Synapse - Africa’s 4IR Trade & Innovation Magazine - 3rd Quarter 2021 Issue 13 (SHOW EDITION)

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3rd QUARTER 2021 | ISSUE 13

SYNAPSE Africa’s 4IR Trade & Innovation Magazine

WOMEN DATA SCIENTISTS

help lead Telkom’s digital revolution

JOHANNESBURG The Artificial Intelligence Tech Capital of Africa?

NVIDIA Features

AI IN AFRICA

From Hype to Reality

Tools, Training and Education for African Developers

MICROSOFT

Exploring AI at Scale

AI EXPO AFRICA 2021 ONLINE SHOW EDITION



Contents SYNAPSE | ISSUE 13 | 3rd QUARTER 2021

p12 AICE to collaborate with NVIDIA on AI, data science projects & training

p24 How to implement Artificial Intelligence in your business and measure its impact

p42 A Data Management Platform for the global ML Community

4 Multichoice introduces AI chatbot T.U.M.I to boost digital customer service 4 World’s first patent listing AI as inventor issued in South Africa 5 The Impact of Artificial Intelligence in Africa 6 SA Visitor & Workplace Management startup WizzPass acquired by US’s FM:Systems 7 WizzPass Workspace Booking - Optimise & streamline your workspace management 8 WHO issues global report on AI in health, Guiding Principles for Design & Use 10 UNDP appoints technical advisory group to steer AL4IR initiative 11 SWITZERLAND - Where Artificial Intelligence Meets Data Safety 12 Ada Labs Africa & AICE to collaborate with NVIDIA on AI, data science projects, training 12 African medtech startups Envisionit Deep AI & GIC Space among Cisco Global Problem Solver Challenge 2021 winners 13 5 Steps To Building A People Analytics Function From The Ground Up 14 Africa IoT & AI Challenge 2021 launched 15 4 Reasons Why You Should Care About AI Governance NOW 16 Egyptian AI, ML-powered lastmile delivery, ecommerce fulfilment startup ShipBlu raises pre-seed round 17 Assoc Prof Moodley joins ICA on intelligence and artificial intelligence 18 RPA: The Next Chapter In The Automation Story 20 HUMAN IN THE SYSTEM: Understanding customer behaviours with ecosystem.Ai

22 Four Decades in Conversational AI 24 How to implement Artificial Intelligence in your business and measure its impact 26 AIZATRON’S AIDAS Platform Delivers an AI Driven Data Analytics Platform at a Fraction of the Cost of Traditional Approaches 28 From Garage to Global: How CompariSure’s conversational AI is driving digitisation within the Insurance industry 30 Leveraging AI to deliver enhanced solutions to African insurance companies 32 AI: The Future of Testing 34 Wisdom and Teamwork OpenSourced Mava’s the Made-inAfrica Multi-Agent Reinforcement Learning Framework 36 Engage and empower your people, one chat at a time Whatsapp chatbot and AI tech for radically enhanced HR performance and employee impact 38 Gap Identification in Real Time Creates Revenue Opportunity 39 ARTIFICIAL INTELLIGENCE (AI) – The Stuff of Star Wars or How the World is Truly Advancing? 42 A Data Management Platform for the global ML Community 44 Is Johannesburg the Artificial Intelligence Tech Capital of Africa? 46 Automation Anywhere Launches the World’s Only Unified CloudNative Platform for Intelligent Automation on the African Continent 52 NVIDIA Features Tools, Training and Education for African Developers 54 Women data scientists help lead Telkom’s digital revolution 56 Exploring AI at Scale 3RD QUARTER 2021 | SYNAPSE

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Editor's Notes Daniel Mpala, Editor and Head of Show Production

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elcome to the Q3 issue of Synapse magazine and to AI Expo Africa 2021 ONLINE. As always in this issue we attempt to do a round up of the major news that's shaped Africa's AI and 4IR industry over the last couple of months. A lot's happened from South Africa issuing the world's first patent listing AI as an inventor, to NVIDIA's continued focus on the African AI and data science scene with its latest collaboration with Ada Labs Africa, & Kenya's AI Centre of Excellence. Another development worth noting is the UNDP appointing a 10-member advisory group to steer its Africa Leading the 4th Industrial Revolution (AL4IR) initiative. This show edition also features thought leadership and insights from those in the African AI and Data Science community and more importantly from our AI Expo Africa 2021 ONLINE exhibitors so be sure to check those out.

JOIN US - ONLINE - ANYWHERE

www.aiexpoafrica.com 2 SYNAPSE | 3RD QUARTER 2021

Dr Nick Bradshaw, CEO AI Media Group & AI Expo Africa founder

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7-9SEP2021

ow – what a year it's been!! We are really proud to welcome our 2021 delegates, exhibitors, partners and sponsors to the 4th edition of AI Expo Africa. This year we are again using the online format to increase the diversity and inclusivity of Africa’s biggest 4IR trade event and welcome Intel and Nvidia as our main Event Partners and Telkom as our new CSI Partner. Last year we saw a jump in numbers and interest both globally and locally and a whole new audience embraced new ways of working and conducting trade online.Its not been without its challenges for us all and we thank you all for your support in 2020 & 2021!! This year is our biggest event gathering so far and we are proud to welcome the Tshwane Economic & Development Agency (TEDA) as the event Destination Partner for the show. Our analysis of the pan AI African landscape has shown that Tshwane and the Gauteng Province of South Africa has the greatest concentration of AI, RPA & Smart technology companies on the African continent and there are some great new partnerships, trade, job and investment opportunities to explore here - make sure you visit them at the show to learn more. We really hope you enjoy this bumper show edition of Synapse Magazine, you will find a copy in your delegate e-Bag and the last 4 editions are available for download from the AI Media eBooth in the expo hall. Have a great time everyone!!


Invest in Tshwane South Africa’s Capital City, the City of Tshwane, is situated in the province of Gauteng, the economic centre of South Africa. As the seat of government, Tshwane is the country’s administrative hub and houses 134 embassies, 30 international organisations making it second only to Washington DC in terms of the concentration of the diplomatic and foreign missions. It is also home to over 30 Johannesburg Stock Exchange-listed companies as well as various multinational companies. The city is home to four universities and various research institutes and its knowledge and information industry is well-developed. Tshwane has a high literacy rate, a large concentration of financial and business services in the region, support of educational institutions and communication infrastructure, including broadband capacity.

Why Tshwane? • Tshwane is the knowledge centre of South Africa. The City has a high concentration of academic, medical, social science,

technology and scientific institutions which produces 90% of medical, science and technology research in the country and 60% of the country’s overall research output. The city has a student population of 60000 and high levels of literacy, giving investors access to a skilled workforce and continuous learning.

• Your investment is safe with us, we are governed by investment protection legislation, The Protection of Investment Act 22 of

2015 which specifically gives foreign investors similar rights and protections available to South Africans.

• We have great investment incentives such as the duty drawback schemes that provide refunds for import duties paid on the

materials used in the production of goods that are re-exported.

• There are no restrictions for foreign investors to acquire property in the country. • There are no restrictions on foreign investors to acquire companies or businesses in South Africa. • Tshwane has a well-developed infrastructure and road network and is centrally situated on the national road network with direct

links to Mozambique, Botswana and Namibia along the east-west N4 route, and with Zimbabwe along the south-north N1 route.

For more information Contact Us 012 358 9999 www.tshwane.gov.za www.teda.org.za www.facebook.com/CityOfTshwane

Block B, 2nd Floor Tshwane House 320 Madiba Street Pretoria 0002

PO Box 440 Pretoria 0001


INNOVATION

MULTICHOICE INTRODUCES AI CHATBOT T.U.M.I

to boost digital customer service

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NEWS

World's first patent listing AI as inventor issued in South Africa South Africa made history in July after it became the first country to award a patent that named an artificial intelligence as its inventor, with the AI's creator Dr Stephen Thaler as the patent owner. US-based Dr Thaler created Dabus (Device for the Autonomous Bootstrapping of Unified Sentience), an artificial neural system that he claims to be capable of independently coming up with several inventions among them a food container that was designed to improve grip and heat transfer. The Global Legal Post reported in July that the patent was secured by British law professor

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edia conglomerate MultiChoice announced in June that it will be enhancing its digital customer service through an Artificial Intelligence (AI) chatbot it launched in May.

Ryan Abbott of the University of Surrey along with his team who have since 2018 tried to apply for the patent in more than 10 jurisdictions among them Australia, Europe, New Zealand, the UK and the US. "The High Court in England and Wales last year sided with the UK Intellectual Property Office in refusing the applications, accepting that while Dabus created the inventions, it cannot be granted a patent on the grounds that it isn’t a ‘natural person’. The European Patent Office and the US Patent and Trademark Office objected on the same grounds, with Abbott’s team appealing," the Global Legal Post reported. The South African Companies and Intellectual Property Commission awarded the patent on 28 July. Only two days later the Australian Federal Court also moved to confirm the patentability of inventions made by AI like DABUS.

The chatbot which is called T.U.M.I -which stands for The Ultimate Master of Information -- is available 24/7 to answer customer queries about products and services. MutliChoice South Africa CEO Nyiko Shiburi said T.U.MI is an "evolutionary leap" in MultiChoice's service capability. "Born and developed right here in Africa, T.U.M.I is a tangible manifestation of our commitment to innovation. This is not innovation for its own sake; the focus is to continue to grow our capacity to give our customers an excellent service experience," added Shiburi. T.U.M.I currently lives on the DStv website and in time, it will live across MultiChoice’s digital ecosystem on the DStv website and app, Showmax (website and app), and Facebook Messenger. T.U.M.I will also act as a concierge to onboard new customers to MultiChoice online-only service, DStv Streaming. T.U.M.I -- which was developed by an inhouse MultiChoice team -- interacts in realtime with customers in an online, text-based conversation. It boasts advanced natural language capabilities, which means that T.U.M.I can recognise user questions and provide responses with information related to DStv products and services. The chatbot can currently customers to clear decoder errors, check balances, reconnect products, make payments, manage holiday home viewing, and change packages. MultiChoice said while T.U.M.I is constantly learning and evolving -- including through feedback from customers -- more functionalities will be added over time. "Thanks to T.U.M.I, MultiChoice is in-step with international technology and customerservice trends. T.U.M.I places us at the forefront of customer interaction providing DStv subscribers with another channel to connect with us,” said Shiburi.


INSIGHT

THE IMPACT OF ARTIFICIAL INTELLIGENCE IN AFRICA T he world is engulfed by an AI revolution. From self-driving cars to automated customer service, artificial intelligence has become omnipresent around the world. But what about Africa? Africa has a knack for inventing and reinvigorating old technologies. To some degree, Africa has managed to sidestep this AI revolution and maintain its traditional way of life. That’s because in Africa, artificial intelligence isn’t really a thing yet - there are no driverless busses or pizza delivery robots that speak your local languages. Many discussions about the future of artificial intelligence in Africa, as well as why and how African countries have been slow to embrace this new technology trend have been had, so how do we move from these Conversations to implementation? The fact that Africa is so behind in artificial intelligence is a matter of concern - what are the effects of this new technology on African countries? The use of artificial intelligence has an impact on how the world functions, and this new technological introduction will also influence Africa. Innovation in AI is expected to significantly help Africa’s development. The rise of AI will change the way African country’s function and interact with each other. Let’s take a look at the future of Artificial Intelligence in Africa. First off, we need to identify what Artificial Intelligence actually is. What is AI? Artificial Intelligence is defined as the theory and development of computer systems able to perform tasks that normally require human intelligence; such as visual perception, speech recognition, decision-making, and translation between languages. This definition makes it seem like artificial intelligence isn’t much different from natural intelligence. But what sets it apart from natural intelligence is its ability to perform tasks without any human supervision or intervention. AI systems are able to improve themselves over time, by learning from their mistakes. In essence -- they become better at whatever task they were designed to do - which makes them more useful in the long run, systems like Roboteur, an RPA and development environment/system that makes it simple and inexpensive to automate processes. As the world of work undergoes fundamental shifts, we are starting to realise that it’s not necessary for a worker to perform every process – at every step of the workflow process, not a human worker. The idea that computers are just as capable of performing

certain tasks is quickly being replaced by the notion that they may actually be more appropriate, efficient and adept at certain repetitive tasks. Robotic process automation (RPA) is all about letting technology take care of repetitive or automatable tasks, thus freeing up human workers to attend to things that require more creativity or non-linear thinking. AI has become popular in Africa despite not being implemented widely yet due to its ability to solve real-world African problems. In order to understand this, let’s take a look at how African countries function currently. The African economy is characterised by a lack of resources and technology. Industries are often not able to improve their production processes or increase productivity due to the use of outdated technologies and processes that have been in use for decades. For example, the food industry requires a lot of manual labour to produce goods that are distributed throughout Africa’s market. The manpower required for these industries is usually something that African countries lack. This causes food prices to be much higher than they would normally be if there were more people available - which can lead to hunger and famine if these products become too expensive for common Africans. In order to have the foresight needed to make decisions much faster, Amathuba AI

offers a solution that is adaptable to multiple sectors. With Brain Everywhere <<BOLD>> you’ll always have an expert on call for every decision, all day, every day. What’s more, Brain Everywhere won’t leave your business or take its set of expertise elsewhere. Let’s face it, there’s nothing more hard-working than a robot, especially one as easy to use and costeffective as Brain Everywhere.

Our Process: Introduction Understand – Current prospective customer challenges / Pain-Points Conceive – Assessment of prospective customer As-Is landscape (Processes/ Systems) Product Recommendation – aligned to prospective customer Current challenges / Pain-Points Pre-Sale Business Proposal highlighting: Product, Pricing, High level details, T&C’s Customer Requisition and registration process Customer license fee Payment Professional Services: Client Introduction, Vision for Client etc Post-Sale Product Scope Configuration – in Conjunction with the customer requirements Quality Control Signed off Scope Configuration

“When Amathuba AI was

founded in 2020 we set out to taking back humanity through technology in doing this at Amathuba AI we want to bridge this gap by being an aggregator of AI in Africa and this means collaboration and finding solutions that solve everyday problems for the African continent and the companies that operate in it

Nomsa Nteleko, Chief Commercial Officer, Amathuba AI

Implementation Product Configuration deployment as per signed Product Scope Configuration User Training User Acceptance Testing Defects Resolution Official Go-Live Post Implementation Preventative Maintenance Customer Query Management Cross Selling Opportunities Continuous Improvement

Contact us Email: nomsa@amathuba-ai.com Tel: +27 76 790 2923 www.amathuba-ai.com

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NEWS

SA Visitor & Workplace Management startup

WIZZPASS

ACQUIRED BY US'S FM:SYSTEMS

"We see great potential to jointly drive innovation and market expansion with FM:Systems, continuing to bring an incomparable full realm of unique digital workplace solutions to market together," said Hornby. Headquartered in Raleigh, North Carolina, FM:Systems provides a strategic, end-to-end solution suite that transforms the digital workplace into a self-sustaining ecosystem and establishes a rich foundation of workplace data that informs decisions as the organisation grows and times change. Intuitive, userfriendly interfaces create a fluid workplace experience in the office, at home or anywhere else in the world an employee might choose to work.

South African visitor and workplace management startup WizzPass was in June acquired by US digital workplace solutions provider FM:Systems for an undisclosed amount

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ohannesburg-based WizzPass was founded in 2015 by Ulrich Stark and Bradley Hornby. The startup specialises in improving the often manual and insecure processes and systems involved with traditional facility visitor management. WizzPass is an alumnus of both Techstars Cape Town (2016) and the Grindstone Accelerator (2019/20) that helped advance its growth journey. The WizzPass Visitor Management System revolutionises the experience, security, convenience and safety of visitors to offices and other buildings, creating improved communications and interactions between businesses and their visitors, be they contractors, suppliers, employees or others. WizzPass is trusted by blue-chip companies at over 300 locations, across 4 continents, and has processed over 10 million secure events.

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“ We are excited to

build on our strategy of providing comprehensive digital workplace solutions with the addition of WizzPass' leading visitor management technologies

"It is with great enthusiasm that we join forces with an outstanding company like FM:Systems," said Stark, co-founder of WizzPass. "This will facilitate exactly the kind of valuable workplace solution combination our customers have been asking for."

FM:Systems said in a statement that the WizzPass acquisition increases the already commanding breadth of the FM:Systems portfolio of products, ensuring that clients can continue to tackle the most daunting real estate and facilities challenges with a single, trusted vendor. "We are excited to build on our strategy of providing comprehensive digital workplace solutions with the addition of WizzPass' leading visitor management technologies," said Kurt von Koch, CEO of FM:Systems. "This acquisition deepens our offerings in visitor management, which is an important aspect of delivering productive, safe, and enriched workplace experiences. With customers that include many marquee companies, WizzPass is recognised as a leader across the industry. We look forward to welcoming and continuing to support existing WizzPass customers as part of the FM:Systems family."


WIZZPASS WORKSPACE BOOKING

ADVERTORIAL

- Optimise & streamline your workspace management

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any people have gotten used to working from home over the last year, but now that companies are opening their offices again, what does the future of our “workplace” life look like? Facility Managers, Health & Safety Managers, Property Managers and C-Level Executives are all faced with a whole new set of challenges to think about for a safe and effective return to the office. How would you manage and optimise employee capacity per department, floor or per area? Which employees should be able to return to work? Which days of the week should certain employees be back at the office? How would an employee book a space or a desk? A challenge or an opportunity?

These decisions can be difficult to make as it requires input from different stakeholders to find the ideal solution for each office space. Many companies, however, have turned this thinking around and are seeing this as an opportunity to streamline their office space and better optimise their workplace. Now that employees are planning or have started returning to the office, this presents a great opportunity for companies to think about their own “workplace of the future” and in doing so, increase productivity and reduce costs in the longterm.

What is workspace booking ? The concepts of “smart desking” or “hot desking” are not new, and have been around for many years. However, when these concepts were created before the Covid-19 pandemic, the first systems were built for a large and mobile workforce that would want the ability to book a space in different offices from day-to-day. However, the challenges of Covid-19 and the new “hybrid model” of alternating workdays between the home and the office, has provided a resurgence for the need for workspace booking systems. Office-space

booking has quickly evolved and now allows you to allocate office-spaces according to Covid-19 social-distancing regulations, and present booking options to employees based on their seating preference, via real-time cloud-based software.

system. This coupled with insightful analytics on space usage allows administrators to gain the control and oversight that they need now and in the future.

Let employees choose

How do we monitor and deny access to an employee who has not made a booking, or who has not passed their Covid-19 screening? Should certain employees be allowed entry without having a booking? These are important questions for each company or office manager to consider. Workspace booking software such as WizzPass, not only allows for real time monitoring but also seamlessly integrates with access control systems. This allows for complete control of who is in the office at a certain time and day. Employee accessmechanisms at the turnstile or door (such as access card, facial recognition, etc) can be automatically blocked by WizzPass so that access to the turnstile or door is denied. Going to the office might never be the same again, but that is a good thing By having workspace booking measures in place, it provides for more structure and control, but also fosters a democratic and transparent operating environment that allows employees to easily book and choose their own workspaces. At the same time, the company is setting itself up to optimise and streamline its workplace effectively, both for current and future needs. WizzPass is a leading cloud-based workplace management system that allows you to effectively optimise your office space. The system is flexible and easy-to-use and is used at all types of offices, including large corporate buildings. To find out more about the WizzPass System, please get in touch with us.

Giving employees the power to manage their days and times within the office not only allows for accountability and higher rates of productivity but also ensures that the highest safety protocols are being practised. The ability to administer seating plans effectively and quickly with cloud-based software is going to be instrumental in how businesses manage their offices – now and in the future. These software systems should allow for immediate bookings and cancellations, thereby shifting the burden of administration away from office managers.

Optimisation and collaboration Administrators should have oversight on which employees intend to be in the office at a particular time and should be able to allocate certain employees to book only in certain areas. This allows for effective planning and increased collaboration on projects between different departments and people. Managers would no longer need to worry about paper trails, and can now manage capacity, health-screening, desk-cleaning frequency and collaboration-planning on one

Flexible settings, but with secure controls

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NEWS

WHO ISSUES GLOBAL REPORT ON AI IN HEALTH,

guiding principles for design & use

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he World Health Organisation (WHO) in late June issued its first global report in Artificial Intelligence (AI) in health, as well as six guiding principles for its design and use. The report, titled Ethics and Governance of Artificial Intelligence for Health, is a result of two years of consultations held by a panel of international experts appointed by WHO. WHO stated in its guidance that AI holds great promise for improving the delivery of healthcare and medicine worldwide, but only if ethics and human rights are put at the heart of its design, deployment, and use. “Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology it can also be misused and cause harm,” said Dr Tedros Adhanom Ghebreyesus, WHO DirectorGeneral. “This important new report provides a valuable guide for countries on how to maximise the benefits of AI, while minimising its risks and avoiding its pitfalls.” Artificial intelligence can be, and in some wealthy countries is already being used to improve the speed and accuracy of diagnosis and screening for diseases; to assist with clinical care; strengthen health research and drug development, and support diverse public health interventions, such as disease surveillance, outbreak response, and health systems management. WHO said AI could also empower patients to take greater control of their own health care and better understand their evolving needs. It could also enable resource-poor countries and rural communities, where patients often have restricted access to health-care workers or medical professionals, to bridge gaps in access to health services. WHO’s new report cautions against overestimating the benefits of AI for health, especially when this occurs at the expense of core investments and strategies required to achieve universal health coverage. The report also points out that opportunities are linked to challenges

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and risks, including unethical collection and use of health data; biases encoded in algorithms, and risks of AI to patient safety, cybersecurity, and the environment. For example, while private and public sector investment in the development and deployment of AI is critical, the unregulated use of AI could subordinate the rights and interests of patients and communities to the powerful commercial interests of technology companies or the interests of governments in surveillance and social control. Furthermore, the report also emphasises that systems trained primarily on data collected from individuals in high-income countries may not perform well for individuals in low- and middle-income settings. "AI systems should therefore be carefully designed to reflect the diversity of socioeconomic and health-care settings. They should be accompanied by training in digital skills, community engagement and awareness-raising, especially for millions of healthcare workers who will require digital literacy or retraining if their roles and functions are automated, and who must contend with machines that could challenge the decisionmaking and autonomy of providers and patients. Ultimately, guided by existing laws and human rights obligations, and new laws and policies that enshrine ethical principles, governments, providers, and designers must work together to address ethics and human rights concerns at every stage of an AI technology’s design, development, and deployment," added WHO.

Six principles to ensure AI works for the public interest in all countries To limit the risks and maximize the opportunities intrinsic to the use of AI for health, WHO provides the following principles as the basis for AI regulation and governance: Protecting human autonomy: In the context of health care, this means that humans should remain in control of health-care

systems and medical decisions; privacy and confidentiality should be protected, and patients must give valid informed consent through appropriate legal frameworks for data protection. Promoting human well-being and safety and the public interest: The designers of AI technologies should satisfy regulatory requirements for safety, accuracy and efficacy for well-defined use cases or indications. Measures of quality control in practice and quality improvement in the use of AI must be available. Ensuring transparency, explainability and intelligibility: Transparency requires that sufficient information be published or documented before the design or deployment of an AI technology. Such information must be easily accessible and facilitate meaningful public consultation and debate on how the technology is designed and how it should or should not be used. Fostering responsibility and accountability: Although AI technologies perform specific tasks, it is the responsibility of stakeholders to ensure that they are used under appropriate conditions and by appropriately trained people. Effective mechanisms should be available for questioning and for redress for individuals and groups that are adversely affected by decisions based on algorithms. Ensuring inclusiveness and equity: Inclusiveness requires that AI for health be designed to encourage the widest possible equitable use and access, irrespective of age, sex, gender, income, race, ethnicity, sexual orientation, ability or other characteristics protected under human rights codes. Promoting AI that is responsive and sustainable: Designers, developers and users should continuously and transparently assess AI applications during actual use to determine whether AI responds adequately and appropriately to expectations and requirements. AI systems should also be designed to minimise their environmental consequences and increase energy efficiency. Governments and companies should address anticipated disruptions in the workplace, including training for health-care workers to adapt to the use of AI systems, and potential job losses due to use of automated systems. These principles will guide future WHO work to support efforts to ensure that the full potential of AI for healthcare and public health will be used for the benefits of all.



NEWS

UNDP APPOINTS TECHNICAL ADVISORY GROUP TO STEER AL4IR INITIATIVE T

he United Nations Development Programme (UNDP) in late June appointed a 10-member technical advisory Group that will steer its Africa Leading the 4th Industrial Revolution (AL4IR) initiative which aims to drive the responsible adoption and use of emerging technologies for Africa's transformation and growth. The technical advisory group is made up of experts drawn from leading global technology firms that include Google, Uber and organisations like the WEF. The group also consists of inventors, lawyers, activists and social influencers, who the UNDP said bring to the initiative a wealth of insights, ideas and expertise in technologyenhanced transformation across several key sectors, including healthcare, governance, education, food systems, elections, access to the internet, mobility and open data for sustainable development.

The technical advisory group comprises of: Crystal Rugege, Managing Director, Centre for the 4th Industrial Revolution, Rwanda Titi Akinsami, Lead, Policy and Government Relations Lead for West and Francophone Africa at Google Ory Okolloh Mwangi, lawyer, investor and policy advisor Rapelang Rabana, Rekindled Learning, 4RSA

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Davis Adieno, Program Director, Global Partnership on Sustainable Development Data (GPSDD) Eniola Mafe, Lead, 2030Vision, World Economic Forum (WEF) Babusi Nyoni, designer and inventor Cezanne Maherali, Head of Policy, Middle East and Africa, UBER Nnenna Nwakanma, Chief Web Advocate, World Wide Web Foundation Philip Thigo, Digital Partnerships Advisor, UNDP and Lead, Africa Leading the 4th Industrial Revolution. Through UNDP, the technical advisory group will help steer a vision of strengthening an African-led 4th industrial revolution with a solid and resilient foundation anchored on five key pillars: data, technology infrastructure, skilling revolution, agile governance and sustainable energy. The experts will collectively enhance Africa's role and leadership within the realm of the 4th industrial revolution through the co-creation of ideas and solutions with institutions and countries within the region. UNDP regional director for Africa Ahuna Eziakonwa said the UNDP is betting on Africans to take the lead in the 4th industrial revolution. "By harnessing and advancing technologies that can steer the continent towards achieving its development goals faster, better and through a greener approach, Africa's role and leadership in this industrial revolution will not only transform lives and institutions

but will also ensure that no one is left behind," she added. Africa's development, the UNDP noted, is increasingly intertwined with new technologies that fuse the digital, biological, and physical worlds. The COVID-19 crisis presents opportunities for accelerated adoption of technologies transforming ways of life, communication, governance, learning and work – ushering in a new era of social, economic and governance disruptions. The UNDP views technology not only as a tool but rather as an artefact for sustainable development and an accelerator of resilience. "If we want to leverage technology for sustainable development and resilience, we must ensure that the foundations for the 4th industrial revolution are firm," explained Alessandra Casazza, Manager of the UNDP Resilience Hub in Nairobi. The UNDP added that while technology is a driver of socio-economic prosperity, previous industrial revolutions significantly increased inequalities with disproportionate benefits and opportunities for early adopters and damage to our natural world. "Therefore, data, energy, internet, skills and agile governance, are anchored on the premise that the 4th industrial revolution must gainfully and meaningfully benefit the African continent. The AL4IR initiative aspires to influence the development and use of technology based on a sound, inclusive and resilient foundation. As the youngest continent globally, Africa's youth are drivers and creators of innovations and, as a result, are poised to benefit most from new jobs and opportunities that were previously inaccessible to them," said the UNDP. The UNDP said that ensuring that rapid and exponential changes don't exacerbate disparities and knowledge or information gaps (especially between governments and the private sector) could advance progress towards the Sustainable Development Goals and the African Union's Agenda 2063. The UNDP further added that it commits to convene, connect and catalyse collaborative actions with partners towards accelerated uptake and responsible use of 4IR technologies for better development decision-making and action.


SWITZERLAND

- Where Artificial Intelligence Meets Data Safety Switzerland - in the heart of Europe - is a hub for future technologies such as artificial intelligence. A key aspect of Switzerland’s efficient technology transfer is the closeness of strong research institutions, small and medium enterprises and multinationals to each other. This facilitates symbiotic relationships between these actors which are encouraged by the favorable conditions in Switzerland. There are countless examples of these, including Microsoft’s laboratory for mixed reality and AI; another is Facebook’s office that works on computer vision. Companies based in Switzerland significantly benefit from its attractiveness to international talent and unbureaucratic support from the government. An added value for AI companies in Switzerland is the framework conditions for education, research and innovation as presented by the State Secretary for Education Research and Innovation.

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NEWS

ADA LABS AFRICA & AICE to collaborate

with NVIDIA on AI, data science projects, training

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airobi-based Ada Labs Africa in June announced that it will, together with the AI Centre of Excellence (AICE), collaborate with NVIDIA on an initiative that will involve a number of data science and artificial intelligence (AI) projects.

Ada Labs Africa CEO John Kamara (pictured) explained that the partnership will strengthen

the capability of data science and AI rollouts in Africa as the two organisations collectively work to build capacity to train AI engineers in Africa. The new initiative is also expected to address the scarcity and cost of skilled AI engineers. "Our journey in contributing to digitize the African continent has taken another important leap because of this initiative. We have invested heavily in infrastructure and platforms that will enable digitization of some of the key sectors affecting African growth and understand the critical role of AI in achieving our mission efficiently. However, if we do not have enough qualified people to manage AI systems, our efforts will be futile. This collaboration will help develop solutions, methodologies and best practices that are mutually beneficial for our companies, clients and market as a whole," added Kamara. AICE has already started training the first cohort of 40 AI engineers as

part of this initiative and launched a CEO roundtable series to demystify AI for C-level executives across Africa. The first roundtable in the series was held in January, and another session took place at NVIDIA’s GPU Technology Conference in April. More sessions are planned for the coming months. Ada Labs Africa said the initiative also aims to strengthen the buildup of the next generation of socially impactful and commercially-driven entrepreneurs who will change the world from Africa. Ada Labs Africa and AICE said they will together contribute to several open-source AI technologies that are expected to help innovators design, develop, deploy and monitor predictive models more quickly and efficiently. NVIDIA’s Head of Emerging Areas, Kate Kallot, commented, “Bringing together our combined expertise and experience in the market will help provide relevant solutions and contribute to a robust AI industry in Africa. We are looking forward to addressing the opportunities and challenges with AI technologies to benefit people and society across the continent.” The companies will collaborate to train over 4 000 AI engineers in five cities in Africa, starting with Nairobi, Kenya, over the next three years and will include fostering applied AI research dedicated to solving challenges relevant to local ecosystems. The collaboration also aims to strengthen relations with key stakeholders, including policymakers in the technology, computing and innovation ecosystem, and to build a transformational tech space on the continent.

NEWS

African medtech startups Envisionit Deep AI & GIC Space among Cisco Global Problem Solver Challenge 2021 winners South African medtech startup Envisionit Deep AI, together with Cameroon's GIC Space in June emerged as second runners-up in the fifth annual Cisco Global Problem Solver Challenge, with each of the companies scoring a $50 000 prize. The challenge is an online competition that awards cash prizes to early-stage tech entrepreneurs solving the world’s most challenging problems. Cisco launched the challenge in 2016 to umpstart innovative ideas that benefit society, catalyze economic growth, and create jobs.

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Cisco's Senior Vice President for Corporate Affairs Tae Too commented in a blog post announcing this year's winners that over the past four years, the firm has had the privilege to see tremendous innovation from around the globe and hash awarded a total of $1.25-million to 43 startups in 15 countries. Over 1745 teams from 126 countries made submissions in this year's challenge. Johannesburg-based Envisionit Deep AI uses artificial intelligence to streamline and improve medical imaging diagnosis for radiologists. The startup, which was founded in 2019 by South Africa's first

paediatric radiologist Dr Jaishree Naidoo along with Terence Naidu, and Andrew Migatchev has developed an AI solution called RADIFY that addresses challenges in medical imaging diagnosis, helping to detect COVID-related pneumonia and early stage breast cancer. Yaounde-based GIC Space, which was also founded in 2019, has developed a series of proprietary technologies to remotely screen and diagnose breast and cervical cancers with real time pathology confirmation.


ADVERTORIAL

5 STEPS

To Building A People Analytics Function From The Ground Up / By Elmen Lamprecht, Managing Partner, COGO People Analytics /

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eople Analytics has taken the world by storm and many South African companies are now looking at implementing some form of advanced analytics in HR. The challenge is that most South African companies are too far removed from implementing predictive and prescriptive People Analytics. In fact, most companies have not even started on their People Analytics journey. This is not a surprise, since a LinkedIn study found that internationally only 29% of companies are using advanced analytics in HR. From our experience, we believe this number is even lower in South Africa. At COGO People Analytics we help clients build a People Analytics function from the ground up. For them, artificial intelligence and advanced algorithms are just a bridge too far at this point. In this article, we provide 5 steps as a guide to building a People Analytics function from scratch.

Step 1: Discovery – Identify where People Analytics can make an impact in the Business HR is valued when it can drive organisational success through the optimisation of its people. Just like Finance enables success through diligent management of money in the business, and IT drives productivity through the right hardware, network & software solutions, HR’s role in the business is to ensure that the people in the business contribute towards company goals. Therefore, the first step in building a People Analytics function from the ground up is to understand that priority should be given to organisational strategies and goals. People Analytics should not measure HR activities with no relevance to the larger business, but rather support the business strategy by measuring how people are contributing to what business deems important. Identify which People behaviours are aligned with strategies and goals.

Step 2: Examination – Selecting appropriate People Metrics Once you have identified where People Analytics will make the biggest business

impact, we need to determine which metrics we are going to use to measure this impact. This is the heart of the process. At this stage, it is important to distinguish between HR Metrics and People Metrics. HR Metrics measures the Effectiveness & Efficiency of the HR function. Very often, this is totally separate from the business and has no relevance outside of the HR Department. People Metrics measure the Effectiveness & Efficiency of the people in the business. This is where people behaviour shows a direct link to company performance. Your C-Level will only be interested in People Analytics.

Step 3: Data Mining – Obtaining Relevant Data Practically, the 3rd step is the most challenging. This is because the data you need for People Analytics is warehoused in several systems. To maximise this step, HR Professionals need to work with their ICT Department to develop a data management strategy that includes the following: Identify the sources of the relevant data (e.g. ERP Systems, stand-alone HR systems, email, Social Media, websites, engagement surveys – just to name a few). Identify the type of data (e.g. Structured/ Unstructured, text/voice/image, GPS, video, etc) Harvest/gather the Data through system integration Normalise the data in a central database/ data lake Enrich data by adding of metadata (i.e. tagging)

Step 4: Assessment – Draw Insight from the Data Once we start receiving information through HR Analytics, we need to start making sense of everything. It is never enough to just take the data at face value. HR needs to interpret the data for the C-Level to clearly link the impact of People Behaviour on the bottom-line. We always propose looking at the following when interpreting data for the C-Level:

Provide Context – Current environment, Industry Benchmarks Compare to Past Performance – Progress/ Regression Provide understanding – Ask ‘Why?’ Predict future outcomes

Step 5: Influence – Communicate People Analytics to Drive Strategic Change The last step is often neglected. Positive, sustainable growth of People Analytics can only be achieved when HR is able to convince and influence the business by clearly communicating success stories to the business. When communicating to the business, take the following into consideration: What should we communicate? Structure message in a business-friendly story To whom must we communicate? Identify all the stakeholders When should we communicate? Weekly/Monthly/Quarterly Reporting Business Cycle (budgeting, Legislative Reports) How should we communicate? (Reports, Newsletters, Posters, Meetings, Emails, WhatsApp) By following these 5 steps, any organisation - regardless of size and sophistication – can build a People Analytics function from the ground up.

If you want to know more about building an HR Analytics function from scratch, please reach out to COGO People Analytics. We are Africa’s No 1 HR Analytics advisory. We offer HR Tech and HR Analytics consultancy services, while our development of HR Analytics Tools (such as advanced HR Reporting which includes Artificial Intelligence and Machine Learning) and HR Virtual Assistants (Chatbots) remain our flagship products.

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NEWS

AFRICA IOT & AI CHALLENGE 2021 LAUNCHED The 2021 edition of the Africa IoT & AI Challenge has been launched in Nigeria, Kenya, Tunisia, and Uganda after opening ceremonies were held for the respective countries in July.

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he challenge is a regional capacitybuilding and pre-incubation programme for senior university students and startups that have innovative ideas in the areas of Internet of Things (IoT) & Artificial Intelligence (AI) and related fields that will enable and leverage a smart future. The Africa IoT & AI Challenge was first held in Egypt in 2016, before expanding to Tunisia and Morocco in 2020. This year's edition will see it being held in five new African countries, namely Kenya, Uganda, Nigeria, South Africa and Rwanda. Participants first compete on the local level, followed by regional finals in which winners from each country meet. The regional finals are held annually as part of the activities of the IEEE Global Conference on Artificial Intelligence and Internet of Things (IEEE GCAIoT). The challenge is owned by Institute of Electrical and Electronics Engineers (IEEE) sponsored by Benya and organised in collaboration with WAKANDAI Ventures. The challenge is organised in each country by a national committee that brings together a number of government and private agencies. The Nigeria challenge comes with the support of the Federal Ministry of Communications & Digital Economy, and National Information Technology Development Agency. In Kenya, the challenge comes with the support of Kenya National Chamber of Commerce and Industry (KNCCI) and Konza Technopolis, while the Uganda challenge is sponsored by the Ministry of Information and Communications Technology and National Guidance, and

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National Information Technology Authority, with a number of universities such as Uganda Technology and Management University (UTAMU), Kampala International University, and Soroti University. In Tunisia, the challenge is organised by Novation City, a public-private programme by the Ministry of Industry.

The challenge is aimed at startups and university students with ideas around: Smart Buildings Smart Homes Smart Agriculture Smart Transportation Fintech Automative Energy Healthcare Education Smart Manufacturing Retail and ECommerce The challenge organisers describe it as not just being a competition, but as a digital transformation movement which delivers training and workshops to participants, providing them with support. With a vision towards developing an environment of innovation and entrepreneurship, the Africa IoT & AI Challenge provides all the technical and logistical support to the participating youth starting from equipping them with advanced technical information in the fields of Internet of Things and Artificial Intelligence to supporting innovative projects and startups to be ready for investment.

The programme also established the ‘IoT & AI Knowledge Hub’, a knowledge complex that provides online workshops, lectures and online learning resources for the IoT & AI community in Africa. Through the hub, the participants learn from the success stories of African engineers and developers working in giant technology companies, and follow their steps to grow in their own careers.

Challenge participants send to benefit from: Technical mentorship Investment opportunities (for selected startups) Networking with top-notch companies, academia, industry leaders, and government representatives Advanced educational technical content Access to international conferences Access to local digital fabrication labs Access to the AIoT Africa Community

Those interested in joining the challenge can check out the challenge proposal here and register here.


ADVERTORIAL

4 REASONS WHY

You Should Care About AI Governance NOW / By KOSA AI CEO & Co-founder Layla Li /

Have you seen AI bias make headlines recently from Amazon’s sexist AI recruiting tool to medical algorithms showing racial bias? In the new age of big data that we live in, this is increasingly relevant for companies that utilise AI’s automation power. If you’re not convinced why you should care, here are 4 reasons to act now. 1. AI Governance is becoming part of the regulatory landscape Maybe you think this is all too soon. AI is just a buzz word, your organisation is just starting a digital transformation, or you’re already market leader waiting to take the next step. Regulatory changes are happening all over the world. According to the OECD, there are already over 300 AI policy initiatives from 60 countries and territories. Most notably, a leaked draft of the AI regulation by the European Commission reveals that the lawmakers are considering fines of up to 4% of global annual turnover or €20M (whichever is greater), for a set of risky AI use-cases. Uber was recently sued by drivers in Europe over automated robo-firing. And Uber lost. The challenge references Article 22 of the European Union’s General Data Protection Regulation (GDPR) — which provides protection for individuals against purely automated decisions with a legal or significant impact. If you haven’t yet started thinking about how you scale your AI operations responsibly,

2. Your customers care The risk mitigation factor pales in comparison of the benefits gained from strengthening your companies’ core values. Companies that embrace responsible AI can make boost their bottom line and differentiate the brand, because your customers care. Organisations with a strong sense of purpose are more than twice as likely to generate above-average shareholder returns, whereas AI without integrity will fail brands every time. Big players such as Salesforce, Microsoft, and Google have publicised the robust steps they have taken to implement Responsible AI. And for good reason: people weigh ethics three times more heavily than competence when assessing a company’s trustworthiness, according to Edelman research.

And more and more customers are consciously choosing to do business with companies whose demonstrated values are aligned with their own are. Companies that deliver positive impact on society boast higher margins and valuations. Organisations must make sure that their AI initiatives are aligned with what they truly value and the positive impact they seek to make through their purpose.

3. Your employees care And not just your customers, your top talent also care about ethics. In UK, 1 in 6 elite AI workers has quit their job rather than help to build potentially harmful products. Timnit Gebru, a leading AI Ethics researcher, was recently fired from Google after criticising its approach to minority hiring and the biases built into today’s artificial intelligence systems. And two engineers quit over the company’s treatment of their top talents. A well-thought-out responsible AI program not only empowers talent to innovate with human impact at front and center of their work, but also can lead to improvements in recruiting and retention, who would have thought?

algorithms, or the teams responsible for managing them. This is not just a problem for gender inequality – it also undermines the usefulness of AI”. Developing an algorithm that accurately performs on the whole spectrum of human diversity is also much more likely to deliver superior value to a broader and varied group of potential customers. Ultimately, expanding your AI program to ensure fairness and transparency will enhance revenue. AI governance is a team effort. It’s not just up to your data scientists to figure out what safeguards to put in place against undesired outcomes from your AI systems. Stakeholders across the organisations need to work together to develop a robust process that ensures accountability, transparency, fairness, safety and resilience. And always keep humans in the loop, be that your customers, your employees or the public.

4. More inclusive AIs = More revenues In the US, BCG research shows that companies lost one-third of revenue from affected customers in the year following a data misuse incident. Lack of trust carries a high financial cost. However, let’s not wait until disaster struck, the latent biases in your AI today are already costing you hidden millions. Most AIs are trained on historic data that are coded with bias and missed opportunities. According to Gartner, “By 2022, 85% of AI projects will deliver erroneous outcomes due to bias in data,

At KOSA AI, we design and build Automated Responsible AI System that help multiple stakeholders in the organisation to evaluate and solve critical issues throughout the machine learning process, so they can understand and trust the AI systems they’re building.

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INVESTMENT

previously at Nestle and Reckitt Benckiser, and most recently leading the communications department at 77 Media of Saudi Arabia. Abdelrahman comes from a computer science background and is currently completing his PhD at Brown University in the US, focusing on the applications of artificial intelligence and machine learning in combinatorial optimisation. Together, the three founders joined forces in 2020 and began building ShipBlu, seeking to redefine the shipping experience in Egypt for thousands of merchants and millions of customers who shop online every day. “People are excited about shopping online in Egypt because it’s effortless and convenient. Unfortunately, a lot of times the delivery experience really puts people off. Some of it is due to a lack of infrastructure, some of it is due to poor resource management. At ShipBlu, we’ve solved both parts of that problem. We promise to

Egyptian AI, ML-powered last-mile delivery, ecommerce fulfilment

STARTUP SHIPBLU RAISES PRE-SEED ROUND

Cairo-based logistics startup ShipBlu in early July announced that it had raised an an undisclosed amount in pre-Seed funding in a round led by Nama Ventures, with participation from Y-Combinator and other prominent angel investors from San Francisco and Saudi Arabia.

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he startup, which was founded last October by Ali Nasser, Abdelrahman Hosny and Ahmed El Kawass. Ali, ShipBlu offers e-commerce fulfilment services, by providing delivery services using artificial intelligence (AI) and machine learning (ML) technology. The startup was recently accepted into US accelerator Y Combinator's summer 2021 batch. Through its fleet across Egypt, ShipBlu offers delivery within three hours to e-commerce customers with shipping transparency. Building on its last-mile technology, ShipBlu also provides e-commerce fulfilment services, with planned fulfilment centres across all of Egypt. Ali, the CEO, comes from an investment banking background, previously at Citi in New York City, returned to Egypt in 2018 and became painfully aware of the woes of last-mile delivery in the region. Ahmed, COO, comes from a supply chain and advertising background,

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deliver a shipping experience that customers will look forward to so that merchants can stay focused on what matters, and customers can continue to enjoy online shopping,” says Ali Nasser. “We are super proud to have ShipBlu be our first investment in Egypt”. says Mohammed Alzubi, Managing Partner of Nama Ventures. “We have really enjoyed getting to know the founders and seeing their passion to disrupt the e-commerce shipping experience. The team is unparalleled to address this opportunity. We are witnessing first-hand what a well-rounded team with complementary skillsets can do in a very short time. Riding with Ali, Ahmed and Abdelrahman on the ShipBlu spaceship has already been exciting, but we can't wait to see what the future holds together”.

“ People are excited about shopping online

in Egypt because it’s effortless and convenient. Unfortunately, a lot of times the delivery experience really puts people off. Some of it is due to a lack of infrastructure, some of it is due to poor resource management. At ShipBlu, we’ve solved both parts of that problem


NEWS

Solving real-world problems Moodley’s research focuses on applied AI, specifically in adaptive and cognitive systems. His work draws from and applies techniques and foundational theories from the Semantic Web, agent-based systems, machine learning, probabilistic graphical models and other areas in AI, with the aim of solving real-world problems. Together with his students and local and international collaborators, Moodley has explored and designed next-generation

ASSOC PROF MOODLEY JOINS ICA on intelligence

and artificial intelligence / By Stephen Langtry /

Associate Professor Deshen Moodley from the University of Cape Town’s (UCT) Department of Computer Science is one of 19 international fellows who will participate in the University-Based Institutes for Advanced Study (UBIAS) network’s 4th Intercontinental Academia (ICA).

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he ICA creates a global network of future research leaders. Some of the best young academics work together on paradigm-shifting, cross-disciplinary research, mentored by eminent researchers from across the globe.

The 4th ICA, which is dedicated to intelligence and artificial intelligence (AI), will start with a virtual opening in mid-June 2021, followed by two main sessions in Paris, France, in October 2021, and in Belo Horizonte, Brazil, in June 2022. Associate Professor Moodley is a member of the recently established Artificial Intelligence Research Unit (AIRU) at UCT, an accredited research unit in the Department of Computer Science in the Faculty of Science. The unit hosts two research groups and the directorate of South Africa’s Centre for Artificial Intelligence Research (CAIR). CAIR is a South African research network with nine established and two emerging research groups across eight universities funded primarily by the Department of Science and Innovation.

architectures for adaptive systems across diverse domains and applications. These interdisciplinary projects contributed to the sensor web, earth observation, biodiversity and digital health research communities. From a computer science perspective, his research explores generic frameworks – comprised of architectures, theories and methods – for designing adaptive and cognitive systems. “My view is that future adaptive and cognitive systems will incorporate both top-down (knowledge representation and reasoning) and bottom-up (machine learning) techniques from different sub-areas of AI,” said Moodley. His future research in this area will focus on three themes: (1) keeping the human in the loop; (2) frameworks and architectures for building hybrid AI systems; and (3) automating knowledge acquisition, discovery and evolution. He believes that some of the research challenges, especially the ones outlined in themes one and three, would require paradigm-shifting, cross-disciplinary research.

Passion for education Moodley’s passion for computers was sparked as a teenager. “I saved up my pocket money to buy a second-hand Commodore VIC-20,” he said. “This was around 1986 – before computers were mainstream.” Growing up in Pietermaritzburg, he completed his studies in computer science at the University of Durban-Westville before completing his honours and master’s degrees at the University of Natal and his PhD at the University of KwaZulu-Natal. He worked in the industry for five years in South Africa, the United Kingdom and the United States of America (USA) before returning to academia.

He was appointed as a lecturer at the University of Natal in Durban and specifically asked to teach first-year computer science students – some of whom had not worked on a computer prior to attending university. “The field of AI is quite broad and a competitive and fast-moving space which is currently led mainly by the US and China,” said Moodley. Compared to other sub-Saharan African countries, South Africa is doing well. However, the country is still lagging far behind the global leaders in the field, due, in part, to a much smaller AI research community than developed countries. According to Moodley, while the South African government has launched several initiatives such as the Presidential Commission on Fourth Industrial Revolution (4IR) and the Digital Economy Masterplan, the country must be more aggressive to transition from the traditional structures and support for science and technology. One important initiative is to invest in and shore up computer science departments, which is at the heart of digital technologies and digital skills. This is one of the ways the country can fully prepare for and embrace the challenges and opportunities presented by the 4IR. The two prominent sub-areas of AI are machine learning and knowledge representation and reasoning. There is a range of AI techniques in the different subareas with different techniques, and even a combination of techniques, that can be used to solve different problems. Areas that could leverage AI in the short term include finance (fintech), smart manufacturing and smart cities. Several South African companies are starting to build AI capabilities. For example, Eskom is exploring its use for predicting electricity consumption patterns and to predict and detect faults at power stations, and the South African Revenue Service is exploring using it for analysing tax returns and detecting tax dodgers. In the longer term, smart logistics and next generation e-market places are two emerging application areas. “AI and digital technologies can play a significant role towards building a more fair and just society in South Africa,” said Moodley. Furthermore, joining, participating in and leveraging networks such as the ICA will be crucial for South Africa to keep abreast of and contribute to the global innovation and development of AI technology.

This article was first published by University of Cape Town News on 7 June here.

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ADVERTORIAL

RPA: THE NEXT CHAPTER

In The Automation Story

/ By Dimitri Denissiouk, Managing Director, IBA South Africa /

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obotic Process Automation (RPA) and Artificial intelligence (AI) are becoming more important for many organisations today, as the COVID19 accelerated the need to remove humans from a number of business processes to avoid the spread of the pandemic.

The benefits of improving workflow with RPA are not limited to the pandemic. These include a more efficient use of the time and potential of employees, improved customer experience, and a stronger control over business processes. Therefore, the focus is on saving time, improving quality, and reducing risk. However, RPA can be a complex and expensive investment.

Total Cost of Ownership Managers always plan the Return on Investment (ROI) of any new project, but most managers are not pricing RPA correctly. One should focus on the Total Cost of Ownership (TCO) rather than an immediate ROI. Naturally, an automation project results in faster processes, allowing the same team to be more productive, but additional longerterm benefits should be also considered. First is the ability to transform your business. Many industries are experiencing a wave of rapid change. Expanding the scope of automation beyond what you can initially achieve is yet another significant advantage. Managers need to understand that RPA is more like a platform on which other solutions can later be created.

EasyRPA The RPA world is largely monopolised by high priced licensed solutions and many businesses are faced with the need to automate, though they feel that automation is cost prohibitive or too complicated. It is much easier to venture on this journey, if you talk to someone that has already implemented real RPA solutions in real companies.

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We believe that every company should have the opportunity to implement RPA. With this in mind and summarising many-year experience of intelligent automation, our experts have built EasyRPA, an RPA platform designed for development, deployment, running, and monitoring of software robots. EasyRPA is not just a set of ready-made bots. It serves as a springboard to intelligent automation across the entire organisation.

Free License Our approach to intelligent automation is that an automation platform should come with a free license. In most cases, a company has to acquire an annual license for each bot in produc-tion. If we have 20 bots and the price is from $5,000 to $10,000 per bot per year, the investment is significant. Moreover, if you add a cognitive capability to your RPA endeavour, the cost will soar. This is why EasyRPA comes with a free license.

Citizen Developer or Professional Developer? The goal of RPA is to streamline manual tasks across a number of applications and business units. The citizen model encourages business users to play a leading role in RPA. The pro-developer approach does not rule out the involvement of business users in RPA. It just encour-ages them to focus on identifying processes suitable for automation and on finding new opportunities for optimisation, while leaving bot development to IT professionals. I would like to explain why we advocate for and use the pro-developer model. Technological complexities and legacies that reside inside organisations are difficult to manage. As RPA scales in complexity, it becomes hard to guarantee that the applications interact effectively or that the bots can navigate the interfaces as easily as humans can. Unstructured data that constitute 80 percent of all organisations’ data make the situation even more complicated. Therefore, trained data scientists and AI engineers come into play to deal with intelligent automation solutions.

Center of Excellence Usually, clients build their centers of excellence to plan and implement RPA across their organisation. RPA, like any enterprise technology, requires input from developers, project managers, business analysts, and other IT staff. As an option, it is also possible to work with a consulting firm instead of building a large center of excellence in-house.

RPA Governance The right bot governance is another requirement. The most feasible way is to integrate the existing governance practices into the RPA development process. Libraries of reusable components must be a part of the core system capabilities.

Human-In-The-Loop RPA is often viewed as a technology that can replace people, but it is smarter to think in terms of how it can help people do their job better. For example, EasyRPA supports the human-in-the- loop feature, where human employees and RPA robots work together.

AI/ML Integration Finally yet importantly, an automation strategy should envision AI/Machine Learning integration. The RPA platform must enable developers to scale RPA initiatives and drive hyper-automation.

Scaling Innovation RPA is just one part of a transformation to a digital business environment. Strategies such as RPA can entirely redefine how a business model works. Technology changes quickly, but with a steady partner, you are prepared to head confidently into the future and open the next chapter in the automation story.


NEWS


ADVERTORIAL

HUMAN IN THE SYSTEM:

Understanding customer behaviours with ecosystem.Ai

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here’s a young man in a crowd of people in a small village. He’s holding a saxophone and he’s dying to play it. The problem this young man has is that he’s missing a vital part of the instrument - the mouthpiece. He’s been searching far and wide, to no avail, until one day he comes across an old woman who offers him a reading of his future. She informs him that she foresees that he will find what he is looking for soon enough, he glances down at the sax and fiddles with the hole where the mouthpiece should be. This old woman deduces what his heart most desires. She calls upon a merchant she knows well, and has worked with before, and tells him that if he can find the instrument’s missing part, they may just have a new customer. In the time when humans consulted oracles, and merchants knew their customers by name, trade was conducted almost entirely by means of personalisation and individual communication. These early forms of business conduct relied heavily on knowing your customer, and the same applied to matters of prediction. While merchants offered handpicked wares for their best customers, misty women

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in large shawls offered grand futures to those they knew best. When the two worked together, they had the perfect business. Both trade and prediction, however, needed to evolve to accommodate customer base growth; and thus the close relationship between the offerors and the offerees was lost. The loss of individualisation though, did not change the fact that customers are unique - despite having been treated as such in broad spectrum sales. Business history has now come full circle. After years of data collection, it is now time to return to a place of personalisation. ecosystem.Ai, with the capabilities of machine learning, has found a way to extract human behaviour from data. Allowing companies to form stronger relationships with their customers, by understanding the clues in their behaviour. By being able to know your customer is behaving like a Jazz King, and communicating in a way that will resonate with them. ecosystem.Ai has been working tirelessly to create an effective set of tools, which companies can use to both understand, and engage, with their customers on a deeper level. Using the theory and practices of Computational Social Science, in conjunction with contemporary psychological constructs,

the chances of seeing the human in your data is not just a business dream. The practical implementation of dynamic behavioural analytics and segmentation, helps to progress common business practices beyond the static boundaries of mathematical analysis and categorisation. ecosystem.Ai offers a tripart selection of products: The ecosystem.Ai Workbench is a versatile, customisable user interface that reduces the complexity of machine learning processes. The Workbench is bolted directly into a business’s internal structure, maintaining exclusive privacy on company/ customer data. There are a vast number of capabilities available in the ecosystem.Ai workbench, including: project creation and management, data science engineering, model creation and deployment; amongst others. The second of the products is a series of pre-written algorithm Modules. These Modules can be used to solve common business problems (such as churn interventions or offer recommenders) using ecosystem.Ai’s behavioural constructs designed for a sector or exclusively for a company. Check out ecosystem.ai/#modules to find out more. The third product is The Client Pulse Responder which is a fully configurable and automated application that uses continuous re-enforcement learning. The Client Pulse Responder is the executable environment that ensures models are deployed, and results are seen. It is the internal core of ecosystem.Ai’s products, providing the space in which business problems are transformed into actionable solutions. The set of tools that ecosystem.Ai has developed equip clients with the knowledge and capacity to learn more about their customers. All of these elements help businesses see that collections of events in an individual’s life are unique enough to provide a view of the human in the data. ecosystem.Ai are the creator of the tools that contemporary merchants and oracles use in their merged business. By using the combination of business practice, the human sciences of social theory and psychology, and computational power; it is possible to reform the close relationship between company and customer. Understanding customer dynamics through data, offers the unique insight needed to know that your customer is behaving like a Jazz King.



ADVERTORIAL

FOUR DECADES IN CONVERSATIONAL AI Let’s set a line in the sand as we start this piece. This discussion is 100% about Conversational AI, not AI in other applications such as facial recognition, or recruitment triaging, or image recognition, or fraud detection, or autonomous cars, or the singularity where GAI (general AI) becomes as, indeed more, intelligent than humans leading to a world where Sundar Pichai’s assertion that Artificial Intelligence will have a more profound impact on humanity than fire will come to pass.

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ll of those topics are entirely relevant and have their pros and cons, controversies and successes but we’ll not concern ourselves with them here as this is not an area where 20 years of professional practice and 40 years of broader interest in how humans communicate with machinery will have any validity. This is the frame of reference, research and reality that the author brings to this piece.

The Author is a veteran player with experience building Conversational A.I. since 1982 when he built his first ChatBot on a ZX Spectrum computer. He and his Associates have created production conversational systems for; customer service, learning & development, technical support, classroom support for learners & teachers and playful systems aimed to generate discussion amongst many others. Since 2002 Elzware has been designing and making Conversational AI systems

using, evolving and training clients, on appropriate technologies and methods using engineering, social and computer sciences.

There have been some prototype systems for healthcare, virtual humans and other outliers, more than 80 systems in nearly 20 years. They are voice/text input and output,

mixed UI environments, multi modal, tied into back office systems for email and SMS, wrapped with code for access to web services, driving social media automatically. Creating nuance to lip-sync and Avatar expressions, we tune Avatars for attitude and see how people react to better understand. That is the position.

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We are old skool, seen it, done it, watched fashions and hype cycles come and go. Now let’s get to the point. There’s been a lot of conversation about Chatbots and Conversational AI over the last few years, lots of talk of revolutions and scary tales of computers taking over the world to make us subservient, not just from the loony out-there correspondents but from respected traditional media outlets around the world both in print, online and on television. This is a shame as the market for Chatbots is far older than the current hype cycle and in many ways should be, the author believes, reflecting back on its roots to ensure that lessons previously learnt are blended into the best of the methods that are currently being trialled around the world particularly in simple transaction based interactions fit for customer service and before less adventurous human interaction sectors get drawn in to further trials. We are on the threshold of an amazing

phase in human to machine conversation but there are some significant roadblocks along the way. Purely data driven Conversational AI is blowing in like fog to cover everything in it’s path and while there is much talk about levels of Conversational AI being more or less autonomous, even sentient or conscious the reality is that fundamental problems with conversational data and large language models are presenting problems for those vendors that are not working with a hybrid architecture.

By hybrid is meant a blending of clear, concise, auditable and governance driven business and process rules and structures in a method that is transparent and explainable,

/ By Phil D Hall, Conversational AI Architect, Elzware Ltd / indeed interpretable to the common businessman and not just the esoteric and slippery methodologies of data science. I’m ignoring the costs of computing these methods and their impact on our planet for this piece. Since Elzware was set up in 2002 it has seen some peaks and troughs in the market for digital conversational. Work before Elzware was with a global systems integration company working with systems that were called ERMS (email response management systems ). These were built according to various NLP methods and worked side by side with call centre operatives to deliver transaction support and handover to humans. 20 years ago and the functionality is essentially no different to that attempted by the recent blizzard of companies that it seems, thankfully, to be thinning slowly out.

Why didn’t these systems continue to evolve? Social media is the short answer, marketeers and information specialists imagined a delivery mechanism where information was delivered once and people would find this through search engines and all would be good in the world, but let’s not get side-tracked into this grimy don’t-call-me-apublisher so I, the Big Tech Companies, can ignore the vitriol, anger and offensive content that is an unacceptable percentage of total social media output. Let’s not open the box on the Tay debacle and start a discussion about feedback mechanisms of autonomous generative and/ or adversarial AI system, let’s keep focussed on Hybrid AI and let’s talk about some of the systems over the last few years that Elzware has delivered that set a target for the heavy lifting that needs to be achieved if the crucial sectors of; healthcare, education and to a lesser extent entertainment are going to be presented with Conversational AI which is fit for purpose.

Echoborg – 2016 and still evolving Actions speak louder than words, so goto www.echoborg.com to check out some of our trailers. As words go though, The AI, built by Elzware, gives the impression of being on the brink of sentience. It speaks through a human or “Echoborg”. It is programmed to


recruit more Echoborgs. Participating humans have to decide whether to become Echoborgs or persuade the AI to agree to a different partnership. Growing show-by-show over five years, “I am Echoborg” experientially engages crowds to discuss emerging assumptions and issues of control. It successfully triggers new awareness and curiosity about the role of AI in our lives. The show is as much about the audience talking to each other as it is about talking to the AI. It is these conversations where the magic really happens. While “I am Echoborg” was originally designed to be run in a theatre setting, now it has been adapted to be run on the Zoom platform in reaction to our global problems with Covid19. This show has been critically acclaimed in the august halls of the UK’s parliament and corporate/control organisation around the world. It was shortlisted for the Innovation in Story Telling at the Future of Story Telling conference in 2018. There is no audience that does not leave the experience richer, wiser and keener to understand how AI is affecting their world and how their world is affecting AI.

GHAIA – 2016 Created as a fully functional prototype system that ensured greater condition management by conversationally engaging the user in their treatment plan & connecting them with medical, care or research professionals. Enabling independent living, conversationally supporting the activities of daily life and improving health & wellbeing. Capturing, storing and securely relaying real time data gathered via conversation to external apps and devices – informing and empowering the user and their medical, care or research team. With a humanistic and deep ecological value base, the GHAIA system was developed through an ethnographic approach, with features & functions designed and developed with significant patient and clinician input. Interviews were conducted with patients & professionals across a number of medical fields, capturing the wants, needs and actions of the patient, and those invested in their care, throughout the patient journey. Questioning, how to support the best possible health outcome, and potential barriers to achieving this. The data captured from these interviews formed the basis for GHAIA’s features and functionality. We aren’t talking about simple intents and entities here, Elzware’s process - since before these ML (machine learning) methods we pumped up - was always meta in as much as the notion of intents is subservient and indeed a small set of steps in a broader process that engages conversation as it is and doesn’t try to dumb it down into IVR (Interactive Voice Response) level flat procedures that are locked into place inside a black box where a probability of success is all that is standing between a correct response and a potential lawsuit or life changing event. Throughout the interviews, clinicians expressed the want & need to keep patients engaged in their treatment plans, and for patients to have access to relevant and

reputable information. They also communicated the want, as clinicians, to have access to up to date and relevant patient health data - providing a clearer picture of a condition and its progress. Patients overwhelming expressed the want to be in control, stay connected to family & friends and have access to information, advice and support. Across the board carers and family members wanted to know their loved one is safe, happy and comfortable. There are other healthcare stories, but let’s move over towards education for a few paragraphs.

Ravensbot - 2012 The Shift was a project that created a concept and working model of an informal, creative, sociable and collaborative online learning environment for young people aged 16-19, who were not in education, employment or training (NEET). The service was driven by our hybrid form of artificial intelligence to match students’ queries or actions with suitable answers or reactions that may be supplied as text, links, diagrams, video or other multimedia. The responsive web pages encouraged a ‘Conversational Learning Environment’ (CLE) supported by an animated figure known as the Ravensbot. All of this was built in collaboration with; students, staff and NEETs over a period of structured meeting, agile development and trials. This was our first comprehensive foray into a website that was completely driven by a Conversational AI system, up to and including the ability to be “aware” of where the users was, their journey and to match the delivery of the conversation accordingly.

TeachBot – 2009 Before the UK educational sector was refactored from a governmental level during Michael Gove’s rein in the Department of Education, Elzware had been working with funding through local and national organisations to turn the UK’s English Reading and Writing curriculum for 11 to 16 years old into an interactive experience on a piece of hardware called an Ameo (between a PDA and a tablet which hadn’t really arrived yet). The aspirations where high and the collaboration between parents, teachers, policy makers and the students bode well for the further development of the system. Highlights of the build system, which fell foul of the financial crash and government policy changes were:

For students: A personalised learning experience, based on your own pace and goals Provided clarification on terminology or topics, through a detailed glossary Provided detailed instructions, guiding them through each subject area, based on National Curriculum requirements

For parents: Allowed them to track their child's performance Based on National Curriculum advised learning techniques and content, they could

rest assured that their child/children would have the support they need, at a pace to suit them

For teachers: Helped them monitor learning for more appropriate intervention – they could track students' progress without the need for detailed analysis of their work to identify strengths and weaknesses Let them get back to their job – TeachBot was designed to meet National Curriculum targets, so they could get on with inspiring young minds, whilst TeachBot dealt with the common questions raised by students Delivered consistent support, allowing them to focus on students who need help with more complex issues, or to cover topics relevant to the whole class, rather than dealing with individual student issues

DesignBot – 2007 The product was developed to take the essence of the Socratic method and provides it to learners through contemporary technology: an onscreen natural language conversational interface. In the designing aspect of Design and Technology students turned to the DesignBot at any point when they want an outside prompt to help them move their thinking forward The DesignBot dealt with any off-task forays in a tolerant manner but quickly drives the student back on task, helping the student determine which direction to take next. On occasion, when a lack of information/knowledge is identified, the designBot would take the student direct to a website, the relevance of which is determined by the preceding conversation Why highlight these systems? It’s not so that you, the reader, can suck your teeth and be impressed about how far ahead of the market Elzware was, but more so that you can attune yourself to what is possible with the right level of collaboration and a methodology that is fit for purposes, both from the perspective of sector governance and the experience of the user. There are a lot of naked emperors right now in Conversational AI and it is worth taking time to interrogate their actual capabilities as well as their underlying methodologies and attitudes to the data which is being generated and used. That said, this is a perfect time to start a journey to enable the delivery of excellent quality information to those people that are interested in your organisation using Conversational AI, we recommend the Hybrid variety. Good luck to you all and do give us a call if you want some support in consultancy, building or training to work with the best of breed tools in this highly confusing and volatile emerging marketplace. Talk to us on hello@elzware.com

3RD QUARTER 2021 | SYNAPSE

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ADVERTORIAL

How to implement

ARTIFICIAL INTELLIGENCE

in your business and measure its impact

A

rtificial intelligence has been considered a godsend to several industries today. Not only does it improve business productivity, but it also allows human resources to focus on creative endeavors instead of being stuck with monotonous work. If you have been looking to improve the operational efficiency of your business, chances are that you would have stumbled across an AI solution that could potentially revolutionize your business operations. However, since AI is still relatively new, implementing AI solutions can be extremely intimidating and challenging. Accubits, a leading AI development company, AI is in the front seat, driving innovation across our customer’s business lines. We are not just a services company that helps our customers with prototypes for trade shows but we help businesses enable AI in their existing systems and drive profitable results. Be it dynamic CRMs powered by intelligent conversation bots, AI integrated analytics for smarter insights, Predictive self-care health diagnostics, we build intelligent systems that coexist with any of your current infrastructures. But the crucial element after implementing Artificial Intelligence in your business is, measuring its impact.

Businesses are leveraging artificial intelligence through various approaches. In fact, global spending on cognitive and AI systems is expected to reach $57.6 billion this year. One of the most common business applications of AI in business intelligence, which involves the use of algorithms to identify trends and create insights from a company’s database. Using AI for business intelligence is expected to be one of the fastest-growing areas in the field over the next ten years. The benefits associated with the use of business intelligence include infrastructure cost reductions and enhancing operational efficiency.

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Another popular method through which organizations are leveraging business intelligence is through the implementation of business dashboards. Many software companies are developing analytical dashboards that are capable of collecting data from other sources to help managers to make informed decisions. AI can also help optimize business management and enhance business security. The applications of AI don’t stop here. Businesses have started using this emerging technology in several other areas as well. One of the primary incentives for AI adoption by businesses is the advantage that they stand to gain over their rivals. However, there are many other drivers that come into play for AI adoption. Some of the main driving forces include - consumer demand, specific business requirements, Internal experimentation, executive decision-making.

How to measure the impact of your AI solution? There is a major discrepancy between research into AI and the tangible results that businesses experience in the real world. This is why measuring the impact of an AI solution and defining the potential return on investment of the AI project is important. Generally, businesses measure this impact by developing a pro forma. Most service providers will offer pro forma development as a service included with ML and AI development. Essentially, a pro forma refers to a method of calculating financial results using certain projections or presumptions. Some of the primary practices that businesses use to measure ROI include:

Defining key performance indicators (KPIs) Generally, this is an essential step in ML training as there is usually a metric that you’re trying to optimize. Organizations use KPIs at various levels to assess their success at reaching targets. High-level KPIs tend

to focus on the overall performance of the business, while low-level KPIs may focus on processes in particular departments in the company.

Having a benchmark of comparison After you have defined the performance metrics, you can maintain a holdout group to get a better understanding of how the AI solution is performing. This refers to a set of customers who will experience the traditional customer service, as opposed to the AI-enabled scenario. By comparing the satisfaction of the holdout group with regular customers, you will be able to gauge the level of impact that the AI solution has.

Monitoring overtime Most AI algorithms are constantly updated as more training data is made available. This means that the models can change greatly over time and need to be monitored to ensure the targets are being met and the metrics for success are sustaining data changes. Establish a process to monitor models, and re-train and measure impact frequently. Implementing artificial intelligence into business operations can be a very complex challenge. More often than not, you will need a reliable development partner to walk you through the process. If you would like to learn how to embrace AI in your organization, reach out to us today for a no-obligation AI consultation. Drop a note to contact@accubits. com



ADVERTORIAL

AIZATRON’S AIDAS

Platform Delivers an AI Driven Data Analytics Platform at a Fraction of the Cost of Traditional Approaches

D

ata is everywhere, where every modern system now produces data. By combining and interpreting data, we create information. From the early 1970’s to the mid 2010’s we saw the rise of the Information Economy which has now lead to the Digital Economy. In the Digital economy, the scale and complexity created by a connected world makes existing Data Analytics systems ineffective. Companies that have the ability to process Petabytes of internal and external human and machine data, from internal and external sources, while rapidly translating this into business knowledge, that results in automated business action, are the ones that will survive and thrive. Current internal BI and data analytical teams cannot meet the growing demand by business who are increasingly coming under treat from emerging competitors who can process data faster than they can – giving them a competitive advantage. Hence, Aizatron developed the Artificial Intelligent Data Analytics as a Service (AIDAS) platform to address these problems.

In analysing the solution, Aizatron realised that most businesses will have to reinvest Millions of Rands into building such a system and those large businesses that have invested into building data lakes and massive data warehouses have largely failed due to the following reasons: They are using old technology. The world has pivoted during the last 5 years and without embracing a more modern approach, these businesses will continue to burn millions of dollars without seeing the results. The vendor has convinced them that if they buy the latest technology they will be able to have a full blown AI data analytics

platform. What the vendors does not tell them is that there is a huge cost to deploying the solution. Also, the skills needed for that platform is very scarce and the customer will have to pay high dollar rates for the services. The hardware provided is not enough to process high speed data processing and it cannot scale when required. The system is also still based on old technologies like day-end and month-end runs to create information cubes from multiple data sources. Users have to wait for the BI team to deliver the results in 2 to 3 weeks later rather than having their own dashboard and interface where they can produce their own queries and results sets in realtime/near-real-time. Systems are built around reactive analytics addressing problems to late. The ability to do predictive modelling for future insights is limited or takes too long. Hence, with these challenges in mind AIDAS enables any business irrespective of size to have a quick-to-implement, lower risk complete Data Analytics Platform. Combined with the resources and hardware AIDAS will instantly begin delivering value to the business at a fraction of the cost of internally built platforms. AIDAS is a cloud-based, multi-tenant Data Analytics Platform solution currently deployed on the Aizatron AWS VPC. It quickly enables customers to implement a modern Data Analytics Platform at a fraction of cost of traditional solutions. Aizatron has decades of experience in deployment of high speed data analytics solutions for the some of the largest Telcos in South Africa. The customers will only pay for what they need, i.e. the AIDAS platform uses a simple formula based on usage of the system.

AIDAS includes the following: 1. The Aizatron Data Analytics System in the Cloud Analytics Database Artificial Intelligent & Machine Learning Toolkits Data Integration Interfaces into platforms such as SAP, Hadoop, Cloudera, Oracle, Microsoft & various OSS and BSS systems Financial Controls Compliance modules API Gateways to enable Secure API integration Secure Interfaces to third parties & Streaming Social Media Platforms Secure Dashboard & Reporting Interfaces

2. A Full Managed Services Team Full Team that does Design, Implementation, support & ongoing enhancement of the solution Resource Roles includes, Solutions Architect, Business Analysts, Developers, Data Scientists, AI Programmers

3. All Reports, Ad Hoc Investigations, Dashboards & Automated Controls Work with Customer BI, operations, Management and audit teams to produce Reports, Dashboards & assist with Investigations Provide Ad Hoc reports and query support for BI and business operational requirements Build Automated Alerting which can be used to initiate automated business flows Provide Executive dashboards Provide external/3rd party dashboards and reports via secure access using Microsoft security settings. This platform can also be deployed under licence on a customer’s virtual private cloud environment which could be on AWS or Azure.

The AIDAS platform currently processes the following types of data: Internal OSS and BSS systems data that is both human and machine generated. Data from social media such as Twitter, Facebook, LinkedIn, etc. External sources such as open data sources and government sources, 3rd Parties such as Credit Bureaus. IOT systems, such as cameras, access control systems, sensors, etc.

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SYNAPSE | 3RD QUARTER 2021


Artificial Intelligent Data Analytics as a Service

AIDAS


ADVERTORIAL

FROM GARAGE TO GLOBAL:

How CompariSure’s conversational AI is driving digitisation within the Insurance industry

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any of today’s tech giants, think Apple, Amazon and Google for example, had their humble beginnings in garage. For Cape Town based tech start-up CompariSure, the journey was no different. Perhaps it was the austere garage environment that led to creative thinking, or perhaps it was something else, but either way, it was one night in late 2018 while working from the garage when CompariSure Founder and CEO Jonathan Elcock (pictured, left) decided to code CompariSure’s first Facebook Messenger chatbot. At the time, CompariSure was still reliant on the same “old school” digital technology that most players in the industry thought would bring a “digital revolution” a.k.a the traditional website. Fast forward to a little more than 2 years later, and over 1,000,000 people across South Africa have now interacted with CompariSure via its conversational commerce chatbot technology. Even before COVID-19 plunged the world into crisis, artificial intelligence – and more specifically conversational AI – was fast becoming a key feature in the digitisation of customer-facing industries like insurance. CompariSure’s focus on digital distribution

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via chatbots has allowed it to pioneer a new way of selling insurance in South Africa. “In one experiment we ran, we found that one of our insurance partners was able to sell 25x more policies via our chatbot than via their website,” notes Monique Elliot, CompariSure’s Head of Marketing. “This for a traditional A/B test of course, controlling for all variables except the call to action of website versus chatbot,” add Elliot. CompariSure’s founding vision was always to build a marketplace that connects users with quality insurance products from South Africa’s most trusted insurance providers. The profound realisation and insight along the way was that doing so via conversational AI would yield gamechanging results. The CompariSure conversational commerce platform allows companies to have personalised engagements with users at scale, driven by machine learning and data science. “At the end of the day, conversation is a deeply human experience and we’ve found it to be more effective and able to deliver better customer experiences than any other form of marketing or channel of communication,” notes Matt Kloos, CompariSure co-founder and CFO (pictured, right). Conversational commerce brings forth many benefits: consumers get instant

service; companies reduce costs; and human agents can spend their time solving more important issues that truly require the human touch. Despite the global industry moving in a digitised direction for some time, South Africa had been slow to adapt, resulting in an industry that is still heavily reliant on call centres to complete sales and provide customer service. As the first and only authorised financial services provider (FSP) in South Africa to have successfully completed sales via a fully-automated Facebook Messenger chatbot, CompariSure is uniquely positioned to support the industry during this time of major digital transition. CompariSure’s proprietary chatbot technology, which leverages popular platforms like Facebook Messenger and WhatsApp, has enabled heavy-weight traditional industry players like Sanlam, Old Mutual and Momentum Metropolitan to have automated, highly-personalised conversations with consumers at scale. “Insurers are choosing to “future-proof” their business by partnering with CompariSure.”, notes CEO Elcock. “After our breakthrough success in using our chatbot tech to distribute Funeral insurance products via our Marketplace, we started building white-label chatbots for insurers. Over the years, our team has perfected the art of partnering human empathy with machine learning to enable financial institutions to seamlessly integrate conversational AI into their service offering.”, says Kloos. Underpinning the tech firm’s chatbot technology is a deep analysis of reams of data being generated via these authentic conversations. To date, the company has had 10s of millions of touchpoints with end-users, with every conversation point and path being analysed in a constant and ongoing effort to improve the customer experience. “By the end of the conversation, it appears that many end-users are genuinely unaware they were interacting with a chatbot. The chatbot often ends a conversation by wishing the user a good day further, to which many users reply Thanks so much - you too!“ chuckles Kloos. “We put significant effort into ensuring the conversation flow is natural, to the point, and even fun and entertaining – the same way a top call centre agent would operate.” CompariSure’s proprietary chatbot tech continues to evolve with the aim of bridging the financial and digital exclusion divide in South Africa and providing end-users access to a broad range of quality products in the most natural way possible – a simple “conversation”.

To learn more about

CompariSure and its tech offering, head to www.comparisure.co.za


A conversational commerce platform that allows companies to have personalized engagements with users at infinite scale, driven by machine learning & data science

www.comparisure.co.za Authorised FSP 48598


ADVERTORIAL

LEVERAGING AI TO DELIVER ENHANCED SOLUTIONS TO AFRICAN INSURANCE COMPANIES / By Curacel /

Nigeria-based earlystage insurtech startup Curacel in May announced a $450,000 seed round raised to power Insurance intelligence in Africa.

T

he new wave of deep learning techniques, such as artificial intelligence (AI) has the potential to live up to its promise to imitate the perception, reasoning, learning, and problem-solving of the human mind.

And it is happening fast in insurance. Every year, African insurers lose more than $12 billion to fraudulent, wasteful, and abusive claims. Curacel’s flagship CLAIMS platform acts as a bridge between primary care hospitals and Africa’s insurance companies, using advanced artificial intelligence to ensure that insurance companies only pay claims for the correct treatment, appropriate medications and recommended patient therapies. AI and its related technologies will profoundly impact all aspects of the insurance industry, from distribution to underwriting and pricing to claims. Major insurers on the continent currently use our products for claims automation and

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fraud detection, like AXA Mansard, Liberty Health and Old Mutual, as well as more than 800 hospitals in Nigeria, Ghana, and Uganda. It plans to expand into 10 new African countries by the end of 2021. Curacel, an insurtech infrastructure company helping insurers & partners in Africa and other emerging markets, increase insurance’s reach and functionality through cloud-based tools and APIs. Curacel is accelerating its expansion across Africa and facilitate the goal of becoming Africa’s premier provider of embedded finance technology for insurance.

We are launching new products Curacel Capital, a cash advance product that makes it easier for healthcare providers to access working capital to mitigate financial challenges. Delayed payments and other inefficiencies in the payment process means many African healthcare providers often have to make the difficult choice between keeping the books balanced or providing healthcare at a loss. With Curacel Capital, healthcare providers can access lump sums of up to three times

their average monthly billings, based on claims processed on the Curacel portal, ensuring that they can continue to deliver essential services without undue disruption.

Curacel Auto is an easy-to-use solution that allows insurance policyholders to submit their auto claims at the point of incidence. Our solution leverages AI to accelerate the claims process while increasing accuracy. It reviews all estimates quickly and efficiently, accurately identifying necessary repairs or total losses thereby reducing the claim cycle by over 70%.

Our numbers and still growing Curacel has helped their clients reduce fraud, waste and abuse claims payouts by up to 25%, saved them a total of $320,000 and have processed more than 700,000 claims and can process an infinite number. Curacel, an AI-powered platform for claims processing and fraud management in Africa.

Learn more about Curacel as an AI intelligent platform. www.curacel.ai | sales@curacel.ai



ADVERTORIAL

AI: THE FUTURE OF TESTING / By Sastri Munsamy, Executive: Research and Development, Consulting, Inspired Testing /

Testing and software quality assurance (SQA) has always been reliant on the human element, particularly in manually-driven exploratory testing and creating automated functional tests. But as we move along the path of integrating elements of Robotic Process Automation (RPA) with artificial intelligence and machine learning, the future of fully automated AI testing is starting to come into view.

M

ake no mistake, we’re still at the foothills of unlocking the benefits of meaningful AI-driven automation in software testing. But that’s not to say we aren’t making any headway.

Today’s leading software testing and SQA tools like Selenium have started integrating elements of AI technology, although we’re not at the point where unassisted AI can take over any meaningful testing functionality from human operators. Still, I can see a time where bots will be ready to take the mantle from human testers, initially for low-level repetitive tasks, but eventually for more complex types of testing, and that’s when we’ll see the real benefits of the technology, that’s still in its testing infancy today, come to the fore.

The State of Play We’re just starting to scratch the surface of injecting quasi-intelligent automation into our testing processes. This is more prevalent in tasks such as minor maintenance on test automation scripts, where basic self-healing AI technology can pick out changes made to user interface fields on pre-existing scripts, and either report or update them automatically. Indeed, there’s already some interesting work being done linking this type of automation technology to libraries like Healenium, which supports Selenium, adding rudimentary self-healing functionality to the automated tests. It then become quite viable to use tools such as Functionize, with its self-healing, auto-updating features, to identify not just a single field but thousands of data elements, which is the basis for machine learning (ML).

The ideal solution: Agile development + autonomous testing Another good example of the current state of play is ReportPortal, a tool that ingests test results and uses AI patterns to analyse

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the nature and changes in these test results over time. Like any AI technology it takes a while until it has enough data to generate meaningful results, but once it does, the results are significant. Using ReportPortal, we can automatically analyse thousands of test results and no longer need to sift through each failure to determine if it’s a valid defect. Instead, the software automatically and ‘intelligently’ differentiates between valid defects and environmental issues and can then provide valuable trend analysis to determine the impact on current functionality. As any experienced tester will know, there’s always the issue of changes being made to real world applications that are poorly communicated up and down the line, so using the tools we have today can give SQA and automation teams a way to quickly – and automatically thanks to AI – determine where the changes are, what type of changes they are, and help them narrow their test focus accordingly.

The Road Ahead All of these examples are early footprints along the road to what I consider the ultimate goal of AI testing: codeless automation. In a nutshell, codeless automation means we’ll no longer need a high level of development capabilities or coding skills to automate testing. We’re only starting to dabble with technology that uses a GUI front-end to guide us through the process of making test automation repeatable, parameterised and

reusable. But while these are the same userdriven wizards we’re already familiar with across many different types of software and platforms, imagine adding real AI technology that can ‘watch’ what a tester is doing, learn from it, and then repeat the process on its own. Taken a few steps further, I can see a time where data-driven intelligence that links to massive cloud-based data repositories take over currently manual functionality, like assigning real values to field types. Eventually this will evolve into technology we already see in tools like Eggplant, that uses images to automate processes, so instead of working with fields, can intelligently identify images (of buttons or other web elements, for example) and populate them with appropriate data. AI tools that operate at the API level will greatly accelerate test automation to the point where we can conceivably, and practically, implement continuous testing, something that’s little more than a pipe dream at present. We’ll be able to test quicker, and for longer periods, at lower costs. Moreover, there’s no reason for the technology to be limited to test automation, it will eventually spill over into other types of testing like performance and exploratory testing. As we continue along the road towards this very real future, AI will drive significant return on investment from speed and time to market, and deliver more value to more clients that no longer have to split their budgets between different types of testing and focus on a collective single offering instead.



ADVERTORIAL

WISDOM AND TEAMWORK OPEN-SOURCED

Mava’s the Made-inAfrica Multi-Agent Reinforcement Learning Framework Many of humanity’s greatest achievements arose from our ability to work together. The complex and distributed problems the world collectively faces now call for a new wave of sophisticated AI cooperation strategies. Responding to that call, an all-Africa-based team at InstaDeep created Mava.

If you want to go quickly, go alone, an African proverb counsels. If you want to go far, go together

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ou can hear the wisdom of generations about the value of teamwork reverberating through these words. As we face challenges such as managing scarce resources under pressure due to climate change, ensuring critical supply routes keep flowing or enlist robots for remote rescue and exploration missions, weaving teamwork strategies into AI tools is crucial. That’s why InstaDeep created Mava: a research framework specifically designed for

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building scalable, high-quality Multi-Agent Reinforcement Learning (MARL) systems. Mava provides components, abstractions, utilities, and tools for MARL. It can easily scale with multi-process system training and execution while providing a high level of flexibility and new creative possibilities. “At InstaDeep, we have a real passion for innovation, consistent with our mission to build an AI-first world that benefits everyone,” said Karim Beguir, InstaDeep’s CEO and Co-Founder. Several frameworks have emerged in the field of single-agent reinforcement learning (RL), including Dopamine, RLlib , and Acme to name just a few. These aim to help the AI community build effective and scalable agents. However, a limitation of these existing frameworks is that very few focus exclusively on MARL – an increasingly active research field with its own set of challenges and opportunities. InstaDeep aims to fill this gap with Mava.

By focusing on MARL, Mava leverages the natural structure of Multi-Agent problems. This ensures Mava remains lightweight and flexible while at the same time providing tailored support for MARL. InstaDeep’s decision to opensource Mava stems from its passion for contributing to the development of MARL, supporting open collaboration, and a commitment to helping develop the wider community, especially across Africa. InstaDeep itself has also benefited from open-source software and wants to give back. “We’re proud to open-source Mava, a world-class framework entirely designed and built by an all-African, all-star team of InstaDeepers,” Beguir said. Mava is the latest in a flurry of 2021 open-source releases by InstaDeep, including three massive bio data repositories as part of DeepChain Apps in May and a natural language processing (NLP) model for the Tunisian dialect, an under-resourced African language, in June. “Working on Mava has been a wonderful experience and a true team effort in collaboration with our African offices in South Africa, Nigeria and Tunisia,” said Arnu Pretorius, the InstaDeep AI Research Scientist who leads the team in Cape Town. “It really showcases the talent we have on the continent. Not only have we begun to enter the conversation of AI,” Pretorius said, adding, “but we are now starting to take ownership of key technologies, helping to shape the future and contributing to making the world a better place using AI.”

Why MARL? In Xhosa, one of South Africa’s eleven official languages, “Mava” means experience or wisdom. Only by working together, has humanity been able to accomplish some of its greatest achievements. This has never been more true. The problems we face are distributed, complex and difficult to solve and often require sophisticated strategies of cooperation for us to make any progress. From the standpoint of using AI for problemsolving, this drives us to harness and develop useful computational frameworks for decision-making and cooperation. One such framework is MARL. MARL extends the decision-making capabilities of single-agent RL to the setting of distributed decision-making problems. In MARL, multiple agents are trained to


Distributed training for multi-agent reinforcement learning in Mava)

act as individual decision-makers in some larger system, while learning to work as a team. The key difference between MARL and single-agent RL is that MARL can be applied in situations where the problem becomes exponentially more difficult to solve as it scales. This could be a problem such as managing a fleet of autonomous vehicles for a growing population, the number of navigation decisions that must be made at any given moment scales exponentially with the number of cars on the road. This quickly becomes intractable for single-agent approaches. For MARL, it’s an opportunity to shine. Many of humanity’s most pressing practical problems are similar to this one. MARL has enormous potential to be applied across various sectors from health to transportation, from logistics to agriculture. MARL can make problems of this kind manageable, however, it introduces other difficulties such as the need for decentralised coordination. To be fully effective at scale in new situations, we’ll need researchers to develop new strategies and techniques.

A research framework for MARL Mava offers several useful and extendable components for making it easier and faster to build Multi-Agent systems. These include custom MARL-specific networks, loss functions, communication, and mixing modules. Perhaps the most fundamental component is the system architecture. The architecture defines how information flows between agents in the system. In Mava, several architectural options are available to help design systems, from independent agents to centralised training schemes and networked systems. Furthermore, several MARL baseline systems have already been integrated into Mava. These serve as examples to showcase Mava’s reusable features and lets developers easily reproduce and extend existing MARL algorithms.

MARL at scale So how does it all work? At the core of Mava is the concept of a system. By system, the Mava team means a full MARL algorithm specification comprising the following components: an executor, a trainer, and a dataset. The executor is a collection of single-agent actors and is the part of the system that interacts with the environment, that is performs an action for each agent and observes each agent's reward and next observation. The dataset stores all of the information generated by the executor. All data transfer and storage is handled by Reverb. The trainer is a collection of singleagent learners, responsible for sampling data from the dataset and updating the parameters for every agent in the system (see illustrations). The system executor may be distributed across multiple processes, each with a copy of the environment. Each process collects and stores data that the trainer uses to update the parameters of the actor-networks used within each executor. How we distribute processes is defined by constructing a multi-node graph program using Launchpad. Consequently, Mava can run systems at various levels of scale without changing the underlying system code.

On the shoulders of giants InstaDeep acknowledges that Mava is indebted to several open-source libraries. In particular, Mava is built on top of DeepMind’s Acme framework and was heavily inspired by its design. It integrates with, and greatly benefits from, a wide range of already existing singleagent RL components made available in Acme. Furthermore, we inherit the same RL ecosystem built around Acme. Most notably, we use Reverb for data flow management and support simple scaling using Launchpad. Mava has also been influenced by, and made use of, other libraries including PyMARL and OpenSpiel as well as environment-specific libraries

such as PettingZoo, Flatland, RoboCup, and the Starcraft Multi-Agent Challenge (SMAC).

From research to development InstaDeep’s engineers tackle some of the toughest real-world problems, not only at a macro level, such as scheduling thousands of trains across a vast network but also at a micro level, such as routing electronic circuit boards in hours, instead of days or months. The collaboration between InstaDeep research teams and engineers has proven to be a key ingredient in these successes. The Mava framework goes one step further by offering a frictionless transition from InstaDeep’s in-house research to product development, creating synergies between its teams. The flexibility of the framework and its capacity to seamlessly scale is a critical ingredient for our research and engineering teams to deliver new products, services, and research breakthroughs that were previously out of reach. InstaDeep is excited about the future of Mava, its growth, and its ongoing development. This release is only the beginning. Not only for making its research in MARL more efficient and scalable and sharing our efforts with the community but also for using Mava directly in applied InstaDeep projects. “To me personally, Mava represents a step in an exciting direction in unison with many others on the continent who are seeking to shape AI’s future,” InstaDeep’s Pretorius said. “This is only the beginning and I’m excited about the possibilities ahead.”

You can find more information, source code, and examples in Mava’s Github repository

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ADVERTORIAL

Mintor is a multi-awardwinning social enterprise that radically enhances HR performance for businesses and community impact for nonprofits through their recruitment, training, payslips, leave management and employee engagement chatbot tools.

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he chatbot integrates with the chat apps that potential or existing employees already use and trust (e.g. Whatsapp) on the one side, and with the business' existing HR systems on the other side, thereby streamlining communications between the business and its target audience. Through using chatbot tools for talent sourcing, screening, shortlisting, interview liaison, onboard training, skills training and even CV-building, Mintor’s customers (medium to large businesses and nonprofits) experienced a 33% reduction in recruitment cost in only a tenth of the usual time, and had a 70% improvement on employee retention within the first year. The tools also enable workers to receive and query their payslips, submit leave requests and sick notes, and receive training on technical and soft skills, anytime and from anywhere. “Our solutions were developed with the focus of being inclusive of those from marginalised or developing country environments where computer and data access is still a barrier to access employment and HR support. It also brings significant improvements in HR efficiency in industries where workforces typically do not have computer-based roles, eg. in retail, manufacturing, agriculture, mining and construction.” says Leànne Viviers, founder of Mintor.

Here are some examples of Mintor in action: A large organisation in the fishing industry turned their traditional paper-based job application process completely mobile through Mintor’s whatsapp recruitment tool. For the first time, applicants can apply from anywhere in the country and only need to travel to the harbour for their final interview after they’ve been screened and shortlisted by Mintor’s matching algorithm. As a result of this chat-based and AI driven process, the organisation doubled their usual number of applicants and saved 40% on their recruitment time in not having to deal with repetitive administrative work, and hence freed up

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ENGAGE AND EMPOWER YOUR PEOPLE, ONE CHAT AT A TIME

Whatsapp chatbot and AI tech for radically enhanced HR performance and employee impact time for the HR team to focus on other employee needs. As part of their give-back campaign, a retail giant leveraged Mintor’s platform to empower their youth customers with a cv-building tool and interview skills training all through their favourite chat app. Within just 4 weeks, over 20,000 youth participated from all over South Africa, most of whom have never had access to career support before. In return, the youth completed a customer survey that provided the retail company with invaluable customer insights on their new product range. Administering training of remote workforce is very costly, time consuming and often ineffective. The largest dairy company in Indonesia decided to turn the tide and converted their training into chat-based delivery leveraging Mintor’s platform. They are now able to upskill their workforce all over the country, enabling them to learn on the go in bite size chunks, as and when it suits their schedule, which not only makes

them more productive, but also improves the quality of their work. Mintor was born in Cape Town in 2015 out of the dire need to help South Africa’s unemployed, a staggering third of the working age population and nearly half of all youth, to find decent work, retain their jobs, become more productive and ultimately improve their living standard. They already supported nearly 100,000 people in South Africa and are expanding their innovation this year further into Africa and South-East Asia. Mintor’s vision is to improve the lives of millions from marginalised communities globally, leaving no-one behind, and they invite you to join the journey. info@gomintor.com | www.gomintor.com


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ADVERTORIAL

GAP IDENTIFICATION

in Real Time Creates Revenue Opportunity / By Andrew Dawson, MD, Cognizance /

We use an open API to integrate into most mobility platforms to ensure "direct to individual tasks". The process, insights and quantification of the revenue changes are made available into a perpetually live dashboard. We use KPI alignment to ensure the relevant data arrives at the right person in time to make a difference in revenue growth. The process, insights and quantification of the revenue changes are made available into a perpetually live dashboard. We use KPI alignment to ensure the relevant data arrives at the right person in time to make a difference in revenue growth.

Cost to Serve

Distribution to Retail In the formal retail sector the bulk of the use of AI and data science has been in the mining of till point, stock on hand and stock in distribution centre data to give meaningful input to the shopper experience, ensure shelves are correctly stocked and that stock from warehouse to the retail store is optimal. This utilisation of AI and Data Science will be further influenced by the advent of “autonomised revenue opportunity recognition platforms” but that is subject matter for another article! My focus here is to touch on one of the most important links in the retail supply chain. The role of the Distribution Business and how AI and Data Science is a pre-requisite to ensure survival and profitability in the midst of the most chaotic turbulent times, where the operating margins and commissions are so thin that delayed response to a costly deviation can cripple operations.

tools, and employ data science teams to interpret the visualised results.

Who/what is Cognizance? We provide similar technologies to the platforms that perform the above functions such as: Tableau, Power BI, AlterX, Qlikview and Yellowfin Data Lake within GCP (Google) ETL Pipelines for data ingestion Advanced processing structured for speed of data ingestion and analysis Proprietary Dashboard structure for visualisation of data Unique data deviation detection methodology aligned to business rules and KPI's

In Closing:

In essence:

We use your data, our technology, experience and expertise to identify gaps in your distribution and retail supply chain (stock and sales) and converting those gaps into revenue. At Cognizance we were blessed to be given the opportunity to work with five significant distribution companies simultaneously, the companies operate in different regional geographies being SA , Namibia, Eswatini, Botswana, and Zimbabwe. The result was the creation of a data lake that was optimally structured to ingest massive volumes of ERP and third party generated data that we were able to mine and visualise in our own proprietary dashboards. We have completely negated the need for the customer to acquire BI and Visualisation

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In a distribution environment where margins are thin, any impact on the “cost to serve” commission negotiated with the principal can have a negative impact on the bottom line, Sales to retail need to be “profitable”, deliveries need to show a profit per load, promotions need to be optimised so that sales pull through and there are no returns to impact on earnings. The Cognizance auto-analytics layer can be set up to report on benchmark deviations direct to the finance person accountable as it is detected through the data. In essence, if given access to the right data feeds the platform will create an income statement per category per principle where profits and losses are identified as they happen and the right individual who is accountable for the action to rectify is advised accordingly.

Gaps are identified as but not limited to: Rates of sale vs stock on hand and stock in manufacturing Product mix per supplier region per community at sku level Stock out and over stocks, including returns Predictive ordering and forecasting Price elasticity defined by Promotional Influence Analysis Our platform identifies, quantifies and directs action and accountability to an individual via task allocation, measurement and tracking.

As our customer base grows our data science team becomes more and more exposed to new territories so our Intelligence layer gets more adept at identifying gaps and pattern changes and making sure the gaps are met with action instructions to resolve. The next horizon is the science of forecasting and scenario planning using the layers of data, Micro and macro influences per market that we are involved in. To make sure that we lead the way in this space we have partnered with one of the leading Global Forecasting and Scenario planning companies based in the USA, The company DecisionNext and Cognizance will use our combined technologies and data science teams to create a differentiated value proposition in the food, FMCG and liquor distribution space. We are working closely with a number of business consulting houses across Europe and Asia and the USA. If our technology offering can add value to your customer base, please reach out directly.


INDUSTRY

ARTIFICIAL INTELLIGENCE (AI)

– The Stuff of Star Wars or How the World is Truly Advancing?

/ By Andre van der Merwe, member & director Cirrus AI , B Sc B Proc LL B SAIIPL Fellow – Patent and IP Attorney /

Interestingly, USA President Abraham Lincoln had once said of US patent law that “it added the fuel of interest to the fire of genius.”

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n present times, AI and AI-related innovations and creations seem to epitomise “the fire of genius” - as mentioned by Abraham Lincoln. AI was once dismissed as belonging to the realm of science fiction, but it certainly is very real, although it does not often feature in the daily news. In fact, the world appears to be on doorstep of an AI-era, with AI being one of the most important technologies of our time. In support of this, Google CEO Sundar Pichai has compared the impact of AI on the world to “the discovery of fire and electricity.” In similar vein, Microsoft CEO Dave Coplin has said that AI is “the most important technology that anyone on the planet is working on today.” In practical terms and for those not familiar with AI, this can be generated only by an ultra-powerful computer system having a massive computing facility (-the hardware being very costly and highly sophisticated), having access to extensive data-banks, being operated by highly skilled personnel (such as software developers/ mathematicians/scientists/engineers), and by using advanced algorithms in the software. Unfortunately to date the lack of resources and capabilities in this field have largely excluded Africa and third world countries from developing and accessing AI locally. Hence the third world is rapidly falling behind the rest of the world especially in the field of technology and business

innovation and development. In South Africa, spite of various shortcomings, the Council for Scientific and Industrial Research (CSIR) both on its own and in collaboration with certain local universities have conducted research inter alia in respect of information and communication technologies, with AI showing some development and growth during the last 15 or so years. See the article by A Ferrein and T Meyer entitled “A brief overview of AI in South Africa” in the magazine Worldwide AI (2012). However, to date it does not appear that much significant practical innovation has emerged from this work - although the author hereof is aware of an instantaneous translation system used at the North-West University (and developed in collaboration with the CSIR), which provides its under-graduate students with lectures

simultaneously in three official languages namely English, Afrikaans and Setswana. Two interesting and thought-provoking short articles on AI were published in the December 2019 edition (Vol 4 Issue 6) of the NewsBriefs publication of the South African Institute of Intellectual Property Law (SAIIPL). The first article was written by Mr Stephen Middleton and focused on AI-generated inventions, and the requirements and problems (such as inventorship) surrounding these inventions and the patenting of such inventions. For those persons who thought that AI-related innovations were limited to the field of technology, the second article by Mr John Foster would have been a surprise because it focused on AI-created works of a copyright nature such as literary works, musical works, artistic works, and sound recordings. In discussing the issue of authorship, the general question was posed whether or not human involvement in the creation of such works is a requirement for the existence of copyright protection. Reverting to the field of technology, of course it always advances more rapidly than the law, especially the law that protects such technology, and this disparity will certainly challenge the fundamentals of the world’s patent and IP legal systems in respect of AI and AI-related developments. This article presents a brief overview of AI and its challenges to the patent system of the USA - and by implication to the patent systems of other countries including but certainly not limited to the EU and its countries, the UK, and South Africa – based on a publication by the World Economic Forum issued during 2018 and referenced below.

How is the World Approaching AI? In 2017 the European Parliament had adopted a resolution and recommendations to the Commission regarding Civil Law Rules on Robotics, and also in 2017 the China State

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INDUSTRY

Council had issued its New Generation of Artificial Intelligence Development Plan. These documents discussed the interactivity between AI and the separate IP legal system of each of the EU and China, respectively. To date, although the Obama administration had issued three reports on AI, the USA has not to date, to the knowledge of the present author, issued a comprehensive discussion document that deals with the interaction of AI and AI-derived innovation, on the one hand, and the US patent system, on the other hand. Against this background, in 2018 the World Economic Forum (hereinafter “the WEF”), from its Centre for the Fourth Industrial Revolution, published a comprehensively researched and interesting White Paper entitled “Artificial Intelligence Collides with Patent Law.” (hereinafter “the WEF White Paper”). Readers hereof are referred to the WEF website for the full text of the WEF White Paper which deals in detail with AI and AI-derived innovation and their interaction with US patent law. The views expressed in the WEF White Paper are those of the (four) authors and do not necessarily express the views of the WEF, or its Members and Partners but such White Papers by the WEF describe research in progress by the authors and others, and are published to elicit comments and further debate.

The WEF White Paper – Developments in AI Background and overview of technological advances – Historically, the English mathematician Alan Turing had introduced AI as a theoretical concept in a paper he had published in 1950. Following that, in 1956 the American computer scientist John McCarthy had accepted and coined the term “artificial intelligence” at a scientific conference in the USA. However, since then no universal definition of AI has been accepted by persons active in this field. It has variously been defined broadly as a computerised system exhibiting behaviour commonly thought of as requiring intelligence – and also as being a system capable of rationally solving complex problems or taking appropriate action to achieve its goals in real-world circumstances. AI is sometimes described based on its problem space such as logical reasoning, knowledge representation, planning, navigation, natural language processing (NLP) and perception, or sometimes on its overlapping sub-fields, including machine learning (ML), deep learning, artificial neural networks, expert systems and robotics. AI is also sometimes categorised based on its intelligence level eg artificial general intelligence (AGI) - a level of intelligence comparable to that of the human mind – or narrow AI that is the form of AI generally seen today that focusses on solving specific tasks. AI’s technological progress has accelerated in the last twenty years based on advances in ML algorithms, massive growth in the

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availability of data, and improved and cheaper computing power. This progress, especially in the last ten years, has led to AI being used to “perform activities which used to be typically and exclusively human” and to develop “certain autonomous and cognitive features for example the ability to learn from experience and take quasi-independent decisions.” Although we do not see much of this in the public realm in South Africa, AI is now revolutionising the way people live, work, learn, discover, innovate and communicate – and placing the world on the threshold of a new and exciting era where the outcomes of AI are set to unleash a new industrial revolution. In the 2nd Obama report (2016), it was stated that the current wave of progress and enthusiasm for AI began around 2010, driven by three factors that built upon each other namely: the availability of big data from sources including e-commerce, businesses, social media, science and government; which produced raw material for dramatically improved machine learning approaches and algorithms; which in turn relied on the capabilities of more powerful computers.” The WEF White Paper reports that global investment in AI has been growing rapidly, with up to $39b being invested in AI development by companies in 2016, including up to $30b by tech giants (and up to $9b by start-ups). This represents a 3 times growth level since 2013. Global revenue in cognitive systems and AI was expected to grow from almost $8b in 2016 to more than $47b in 2020. Similarly, AI has led to a race for patents and IP rights among the world’s leading technology companies. In fact, the number of AI patents granted globally increased by a factor of three from 2012 to 2016, with the USA alone seeing an increase of 1 628 AI patents issued in the same period. Advances in AI’s “inventiveness” – AI is no longer simply “crunching numbers very quickly” but is generating works of a sort that have historically been regarded as “creative” or as requiring human ingenuity. AI can now independently learn how to perform complicated tasks, prove mathematical theorems, and engage in artistic endeavours such as composing musical works and creating sophisticated artistic works. Using techniques that have developed from our understanding of evolution, molecular biology, neurology, and human cognitive processes, AI is transforming computers into “thinking machines” that are capable of performing creative and inventive tasks at unbelievably high speeds. Already in 1994 the AI pioneer Stephen Thaler had developed the so-called Creativity Machine which was capable of generating new ideas through artificial neural networks. These networks are collections of on/off switches that automatically connect themselves to form software without human intervention. This system can “brainstorm” new and creative ideas by combining an artificial neural network with another network that assesses the value

of the output. The Creativity Machine was apparently involved in generating an invention that was ultimately granted as US patent No. 5,852, 815 in May 1998. This became the first US patent issued on an AI-generated invention. Thaler had cited himself as the sole inventor without any mention of the Creativity Machine. Another interesting example is the so-called Invention Machine developed by the computer scientist, John Koza. This system is based on genetic programming and is modelled on the process of biological evolution. It is understood that the Invention Machine created an invention that resulted in US patent No. 6,847, 851 granted in January 2005. Likewise, Koza and two other persons were cited as inventors, with no mention of the Inventive Machine. Further examples of AI inventiveness include computer systems programmed to independently design a new nose cone for a Japanese bullet train; to design new piston geometries for reducing fuel consumption in diesel engines; and to help develop new pharmaceutical compounds. In a totally different (and surprising!) field of activity - and one that will interest patent attorneys and other patent practitioners - AI technologies have emerged recently to help draft patent applications ie patent specifications and claims. This of course encroaches on territory historically requiring human ingenuity and input from inventors and more particularly by patent attorneys/ practitioners. Is this a challenge or an opportunity, or both, for patent attorneys/ practitioners world-wide? Some examples of this early capability are mentioned below. An example of the above has been developed by Cloem, a French company, that uses NLP technologies to assist patent applicants to generate patent claims and variants of patent claims, called “cloems.” Another example is AllTheClaims.com and its sister project, AllPriorArt.com (collectively AllPriorArt) which can autonomously generate patent claims and descriptions after parsing and randomly re-assembling patent texts and published applications from the US patent database. A more recent AI-based service called Specifio can prepare software-focused patent applications, even drafting patent specifications and figures after receiving a set of patent claims from a user of the system. Apparently, this system can generate patent applications that are about 90% complete, requiring a substantial reduction in professional time for a patent attorney/ practitioner to complete. Although such AI platforms still have challenges to overcome at this stage, these appear to forecast a future where AI could reliably and accurately generate parts of, or entire, patent applications, at least in a draft form, without much or extensive input from patent attorneys/practitioners. Taking the above to its logical conclusion, although it may sound highly speculative at this time, the question arises - would it be


INDUSTRY

possible in future for invention-creating AI to autonomously complete both the inventive and patenting processes ie without human intervention? AI’s entry into fields that have historically required “human ingenuity” raises various critical legal and policy questions that need to be addressed. For example, should AIgenerated inventions be protected, and if so, to what extent? And if the patentability of AI-output inventions becomes legally accepted, then should AI also receive inventorship status? Increased acceptance of AI – The public’s view on AI has become friendlier and more acceptable in recent years – possibly coupled with a better understanding of AI and its potential benefits to society. This can impact on legal and policy considerations. In the USA, for example, the approach to AI has been more conservative and guarded than in far-Eastern countries and in Europe. By contrast, a survey conducted in recent years by the European Parliament has shown that 68% of people surveyed expressed positive views on AI while 79% had positive outlooks on robotics.

AI and How it Interacts - and Conflicts - with Patent Law In the USA, as far as the author hereof is aware, no official guidance or law amendment has been provided (-other than a decision by the US Patent Office recently – see the Updating note provided below), and very little discussion has taken place, regarding the repercussions or impact of AI on US patent law (which in certain respects could also in due course apply to the patent law of other countries). The WEF White Paper concludes, after comprehensive and objective reasoning but without coming to any definite proposals, that the US patent law “governance” and treatment of AI can have significant impacts on innovation, the economy and society. Given how quickly AI is advancing, it is paramount that the relevant stakeholders – patent and non-patent professionals alike – proactively and urgently engage in further research and discussions with one another to find ways for the patent system to promote innovation while minimising any negative social and ethical implications. More particularly, the WEF White paper explores four main patent issues affected by AI that merit further discussion, as set out briefly hereunder. The present US standard on patent-eligible subject matter – Firstly, the present US standard on patent-eligible subject matter needs to be carefully evaluated to determine whether it has any material negative impact on AI or AI-driven technologies per se (such as computer software). If so, the relevant actors should search for possible adjustments to the standard that can better achieve the main objectives of patent law such as promoting innovation, disseminating useful information and incentivising investment in helpful technologies.

The anticipated benefits from the contemplated changes must then be weighed against any possible negative social and ethical implications that may arise from such changes. The relevant actors should also consider other available mechanisms for promoting and protecting AI innovation (eg laws on trade secrets or copyright) to help assess whether any of the identified shortfalls in the present patent law subject-matter eligibility standard can be rectified through other means. Protection of inventions created entirely by AI? – Secondly, the question of whether inventions that are created entirely by AI should be protected by patent law needs to be answered. To help arrive at an effective solution, the relevant actors must diligently analyse the potential positive and negative effects – from technological, socio-economic and ethical viewpoints – by patenting AIgenerated inventions, and then assess these effects in view of one another. Possible middle grounds between the competing interests must be identified to help the patent system achieve its main objectives in a well-balanced manner. If the relevant actors ultimately decide to allow AI-created inventions to be patentable, then they must also decide whether inventorship should be awarded to AI’s that have generated those inventive concepts. An updating note by the author hereof – The recent US Patent Office decision of 27 April 2020 on US patent application No 16/524,350. In this decision the US Patent and Trade Mark Office ruled that AI systems cannot be listed or credited as inventors on a US patent. The decision further stated that an “inventor’ under current (US) patent law can only be a natural person. This ruling follows similar stances adopted by IP Offices in other countries – and clearly confirms the present approach of the US Patent Office. In order to change this approach by the USPTO, an amendment to US patent law would be required. Patent infringement by AI and related liability – Thirdly, present US liability laws do not account for situations where patent infringement is committed by AI. The relevant actors need to explore “who” (or “what”) should be held liable in those situations and how compensation should be assessed. The different existing liability frameworks must be analysed to identify their relative strengths, and new approaches should be explored to see if these can function more effectively than existing liability systems. “POSITA” - Are changes required to the present definition? – Fourthly, further discussions are required to determine whether changes are necessary to the present definition of “a person of ordinary skill in the art” (“POSITA”) which is a hypothetical person with which obviousness of an invention is assessed in terms of US patent law. As the use of AI becomes more prevalent, the actual persons “of ordinary skill” who work in various industries will increasingly rely on AI. On

the one hand, a categorical exclusion of AI’s involvement from the definition of a POSITA can risk having a non-obviousness standard that fails to accurately reflect the real-world level of obviousness. However, on the other hand, as AI becomes “smarter,” incorporating the use of AI into the definition of a POSITA would likely result in more inventions being deemed obvious, which would likely result in fewer patents being granted. In this scenario, if AI reaches super-intelligence at some future time, would that not mean that everything, or at least most inventions, will be considered obvious? These questions must be studied to help arrive at a non-obviousness standard that is realistic and accurate.

Conclusion Consideration by role players in the United States on the above issues (which are discussed in considerable detail in the WEF White Paper) needs to be comprehensive and multi-faceted so that an optimal balance can be struck between the various competing factors. This will improve and assist US patent law to continue adding “the fuel of interest to the fire of genius”, as stated by President Abraham Lincoln, in ways that are socially inclusive, ethically responsible and legally/ technically meaningful. This could also in due course, by analogy, point or guide the way to corresponding or similar AI patent law reform in other jurisdictions. Andre van der Merwe

The author hereof is a member and director of Cirrus AI. This is a South Africa and Africa AI effort – a private sector-led initiative to create a world-class AI capability to support African research and development across academia and industry. Another member of the Cirrus team, Dr Jacques Ludik, has recently authored a book (published by Amazon) entitled “Democratizing Artificial Intelligence to Benefit Everyone” that is recommended for further general reading on AI issues.

The article was first published in the Newsbriefs Publication of the South African Institute of Intellectual Property Law

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INSIGHT

A DATA MANAGEMENT PLATFORM FOR THE GLOBAL ML COMMUNITY To engineer our way out of data engineering and deliver a systematic solution to the data crisis / By Gregg Barrett, Head of Cirrus /

Introduction If there is one immediate action that the Machine Learning (ML) community needs to take, it is to deal effectively with the data crisis. The data crisis that is upon us is driven by the cost and complexity of data engineering and the hodgepodge mix of nascent tools and approaches that are being thrown at it. For far too long there have been discussions about “democratizing AI”, with initiatives and efforts spanning the full gamut of the imagination. Yet the key ML ingredient, data, has seemingly been absent from the discussion. At the risk of pointing out the obvious, a properly functioning ML pipeline requires the democratization of data before there can be any democratization of ML. 42

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here are very few organizations whose ML efforts are not severely hamstrung by the resources needed to develop and maintain datasets. From academia to industry, from materials science to law, the need to store, manage, share, find and use data for ML research and application is an obstacle that few can surmount. This is significantly impeding the advancement of ML across academia and industry with substantive societal impacts, including hindering scientific research.

To be clear, the democratization of data is not just about access to data, but the empowerment of those that need to use it. That we have placed a name on the skills and tooling needed — data engineering — does not help in solving the problem one iota. While eliminating data engineering altogether is unrealistic, there must be an effort to significantly mitigate it. To do so a dominant open source design for a data management platform for ML is called for that is 1) available to the global community (commons) and 2) usable by persons who are not programmatic experts. A handful of the world’s foremost technology firms have the resources and expertise to undertake the development of data management platforms for their own internal needs. This however only reinforces a situation of the have and have-nots. A dominant open source design originating from the cloud is unlikely as the major cloud providers are each building their own proprietary platforms. There is however nothing stopping the cloud providers from implementing their own version of a common platform that is fully integrated into their stacks.


Requirement When referring to a platform I am are referring to an all-in-one solution for data management. At a high level the data management platform needs to target the following: Empower data science and ML users around the world by taking the engineering out of data engineering. Support management of the full data life cycle in ML. Support the ML lifecycle with integration across the ML workflow. Support the predominant ML frameworks. Support a variety of ML data including sensor and instrumentation data.

What needs to be recognised While this all sounds great, the problem is that no such platform exists, so the question is how do we get there? In recent times I have been debating this with several individuals and organisations and have concluded that the following needs to be recognised: Not an engineering problem It must be realised that the absence of a dominant design for a data management platform is not an engineering problem but a strategic issue that goes beyond engineering. Expertise and resources Big tech has the expertise and resources to develop and maintain such a platform and have viable internal development efforts underway to serve their own internal needs. Startups by contrast do not have the resources, experience, and user base to initiate it. To kick-start the effort it should be “capitalized” with the contribution of one or more of the internal platforms currently under development within big tech. Initial user base and adoption If the platform is to have any sort of future, it will need to have a roadmap for widespread adoption — including the internal requirements of big tech. The best way to obtain this adoption will be through collective collaboration on the platform itself. A big tech genesis will give the platform the necessary adoption due to large internal and external user communities. A large user base amongst other things will help to future proof the platform, justify the cost of development and maintenance, and drive continued adoption and investment in the platform. It will also signal to the broader community that it is worth making the investment in learning and adopting the platform from 1) a people standpoint — think universities incorporating training programs pertaining to the platform within their curricula, and 2) startups and technology provide supporting integration from a technical standpoint.

Collaborative economics Data engineering challenges are a cost even for big tech and collaborating on a dominant design for a common platform would reduce development costs for big tech and significantly so for everyone else. The platform would not be a direct revenue driver, rather a useful contributor to a revenue generating ecosystem by increasing the application and adoption of ML. Collaboration at this level makes economic sense — coopetition. Architectural debate in waiting A Data Orientated Architecture vs Microservice Architecture is a key point of discussion that may not be resolved, at least in the near term. Pursuing one or both is feasible as long as there are several firms pursuing it.

Next steps To move beyond talk a growing group within the ML community are reviewing options to initiate the following:

1. Establish an engagement mechanism and facilitate the coordination and collaboration of industry on the development and release of a data management platform. 2. Recognise the needs and call out the requirements of a dominant design for a platform. 3. Review current development efforts and database technologies and the sharing of experience in building such platforms. 4. Devise new solutions and a development roadmap that accounts for future challenges. 5. Coordinate the development and release of a platform for the commons. 6. Coordinate the maintenance and future development of the platform. Summary The data science and machine learning community in collaboration with leading technology firms need to bring together existing development efforts to coordinate the eventual release of a data management platform for the commons. A data management platform for ML that ensures the consistent and high-quality flow of data throughout the ML lifecycle will make building high-quality datasets, and the building of ML systems more repeatable and systematic. Now is the time for a united effort to make data engineering uncool again. It starts with recognizing that substantively more can be achieved by a group of organizations collaborating on a platform as requirements are broadly similar and where there are differences within the group those differences are shared by others around the world.

Related reading: A review on data cleansing methods for big data An evolution of data-oriented programming Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess? Challenges in Deploying Machine Learning: a Survey of Case Studies Cloudy with High Chance of DBMS: A 10-year Prediction for EnterpriseGrade ML Data Engineering for Everyone Data lifecycle challenges in production machine learning Data Management Challenges in Production Machine Learning Data Platform for Machine Learning Data Scientists in Software Teams: State of the Art and Challenges Data Validation for Machine Learning Detecting data errors: Where are we and what needs to be done? Extending Relational Query Processing with ML Inference Firebird: Predicting fire risk and prioritizing fire inspections in Atlanta. For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights ItemSuggest: A Data Management Platform for Machine Learned Ranking Services Modern data oriented programming Data readiness levels People, Computers, and The Hot Mess of Real Data Rethinking Data Storage and Preprocessing for ML Rules of machine learning: Best practices for ML engineering Technology readiness levels for machine learning systems tf.data: A Machine Learning Data Processing Framework Uber’s Big Data Platform: 100+ Petabytes with Minute Latency Zipline: Airbnb’s Machine Learning Data Management Platform

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INSIGHT

IS JOHANNESBURG THE ARTIFICIAL INTELLIGENCE TECH CAPITAL OF AFRICA? We are often asked, “How big is the Artificial Intelligence Tech Sector in Africa?” So we set out to research that very question and the results of two years of analysis are now starting to emerge.

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owards the end 2017 we set out to fill a gap in the Africa artificial intelligence / 4IR tech business sector in Africa by creating The AI Media Group. Launching in early 2018 we set out to create a new hybrid analysis, trade, consulting and media agency that would have at its heart, a thriving community discussing the application of 4IR technologies. We also wanted to help unite decision makers, investors, buyers, suppliers, innovators, SMBs and global brands in the Africa region. Over the intervening three years, we created the AI Expo Africa community, with 17K+ practitioners and the largest B2B Artificial Intelligence, RPA & Smart Tech community trade event in Africa. Coupled to our curated business audience we also launched Synapse trade magazine, a unique quarterly trade journal giving a voice to small and large companies alike and a platform to showcase Africa 4IR products and services to a much wider global community. As the now de facto regional trade body focusing solely on B2B 4IR trade in this technology category, we are now a trusted soft-landing entry point into the Africa market for trade missions, corporates and start-ups seeking to launch in this region. Dr Nick Bradshaw, CEO and founder of AI Media Group states, “It wasn’t like that in the beginning, starting as a small bootstrapped company ourselves, we quickly realised the analysis landscape for this sector was literally zero and what available reports we could find lacked depth and country coverage. Established analysis firms had fairly “blank” maps or fragmented data sets on the region which was surprising to us as this was not the reality we saw on the ground. So a key goal for us was to shine a light on the growing and vibrant AI tech ecosystem on the African continent.” Bradshaw adds, “Over the last two years we have been building a much more detailed picture analysing organisations that are active in the African AI tech ecosystem both local, regional and global, as well as commercial and non-commercial entities. We also looked at No. employees, Industry, HQ City, Country, Sector, Year founded and Specialities.”


INSIGHT

“ Over the last two years we

have been building a much more detailed picture analysing organisations that are active in the African AI tech ecosystem both local, regional and global, as well as commercial and noncommercial entities

The trends are now emerging with some top level insights being….. We have so far looked at 1500+ companies to establish a baseline data set Of the 1389 companies we could find consistent data on, 826 (60%) are based in the Africa region Of those 74% are based in South Africa, 6% Tunisia 6% Nigeria 3% Kenya 2% Egypt 2% Ghana (with 18 other countries make up the remaining 7%) Of those in South Africa, 405 (67%) are based in the city regions of Johannesburg and Pretoria while Cape Town accounts for 174 (29%)

Distribution in Africa by country The most common commercial categories these companies operate in include; Information Technology & Services, Computer Software / Hardware, Financial Services, Internet, Telecommunications, Management Consulting, Marketing & Advertising, Electrical & Electronic Manufacturing, Information Services and Industrial Automation. 62% of all African companies active in the region are privately held with the vast majority (50%) having less than 20 employees. Based on year founded, the last 5 years have seen a significant rise in the number of companies active in this sector, most likely due to the combined effects of available funding, lower start-up costs for tooling, cloud, compute and open source resources allied to the growing demand in the B2B space.

with South Africa accounting for the most frequent country of origin for companies in this sector. Johannesburg looks like the No.1 contender for the “AI Tech Capital of Africa” based on the organisations we have assessed so far. Its not clear the exact reasons why South Africa is so dominant but a larger and more established education & skills base, coupled to higher economic demand and

an active tech entrepreneur / supplier ecosystem may (in part) account for this. A larger parter / vendor ecosystem built around the likes of Microsoft, Google, AWS, IBM, Oracle & SAP who have historically had their Africa HQ operations in South Africa may also account for the landscape we currently see. This is by no means an exhaustive survey and the data is continually changing and evolving.

If you want to learn more about our analysis, we will be presenting our in-depth findings at AI Expo Africa 2021 ONLINE that will run as a three-day LIVE event 7-9 September followed by a 30-day ON-DEMAND archive. We hope you can join us.

Join AI Expo Africa 2021 Online • CALL FOR 2021 SPEAKERS is now open – Join our prestigious speaker line via https://aiexpoafrica.com/speakers/ • RESERVE your FREE 2021 tickets NOW – Invite your team to join us TODAY via https://aiexpoafrica.com/registration/ • BECOME a sponsor / exhibitor – Find new clients for your 4IR products and services via https://aiexpoafrica.com/sponsor/ • Press & Media enquiries – enquiries@aiexpoafrica.com

Initial Conclusions The top 5 most “active” countries are South Africa, Tunisia, Nigeria, Kenya and Egypt – this broadly maps to the top tech investment hubs in the region as indicated by the likes of Maxime Bayen & Max Cuvellier It’s clear we have we have growing and dynamic AI / 4IR tech ecosystem in Africa

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3RD QUARTER 2021 | SYNAPSE

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ADVERTORIAL

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dvancements to the previous Enterprise A2019 platform along with a new Automation 360 brand transforms the employee and user experience and delivers comprehensive process discovery, digitisation, automation and optimisation capabilities on a single, integrated platform to enable users to automate 2X more processes, at 3X the speed to scale an enterprise, and at a cost that is 1/5 of the infrastructure costs of legacy solutions.

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Automation 360 allows enterprises to: Discover: The platform's Discovery Bot records user activities, documents business processes, and analyses process variances to identify which automation opportunities offer the highest business impact, and then generates bot blueprints that can be used to build software bots to automate these processes. Automate: The platform's foundational intelligent automation product, RPA Workspace, includes significant enhancements to the low code bot building experience, SaaS delivery models, and the centralised deployment, administration, and governance of bots. Citizen developers are equipped with more visual drag-anddrop experiences. RPA managers and IT teams benefit from the Cloud Preview Sandbox, with pre-release access to the next update to plan and develop ahead. Digitise: Automation 360's IQ Bot has advanced its integrated Intelligent Document Processing (IDP) solution by combining RPA with pre-trained and custom-trained Artificial Intelligence (AI) models to automatically classify, extract and validate the information from business documents, emails and other unstructured and semi-structured data, with minimal setup time. Optimise: With Bot Insight, the platform analyses RPA and other bot activities in

real-time, generating valuable business and operational insights for enterprises on how they can optimise their bots' performance at scale. Meet AARI: Automation Anywhere Robotic Interface (AARI) is your digital assistant for work. Automate from anywhere. Increase RPA adoption with Citizen Development. Be more productive and improve your office processes by automating repetitive, error-prone tasks across multiple systems. Front Office Automation with AARI Desktop | Digital Assistant Demo

Cloud-Native Platform Advances Front Office and Back Office Automation According to a recent survey from Automation Anywhere, RPA is rapidly gaining ground in the front office. High volumes of users, paired with a surge in customer demand during the pandemic, attribute to the growing popularity of automation in customer-facing functions, such as call centres. Customers are turning to a cloud-native platform, like Automation 360, which can be deployed in the cloud or as a hybrid solution that combines on-premises infrastructure with the cloud. The platform supports both legacy systems and modern applications, including support for SAP, Oracle, Workday, Salesforce, Office 365, G Suite and other enterprise applications– from the front office to the back office – enabling companies to extend end-to-end automation across their organisation. "Organisations are quickly realising that deploying their RPA initiatives in the cloud, and the productivity gains that come with it, offers increased efficiencies, reduced time to market, and improved customer and employee satisfaction," said Holly Muscolino, Research Vice President for IDC's Content Strategies and the Future of Work research services. "In fact, IDC predicts that by 2022, 45% of repetitive work tasks in large enterprises will be automated."

About Automation Anywhere Automation Anywhere is a global leader in Robotic Process Automation (RPA), empowering customers to automate end-toend business processes with intelligent software bots – AIpowered digital workers that perform repetitive and manual tasks, resulting in dramatic productivity gains, optimised customer experience and more engaged employees. The company offers the world's only cloudnative and web-based automation platform combining RPA, artificial intelligence, machine learning and analytics, yielding significantly lower TCO, higher security, and faster scalability than legacy monolithic platforms. Its Bot Store is the world's first and largest marketplace with more than 1,200 pre-built, intelligent automation solutions. Automation Anywhere has deployed over 2.8 million bots to support some of the world's largest enterprises across all industries in more than 90 countries. For additional information, visit www. automationanywhere.com

TreasuryONE Automation, part of the TreasuryONE Group of companies, is a certified preferred Automation Anywhere partner that design, implement and support RPA solutions in South Africa and Africa. With more than 20 years of experience in financial automation, your RPA project will be in the hand of experts. 1ai where we automate and innovate to save time, save costs and increase process compliance. Visit www. rpa.treasuryone.co.za

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COMMUNITY

NVIDIA FEATURES TOOLS, TRAINING AND EDUCATION FOR AFRICAN DEVELOPERS

Developers don’t just come from the United States — they hail from Kenya, Nigeria, Senegal and beyond. Africa is home to close to 1 million developers who are driving technology innovation through regional, local, and grassroots communities. NVIDIA is focused on supporting these developer ecosystems in emerging markets and opening access for communities to solve pressing regional problems with AI.

N

VIDIA is working with communities across Africa to engage with on the leading edge of AI and other groundbreaking technologies. These technologies affect everyone, and to ensure all voices are heard, we’ve designed a program that seeks to inspire, influence, and impact developers from emerging countries. NVIDIA offers developers a range of tools, from software development kits (SDKs) for conversational AI and ray tracing, to handson courses from the NVIDIA Deep Learning Institute (DLI). They’re available to all members of the NVIDIA Developer Program, a freeto-join global community of over 2.5 million technology innovators who are revolutionizing industries through accelerated computing. The DLI curriculum encompases a comprehensive learning experience covering a wide range of important topics in AI, data science and accelerated computing. SDKs are a key component that can make or break an application’s performance. Dozens of new and updated kits for high performance computing, computer vision, data science, conversational AI, recommender systems and real-time graphics are available so developers in every market can meet virtually any challenge.

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NVIDIA is also engaging developers, researchers and data scientists through NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data sciences. NVIDIA Inception helps startups during critical stages of product development, prototyping and deployment. Every NVIDIA Inception member gets a custom set of ongoing benefits, such as DLI credits, marketing support, and technology assistance, which provides startups with the fundamental tools to help them grow.

We’re still growing our emerging markets initiatives to better connect with developers worldwide. Here are the areas that we’re working toward: Remove Barriers to Access Digitization has boosted the growth trajectory for African developers, researchers and startups. Where a previous barrier lay in lack of access now that the world has moved online so has the knowledge. The ability to attend more conferences around the globe means more information shared between experts. Shared world-class expertise is key to creating competitive companies, and it’s a way developers can

keep on top of development trends, keep their skills up to date and, fundamentally, keep them relevant. One avenue to democratizing compute is the cloud, especially for startups and developers who want to learn. By using cloud-based computing services, they’re able to access the type of compute power they need, without having to actually have the hardware on hand.

Embrace Regional Differences, and Spotlighting Success and Challenge Developers across the globe will all face different challenges. When sharing knowledge it’s important to cater to those global perspectives, particularly at technology conferences. Ensuring there are diverse panels, speakers and languages for emerging markets can contribute to significant growth and development across regions. Community opportunities like this don’t have to be developed by larger corporations. Smaller companies and leading startups are just as important to support and facilitate growth regionally, whether it’s through conference sponsorships, talks, workshops or mentoring for other startups. This means the opportunity for practicing communities to develop. Ideas can be shared within these groups, and people from various backgrounds and industries can come together to solve problems. Challenges being faced in the East might have already been solved in the West, or vice versa, and these different experiences and learnings can be shared and built upon. It's these communities in Africa that are driving growth and innovation in technology.

Foster Opportunities to Learn and Connect, by Thinking Outside the Box Developers themselves are investing time and effort into AI in Africa, and local challenges are being solved by local developers. Bigger companies can and are also investing into Africa to develop the best talents in the continent. With Accelerator and Incubator programs, they can work with brand new startups, and the startups who are supporting the development of communities and ecosystems. It’s important that larger organizations learn from the African AI ecosystem by tapping into these communities, bringing in diverse teams to advise on their own policies.

New Ways to Knowledge Access to knowledge is key to the success, growth and development of AI in Africa, and the AI ecosystem is well on the way to solving that problem. By thinking outside of the box, the continent has carved a new path for themselves, and now the continent is well on their way to becoming the future leaders of AI.

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3RD QUARTER 2021 | SYNAPSE

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WOMEN IN TECH

Jess Ferguson

Tsholo Madi

WOMEN DATA SCIENTISTS HELP LEAD TELKOM’S DIGITAL REVOLUTION Telkom’s ongoing innovation focus has seen the organisation training and hiring dozens of data scientists across its operations, applying the power of data to solve organisational and societal problems – revolutionising efficiencies at the telco, and boosting the services it can offer clients.

W

omen data scientists are in the vanguard of this drive, bringing their skills to bear on the diversified company’s innovative, digitalfirst focus. The data science revolution has also been an opportunity to drive Telkom’s Female Leadership Development Programme (FLDP), creating opportunities for dozens of women across the organisation with wide experience across adjacent sectors. Ops specialist: data science Tsholo Madi has been part of the FLDP, having changed careers to become a data scientist after working as a cost and management accountant and an events entrepreneur. “While I was running my events company, I read that data science was likely to be the sexiest profession of the 21st century,” says Madi. “I knew I had to be part of that. My husband works in the IT space, and he encouraged me to study coding. That was my first step towards a data-science career.” “Data science can help companies to maximise revenues, build business strategies and communicate to their stakeholders by visualising data in clear, compelling ways,” says Madi. “Our role as data scientists is to add value; to help find data-driven solutions and process improvements.”

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Madi’s colleague and fellow data scientist Jess Ferguson is also part of the Telkom women’s leadership programme. “Telkom has shown a great commitment to growing women leaders through the programme,” says Ferguson. “Women leaders are empowered and it provides a strong foundation for ongoing development.” Ferguson became a data scientist after a successful career as an occupational therapist. “Data science allows me to make a difference for vast numbers of people – not just individuals,” she says. “My goal is to use occupational science within my data science role to work at a societal level, to improve how people live, work and play. “Technology is a more male-dominated industry,” says Ferguson. “That's not a secret, but we do have several strong women in our team. The FLDP is a leading initiative by the Telkom Group to develop women leaders. It's a fantastic, year-long intensive programme run by the UCT Graduate School of Business. “I feel really empowered seeing this desire to grow female leaders,” says Ferguson. “My voice is heard. That speaks to the culture of our team, and the broader organisation. I am also inspired by the work we do.

“We use data to support school-subject programmes, digital skills, as well as learner wellness,” says Ferguson. “Data helps us identify trends, spot learners who may have learning challenges, and to find top learners we can use for modelling new approaches.” Through the Telkom Foundation, the company is also helping to develop the next generation of data scientists, by encouraging learners – girls and young women as well as male learners – to follow careers in science, technology, engineering and maths (STEM) fields. Data science is also used to optimise the programme’s effectiveness. Madi says the discipline is about asking the right questions in order to be able to solve society’s problems. “I even tell my own children that we ask questions to be clear on what the problem is, so that we can all work together to solve it,” says Madi. “I encourage children to actively listen; as listening is another form of gathering data. “When we listen carefully, and we ask the right questions, we get to understand where we are as a country,” she says. “Then we can gather data to address our challenges. Data science can help with that too.”


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INNOVATION

EXPLORING AI AT SCALE At Microsoft, we are pioneering a new approach to artificial intelligence that is fuelling the next generation of AI at Scale, which is working at unprecedented levels of complexity to solve some of today's biggest challenges. To achieve the next level of intelligence in AI we need to be able to train even larger, more sophisticated models on massive amounts of data requiring intensive compute power, time, and resources. We also need to be able to leverage the progress we make across various scenarios to scale to multiple use cases and applications.

We are enabling AI at Scale across infrastructure, model development, and deployment. Infrastructure: we needed to rethink both our compute hardware and networking. This led to investments in purchasing the best AI accelerators including the latest Nvidia GPUs and InfiniBand networking Large AI models: we found larger AI models performed better in tasks so we needed to train the largest models possible Deployment: although large models lead to higher accuracy, they are expensive to deploy. We need to solve the deployment problem with extremely high loads to deliver this AI into every product Today, AI is bound by the limitations of infrastructure, effectiveness of machine learning models, and ease of development. AI at Scale expands beyond these limitations to allow rapid acceleration of AI innovation. Advancements in models are changing how AI is developed and advanced through the creation of comprehensive, centralised models that can be scaled and specialised across product domains. Supercomputing is crucial to leveraging data with billions of parameters.

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AI at Scale is unlocking breakthroughs in natural language processing (NLP) across text, images, and video, allowing humans to interact with data more naturally than ever before. NLP powers virtual assistants, analysis of research or records, and more. Beyond interpretation, NLP can produce content— generating tests in education, or imagining new ideas for movies, books, and other media. Supercomputers are for example used by corporations and government organisations around the world to simulate outcomes, make wholesale improvements, and increase efficiency to supplant competition. We see organisations across industries using supercomputing capabilities for everything from manufacturing cars and consumer packaged goods to finding new oil repositories or assisting in research and development of new life-saving medicines. By working collaboratively and continuously – both internally and with the global community – we continue to introduce new AI capabilities into our products, platform offerings to our customers, partnership endeavors, and the many Microsoft initiatives that support our mission of human empowerment. These collective AI efforts are resulting in breakthroughs destined to help our world solve some of the toughest challenges. Together, we will build increasingly powerful models that will teach AI to understand our world and help amplify our own ingenuity more effectively. Transparency and ethics are very important factors to consider when adopting AI. How does AI at Scale achieve this? We are committed to the advancement of AI, driven by ethical principles that put people first, and AI at Scale is aligned with these. By creating a common AI foundation that every team can work from, we can efficiently deliver high-value, AI-driven user experiences at massive scale, and with all the privacy and responsible AI considerations our modern society requires. We have reimagined and redefined how we are developing AI from hardware, to networking, software algorithms, and computing systems. And we’ve made all of this available on Microsoft Azure for any organisation to take advantage of to accelerate its potential for innovation and business transformation. With access to this worldclass technology, technical teams will be able to fasttrack the AI development cycle and deliver business value at an unprecedented pace, quality and price performance.

You can learn more about Microsoft AI at Scale: Explore AI at Scale - Microsoft Innovation


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2nd QUARTER 2021 | ISSUE 12

SYNAPSE Africa’s 4IR Trade & Innovation Magazine

NVIDIA INCEPTION: 3 African Startups Accepted into the Programme

TUNBERT: 1st AI-based

Tunisian Dialect System

3 AFRICAN STARTUPS using AI, Data Science for Financial Inclusion

AFDB BACKS AI-BASED

National Consumer Management Systems

HOW THE PANDEMIC Gave Birth to SA’s latest 4IR SaaS platform

LACUNA FUNDS DATASETS for Low Resource African Languages


SYNAPSE

2nd QUARTER 2021 | ISSUE 12

SYNAPSE Africa’s 4IR Trade & Innovation Magazine

Africa’s 4IR Trade & Innovation Magazine

NVIDIA INCEPTION: 3 African Startups Accepted into the Programme

TUNBERT: 1st AI-based Tunisian Dialect System

REACH AFRICA'S LARGEST ARTIFICIAL INTELLIGENCE & 4IR COMMUNITY WITH SYNAPSE MAGAZINE

3 AFRICAN STARTUPS using AI, Data Science for Financial Inclusion

AFDB BACKS AI-BASED

National Consumer Management Systems

HOW THE PANDEMIC

Published Quarterly

Gave Birth to SA’s latest 4IR SaaS platform

LACUNA FUNDS DATASETS for Low Resource African Languages

Official Publication of AI Expo Africa

RATE CARD RATE*

AD SIZE Company Listing (logo, company description & hyperlink) 1/4 Page (includes 1/4 page editorial) 1/2 Page (includes 1/2 page editorial) Full Page (includes full-page editorial feature) Double Page Spread

R2500 R3500 R4500 R6500 R10,000

* All rates exclude VAT & agency commission. Rates are based on casual advertising. Discounted rates are available for longer ad / editorial runs

READERSHIP / SOCIAL MEDIA REACH Synapse Magazine is Africa’s first and only business quarterly publication covering developments across the continent in Artificial Intelligence (AI), Data Science, Robotic Process Automation (RPA) and Fourth Industrial Revolution (4IR) smart technologies. Synapse offers industry executives, practitioners, investors and researchers relevant news, in-depth analysis, and thought leadership articles on trends around 4IR innovation and digital transformation in industries that include banking, retail, manufacturing, healthcare, mining, agriculture, education, and government, among others.

Over the years the magazine has established a significant following across Africa as well as globally, with readers from as far afield as the North America, South America, Europe and Asia. This makes Synapse a great marketing platform for startups and established tech companies to reach a broader community of buyers, investors and partners.

Readers around the world

With its insights, interviews and case studies, the magazine aims to be a voice for African 4IR practitioners, researchers, innovators, thought leaders, and the wider African AI community. Since its launch in 2018, Synapse has amassed a combined readership of 31,300 across the Issuu platform (on which it is published), the AI Media Group’s email database, the AI Expo Africa Community Group on LinkedIn and the AI Media Group’s social media channels where the magazine is distributed. It also links to AI TV, Africa’s only dedicated YouTube streaming channel focused on 4IR business users and trade.

BOOK YOUR AD / EDITORIAL FEATURE

Advertising and artwork to be supplied as a high resolution PRESS-ready PDF of at least 300dpi. Art and editorial features to be submitted to: daniel.mpala@aiexpoafrica.com