Synapse - Africa’s 4IR Trade & Innovation Magazine - 3rd Quarter 2018 Issue 01 (Launch Edition)

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3RD QUARTER 2018 ZAR25 | US$2.50 | Euro1.60

The Voice of African AI & Data Science

AI has the Potential to Transform Africa, leveraging its benefits for the Social & Economic Good of the Continent. AI Expo Africa will see a gathering of Africa’s AI Leaders to plot the way forward for the 4th Industrial Revolution






Dr Nick Bradshaw Co-Founder AI Media & Community Director

Dr Nick Bradshaw Co-founder AI Media & Community Director

Roy Bannister Co-Founder AI Media & Editor in Chief

Roy Bannister Co-founder AI Media & Editor in Chief

WELCOME TO the launch edition of Synapse Magazine, brought to you by AI Media and the mouthpiece of the African Artificial Intelligence & Data Science Community.


3RD QUARTER 2018 ZAR25 | US$2.50 | Euro1.60

The Voice of African AI & Data Science

AI has the Potential to Transform Africa, leveraging its benefits for the Social & Economic Good of the Continent. AI Expo Africa will see a gathering of Africa’s AI Leaders to plot the way forward for the 4th Industrial Revolution






Send us your articles to enquiries@ and join our community today... LinkedIn Group Facebook Twitter Instagram U Tube Issu


that would allow Africa to harness AI for the greater economic and social benefit of the African continent. We hope that, as a community, you will all enjoy and contribute to our “FREE” to read Synapse magazine and shout about your own AI achievements, share knowledge with peers and connect with other members of this emerging community - from business and suppliers, to academics and venture capitalists alike. Our vision is that Synapse will chronicle the 4th industrial Revolution as it unfolds in Africa, and play some part in connecting the members of this growing community.


As you may know, a synapse is a structure in the human brain that connects neurons and allows transmission of information from one neuron to another, which is a name we found fitting for our publication, as Synapse is meant to transmit information about AI and its related fields and connect the various elements of the African AI Community. From the outset, our goal was to build a community where real world AI and data Science in the business landscape could be celebrated, explored and showcased to a much wider audience When Nick and I conceived the idea of building this community we wanted AI Expo Africa to the be the annual rallying point and for the Synapse magazine to serve as a communication platform with the wider African community all points, North, South, East and West and a fundamental part of building an AI Community. Our vision is to build the largest business focused AI Community in Africa and to build a bridge between the academic pursuit of AI and the realworld possibilities for this new technological frontier,


1 Introducing AI Media’s Synapse Magazine 4 Kenya Establishes Artificial Intelligence Task Force 6 Sas - Integrating Artificial Intelligence into Your Analytical Strategy 8 Artificial Intelligence is here to Stay – but South Africa may not yet be Fully Prepared for the Coming Change 10 Africa Needs To Get Into The Artificial Intelligence Race 12 Machine learning: Extracting your Strategy from your Data 14 AI Expo Africa Floorplan & Sponsors 16 LTI - Let’s Solve 18 Hikvision Launches Face Recognition Terminals 22 SqwidNet - Enabling the IoT & AI Journey through an Open-Access Ecosystem 24 AI isn’t Taking over the (Business) World just yet - Here’s why 25 West African AI Scene 26 Beyond the Hype: Toward Improved Data Science enabled by Decades-Long Domain Knowledge 27 Data Science Competition Platform for Africa launches at AI Expo Africa 28 Africa Business Integration – An African Tech Start Up on a Mission 29 Investing in AI – The VC Perspective 30 Machine Intelligence Institute of Africa at AI Expo Africa 31 Converting the disruptive potential of AI to transformative business opportunity – Microsoft SA 32 Intel Corporation join AI Expo Africa 2018 as Premier Sponsor and Boost Artificial Intelligence Skills Development in the Region 33 Which horse to pick? When it comes to AI it is all about Horsepower 34 AI Expo Africa: BCX Sponsor Spotlight 36 How to Build a Content-Based Recommender System For Your Product 39 Synapse Magazine Rate Card

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Kenya Establishes Artificial Intelligence Task Force


Professor Bitange Ndemo is the Chairman of Kenya’s Distributed Ledgers and Artificial ntry’s digital transformation. Intelligence Taskforce that will develop a road map for the country’s Prof. Ndemo is an Associate Professor of Entrepreneurship at thee University of Nairobi’s Business School. His research centers on the link between ICTs and small and medium ment in Africa. enterprises with emphasis on how ICTs influence economic development He is also an advisor and Board member to several organizations including ica, Mpesa Safaricom one of the leading telecommunication companies in Africa, ca. He is a former Foundation, and Research ICT Africa that is based in South Africa. unication Permanent Secretary of Kenya’s Ministry of Information and Communication where he was credited with facilitating many transformative ICT projects. He is an Open Data/Big Data evangelist and dedicated to simplification (visualization) of data for ordinary citizens to consume. He writes two columns every week for the Business Daily and Nation on-line.



Leading the way on the African continent, Kenya’s Information, Communication and Technology Cabinet Secretary, Joe Mucheru, has appointed Dr. Bitange Ndemo, the former ICT Permanent Secretary, to lead a 10-member taskforce that is set to explore the use of the artificial intelliegence and blockchain for Kenya’s development. The 11-member team, officially called the ‘Distributed Ledgers and Artificial Intelligence Taskforce’ has been tasked with plotting out a 15-year roadmap for the the country to harness artificial intelligence and blockchain technology, with the main focus being on making Kenya a leader in the job creation space. Synapse Magazine had the pleasure of interviewing Professor Ndemo in anticipation of his appearance at AI Expo Africa - Africa’s leading Artificial Intelligence community event:

ranks 1st in Africa in terms of broadband speed in the World – see report) is accessible and affordable throughout the country; • Encourage content and apps development (we subsidized laptops to all University Students, and facilitated creation of innovation hubs as well as accelerators in virtually all universities (public and private); • Encouraged public private partnerships in ICT projects including the undersea cables; • Started deliberate capacity development • Created ICT employment opportunities in back office as well as business process outsourcing and software development. The Government took the lead in all these investments and creating enabling environment especially through the Open Data initiatives that availed data that fuelled development of Apps in health, agriculture and education.

Synapse: Kenya is seen as a leader in the tech space, embracing technology and using it for the greater social and economic good of the country. What can other African countries learn from Kenya in this regard? Our story started with five simple policy statements that we implemented to the letter. These were: • Build ICT infrastructure and ensure broadband (Kenya

Synapse: Please give us a broad overview of the tech space in Kenya, with specific reference to AI. Are there many start-ups and companies entering the AI sector? Given the fact that the Fourth Industrial Revolution will be underpinned by the emerging digital transformation technologies such as Blockchain and AI, the Government set up a taskforce to develop a road map for the implementation of these transformative technologies. Several private sector companies indeed now leverage these technologies

Synapse: Is the Kenyan government looking at assisting AI start-ups financially? Indirectly, the Government will support AI start-ups. In the next few months, we start leveraging AI technology solutions from start-ups to improve public service delivery and in the fight against cybersecurity. Several voluntary groups meet to learn more about Machine Learning, Deep learning Data Analytics and other supporting technologies. Many have realized that it is an exercise in futility to fight these emerging technologies as disruptive – that they take away jobs. The best solution is to understand them and use them to build new solutions. Synapse: The Kenyan mindset with regard to embracing technology is forward-looking. Kenya appears to be at the forefront of embracing technology - do you think this approach is what has made the country the traiblazer on the continent that it currently is? The country has had many champions (risk takers) who made it easy for the youth to collaborate with several other people from other countries and develop new solutions. The regulatory environment too is very supportive. In our Distributed ledgers and AI report, we are seeking to create a supportive legal framework to allow many others to take risk in new technologies. Synapse: Tell me more about the AI and Blockchain Taskforce - what are its aims and what does Kenya aim to achieve with the taskforce? The purpose of the taskforce is to explore use cases of these emerging technologies and ensure their applicability in the Kenyan context. The aim is to leverage these technologies to improve public service delivery in the best inclusive way. The taskforce will also develop a trusted identity to enable private sector too to invest in these emerging technologies especially in the development of the supply chains to reduce waste especially in food. Further the taskforce will explore areas that require a legal framework in order to exploit the benefit of the technologies. For example, there is urgent need for a Digital Asset Framework before the private sector embarks on issuance of coins (tokens). Synapse: Technology has capacity to radically change the African narrative and make meaningful impact on service delivery, Do you foresee Kenya’s economy and society being radically transformed by AI and technology over the next decade? We are looking at the next five years to radically transform

the economy much more inclusively than ever before. We are hoping that at least more than 60 percent of transactions will be on digital currencies (including mobile money). If this happens we shall raise the level of transparency through traceability and this will have a huge impact on corruption that dominates the African narrative. Already we are seeing transformation in supply chains using AI, Blockchain, IoT and Big Data. Synapse: What sectors do you think will be most changed? Healthcare, finance, service delivery? The immediate focus of our implementation will be in Healthcare (track and trace pharmaceuticals), Food Security (Track and trace from source to fork to eliminate opportunistic businessmen), Government Registries (for improved transparency), finance (for greater inclusivity), Housing (ensuring affordable housing is allocated to the needy) and Manufacturing. Synapse: On the African continent, where many countries have a labour-intensive workforce, do you believe that AI will create joblessness or rather have the potential to create a new, skilled workforce that will oversee and drive automation? AI will create many more jobs than it will destroy. That AI can enable us to translate our many languages to enable us communicate and comprehend means a lot to the development of the continent. These will be new jobs that do not exist today. Already the little AI experiences we have without handsets is Eurocentric. Africans must learn to do it for themselves and many of those who are not able to comprehend English. When computers were introduced, we feared that they will take away jobs but they have created more jobs. In AI I hope to see many new solutions requiring new skills. We therefore must retrain the people. Synapse: Are there any examples of Kenyan companies using Ai at the moment? Yes, there are Apps enabling farmers to know crop diseases through AI. For example, the Twiga IBM behavioural analytics that enabled companies to develop a credit score used to provide unsecured loans to farmers. A few large companies are using AI in their cybersecurity initiatives. Banks are using AI in their back office automation. Synapse: Any other important information about AI in Kenya that you would like to share with our readers? I am one of the senior advisors of UN’s Global Pulse, a big data initiative with a lab in Kampala and I also represent World Data Lab in Africa and in both organizations, we are actively using Machine Learning to establish alternative ways of validating population data as well as understanding poverty dynamics throughout the World. ai


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Integrating Artificial Intelligence into Your Analytical Strategy


When it comes to driving strategy, using the right tactics at the right time is essential to achieving success. Despite the hype, artificial intelligence is not the correct solution to every problem. Understanding where, when and how to apply these capabilities within a larger strategy requires expertise in both industry and analytics.



TODAY’S MACHINE learning tasks are tackled in four primary ways • Machines that need to be taught by example before they can apply the resulting insight to similar tasks • Machines that can extrapolate from a general pattern and apply it to other data • Machines that can, unsupervised, study data to find patterns, getting better with experience (though never autonomous) • Machines that can work with and exploit a given set of rules to move towards a desired outcome But as machine learning gets deeper, we are embarking on the next step towards increasingly sophisticated AI: deep learning. The sophisticated analysis of deep learning is achieved through “neural networks”, so called because they loosely mimic the interconnected structure of the human brain to provide a many-layered functionality. “Deep learning is only going to be used when it really makes sense—where it can quickly find intricate, variable relationships hidden in large volumes of data that we haven’t been able to pull out in any other way yet,” explains Mary Beth Ainsworth, global product marketing manager of artificial intelligence and text analytics at SAS. “But deep learning means a machine can look at a problem through a completely different analytic lens than its human counterpart. It could be used to tackle all sorts of issues. The potential in all the data we collect every day is yet to be realized.” At the same time, artificial intelligence should not be a black-box tool that operates separately from the rest of your organizational strategy. There will be problems that are best solved by more traditional methods and others that are ideal for the application of artificial intelligence. A differentiator for SAS is our ability to combine traditional and modern machine learning methods in the same platform, working on the same data with an integrated security model. It’s simple – one platform, any analytical method. We believe that analytics should be applied wherever there is data. Therefore, SAS is embedding artificial intelligence capabilities into our SAS® Platform. As we continue to make advances in the field of artificial

intelligence, you will automatically benefit through future releases of SAS technology. Our platform is designed to support the entire analytics life cycle because we understand that a carefully designed and well-implemented analytics strategy helps organizations achieve more. You understand your strategy; we understand analytics. With our guidance, you can integrate advanced analytics, including artificial intelligence, into your strategy – and understand the strengths and weaknesses of various methods based on your goals. Together, we’ll apply practical, real-world analytics that set a clear path for realizing your vision and accomplishing your objectives. SAS delivers AI solutions that incorporate machine learning, computer vision, natural language processing (NLP), and forecasting and optimization technologies to help you unlock new possibilities. Built on the powerful, trusted SAS® Platform, our artificial intelligence solutions support diverse environments and can scale to meet changing business needs. ai

Artificial Intelligence is Here to Stay – but South Africa may not yet be Fully Prepared for the Coming Change Artificial intelligence will transform how the world lives and works. Recent announcements by Google have shown just how powerful Artificial Intelligence (AI) can become. The change is inevitable – but what steps do countries need to take today to ready themselves for an AI-driven future? And is South Africa prepared?


A RECENT industry-focused AI summit held at the White House put a spotlight on the debate. The event was attended by more than 100 senior government officials, technical experts, business leaders and heads of industrial research labs. At the core of the summit: the US government’s focus on leveraging AI for the benefit of US workers and removing barriers to innovation. With it came an awareness of what achieving that goal will require – including the skills people will need to make the most of the new world of work. “AI and related technologies are creating new types of jobs and demand for new technical skills across industries,” noted a release issued by the White House’s Office of Science and



Dr Caroline Belrose, Chief Data Scientist & Managing Director at Accenture South Africa

“In South Africa, Accenture research found that some 78% of local executives say they need to boost their organisation’s competitiveness by innovating through investments in AI technologies. However, only about a third of these organisations are planning significant AI investments over the next three years.”

Technology Policy. “At the same time, many existing occupations will significantly change or become obsolete. Attendees discussed … a renewed focus on STEM education throughout childhood and beyond, to technical apprenticeships, reskilling, and lifelong learning programs to better match America’s skills with the needs of industry.” The Question Remains: How are we faring at home here in South Africa? “If South Africa embraces AI, we can create jobs, grow the economy and improve productivity. All very relevant given our current economic climate,” notes Rory Moore, Innovation Lead for Accenture in South Africa, emphasising the importance of government’s role in enabling AI as a catalyst for growth. “AI can open up opportunities to create new value, reinforcing how people drive growth in business,” Moore notes. “It can also help people be more productive – by some estimates, leading to a 40% increase in labour productivity by 2035.” Yet change isn’t far off. According to 2017 research conducted by Accenture, in five years’ time, more than half of consumers and enterprise clients will select products and services based on a company’s AI, instead of the company’s more traditional “brand”. In seven years’ time, most interfaces will not have a screen and will be integrated into daily tasks.

According to Karthik Venkataraman, Head of Artificial Intelligence and Intelligent Automation at Accenture Technology, “South African companies need to shape their own journeys towards becoming responsible users and creators of AI. This will require an understanding of our unique business and economic environments, as well as finding the relevant partner for this endeavour.” In South Africa, Accenture research found that some 78% of local executives say they need to boost their organisation’s competitiveness by innovating through investments in AI technologies. However, only about a third of these organisations are planning significant AI investments over the next three years. “Given the sudden pivots and opportunities enabled by rapidly evolving technology, we need to remain mindful that the business landscape is constantly being redefined in response to this,” notes Dr Caroline Belrose, Chief Data Scientist and MD for Analytics at Accenture. “Given this dynamic, we need to help business leaders to embrace the advancements of technology and AI to evolve and orientate within an everchanging market.” Obstacles remain to AI uptake in South Africa. On one hand, companies are often weighed down by legacy infrastructure, technologies, systems, business models and outdated corporate structuring. At the same time, our workforce is not yet ready for the AI revolution already underway in other parts of the world. Indeed, like workers in many countries elsewhere, South Africans are concerned that AI may affect their jobs and even worsen income inequality. Further issues relate to the quality of education in South Africa (from primary to university levels), the capabilities of our scientific research institutions, as well the quality of our national innovation ecosystems and our lack of a national collaborative mindset. A July 2017 AI roundtable hosted by Accenture and GIBS Business School revealed both concerns and possible solutions. Rather than replacing humans, it was argued, AI should be harnessed to increase workers’ productivity, with organisations focusing on reskilling their workforces and ensuring inclusive economic growth remains the ultimate goal. Given that AI has the potential to see certain jobs automated – potentially worsening inequality and eroding incomes for some parts of the population for a period – the imperative for responsible AI, and for policymakers to proactively address and pre-empt its downsides is real. Among the critical issues: identifying the groups most at risk of job displacement and creating strategies that focus on reskilling and retraining people so they can be successfully reintegrated into an AI-driven economy. Sound rules, regulations, governance guardrails and economic policies will also play critical enabling and protective roles. At the core, the most significant challenges to the adoption of AI are no different in South Africa than anywhere else. They are about preparing people for the intellectual, technological, political, ethical and social questions that will arise as AI becomes more deeply integrated into our lives. To prepare South Africa – and South African companies – for what’s to come,

policymakers must clear the path and help prepare the next generation accordingly. More than this, a strong code of ethics will be needed to make sure growth is inclusive; infrastructure barriers must be removed and a collaborative ecosystem put in place to support AI development. South Africa must start building the competencies it needs to participate in an AI-driven future today. ai

Africa Needs To Get Into The Artificial Intelligence Race AI Expo Africa speaker Darlington A. Akogo, Founder, Director of AI, Deep Learning-Machine Learning Technologist at minoHealth & GUDRA, talks about the need for Africa ‘To Get Into The Artificial Intelligence Race’.

The importance of such AI plans lies in the power that AI currently has and would have on the future. AI would add $15.7 trillion to the Global Economy by 2030 and potentially increase labor productivity by up to 40% by 2035. AI also has the potential to double the economy of a nation like the USA (GDP: 18.57 trillion) in just 20 years. This has led to the AI Race among nations. Russian president, Vladimir Putin recently said “Artificial intelligence is the future, not only for Russia, but for all humankind, It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” It’s important for Africa to start planning now towards an AI future. Developing Economies like ours are the most threatened by such developments when not addressed


SEVERAL NATIONS have already drawn their plans and reports on Artificial Intelligence(AI), Canada first published its AI plan in March 2017. China later published its detailed AI plan, A Next Generation Artificial Intelligence Development Plan in July 2017 where they outlined initiatives and goals to become equal to other AI powerhouses by 2020, lead the world in some aspects of AI by 2025 and entirely dominate as the primary center for AI innovation by 2030. Other nations and bodies have reacted by drawing theirs since then, among them are France, UAE, South Korea, India, EU, Germany, Mexico, Australia and USA. In just 17 months, at least 23 nations and bodies have published some form of plan for AI development. Kenya and Tunisia are the only nations from Africa among them with any attempt towards developing an AI plan.



List of nations with outlined plans for Artificial Intelligence, from March 2017 till date.

African countries like Ghana, where the local population has to rely on 1 Doctor for every 11 000 citizens, can benefit tremendously from harnessing Artificial Intelligence in fields like Health Care.

ABOUT MINOHEALTH minoHealth is a Futuristic Medical Health System seeking to Democratize Quality Healthcare with Artificial Intelligence(A.I) Medical Predictions/ Diagnostics Systems, Cloud Medical Records System for Hospitals, Ministry of Health and Patients separately and “Big Data” Analytics. minoHealth uses Artificial intelligence to make Health Predictions and Diagnostics, stores Patient Medical Records online to be available to all physicians within a hospital in Real Time, all the data collected are intelligently organised and with “Big Data” approaches and technologies, they are continually analysed so important health information/stats on general hospital patients are visualised and made available to physicians. And furthermore, on a direct National level, we have the Ministry Portal that collects patient data from all the registered hospitals within the country with their permission and organises and analyses them and makes them visually available to the Ghanaian Ministry of Health. With this portal, we can actually collect national health data at hardly any cost on a repeated timely basis without any human effort.


ABOUT DARLINGTON AHIALE AKOGO Darlington Ahiale Akogo is a Deep Learning/ Machine Learning Engineer & Researcher who founded minoHealth in order to democratise Quality Healthcare in Africa. minoHealth develops Artificial Intelligence systems for medical diagnoses and prognoses. They also use Data Science and Cloud Computing to collect, analyse and visualize health data. Darlington Ahiale Akogo also heads minoHealth AI Labs where he and his team research and apply Artificial Intelligence to fields like Biotechnology, Regenerative Medicine/Tissue Engineering, Optometry, Epidemiology, Dietetics/ Nutrition and Agriculture. And with Gudra AI Studio, they explore Artificial Intelligence applied to general domains like Energy, Art, Education and Linguistics.

places like Ghana have close to 11,000 people to 1 doctor ratio and Malawi has close to 60,000 people to 1 doctor ratio. We therefore potentially can benefit the most by focusing on and investing in Artificial Intelligence and its many applications. As my team and I at minoHealth are focusing on, we can solve our lack of Quality and Accessible Healthcare issue with AI and Deep Learning. We can also solve poor Food Production and Distribution issues plaguing us and someday solve our transportation issues and high records of road accidents with Autonomous Vehicles. There are several things that Africa needs to start doing in order to get into the AI Race. We need to start getting the general public informed about AI and how they can use it to improve their labour productivity and value today in an Augmented Intelligence approach. This is applicable to all Africans, be they small scale farmers using AI apps to ensure their crops are healthy, marketers using predictive models for Market Forecasting or medical doctors using AI to improve their Healthcare Delivery. Additionally, we need to galvanize existing African AI talents into planning towards AI for Africa and deploying AI in Africa. My team and I at minoHealth develop and research AI systems mainly for healthcare in Africa in order to ensure we enjoy some of the benefits of AI. We have also started conversations about AI in the forms of seminars, lectures and interviews in order to get the Ghanaian and African public informed about AI and get the attention of the government so we can start developing national plans. We have to start planning towards developing the right infrastructures and frameworks in Africa in order to enjoy great national and continental developments from AI and curb possible repercussions. ai


properly. For example, a report recently showed automation technologies could perform 65% of the jobs in Nigeria, 67% of the jobs in South Africa and 85% of the jobs in Ethiopia by 2030, compared to 35% and 47% of the UK and USA, respectively. Even if we fail to locally develop AI systems, just like Africa consumes softwares and services from America like Facebook or Google, institutions in Africa would end up as consumers for foreign AI systems and we would all just be data points. In the long run, when the world starts approaching the Post-Work era due to high levels of automation and many countries begin implementing systems such as Universal Basic Income (UBI) by heavily taxing AI and Automation companies, we would likely be left even poorer and incapable of implementing such social safety nets since there aren’t enough local AI companies to tax. The recent most complete GDP of the whole Africa (excluding Eritrea and Western Sahara) was $2.74 trillion which was less than that of France in that same year, 2014, which was $2.83 trillion. We could end up with an even larger economic gap when compared to Western and some Eastern nations, too large to ever be realistically closed if we don’t plan towards the 4th Industrial Revolution. AI, especially Deep Learning has the potential to solve a broad array of problems, which is why a large portion of Industry, Academia and Governments around the world are already investing so much into it. Africa has some of the worst and largest problems in the world. 501 million people, 47 percent of the population of sub-Saharan Africa lived on $1.90 a day or less in 2012 and 233 million people in sub-Saharan Africa were hungry/undernourished in 2014-6. In 2015, 9.2 million deaths were recorded in Africa, majority being caused by communicable and non-communicable diseases, whilst some

Machine learning: Extracting your Strategy from your DATA 2018 is becoming the year where everyone is putting Artificial intelligence on their key strategy list. Because AI is a nebulous term, for many having an “AI Strategy” is demonstrated by a plan to roll out a chatbot to their customers. Don’t get me wrong, chatbot technology is maturing fast and can bring great value when applied in the correct way, but many organizations are missing something big here.


DATA SCIENCE and machine learning, the tools and technology often associated to “AI”, are more mature in many ways and can transform a business at a more fundamental level. There is an underutilized goldmine of data in every business that may be hiding the answers to some important questions. I’m talking about the unstructured and logging stores generated by the sales process - your mobile app, website and call centre - every minute of every day. At best this data ends up in a graph on a specific dashboard but most often it’s simply stored and archived. Organizations have started using machine learning as part of a data science program to put this data to work. Some proven ways of using the predictive power of historic data include: • Predicting when systems and parts will fail enabling proactive maintenance. • Identifying fraudulent transactions and clients • Predicting the success of a product or item and highlighting which factors make particular products popular or unpopular • Predicting the likelihood that a discount will help close a deal or which customers are thinking about abandoning your brand



AI-driven logistics scheduling So how does putting your data to work become ingrained in your strategy? It’s about putting data at the heart of every decision. Management’s role needs to change from answering questions, to crafting the right questions. Two organizations that have undergone this change are Google and Uber where pricing, product details, logistics and marketing decisions are all driven by data and almost entirely automated using that data. Here, management’s role is to ask the right questions of the data. In 2006, Google’s then-CEO Eric Schmidt said: We run the company by questions, not by answers ... and that stimulates conversation. Out of the conversation comes innovation. Innovation is not something that I just wake up one day and say ‘I want to innovate.’ I think you get a better innovative culture if you ask it as a question.” This is very different to approaches of the past where management’s over-reliance on experience resulted in a lack of

by Craig Heckrath, Mint Intelligent Insights Delivery Lead

innovation and group think. Yes, data was used, but it was often cherry-picked and assembled to support an existing decision leading to confirmation bias rather than real findings. A key component of the new data-driven business is the data scientist. Guided and focused by the big questions defined by management, the data scientist’s objective is to navigate the many pitfalls associated with extracting truth from raw, unprocessed storage. Statistics and machine learning are full of pitfalls. As data is the fuel that powers a machine learning strategy, the machine’s performance will depend on the quality and quantity of the fuel. Taking a data-first approach to decision making therefore has a huge risk. Without a critical analysis of the data, it will tell you untruths leading to rickety, ineffective strategies. Bias is data’s biggest affliction. Perhaps your sales team only captures leads from specific market segments - this will result in a bias which could lead to flawed conclusions if this data isn’t normalized. Another common pitfall is confusing correlation with causation. A correlation needs to be investigated, often in the real-world, to determine what the causation factor is. An over-simplified example would be to assume that fire trucks are causing fires as they always seem to be where the fires are - the mistake here is obvious, yet businesses will often make similarly bad choices based on not clearly understanding the correlation/causation pitfall. Ultimately, the data scientist needs to prove the predictability of the data and test the accuracy of predictions made by the trained AI models. This approach of testing evidence and proving the reliability of outcomes and business impact is what puts the “science” in data science. Machine learning has the ability to bring predictability and optimization to organisations, which was simply not possible in the past. It enables a business to have predictive maintenance schedules, minimizing logistics and minimizing failures with less staff. Customers can have an entirely personalized experience that will feel more human and less like advertising. As with all revolutionary technology, it comes with huge organizational risk if rolled out inappropriately. But ultimately, businesses not utilizing their data to drive their strategy simply won’t be able to compete. ai























































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LTI PRESENCE & Operations across Africa: LTI has a significant presence in the African continent, servicing a variety of clients. LTI started operations in 2005, and is one of the first Indian companies to develop a 150+ seater near-shore development center in Johannesburg, SA in the year 2012. The company currently has stateof-the-art delivery center strategically located at the Rosebank Towers. This delivery center is the hub for 24x7 Command Center, providing AD/AM services to multiple clients in the African region. We are committed to developing local talent, and through our training academy, have ensured job enablement by training multiple batches of SA graduates. LTI is a BBBEE Level 2 organization, and we ensure compliance through our strong local HR, and Compliance & Legal presence. Accelerating Digital Transformation with Mosaic: LTI’s Mosaic Suite is a converged offering of Data, AI & Automation solutions, which addresses all key digital transformation tenets. This is accomplished by simplifying “Data-to-Decision” in hyper-distributeddata and hybrid-computing environments, with AI Logistics, Automated Intelligence & Actionable Insights. Mosaic is uniquely positioned to address the convergence of digital & physical boundaries; convergence of technologies & skills; and convergence of business context & knowledge, with an inherent Engineering mindset - an identity & legacy of the L&T Group. With its best-fit-for-the-purpose technology stack, Mosaic provides proven capabilities to carry out a rapid iterative solution build, and empowers us to integrate, orchestrate and manage data & analytics, to provide scale, future readiness, and speed to our users. Powered by Mosaic, LTI has solved many interesting cases leveraging AI, and has delivered amplified outcomes to clients.

Some interesting examples of problems solved for our clients include: Transforming End-User Experience with Cognitive Agents: • Helped a global market leader in beauty products reduce 8-10% tickets through cognitive end-user assistance, resulting in a 30% upfront cost reduction. • Empowered 200 million citizens, by providing an intuitive tax cognitive agent to assist taxpayers through the tax payment process, by handling issues regarding reporting, compliance and data exchange. • Empowered & engaged employees with self-service, using cognitive agents, in the areas of Admin Services, HR Services & IT Support. • Processed claims in minutes for a large insurer, using image processing & assessment for motor vehicle insurance. Delivering Outcomes with Intelligent Automation: • Assisted process automation in areas of trade finance, policy documents, KYC, email requests, invoice processing, and procurement, leveraging intelligent extraction of documents & unstructured content. • Automated video content tagging for a large media company, for delivering extreme personalizationdriven content monetization. Reach us at: ai

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Continuous learning

Hikvision Launches Face Recognition Terminals Hikvision, the world’s leading supplier of innovative video surveillance products and solutions, has launched a range of groundbreaking face recognition terminals. Hikvision’s face recognition terminals are embedded with deep-learning algorithms for access control and office scenarios for improving building operat operations, workforce management and safety operations.


THREE FACE recognition terminal models are available, each one uniquely and flexibly designed for a wealth of applications and scenarios: • Wall-Mounted Face Recognition Terminal – DSK1T604MF & DS-K1T606MF • Base- or Wall-Mounted Face Recognition Terminal – DS-K1T605MF • Face Recognition Component for Access Turnstile – DS-K5603-Z DS-K1T604MF & DS-K1T606MF DS-K1T605MF DS-K5603-Z



Face Recognition Component for Access Turnstile – DS-K5603-Z

Wall-Mounted Face Recognition Terminal – DS-K1T604MF & DS-K1T606MF

All three of these models are easy to install and use. The wall-mounted terminal is well-suited for quick access at an entrance. The base & wall-mounted terminal can be mounted on convenient vertical surfaces or on the front desk. The face recognition component is designed for use in conjunction with access turnstiles. If needed, this model can be rotated horizontally upon installation for capturing facial images at the most

effective angle. Various models and types are available with a wide variety of front-end or back-end combinations to meet a multitude of scenarios and environments. Hikvision’s face recognition terminals support 1:1 or 1:N matching modes, authentication via Mifare cards, and TCP/IP or RS-485 connectivity.

Accurate and Fast Face Recognition Hikvision has embedded Deep-Learning algorithms into its face recognition terminals, providing fewer transmission delays and a reduced load on backend components. As a result, the terminals have a high success rate – the face-capture rate can hit 99% accuracy at less than 0.5 seconds. During rush hours, access turnstiles equipped with Hikvision’s face recognition terminals can respond in less than a half-second, passing up to 40 persons per minute. Hikvision’s face recognition terminals apply to a wide variety of scenarios and environments and can be highly personalized. Multiple authentication modes are available: face DS-K5603-Z with images, swiping ID card and comparing access turnstile images, custom modes, and more. Applications range from commercial real estate, government agencies, small to very large businesses or factories, just to name a few. ai Base- or Wall-Mounted Face Recognition Terminal – DS-K1T605MF


ABOUT HIKVISION Hikvision is the world’s leading provider of innovative video surveillance products and solutions. Featuring the industry’s strongest R&D workforce, Hikvision advances core technologies of audio and video encoding, video image processing, and related data storage, as well as forward-looking technologies such as cloud computing, big data, and deep learning. In addition to the video surveillance industry, Hikvision extends its reach to smart home tech, industrial automation, and automotive electronics industries to achieve its long-term vision. Always creating value for its customers, Hikvision operates 33 regional subsidiaries all over the world to achieve a truly global presence. For more information, please visit us at



As the Acting CEO of SqwidNet, Phathizwe Malinga is responsible for building an IoT connectivity business in South Africa in partnership with International IoT giant SIGFOX. In addition to his CEO role, he will continue overseeing the solutions division for SqwidNet, a fully owned subsidiary of Dark Fibre Africa. Malinga has made a natural transition into the role of leading SqwidNet because of the various leadership roles he has fulfilled over the years. He is no stranger to the role of a strategist, as he consulted with both Max Healthcare and Life Healthcare Group in his previous position with the organisation. He has been involved in the information technology and telecommunication industry for over two decades, having held senior management level positions. Before joining SqwidNet, Malinga was the Head of Application Strategy at Life Healthcare Group, and he was in charge of the IT Application strategy and Software Development for the group. Phathizwe completed his Executive MBA from the Graduate School of Business, Cape Town. He continues to guest lecture with the university and he sits on the board of Bizmod Consulting. Malinga is a Singularity University Faculty Candidate. Phathizwe Malinga, Acting CEO SqwidNet


SqwidNet - Enabling the IoT & AI Journey through an Open-Access Ecosystem



SQWIDNET, A wholly owned subsidiary of DFA, operates the Sigfox IoT Network in South Africa. Founded in November 2016, SqwidNet fosters an open-access IoT ecosystem that is simple for South African businesses to use, is ultra-low cost, and is extremely well suited to connecting remote assets and assetsin-transit. We provide a business-grade nationwide network that brings reliability to message delivery, with over 700 base stations that cover over 85% of the country’s population. This means SqwidNet has made connecting assets to the Internet ubiquitous. We make IoT ubiquitous. And it needs to be because IoT is the cornerstone of innovation and digital transformation journeys. In today’s increasingly complex marketplace, companies need to constantly look for ways to improve their offering in order to survive. In order to gain this competitive advantage, today’s businesses want to gather more and more data about the nuances that exist in their operations and the insight that they seek is what will drive their innovation. The rise of IoT that SqwidNet ushers into the mainstream have been met by the dramatic decrease in the cost of cloud computing. This ability to collect and store all the near real-time data associated with changes in the environment that all assets operate in, gives the opportunity for a business to look for patterns in the data that lead to better decision making. With SqwidNet, the potential of an asset generating data every 10 minutes for over 5 years, makes this pattern-recognition task beyond human ability. This is where artificial intelligence (AI) is

required, using an algorithm in the cloud to analyse this data to provide insight. We have seen our customers use AI: to detect water leaks, based on the rate of water flowing into various parts of a building; to sense a deterioration in air quality; to determine the optimal cement curing time required for a more structurally strong building. Sigfox, themselves, use AI to approximate the location of a Sigfox Ready™ asset that is not fitted with GPS, giving customers the ability to track assets that only require a few kilometres accuracy at an even lower cost and longer battery life. With an easily accessible IoT repository (visit partners.sigfox. com) that boasts of 455 local and global devices, SqwidNet is the beginning and the cornerstone of a richer AI journey, enabling any size business the ability to improve their competitiveness. Any size? Challenge accepted! SqwidNet introduced an entrepreneurship programme called IoTE, IoT to the power of E, and we had over 20 entrepreneurs go from idea to product in just 3 months. We have a second iteration of this programme starting in the next few weeks and this time we are aiming at 60 entrepreneurs. Our technology is so simple, that it is not limited to just business. Even a student can use it to improve their quality of life. Our University Challenge saw various students attempt to solve one of the UN’s sustainable development goals using Sigfox. The winners will be presenting their rhino tracking solution in Germany in October 2018. SqwidNet truly makes assets come alive. ai

AI isn’t Taking over the (Business) World just yet - Here’s why…


AI has become, in a very short time, an established part of business. Much time and energy has been spent on evaluating the impact that this digital disruption will have on business and specific industry sectors. Significant sums have also been invested in testing various forms of AI – in most instances targeting improved operational efficiencies through robotic process automation or improved digital self-service through chatbots.



FEW WILL have managed to move beyond the pilot phase into full scale production, however. This is, for the most part, due to legacy infrastructure issues. Data continues to be stored in different places and in different formats. Many operating systems are not fully integrated. And many decisions and actions continue to be taken by staff, outside of systems. If key pieces of the puzzle are not connected, it becomes difficult to automate these. To connect all of said pieces usually requires significant changes to systems, processes and people. That is why disruptive businesses tend to be started from scratch rather than emerging from existing business models, unlocked by the application of AI. The disruptive implications of AI remain daunting for most established businesses, which slows down adoption. Breaking down what has worked and replacing it with what should work takes courage, leadership, time, and money. In many cases, it is forced on companies as they scramble to respond to a disruptive market entrant that threatens their very survival. So, how do executives prepare their organisations for the digital era while driving improved performance via their existing business models? How do you migrate a legacy business model to a digital reality without running parallel business models or engaging with a transformational overhaul from day one? Take the contact centre. It’s tempting to get rid of all your agents and ask your customers to self-serve via chatbots. The financials make sense. But, this means you have to build a chatbot capable of answering all customer queries in a way that satisfies the customer. Doing this in an Innovation Lab and hoping the

digital team will get it production ready is wishful thinking. There are simply too many possibilities to capture using traditional decision tree coding techniques, and the amount of rich unstructured data is seldom enough to achieve acceptable predictable outcomes via machine learning. These projects struggle to get out of pilot phase simply because the risk of a poor customer experience is too high. A more pragmatic approach is to stick with your existing human interfaces and augment them with digital intelligence. This gives you room to learn and make mistakes, because your staff can step in when your digital logic is found wanting. It means approaching AI from a both/and position, not an either/or one. In other words, including, not excluding staff in your digital mix. Only once you have perfected your digital logic do you then look to adopt purer forms of digital autonomy. This approach can help realise that mythical call centre creature - the super agent, capable of handling any and every query, changing from business unit to business unit or product to product without missing a step. Call navigation technologies specialise in the capture, maintenance and deployment of structured, expert logic that can navigate agents through real-time calls, ensuring that, based on the customer’s specific context, they ask the right questions to diagnose the right problems, and then identify the right solutions that result in them taking the right actions. In addition, this expert logic can also feed self-service bot interfaces, guiding a digital conversation in line with structured, consistent and compliant advisory logic. continued to page 27

Ryan Falkenberg, co-CEO & co-Founder, CLEVVA

Ryan Falkenberg is co-founder and CEO of CLEVVA, an augmented intelligence technology that allows non-coders to capture expert logic into Navigation Apps. Navigators remove the need for people to learn decision-making formula, guiding them through any required decision and action in real time. Termed ‘Artificial Intelligence for People’, CLEVVA gives people access to a digital brain so they can rather focus their learning and efforts on more differentiating and value-adding behaviours.

Artificial Intelligence In West Africa

by Darlington A. Akogo

establishing ‘National Agency for Research in Robotics and Artificial Intelligence (NARRAI)’. Nigeria’s large population of 186 million puts them in a position where if they are proactive about Data and Artificial Intelligence, they could have a strong AI ecosystem in Africa, and maybe even globally. However, Ghana’s stable socio-political atmosphere and relatively stable economy gives it strong potentials to become an international hub in AI development in Africa. These are likely some of the reasons behind Google chosing Ghana to establish their first African AI center. However, local proactivity is required. Hence, I’m advocating a proactive approach in Ghana and setting up a taskforce to develop an open sourced “Ghanaian Artificial Intelligence Plan and Report”, one we intend to propose to government. The home of jollof rice, West Africa, has a lot of potentials in growing as an AI ecosystem, which they can leverage to improve the quality of life in the region. Current efforts are quite scattered. To harness its full potentials, proactive and strategic efforts are required from Governments, Private Sectors and Academia. And a lot of collaborations across current key players is also necessary in developing a strong West African AI ecosystem. And the regional economic union, ECOWAS, should consider creating a taskforce to develop a West African Artificial Intelligence plan. ai

AI isn’t Taking over the (Business) World just yet - Here’s why… continued from page 24 This can also be used in sales, technical support and any number of other operational areas. By initially building digital experts that augment, not replace staff, it allows you to realise significant business benefits without being forced to transform the business model to make it work. And not only will it enable staff to do more with less training, but you start building critical data that helps you shape and optimise your digital logic. As the logic gets more robust and accurate, you can look to adopt more digital interfaces where relevant. By that time, you will have empowered your staff to think and operate within a digital world; one where prescribed decision logic will increasingly

be tackled by technology and where the human role shifts to perfecting the behaviours that help differentiate the customer experience. Intelligence augmentation (IA) offers existing businesses a very pragmatic first step into the digital era. It focuses on getting the back-end logic or intelligence perfected before you try perfect the front end experience. It also allows staff to increasingly develop their EQ given that their IQ can increasingly be supplemented digitally. 2018 is the year where AI is moving out of the labs and into the mainstream. And intelligence augmentation solutions are one of the key ways existing businesses achieve this outcome. ai


FOR AI communities, Data Science Nigeria, led by Bayo Adekanmbi, is very active in building a strong community of AI and Data Science practitioners. They’ve held events like DSN PhD4Innovation hub, Telecoms Churn Kaggle Competition and MentorAfricanDataScientist. In Ghana, strong dedicated AI hubs and communities don’t exist yet, however, I have worked with some of the various STEM hubs and communities and held seminars and lectures on Artificial Intelligence. Atlantic AI Labs opened an AI Research center also in Benin. And a workshop on AI was held in Benin recently. Ivory Coast launched a project to use drones and AI the monitoring of their power grids. And a new Master’s program in Data Science was inaugurated in Ivory Coast. And Cape Verde Economic bank is using AI to allow customers make payments. The Minister of Communications in Nigeria, Mr Adebayo Shittu, has promised government support for the creation and growth of an Artificial Intelligence ecosystem in Nigeria. He said “We shall consider and explore pillars like connectivity, digital inclusion, trade, security, innovation and policy. We will also examine challenges and prospects of AI to foster greater interoperability and security.” And the Nigerian government recently said they are planning on


We are in the very early phase of Artificial Intelligence (AI) Development and Democratization in West Africa. There are currently few Artificial Intelligence startups and initiatives to grow the community of practitioners. Most of these efforts are driven by civil entities, with little current government support and appropriate academic presence. And the main leaders currently are Ghana and Nigeria. Some of the key players in Ghana and West Africa include minoHealth, where Artificial Intelligence systems, specifically Deep Learning and Machine Learning systems are developed for forecasts, diagnoses and prognoses of medical conditions including Breast Cancer, Pneumonia, Fibrosis and Hernia, and its lab, minoHealth AI Labs where research work is being done in applying Artificial Intelligence to domains like Biotechnology, Nutrition/Dietetics, Optometry, Neuroscience and Epidemiology. And in Nigeria, Ubenwa uses Artificial Intelligence to detect birth asphyxia from infant cry.

Beyond the Hype: Toward Improved Data Science enabled by Decades-Long Domain Knowledge

Data science and machine learning have become catch-all phrases, positioned as solutions to all the world’s problems. But for one South African firm deeply rooted in applying data science and machine learning to develop bespoke industrial solutions, the key to success lies in a deep understanding of key industry verticals.


StoneThree CEO, Derick Moolman



STONE THREE is an industrial IoT company that leverages its extensive domain knowledge, data science and enterprise software expertise, and device capabilities to develop sustainable solutions for repeating problems in key global industries. Founded in 2000, Stone Three has evolved to become one of South Africa’s leading data science firms, leveraging its digital signal processing experience to develop bespoke devices and sensors that generate unique data sets for industrial applications. According to Stone Three CEO Derick Moolman, the company distinguishes itself through a combination of signal processing experience, enterprise-level software engineering, and deep domain expertise built up over the past two decades. “Our team of engineers, developers and business analysts research and develop real applications for industrial solutions in the mining, healthcare and maritime telecommunications industries. Our data science philosophy is guided by the belief that it’s undesirable to implement effective machine learning solutions without having deep domain expertise.” The company prides itself on a long track record of developing industry-specific solutions to some of the most complex and multifaceted industrial problems. “Our machine vision based smart sensor technology, remote process diagnostics and advanced process control services have improved minerals processing operations with digital productivity solutions,” says Moolman. “Through partnerships in the maritime and telemedicine industries, we have developed leading edge software-defined radio and precision telemedicine offerings exported to several countries. We have always sought to go beyond the hype of new technologies and uncover real-world applications that solve specific

problems in the industrial value chain.” Stone Three consists of four key business units, namely: Stone Three Mining, which develops solutions focused on digital productivity; Stone Three Healthcare, which owns and operates precision telemedicine solutions for the North American market with a strong focus on diagnostics; Stone Three Communications, which develops hardware and software solutions for the global maritime industry using industrial IoT technologies; and Stone Three Labs, an internal R&D lab that supports the company’s vertical business units while also incubating new ideas and innovation through contract research to develop new solutions. Moolman says the company fosters a strong entrepreneurial spirit among all team members. “Our business units were created by people within the company who had the necessary passion, expertise, skills and experience in key verticals to build those business units into profitable businesses that can scale at a global level. By empowering our 65-strong team, we create an environment that nurtures and enables a culture of innovation and invention and unlocks growth opportunities across the company.” ai Stone Three is headquartered in Somerset West near Cape Town. For more information, please visit

Data Science Competition Platform for Africa launches at AI Expo Africa Ixio Analytics is pleased to announce the launch of Zindi, a data science competition platform on 9 September 2018 at AI Expo Africa. Zindi provides a central platform for a community of mostly African data scientists to convene, collaborate, and compete to solve the continent’s most pressing challenges. Zindi is the first platform of its kind in the African market.

Megan Yates, Chief Scientist, Ixio Analytics “With Zindi, organizations will be able to quickly and affordably source the best of the best data science solutions and talent. This will give them a true competitive advantage in the market,” says Megan Yates, Chief Scientist, Ixio Analytics. Data scientists download the datasets to build machine learning and artificial intelligence models. Competitions are open for two to four months. Upon submission, solutions are automatically evaluated by the platform based on accuracy and data scientists can see their ranking on a live public leaderboard. Data scientists can win up to $50,000 USD in prize money.

“Whether you’re a novice or an expert, data scientists can come to Zindi to sharpen their skills on real-world problems, connect with their peers, access tutorials and job listings, build their profiles, and win money while doing it!” says Celina Lee, Zindi.

the ideal environment for analytics to be brought to life and thrive in an unstructured world, with solutions being built in Africa, for Africa.” How does Zindi work? By hosting a competition on Zindi, organizations- corporates, startups, non-profits, and governments- can get their problems solved by a growing community of Zindi data scientists. Zindi works with the organization to define its business problems, prepare and source the datasets, and post the competition for the Zindi community to solve. How to get involved Zindi will have a start-up booth in the Innovation Cafe (No.14) at AI Expo Africa running from 10-11th September and will also present in the Innovation Track of the speaking programme. Organizations that are interested in hosting a competition can contact Zindi for a free consultation. Data scientists can enter competitions on Zindi starting 9 September by registering at For more information, contact Ixio Analytics Ixio Analytics is a data-led modelling and analytics company that serves clients across the African region, with offices in South Africa and Ghana. Ixio takes a business-led and science-driven approach to data. Problems are solved using the full weight of advanced statistical programming ecosystems, like R and python. Ixio specializes in statistics, machine learning, unstructured and NoSQL data mining, pattern recognition, iterative solution engines, and heuristic techniques. For more information, visit www.ixioanalytics. com. Press contact: Celina Lee ai


Zindi is the first platform of its kind in the African market “As a start-up looking to digitize the transport sector through online/mobile bookings and cashless payments, our data is growing exponentially. We don’t have the resources to spend on expensive data science consultants. Zindi allows us to crowdsource a custom-built machine learning solution for our business and identify local data science talent,” says Daniel Mutonga, Sales Executive of Mobiticket, one of Zindi’s inaugural competition hosts. Sameer Jooma the Director of Innovation and Analytics for the African region at AB InBev says, “Platforms like Zindi enable us and other organizations to delve into uncharted territory through data exploratoration. It creates

Celina Lee – Zindi


ZINDI WILL launch with a set of inaugural competitions that tackle challenges such as optimizing public transportation in Nairobi, automatically processing text in documents to classify them according to the United Nations’ Sustainable Development Goals framework, and maximizing the impact of social media posts. The total prize money up for grabs with this first set of competitions is expected to be at least $20,000 USD, with new competitions continuing to roll-out every month.

Africa Business Integration – An African Tech Start Up on a Mission Africa Business Integration (ABI) is a sponsor and speaker at AI Expo Africa 2018, they are one of the few startups in Africa enrolled on the NVIDIA Inception Program. Their CEO, Tebogo Nakampe is a member of the Intel® Student Ambassador Program focusing on AI.


AI Expo Africa Chairman Roy Bannister (left) & Event Director Nick Bradshaw (centre) meet Team ABI; Itumeleng Madisha (left) Tebogo Nakampe (centre) & Thabo Koee (right) and get hands on with Windows Mixed Reality headsets.



ALONG WITH his co-founders; CIO Itumeleng Madisha, CTO Thabo Koee, CFO Ipeleng Dube and SVP Nolo Lerumo, they have built a innovative company focusing on virtual reality, augmented reality and artificial intelligence interactive technologies based in Cape Town, South Africa. They are a provider of accelerated computing systems and offer systems integration consultancy on a range of projects. Their AI research scientists work to help African businesses leverage new hardware and software tools to harness the full power of computational innovations. Building Head Mounted Devices leveraging deep learning to help mining and Geo-technical engineers in Africa As a specialist in building intelligent computer vision devices and High-Performance Computing, Africa Business Integration has completed its prototyping stage for the Intelligent Industrial Head Mounted Device called HoloXR. HoloXR is an Augmented Reality head-mounted device that uses deep learning to help mining and Geo-technical engineers in Africa. HoloXR will analyse and predict future seismic events using seismic data in real-time, helping improve the productivity and efficiency of mining workers and operations across Africa.

The NVIDIA Inception program driving African Innovation The NVIDIA Inception program has helped Africa Business Integration with access to NVIDIA GPU technology. Africa Business Integration is also utilizing NVIDIA’s Deep Learning Institute. Access to these resources has helped Africa Business Integration rapidly prototype the HoloXR device and accelerate seismic data analysis while predicting future events. NVIDIA Inception program is a virtual accelerator program that helps start-ups during critical stages of product development, prototyping and deployment. Every Inception member gets a custom set of ongoing benefits, from hardware grants and marketing support to training with deep learning experts. ABI are helping Africa lead the 4th Industrial Revolution using Machine Perception Africa Business Integration is on a mission to help Africa lead the 4th Industrial Revolution using Machine Perception. ABI see HoloXR offering the ability to revolutionise the efficiency of the African labour force by utilizing innovative AI- accelerated technology. With the help of NVIDIA’s AI computing tools, ABI will enhance the perception of billions of Africans with the HoloXR device. The HoloXR’s graphics and software are accelerated by NVIDIA’s GPU technology, which places ABI at the forefront of AI innovation in Africa. ai

Investing in AI – The VC Perspective Valuable insight from Andrea Böhmert, Co-Managing Partner Knife Capital, about the VC’s perspective on Investing in AI (Artificial Intelligence).

Each of the company types has its challenges. Type A, the acqui-hire potential, needs to move from consulting job to consulting job, hoping that at the end one of the clients (or a big consulting house) wants to have these skills for themselves and is prepared to pay for it. Type B, the potential unicorn, needs to develop this unique IP and validate that it works. This will most likely require significant funding at a very high risk, while waiting for the validation to materialize. Hoping that nobody solves the problem before you. Type C has to crack the challenge of matching the AI expertise with industry knowledge and finding a sufficient amount of early adopters to make the business model work. So over and above working with cutting edge technology these entrepreneurs also have to build a real business. Analysing the publicly available portfolio of the South African VC community (limited to those that are registered on the SAVCA database), only a very few have done investment in companies that mention AI on their website. So why is that? Are there not enough investment ready AI companies in South Africa? Are the majority of investors not ready for it? Or is it just too difficult to generate significant returns from such an investment if you are based in South Africa? A investors, we get bombarded with business plans full of ”Buzzword – Bingo”, hoping that for every AI, IoT, VR, AR and Crypto, another 0 gets added to the valuation. But how do we distinguish between “Substance” and “Hype” when you are actually struggling to find experts to even conduct a technical Due Diligence on these companies? Looking at the month of September where in one week you have the Learning Indaba in Stellenbosch and the AI Expo in Cape Town, it will be very interesting to experience Africa’s AI community in action. And assess which one it is – substance or hype. About Knife Capital Knife Capital is an independent growth equity investment firm focusing on innovation-driven ventures with proven traction. By leveraging knowledge, networks & funding, Knife Capital accelerate the international expansion of entrepreneurial businesses that achieved a product/market fit in a beachhead market. They have offices in Cape Town and London and invest via a consortium of funding partnerships, including SARS section 12J Venture Capital Company: KNF Ventures and select Family Offices. Knife Capital also managed HBD Venture Capital’s portfolio of investments in SA and builds high-growth tech-enabled SMEs through its Grindstone Accelerator. Knife Capital has invested in DataProphet, a Machine Learning company that focuses on providing its AI enabled process control solutions to the global Manufacturing industry. ai More Information Contact: Andrea Böhmert +27 82893 2520 Web: Twitter: @KnifeCap @andreaboehmert


IT IS 2018. Can you rightfully call yourself a VC if you have not invested in at least one AI company? After all – according to the McKinsey’s and Deloitte’s AI is the disrupting force, the game changer. The Gartner Hype Cycle for Emerging Technologies, 2017 names one of the three emerging technology megatrends: Artificial Intelligence (AI) everywhere. And when Gartner says “By 2019, startups will overtake Amazon, Google, IBM and Microsoft in driving the AI economy with disruptive business solutions“, we better start investing now. According to the latest PwC/CBInsights MoneyTree Report, which tracks global VC investments across all industries, total funding for AI-related firms accounted for a total of $5 billion invested across 444 deals in 2017. That’s a 28% increase compared to 2016, when $3.9 billion was invested across 417 AI-related deals, PwC/CBInsights says. And some of the funding rounds and exits are staggering. But what defines an AI company? Does employing a Data Scientist make you an AI company? Is a company that is using older data analytics tools and labeling it as AI for a public relations boost an AI company? What if you use the algorithms offered by the likes of Amazon, Microsoft and Google? Is AI your core purpose or are you adding AI functionality to an existing value proposition? Or are you creating real business solutions leveraging your own AI algorithms? And which one of the above justifies the sometimes crazy valuation expectations that seem to come with including the term AI in your pitch deck. As VCs, our mandate is not to invest in disruption and latest technology – it is to make significant returns for our investors. So, do AI companies make money? How does one generate the returns our investors are looking for? Dividends require significant positive cash flow. IPO – sounds cool but you have to have a constant narrative that satisfies your investor base – quarterly. M&A seems to the most logical choice, with AI acquisitions up by 44% in 2017 according to research published by CBInsight. And AI startups are acquisition targets not only for big tech companies, but also for the likes of traditional insurance, retail, healthcare, and automotive incumbents. Applying Knife Capital’s principle of building exit-centric but sustainable businesses, what exactly are the acquirers buying? The largest percentage appear to be acqui-hires, trying to buy skills in this still relatively small talent pool. And at a rate of US$2m to US$5m per person, this is not a bad way to generate returns. If you analyse these companies you will often see a well-known and highly respected founder, surrounding himself (and yes, unfortunately in all the cases I analysed it is a HE) with a group of highly skilled scientists consulting to industry. The next acquisition rationale seems to be Intellectual Property – companies that have managed to solve a “real problem” by applying their own AI solution to a very specific, often narrowly defined issue. These are often the so called Unicorns. And then there are those who are building businesses. The founders understand AI and they have a solid understanding how the insights that can be obtained can add measurable benefit to a specific industry.


Andrea Böhmert – Knife Capital

Machine Intelligence Institute of Africa at AI Expo Africa The Machine Intelligence Institute of Africa (MIIA) - a non-profit organization founded by Dr Jacques Ludik in 2015 - has a wide reach across the continent, as it sets out to build a strong and innovative Machine Intelligence and Data Science community in Africa. MIIA will have a strong presence at a AI Expo Africa, as the event will serve as a gathering for the pan-African AI Community, of which MIIA is an intergral part. Dr Jacques Ludik is also considered one of the leading thinkers and proponents in AI and its assocaited disciplines globally and on the African continent.

MIIA’s AIMS are to help transform Africa by networking together the critical mass of resources, promote and sponsor learning activities, and strengthen scientific and technological excellence, mentoring and collaboration on the continent. “MIIA’s vision is to accelerate and deliver breakthrough Machine Intelligence and Data Science research and practical applications to solve African problems, support entrepreneurial activity, and help drive longterm inclusive and sustainable scientific, technological and socio-economic development on the continent,” says Dr Ludik, who will also serve as Track Moderator at the event. “MIIA looks to partner with governments, business, startup incubators, non profit organizations, universities, and research organizations from around the world to support and help mould the future of Machine Intelligence and Data Science research and applications in Africa.” ai


To Join the MIIA Community,visit:



“It’s great to see AI Expo Africa focusing on the real world applications of AI. Their novel approach to include large enterprise platform and service vendors, alongside AI start-up innovators coupled to a CxO audience containing decision makers and investors makes for a perfect mix. It’s a great opportunity to see how AI is now impacting many aspects of Commerce and Enterprise in Africa, and delegates will gain great value from attending.” - Dr Jacques Ludik, Founder & President of MIIA & Cortex Logic CEO, will be serving as Track Moderator at the event.

Converting the disruptive potential of AI to transformative business opportunity – Microsoft SA

Overcoming hurdles in the AI adoption race However, fears about the potential dangers of AI remain high, especially with regards to the job market. While it’s true that the advent of AI and automation has the potential to disrupt the labour market,

disruption can be minimised by reskilling current employees and upskilling students at a secondary and tertiary educational level to cater to the future needs of the digitally transformed workplace. In fact, Gartner predicts that by 2020, AI will create half a million more jobs than it eliminates. When designed with people at the centre, AI can extend people and employees’ capabilities, free them up for more creative and strategic endeavours, and help them as well as their organisation achieve more. Whether it’s building the next breakthrough product, creating a personalised customer experience, or redefining business processes, AI can help support the performance of the business. Considering the prominent role AI will play in the future workplace and society as a whole, it is essential that the technology is transparent, secure, and sets the highest bar for protecting privacy, while also being inclusive and respectful to all. To ensure this, Microsoft grounds its work in a set of design principles. Amongst others, these include the need for AI to be transparent, that AI has to maximise efficiencies without destroying the dignity of people, that AI must be designed for privacy and possess algorithmic accountability. Furthermore, AI must guard against bias, ensuring representative research that doesn’t discriminate. It is not an exaggeration to say that AI will have an impact on all aspects of our personal and professional lives in the future. This provides us with tremendous opportunities and, for sure, significant challenges. As a starting point, education is critical to ensure that we arm today’s and tomorrow’s workforce with the skillsets they need for success for that future, while ensuring that we democratise AI for all. ai


Microsoft South Africa In the digital era, companies and people have access to massive amounts of data through the internet as well as ubiquitous IoT sensors, along with the big computing power of the cloud that enables us to access and do more with data at a greater scale than ever before. Add to this, the powerful algorithms that allows companies to make research breakthroughs in order to train computers to accomplish more sophisticated tasks on their own and you end up with a technology trend, namely Artificial Intelligence (AI), which is set to fundamentally reinvent how organisations of all sizes are run, compete and thrive. According to PWC, AI will contribute up to $15.7 trillion to the global economy by 2030, making it a massive commercial opportunity in today’s fastchanging economy. There is no better time than now for African companies to solidify their long-term AI strategy. Spending on cognitive and AI systems in the Middle East and Africa (MEA) region will reach $114.22 million by 2021 according to data analytics firm IDC. All of this translates in the need for businesses to start focusing efforts on using AI to enrich people’s jobs, reimagine how traditional tasks are done and create new industries. In fact, while some industries and businesses are more advanced than others in implementing AI, it is still in its early stages of development, which is a positive for emerging markets, as they have the ability to leapfrog more developed counterparts.


Microsoft SA join AI Expo Africa as Platinum Sponsors stating “AI will have an impact on all aspects of our personal and professional lives in the future”.

Intel Corporation join AI Expo

Africa 2018 as Premier Sponsor and Boost Artificial Intelligence Skills Development in the Region


Intel Corporation’s involvement in AI Expo Africa as a premier sponsor will drive regional skills development in Artificial Intelligence by supporting the training of 200 young African engineers in partnership with event organisers AI Media and local Ecosystem Enabler Silicon Cape.



“With technology breakthroughs across all sectors of industry now going mainstream, understanding the business opportunity from both a supplier and client perspective has never been more paramount.” said Roy Bannister, Event Chairman. “AI ExpoAfrica 2018 has at its core a focus on real-world applications and trends driving the AI Economy in Africa and seeks to build an AI Business-focused community across the continent,” Bannister concluded. AI ExpoAfrica 2018 is aimed at the C-Suite decision makers, senior executives, investors and innovators focusing on the disruptive nature of Artificial Intelligence (AI), Machine Learning, Deep learning, Data Analytics, Robotic Process Automation (RPA), AI cloud platforms, hardware, devices and Internet of Things (IoT). “With the signing of Intel Corporation as a premier sponsor, we are now able to deliver on one of the key themes and goals of the event, namely skills development and education”, said Nick Bradshaw Event Director of AI Expo Africa. “When we launched the show at the end of January 2018, we conducted a very high level survey on the challenges we face in deploying AI Technology in Africa. It was clear that skills availability was the No. 1 challenge closely followed by education as the two biggest challenges facing companies seeking to grow competency in AI and related disciplines. So in response to this, and in partnership with Intel Corporation, we are offering the opportunity to train up 200 young engineers during the event by a series of AI workshops we have named the “AI Talent Tank”. This new aspect to the event will be unique and FREE for young engineers to take part in and it is sure to be popular. Tickets for participation in the AI Talent Tank workshops have been snapped up quickly, highlighting the thirst for training in this sector,” concluded

Bradshaw. Intel Corporation joins a growing list of sponsors and supporters of AI Expo Africa. Roy Allela, Intel Corporation Program manager for the Intel Innovator Program in EMEA and Royal Academy of Engineering LIF Fellow 2018, commented, “We are proud to be helping facilitate much needed AI skills development and education. It is incumbent on us all to foster collaboration with events like this to ensure young engineers gain an opportunity to not only learn new skills but interact with Expo sponsors and delegates to create new relationships, swap ideas and even secure job opportunities.” Allela concluded, “The AI Expo Africa Event Team at AI Media were only too happy to configure a sponsorship package to meet our mutual goals and the AI Talent Tank will truly be a win-win for all concerned, we can’t wait to share this with the community and leverage the local skills development partners they are working with to make this happen”. Tumi Menyatswe, Ecosystem Manager at the The Silicon Cape Initiative commented, “Silicon Cape is committed to building and catalysing the Technology Ecosystem in the Western Cape and beyond. We are proud to be a partner of AI Expo Africa and the team has been very proactive with us in achieving our mutual goals. We support ideas such as the AI Talent Tank at AI Expo Africa as it aligns with our vision to work with entrepreneurs, regulators, governments and corporate businesses to create an environment where startups and skills can thrive”. Menyatswe concluded, “We need to grow the technology talent pool on the Continent and foster the creation and growth of World-class solutions, services and companies in Africa. We will be helping AI Media and Intel Corporation reach into our community to find and train the new, young AI talent of tomorrow, we can’t wait to get started.” ai

Which horse to pick? When it comes to AI it is all about horsepower Brett StClair, CEO Siatik -

Meanwhile, too many South African businesses rely on locally hosted machines; some even try do statistical solutions using Excel, because these are tools that have traditionally been used. But it’s clear that it is time to reskill and harness new modern toolsets. The next major evolution has been in the world of Machine Learning (ML), where processing units are being configured into “neural nets” like Google’s Deep Mind, powered by either graphical processing units or, more recently, Google’s Tensor Processing Units (TPUs). In its current version, the latter can process 96 Petaflops (1 million servers in a datacenter is the equivalent)! All of this power is available from Google on a pay-as-you-use model, which saves business and researchers millions of dollar in upfront investments. We’re proud to say that Siatik is part of this extraordinary tech revolution, as one of Google’s leading Premier Cloud partners in Africa. We help business move compute to the cloud and to modernise infrastructure, from Virtualisation to Containerisation with Kubernetes. We also help your business productionize your dataset from SQL (or any database) into Google’s Big Database services, as well as use simple tools like Auto ML, which starts to make AI as easy as using Excel. What we are seeing in the market is that most South African businesses want to be using the sexy stuff like Tensorflow and Docker, but they are still hosting on servers on site. This is where we come in to migrate and modernise your workloads. At Siatik we see the future business as a business that is driven by data and powered by algorithms. We offer a worldclass service where we know we are backing the winning horse. ai


Tensor Processing unit v3, water cooled


IT HAS always been a dream to build machines that could imitate humans and perform human-driven tasks. The world of Artificial Intelligence (AI) has been around since the early 1950s when scientist Alan Turing determined how to measure the maturity of AI, including testing a machine’s ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Ray Kurzweil, well known Futurist, Director of Engineering at Google, and co-founder of the Singularity University, has predicted that by 2045 we will reach the point where machines will have become more advanced than humans. This ‘singularity’ hypothesis describes the point at which artificial superintelligence will trigger both unimaginable technological growth and changes to human society. AI brings with it scary ideas about robots taking over the world but, as the field advances, it is clear that the technology is still in its infancy and will rather play an augmenting role in business and in bettering human lives. Currently the field of machine learning is undergoing a huge rise because of two key technology trends: the first being Cloud Computing and the second Big Data. The two go hand in hand. However, far too often we see South African CIOs trying to separate the two fields. The reason that they go hand in hand is that firstly you need vast amounts of very fast, very cheap storage. Next, you need access not only to unlimited compute but the fastest compute. This is what you ultimately need to properly programme machines.The more compute you have, the faster you can train machines and save time, from weeks to minutes. A great example comes from PPS: they ran a neural net to do propensity modeling on the likelihood of whether an existing client will buy finance products. Previously this would have taken a huge amount of code and over 3 days to process. Using Google’s neural net the machines were trained twice in just 19 minutes. Google has been leading the field in machine learning for over 10 years. It all started with Google releasing a white paper back in 2003 on its File Storage system, which re-invented how data can be stored across many virtual devices. This was not long after Google released its very important Mapreduce white paper which explained to the world how the company manages the huge task of indexing the world’s internet data. This paper was released to the open source community where the world of hadoop and unstructured databases sprouted from. This was the start of Big Data being available to all. Today Google is still releasing Big Data solutions in the format of Big Query and Big Table, which are massive databases powered by a lot of compute or horsepower, on a pay-as-you-go model.

AI Expo Africa: BCX Sponsor Spotlight As a leading digital transformation partner, BCX is excited to be a part of AI Expo Africa’s inaugural event – an event that we believe will play a pivotal role in the South African technology landscape.


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How to Build a Content-Based Recommender System For Your Product Presenting users with the most relevant information is an important task for any product to fulfill. To do this properly, you need to be able to extract their preferences from your raw data. Here’s a framework for you to start doing that.


Written by Helge Reikeras, Data Scientist at OfferZen



DEDUCING INTERPRETATIONS from your raw data can be tricky, because to succeed you need to: Understand what the users’ needs are: You will typically only have very limited, implicit data of what a user might be interested in. For instance, Netflix needs to infer their users’ preferences of movies based on the movies they have watched previously. The users won’t explicitly tell Netflix what they like. Prioritise all matches: Even if a company like Netflix is able to satisfactorily model user preferences in movies, they still have a big problem: There are >50,000 movies out there of which thousands may fit with the user’s preferences. Which movies should Netflix recommend first? As a data scientist at OfferZen I was recently involved in implementing a recommender system. Since everybody knows how Netflix works, we are going to explain the underlying concept at the example of movie recommendations. I’ll give you some guidance on how you can get started building your own and share some practical learnings from our own implementation. Recommender systems There are two main data selection methods: Collaborative-filtering: In collaborative-filtering items are

recommended, for example movies, based on how similar your user profile is to other users’, finds the users that are most similar to you and then recommends items that they have shown a preference for. This method suffers from the socalled cold-start problem: If there is a new movie, no-one else would’ve yet liked or watched it, so you’re not going to have this in your list of recommended movies, even if you’d love it. Content-based filtering: This method uses attributes of the content to recommend similar content. It doesn’t have a coldstart problem because it works through attributes or tags of the content, such as actors, genres or directors, so that new movies can be recommended right away. Based on this, I’m going to introduce you to content-based filtering for a movie recommender system. I’ll use Python as the programming language for the implementation. Step 1: Choosing your data The first thing to do when starting a data science project is to decide what data sets are going to be relevant to your problem. This stage of the project is referred to as data selection and is highly important because if you choose the wrong data source, you won’t get successful performance. Whenever you’re dealing with content-based filtering, you’ll

need to find those attributes of your content that you think are relevant to the problem. That way, you can later rank the content for your users or recommend relevant parts to them. Here’s how this would look for our movie recommendation example:

The formula used to calculate TF-IDF weight for term i in document j is:

tf is the term frequency, df is the document frequency and N stands for the total number of documents in the dataset. A vector-encoded document will look like this when encoded:

Each element in the vector represents a TF-IDF weight associated with a term in a document.

Cosine similarity I’ll use Python and the numerical library Numpy for illustration where x and y are two documents representing the feature vectors introduced in Step 1:

Vectors have direction and magnitude. Because of this, we can calculate the angle between two vectors. A popular measure in data science is the cosine of this angle computed as follows:

This measure will equal 1 when the vectors are parallel (they point in the same direction) and 0 when when the vectors are orthogonal. Vectors that point in the same direction are more similar than vectors that are orthogonal. Now we start to see how this can be helpful to us: For example, the movies Toy Story and Monsters, Inc have a cosine similarity of 0.74. We would have expected these movies to have a relatively high similarity. In contrast, the cosine similarity between the movies Toy Story and Terminator 2 is 0.28 - as expected much lower. We can now recommend movies based on the movies that a user has already watched or rated using the cosine similarity. We would recommend movies with the largest similarity to the


Step 2: Encoding your data There are a number of popular encoding schemes but the main ones are: One-hot encoding Term frequency–inverse document frequency (TF-IDF) encoding Word embeddings For our example, we will use the term frequency–inverse document frequency (TF-IDF) encoding scheme. The advantage of TF-IDF encoding is that it will weigh a term (a tag for a movie in our example) according to the importance of the term within the document: The more frequently the term appears, the larger its weight will be. At the same time, it weighs the item inversely to the frequency of this term across the entire dataset: It will emphasise terms that are relatively rare occurrences in the general dataset but of importance to the specific content at hand. That means that words such as ‘is’, ‘are’, ‘by’ or ‘a’ which are likely to show up in every movie description but aren’t useful for our user-recommendation, will be weighed less than words that are more unique to the content that we are recommending.

Step 3: Recommending content Recommending content involves making a prediction about how likely it is that a user is going to like the recommended content, buy an item or watch a movie. There is a large amount of methods and literature available on recommender systems. Popular methods include: Similarity-based Methods One-class SVMs Matrix Factorisation Supervised Learning Deep Learning We are going to use a simple similarity-based method called cosine similarity as it is easy to understand, but does a good job at illustrating the fundamental concept of making recommendations.


I’m using the publicly available MovieLens data set. This data set consists of a sequence of tags such as actors, genres, moods, events or directors for each movie. These tags were generated using user-contributed content including ratings and textual reviews. We’ll collectively refer to the tags associated with a given movie as a document. For example, the movie Toy Story has 178 tags in our chosen data set, some of which are: How do you extract data that is relevant for the content that you want to recommend? This depends on your specific problem and what data is available or can be collected: At OfferZen, for example, we used a company’s activity on our platform as the main data to build the indicator for company preference and what they are looking for. The models used in data science are fundamentally mathematical in nature and thus require us to represent the data in vector format - an array of numbers stored in memory. These vectors are called feature vectors. In content-based recommender systems, the term content vectors is also used. So how do we convert the above tags into a vector representation?


ones already highly rated by the user. Generating user preference profiles Instead of recommending movies based on specific movies that a user has already watched, we could also attempt to build profiles of the users’ preferences. This will allow us to gain an aggregate view of the users’ preferences and then recommend content based on their behaviour over time without skewing the recommendations by outliers. Let’s take user #1 in the dataset. This user has rated the following movies from 1: dislike to 5: like.



preferred items. This can easily be a problem when our user’s interest sits on opposite sides of the spectrum. In order to address this issue, we could resort to using machine learning methods.

What to do if you don’t have explicit user ratings? Following our example of using movie ratings to recommend content, you might have realised that we are implicitly assuming that the user ratings are available. However, frequently there is no such explicit data. What to do in this case? The solution is to determine implicitly when a user liked or disliked an item. At OfferZen, we deal with this in two ways: We track whenever a job seeker profile has been skipped and when it is viewed.On Netflix, one would be able to track if a user has actually watched a movie all the way through or stopped after the first few minutes. We register interview invitations to job seekers as a stronger signal of intent than a profile view. This way, we can generate an implicit rating even though we don’t explicitly ask companies to rate their interest, which will frequently cause unnecessary cognitive load on the user. On Netflix, a user’s “saved movies list” might be considered less weighted than a “like”. I’d love to hear your feedback and suggestions, are you currently or planning on implementing data science projects in your company?

How do we build a preference profile for this user? There are many ways to build the preference profile. For simplicity, I will take a less principled approach and take the weighted mean of the user’s ratings and the TF-IDF vector representations of the respective movies. This simple weighted mean will then constitute the user’s preference profile. All we have to do now, is to take the cosine similarity between the user profile vectors and content vectors to find their similarity. Now we can recommend the most similar items. Based on this, user #1’s top recommendations are: Based on the user’s rating of other movies, these appear to be good recommendations for this user. A drawback of the weighted mean approach is that it will tend to give recommendations that are just that - the mean of

Resources Github repo containing the source code for this article Good tutorial on TF-IDF Useful tutorial on cosine similarity Coursera specialisation on Recommender Systems The MovieLens dataset This article has originally been published by OfferZen. OfferZen is an online marketplace used by hundreds of SA’s top tech companies to hire talent for their software teams. Every Monday; Software Developers, Data Scientists, UX Designers and Product Managers who are actively looking for work are listed on the platform. If your company is looking to hire or you’re looking for your next challenge in tech then find out more here. ai



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AI has the Potential to Transform Africa, leveraging its benefits for the Social & Economic Good of the Continent. AI Expo Africa will see a gathering of Africa’s AI Leaders to plot the way forward for the 4th Industrial Revolution

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