__MAIN_TEXT__

Page 1

YOUR EYE ON INNOVATIVE MACHINE LEARNING SOLVING REAL WORLD PROBLEMS

Azafran Capital Partners

INSIGHTS Data Evolution and Revolution Sound and imagery - our first means of communicating and recording history, from the oldest cave art dating back over 60K years to over 100K years when the instrument of modern speech, voice, acoustics and all that comes with it began to form. Fast forward to the past decade and the role of these two mechanisms had not advanced much beyond the simple dimensions we had been using them, as a call and response, communications, recording, art, music, etc. All wonderful and amazing, these primary elements of what makes and defines us as a species. Now, with the enormous advances in data, machine learning, and deep learning, we are beginning to unlock the next dimension of voice, acoustics and imagery. Over the past decades, we have been recording, scanning, storing, amassing huge troves of digital data which are now being put to use by innovative entrepreneurs and companies as they create applications, platforms and solutions to some of life’s most vexing and intractable problems.

issue six FOCUS At Azafran Capital Partners, we invest in companies using Deep Learning and Machine Learning, emphasizing voice, acoustics and imagery datasets in the health, wellness, robotics/sensory and enterprise spaces - a $1 Trillion market by 2025 (see Market Predictions on following page). In the following pages we visit trends and predictions focused on data and datasets through the lens of voice, acoustics and imagery as the inputs to solutions built on deep and machine learning solving real world problems.

Think of all the data Facebook and others have collected, hospital systems, insurance companies, governments, clinical trials but until the last 10 years we did not have the tools to “mine” this data. These data sources provide incredible volume of baseline information for startups to train their models and train their algorithms. Starting with unformatted data (See page 3 for piece on Data Lakes) - really a bunch of noise - and then you run models against the sounds, against the imagery and then you begin to see patterns emerge. It’s all about benchmarking the data and looking for patterns. When you look at the mountains of data that entities of every size have been saving, we are now using it to create algorithmic models to solve problems that we could not imagine solving before the marriage of data, deep learning and machine learning. Once solutions begin to appear, problems get solved via deep learning and machine learning engines are applied as the datastores are constructed and organized. We now can correlate patterns, trends and garner data from these mechanisms that was previously not possible. Now, that’s a revolution. Over the rest of this ISSUE of INSIGHTS, we will explore dynamics and use cases related to this huge new opportunity and reality. Companies already cracking the code, from healthcare to safety, security and enterprise applications - all with consequences and improvements for humanity that we could not imagine even ten years ago, and at the core, we focus on data + voice and acoustics + imagery + machine and deep learning, a $1 Trillion-plus opportunity (see Market Predictions on next Page) within the next 5-7 years, and the core of Azafran’s Investment Thesis.

One of the significant challenges that the current research community is trying to address is how to equip the machines to recognize, process, and infer decisions from sounds and visuals. A lot of technologies are powering the research works. However, machine learning (ML) is a promising technology that is expected to impart the highest value to a range of interactive real-world applications such as image and speech recognition. The repetitive style of ML is essential for interactive models as they can adapt independently when exposed to new data sets. ML can easily apply knowledge and experience from an extensive collection of data repositories to allow face recognition, speech recognition, and much more.” - Future of Image and Speech Recognition with Machine Learning | CIOReview | July 9, 2019

Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved

Volume 1 Issue 6 - Page One


Data Use Cases

market PREDICTIONS

Using the framing outlined in the lead article on the previous page, entrepreneurs are making exponential progress utilizing incredible volume of digital data that has been amassed. Now turning their deep and machine learning engines on voice, acoustic and imagery modalities that, until recently, was actually “dumb” and representational. Now the data can be parsed and patterns are being found driving some of the most incredible innovations the world has seen. Following are snapshots of some of the more innovative and interesting datadriven solutions entrepreneurs in our partner ecosystem are building right now: ● ● ● ● ●

Using video cameras for security detecting possible threats and for tracking objects through image recognition and machine learning. Security surveillance using millimeter and infrared, plus image recognition to detect a possible threat. Developer of patent pending acoustic and ML technology that manages pediatric asthma in India and the USA. Developer of world’s first ML-based voice and audio testing platform for agencies and brands. Developer of ML platform for trustworthy and ethical machines in healthcare delivery.

Voice Startup Funding in 2019 Set to Nearly Triple Says European VC Mangrove and “Voice Economy” to Be a Trillion Dollar Market in 2025 “Investment in voice startups is now accelerating according to our analysis. Voice startups have raised $786m already this year, significantly exceeding the $581m raised in 2018 and $298m raised in 2017. This rise in funding reflects a growing belief that voice technology will be transformative. The size of fundraisings has also increased markedly–with an average of $30m so far in 2019 versus $18m in 2018 and $17.5m in 2017. Mangrove’s report goes a step further, however, by suggesting “Apple’s launch of SiriOS in 2020 will unleash huge innovation in the voice economy, which will be worth $1 trillion by 2025.” Source: Mangrove Capital Partners Report 2019

One thing all these innovative solutions have in common is that they rely heavily on benchmarking massive amounts of historical data, which then informs and helps create the predictive models and datastores they use to develop solutions for the market. Ranging from health, to wellness, security, and enterprise, data is driving the revolution and the “new” modalities of voice, acoustics and imagery are helping drive the most innovative discoveries

NEWSWORTHY… AI/Machine Learning is the Future of Financial Services, Adoption by Frontrunner Firms Accelerating Frontrunner financial services firms are achieving companywide revenue growth of 19% directly attributable to their AI/ML initiatives, much greater than the 12% of follower firms achieve. 70% of all financial services firms participating in the study are using machine learning in production environments today, and 60% are using Natural Language Processing (NLP). - Excerpted from Deloitte’s AI Leaders In Financial Services 2019 Report

Data Security and Privacy A key element that we stress to our portfolio companies is to have ironclad terms of service and, in addition, data security and privacy policies are crucial. In today’s environment, playing loose on this front could bring down a beautiful, hard fought vision and company, especially in the areas of health, wellness and security.

Big Data Grows Exponentially Worldwide Big Data market revenues for software and services are projected to increase from $42B in 2018 to $103B in 2027, attaining a Compound Annual Growth Rate (CAGR) of 10.48% according to Wikibon. According to an Accenture study, 79% of enterprise executives agree that companies that do not embrace Big Data will lose their competitive position and could face extinction. Even more, 83%, have pursued Big Data projects to seize a competitive edge. Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved

At a high level and as an example, within a database the various data points associated with an individual can be correlated from a privacy standpoint by the type or class of information they contain. “A dataset is made up of data points (e.g. specific members of a population) and features (e.g. values of the attributes associated with each individual). Machine Learning and its subcategories (i.e., Deep Learning), represent powerful statistical techniques. However, it is exactly their ability to derive actionable insights from large multidimensional datasets that presents an unforeseen challenge to the field of data security.” Excerpts from: Rethinking Data Privacy: The Impact of Machine Learning, Arianna Dorschel in Medium. Volume 1 Issue 6 - Page Two


Investment Segment Highlight: Data Lakes Component: Data stored in its natural format and transformed data used for tasks Data Lakes Defined A data lake is a system or repository of data stored in its natural format, usually object blobs or files. A data lake is usually a single store of all enterprise data including raw copies of source system data and transformed data used for tasks such as reporting, visualization, analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails documents, PDFs) and binary data (images, audio, video). Source: Wikipedia

IN THE KNOW

Examples One example of technology used to host a data lake is the distributed file system used in Apache Hadoop. Many companies also use cloud storage services such as Azure Data Lake and Amazon S3. It is important to note that for data lakes, the way data is being stored has moved away from traditional relational databases to Binary Large OBjects (BLOBs). A Blob is a collection of binary data stored as a single entity in a database management system. Blobs are typically images, audio or other multimedia objects, though sometimes binary executable code is stored as a blob. (Source: Wikipedia)

The Azafran Take Data lakes have become an economical option for many companies rather than an option for data warehousing. Data warehousing involves additional computing of data before entering the warehouse, unlike Data lakes. The cost of maintaining a data lake is lower than maintaining a Data lake owing to the number of operations involved in building the database for warehouses. The growing use of IoT in many offices and informal spaces has further emphasized in the need for data lakes for quicker and efficient manipulation of data.The global data lakes market was valued at USD 3.24 billion in 2017, and is expected to reach a value of USD 14.01 billion by 2023 at a CAGR of 27.4%, over the forecast period (2018-2023).

The University of Maryland and the City of Baltimore team up to design machine learning models to overcome possible data security risks. A new agreement between the University of Maryland, Baltimore (UMB), and the University of Maryland, Baltimore County (UMBC), will aim to leverage UMBC’s expertise in machine learning and cybersecurity to improve data security and protect medical information from cyberattacks. Through the partnership, UMBC will provide core resources to the UMB Institute for Clinical and Translational Research (ICTR) Informatics Core, adding a new Cybersecurity and Artificial Intelligence Core. This will enable the design of machine learning tools that can analyze large datasets, determine what additional data could be collected to potentially improve analysis, and uncover possible security risks associated with devices or systems. The most exciting area for the partnership is looking through the vast amount of data being compiled and finding occurrences or finding opportunities, maybe a unique link that we didn’t discover through natural language processing, through artificial intelligence, going through this data and really looking to see is there this needle in a haystack, except the haystacks are huge and the needles are really, really tiny, to see how we can really help our colleagues here with some discoveries,” noted Karl V. Steiner, PhD, vice president for research at UMBC.

Source: Guru99

Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved

-Excerpted from Health IT Analytics, August 2019

Volume 1 Issue 6 - Page Three


Feedback, going forward Thank you for the work you are doing in the world and your continued support of Azafran INSIGHTS’ monthly journey into the intersection of machine learning driven by voice, acoustics, language and image data. Our intention is to use this as a vehicle to open a dialogue with each of you, together as a group, and we strongly encourage and welcome your feedback. We’ve made feedback/comments simple, you can quickly and securely leave us a voice message by clicking here. If you are reading in print, please just visit the contact section of our website at AzafranCapitalPartners.com. In either case, just click on the “Start Recording” button and leave your thoughts and suggestions. Or you can always send us an email to insights@azafranpartners.com - thank you. We will be publishing INSIGHTS each month going forward, exploring the opportunity and intersection of voice tech and AI. We look forward to building this sector together and all the benefits for humanity that are soon coming down the road. From the Azafran team, we wish you all the best and a successful year ahead.

voice-techINDUSTRY At a Glance: Top 5 Markets & Global

Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved

413 West 14th St. - Suite 200, New York, NY 10014 p: +1.212.913.0700 insights@azafranpartners.com AzafranCapitalPartners.com

Volume 1 Issue 6 - Page Four

Profile for James F. Kenefick

James F. Kenefick - Azafran Capital INSIGHTS Vol. 6  

Read the latest issue of Azafran Capital INSIGHTS.

James F. Kenefick - Azafran Capital INSIGHTS Vol. 6  

Read the latest issue of Azafran Capital INSIGHTS.

Advertisement