Azafran Capital INSIGHTS — Vol. 15 by James F. Kenefick

YOUR EYE ON INNOVATIVE MACHINE LEARNING SOLVING REAL WORLD PROBLEMS

Azafran Capital Partners

INSIGHTS Why the Future of Machine Learning is Tiny, and the Future of AI is Augmented Augmented AI and TinyML are fast becoming key elements of the ML and AI revolution - Issue Fifteen of INSIGHTS takes a deep dive into these exciting technologies through the lens of Azafran’s Investment Thesis

Augmented AI For some time, our team has been scratching our heads at the loose interpretation of AI as a catch-all term, serving as both the umbrella for any tech or platform using machine learning, deep tech or science and their associated components, alongside the strict definition where AI is sentient machine intelligence, making decisions without human interaction. This dynamic is confusing for the public at large, who could be watching an episode of Battlestar Galactica (cyborgs = true AI) when a commercial for IBM breaks in talking about how they are using AI at Wimbledon to improve scoring, etc. In the various markets where AI is at work along the lines of the first definition above (i.e. not sci fi), this also creates confusion and different perspectives and definitions. At Azafran, our focus is not on robotics or the strict definition of AI, but rather, machine learning, deep science/tech served by the input modalities of voice, acoustics and imagery. Through this lens, we see Augmented AI playing a key role in our Fund Two and near to mid term investments, through the current decade. Augmented AI (some call it the Future of AI), combines humans and machines together to make a decision & requiring human input, hence the augmented moniker. Further, Augmented AI by design helps humans both make decisions and eliminate tasks, and does not seek to replace humans in the equation. Examples of Augmented AI that our readers might have experienced include Grammerly and Adobe Photoshop’s Select Subject feature. Grammerly assists writers and editors, making suggestions based on strict grammar interpretations but also learns a particular writer’s style and over time adjusts recommendations to fit that style. Adobe’s Select Subject, examines an image & then utilizes a fairly sophisticated Augmented AI to select that part of an image that looks like it’s the focus of the shot. This is an operation that can be done by hand, but it is slow, tedious and error prone. Azafran INSIGHTS © Azafran Capital Partners 2020 - All Rights Reserved

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Issue Fifteen issue ﬁfteen FOCUS Azafran Capital Partners is a 2020 vintage early stage venture fund investing in companies ($2M to $8M) that are using deep learning and machine learning, emphasizing voice, acoustics and imagery datasets in the health / wellness, and IoT / enterprise markets. Issue Fifteen focuses on the emerging technologies TinyML and Augmented AI, as they begin to take their place in the world of machine learning and deep science technology and product development. We especially see these technologies furthering the modalities where Azafran has focus: Voice, Acoustics and Imagery.

Human fears around artiﬁcial intelligence stem from an assumption that the ultimate goal of AI is to replicate, and surpass, human intelligence, thereby threatening our own existence. Indeed, worries about AI supplanting jobs have been constant since the technology’s advent. However, the theory of AI augmentation challenges this end goal, reimagining AI as a way to develop technology to supplement and support human intelligence, with humans remaining at the centre of the decision-making process. Augmented intelligence can therefore be deﬁned as the human-centred partnership model of people and AI working together to enhance cognitive performance and business operations.” - Why businesses must invest in AI Augmentation - Imam Hoque

Issue Fifteen - Page One

Why the Future of Machine Learning is Tiny, and the Future of AI is Augmented. (Continued from Page One)

TinyML Shifting gears to TinyML, another technology that is on the cusp of advancing the benefits of machine learning, in this case to the edge and more so into our everyday lives. As defined by VentureBeat: “TinyML broadly encapsulates the field of machine learning technologies capable of performing on-device analytics of sensor data at extremely low power. Between hardware advancements and the TinyML community’s recent innovations in machine learning, it is now possible to run increasingly complex deep learning models directly on microcontrollers. A quick glance under the hood shows this is fundamentally possible because deep learning models are compute-bound, meaning their efficiency is limited by the time it takes to complete a large number of arithmetic operations. Advancements in TinyML have made it possible to run these models on existing microcontroller hardware.” Further, as noted by Azafran General Partner, Martin Fisher, “The big difference between full stack machine learning and TinyML is in the constraints. TinyML is running in a micro space, with low energy, versus a phone, desktop, servers or the cloud. You have to put a battery in there, it has to last a few years. In terms of the code, there is no hard drive for data so the data has to be written into the code for the chip / device to truly operate and process inputs on the edge. In addition, that code is probably in Assembly or C programming, which is not a friendly environment, very strict. Significant size, power, data and code constraints, that about sums up why TinyML is so challenging, but the benefits will be huge.” Thinking of the use cases and applications that are already being imagined and deployed (see Use Cases on the next page) the possibilities are staggering, ranging from sensors in fields helping grow crops to sensors listening to machines for faults. One of our favorites is a successful initiative in the African Savannah (spearheaded by Paul Allen and the Allen Institute prior to his passing) where acoustic sensors driven by machine learning have basically eliminated elephant poaching in some reserves that have implemented the technology.

from the FRONTLINES Augmented Intelligence is the New “AI” (Deﬁnitive Healthcare) The American Medical Association (AMA) is making the shift, stating that “augmented intelligence” more accurately represents the role these new technologies play. In terms of policy, the AMA has already set some ground rules and aspirations concerning augmented technology implementations.

The AMA seeks to: ●

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Leverage healthcare engagement to improve patient outcomes and physician satisfaction, and set priorities for AI in the industry Integrate physician experiences into the development and implementation of AI Encourage the development of high-quality AI that is designed with clinicians and other end-users in mind and safeguards patient data privacy Promote education for care providers, medical students, and administrators on the applications and limitations of AI Study the legal implications of healthcare AI, including liability, and advocate for the appropriate oversight to ensure safety and equitable use of these technologies

SPECIAL REPORT SERIES Part Two

augmented ai AS A SERVICE

Returning to the challenging development of the TinyML space, the chip tech and infrastructure, how you create MCUs, how you deﬁne device trees - almost all of that is in China. Getting just a prototype spun up for testing can be a months-long process with support slow and painstaking. If a developer needs to make changes to the ﬁrmware, for example, they are dealing most likely with poor documentation and support and the expertise is highly specialized (electrical engineers), presenting a talent gap on top of these other challenges.

Amazon Web Services recently announced the general availability of Amazon Augmented Artiﬁcial Intelligence (A2I), a fully managed service that makes it easy to add human review to machine learning predictions to improve model and application accuracy by continuously identifying and improving low conﬁdence predictions.

But the payoff is going to be a game changer and our team expects to see a lot of movement and development in this space in the next 2-3 years. Ending on a note looking back to Martin’s first startup (Rainbow Software) he recalled a similar environment to the challenges facing TinyML companies and developers. They built an MS-Office-like software suite (long before Office was imagined) on Commodore 64s. Everything was done moving in and out of registers - no hard drive, and a limited memory environment, not the same but evocative of what currently TinyML developers are facing. Marty eventually sold Rainbow to IBM, his first of many successful exits to come.

Amazon A2I helps developers add human review for model predictions to new or existing applications using reviewers from Mechanical Turk, third party vendors, or their own employees. With Amazon A2I, developers can add human review to machine learning applications without the need to build or manage expensive and cumbersome systems for human review.

Issue Fifteen - Page Two

Augmented AI and TinyML Use Cases Focusing on solutions within Azafran’s Thesis, following are a number of Augmented AI and Tiny ML use cases already out there in the world:

TinyML One recent event in the advancement of TinyML was Apple’s acquisition of Xnor.ai, a Seattle startup spun off from the Allen Institute specializing in low-power, edge-based ML tools. Xnor.ai’s technology embeds ML on the edge, enabling facial recognition, natural language processing, augmented reality, and other ML-driven capabilities to be executed on low-power devices rather than relying on the cloud. Another recent key milestone in development of TinyML was Amazon Web Services’ recent release of the open-source AutoGluon toolkit. This is a ML pipeline automation tool that includes a feature known as “neural architecture search” which finds the most compact, efficient structure of a neural net for a specific ML inferencing task. There are also commercial implementations of neural architecture search tools on the market. A solution from Montreal-based startup Deeplite (under LOI with Azafran) can automatically optimize a neural network for high-performance inferencing on a range of edge-device hardware platforms. It does this without requiring manual inputs or guidance from scarce, expensive data scientists.

Augmented AI Radiologists are already using machine learning and assistive technologies as diagnostic tools. Retinal scanners in smartphone apps can detect “white eye,” or retinal reflections, in infants – often a sign of cancer or cataracts. Facial recognition technology is also being used to diagnose infants. Boston-based company FDNA developed an AI that cross-references photos of infant faces to flag potential genetic conditions. With identity theft and account takeover on the rise, it’s increasingly difficult for businesses to trust that someone is who they claim to be online. Jumio’s identity verification and authentication solutions leverage the power of biometrics, informed AI and the latest technologies to quickly verify the digital identities of new customers and existing users.

NEWSWORTHY… Why businesses must invest in AI augmentation Augmented AI sees humans and technology working in tandem to transform the decision-making process. “Gartner predicts that AI augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally, while by 2030 augmentation will surpass all other forms of AI initiatives to account for 44 percent of the global AI-derived business value. Analysts believe that as AI evolves, combined human and AI capabilities harnessed through augmented intelligence will deliver numerous beneﬁts for business.” Imam Hoque for ITProProtocol Azafran INSIGHTS © Azafran Capital Partners 2020 - All Rights Reserved

in the HEADLINES Five Trends in Hardware to Watch 1. TinyML. (DAI) Machine learning frameworks that allow implementers to train a computer model based on example conclusions from a set of training data are making profound impacts on the way we will use computers. This may sound like a software trend, but the reality is these models need to run on hardware in order to apply them to real-world problems. Until recently, that meant most of the models lived in the cloud.

Over the last few months, however, we have seen the explosion of tinyML—frameworks that allow for those models, once trained, to run on very common, low-cost, and low-power hardware. While tinyML is so new it doesn’t even have a Wikipedia page (as of this writing at least!), we can expect to see commercial products and small-scale projects using these frameworks very soon. Development stakeholders will need to quickly become conversant about the opportunities and risks associated with artiﬁcial intelligence, especially as it becomes available everywhere.

they wrote the book on TINYML Deep learning networks are getting smaller. Much smaller. Pete Warden and Daniel Situnayake explain how you can train models small enough to ﬁt into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-bystep.

Issue Fifteen - Page Three

from the “ROAD” The Azafran team adapts to the New Normal shifting to virtual conferences, pitch days, and openings around the U.S. & world Standard procedure and part of the Azafran team process - we are out on the front lines every week, attending and speaking at conferences, events, pitch days, openings. We are all seasoned entrepreneurs, operators and start up geeks so this part comes easy for us. As we shift to attending and helping host virtual events, this dynamic continues to be a crucial element helping us to deliver on the Azafran investment thesis. Through this work, our team stays on top of the latest trends, companies and partner opportunities, especially away from the obvious venues in the world.

Azafran Pivots to Virtual Events Over the past months we have had to cancel many planned events and trips such as SXSW, HIMSS and others - but Team Azafran has kept up the pace attending and helping host a number of events, panels and pitch days - following are a few we’ve attended and will be attending in the coming month:

BCI TALKS Series Azafran’s Managing Partner James Keneﬁck was on a virtual panel event this past month, Digital Transformation in Turbulent Times, alongside leaders from Morgan Stanley, UBS and BP. Lucy Condakchian, general manager of robotics at Maxar Technologies,, onstage @ TechCrunch demonstrating and talking about the company’s work in space, including NASA’s Mars 2020 rover.

TechCrunch Sessions: Robotics + AI 2020 Berkeley, CA

At one of the last place-based conferences since the Covid-19 crisis broke, team Azafran was on the ground at the TechCrunch Robotics + AI 2020 Conference in Berkeley. There was a mix of great speakers, panels, and startups on hand - TC had held a private pitch competition (culled from a few thousand applications) at their offices the night before the conference, and the four finalists presented - three of which fit the Azafran thesis and we have had follow ups and discussions & added them to our watchlist. In addition, there were a few dozen companies from startups to established world leaders on display outside the presentation hall. We had great discussions with a number of them including Toyota Ventures and Sony Innovation and look forward to speaking with them on the partnership front as the dust settles from the current crisis.

Google VOICE Talks The Azafran team was online for this monthly deep dive into the rapidly-evolving voice industry. Presented by Google Assistant, each livestream features 60 minutes of insider content from the world's leaders in voice technology.

Venture University's Cohort 7 Demo Day Held on Zoom for the ﬁrst time, Venture University presented their unique format where they showcase new investments and the teams that will be managing the investments.

ITU AI For Good Pitch Event The Innovation Factory Live Pitching Session showcased a diverse set of emerging entrepreneurs with promising AI ventures whose solutions can accelerate the progress towards meeting the UN Sustainable Development Goals.

Upcoming Events HIMSS: Accelerating Health Systems’ Digital Transformation: Why Digital Health Must Be the New Standard in a Post-COVID-19 World The 2nd Annual Innovation Works and Carnegie Mellon University AI & Robotics Venture Fair As always, please email us at insights@azafranpartners.com if you would like to schedule a Zoom video or audio call until physical travel and gatherings pick back up.

Issue Fifteen - Page Four

Azafran Fund I Portfolio Focus Aspinity’s Solutions Help Enable the the TinyML Revolution The Growing Demand and Market Drivers for Always‐on Sensing and Listening In the next five years, billions of handsfree, always‐on sensing devices that run on battery will assist us in our daily lives at home and at work: playing music on request, controlling our home’s temperature and lights, alerting us to danger, monitoring the wear and tear of factory equipment, even continuously monitoring our health. These devices are always‐on, continuously digitizing and analyzing all sensor data as they wait to detect a random sporadic event, such as a voice, an alarm, a slight variation in the vibrational frequency of an engine or a change in heart rhythm. This constant analysis of mostly irrelevant data is grossly inefficient―expending precious system resources on data that will ultimately be thrown away. Aspinity’s Reconfigurable Analog Modular Processor (RAMP) technology enables a fundamentally new, more efficient edge architecture that does just that. RAMP technology incorporates powerful machine learning (ML) into an ultra‐low‐power analog neuromorphic processor that detects unique events from background noise using the raw analog sensor data, before the data is digitized.

Aspinity Analyze-First: Eliminating the Irrelevant Data - While it's Still Analog

TinyML Makes a Huge Impact on Mobile Devices At last month’s tinyML Summit, Aspinity joined other industry thought leaders, including Qualcomm, Samsung, Google, and Arm, to explore the size and power challenges of using tinyML to extract intelligence from the physical world. Eliminating the bottlenecks in developing a powerand size-optimized neural network through more efﬁcient algebraic functions and memory access was a big part of the conversation. There was also a collective buzz on establishing industry benchmarks for comparing the energy efﬁciency of the digital cores that comprise today’s tinyML chips. For more information: https://www.aspinity.com/ Contact: Thomas Doyle, CEO - tom@aspinity.com

Aspinity and German chip manufacturer Inﬁneon Announce Partnership

RESEARCH

voice - acoustics imagery & AI/ML Company Tracker & Industry Snapshot At a Glance: Top 6 Markets & Global Totals

Issue Fifteen - Page Five

A Year of INSIGHTS

Below is a quick snapshot of some of the covers from Azafran’s INSIGHTS over the past year - please let us know if you would like PDFs of any issues - just email us at insights@azafranpartners.com and we’ll send along.