James F. Kenefick - Azafran Capital INSIGHTS Vol. 7

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Azafran Capital Partners

INSIGHTS issue SEVEN A Picture is Worth a 1,000 Words This age-old anecdote is now even more true than we could have imagined even 5 or 10 years ago. As the Azafran team strengthens and deepens its focus on the modalities of voice, acoustics and imagery, we dedicate this issue of INSIGHTS to the emerging world of imagery as a modality for machine/deep learning, science and the incredible products and companies that are emerging in the space. As a human expression and much like voice and acoustics, imagery has been with us since ancient times, with the oldest-known example of figurative art anywhere in the world—the world’s very first picture - found in the caves of Indonesia dating back over 35K years ago. Also, like voice and acoustics, imagery has had a place in society and science but with the advances of the past decades in data, machine learning and deep science, imagery has taken on a new role, as a new modality feeding these incredible engines with sources of data never imagined before. Another important consideration is that machines see images differently than humans, where we largely associate by shapes, machines tend to interpret textures, so each has particular advantages/disadvantages in evaluation. As reported in a recent Quanta Magazine, “To make deep learning algorithms use shapes to identify objects, as humans do, researchers trained the systems with images that had been “painted” with irrelevant textures. The systems’ performance improved, a result that may hold clues about the evolution of our own vision.” The implications and opportunities here are staggering and the Azafran team is focused on this segment, with active investments partnerships forming. We’ll continue to write and communicate our stories on this front, with this issue as a starting point for much to come.

Company Focus: Mobile Based Security “SWORD is the single most exciting project I have ever worked on. Bringing a challenging and disruptive new IoT device to the market has also proven to be the most challenging project I have ever been part of.” - Barry Oberholzer CEO & Founder, SWORD

As a prime example of benefits and product impact not even conceivable a decade ago, SWORD capabilities include the proactive, non-invasive, remote scanning of individuals, objects and packages for detection and identification of concealed weapons and persons of interest. Our threat detection functionality is a combination of 3 technologies connected by proprietary software algorithms and custom low frequency millimeter wave technology. SWORD features are

issue seven 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. Issue Seven of INSIGHTS focuses on new advances and opportunities using imagery as the input and modality to machine learning and deep science engines. Alongside voice and acoustics, incredible innovation is occurring from picture imagery fueling complex algorithms to sensory devices using live images and imagery driving companies, technologies and advancements for humanity.

The computer-assisted analysis for better interpreting images have been long standing issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances.”

agnostic to environmental conditions and can operate in rain, fog or total darkness. Our multi-tiered threat detection platform can simultaneously scan for known persons on a custom database, over 8,000 known weapons and runs continuous gunshot detection technology, all within milliseconds. “The Azafran team has been impressed with

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Deep Learning in Medical Image Analysis National Institutes of Health

SWORD’s founders and technology since our first meeting,” notes James F. Kenefick, Azafran Managing Partner. He adds, “the benefits are game-changing on so many levels, for society, our fund and our investors, we are excited to have a term sheet signed with SWORD and look forward to working with them in the months and years to come.” Volume 1 Issue 7 - Page One

Imagery Use Cases


Imagery has been a constant and a large source of the mountain of data that has been building, but now it is finally being put to use in ways that both truly create benefit for society and potentially, hugely successful companies and platforms. Following are snapshots of some of the more innovative and interesting solutions entrepreneurs in our partner ecosystem are building right now, using imagery as a focal point and modality: ● ● ●

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Security surveillance using millimeter and infrared plus image recognition to detect a possible threat. Uses video cameras for security detecting possible threats and for tracking objects through image recognition and machine learning. Proactive, non-invasive, remote scanning of individuals, objects and packages for detection and identification of concealed weapons and persons of interest. Making Touchless Auto Claims A Reality with AI. Instant point-ofaccident damage assessment using AI image analysis. Crop yield detection relying on satellite imaging data and a machine learning algorithm to figure out how healthy a corn crop is from space. Creating accurate forecast data (including imagery) with AI, analytics, and the Internet of Things (IoT) to help consumers and businesses make faster, smarter decisions such as better inventory allocation, staffing and operations based on upcoming weather.

NEWSWORTHY… Facial Recognition Expanding To More Airports

Facial Recognition Continues to Grow Exponentially “We are already aware of Facebook’s Deepface program that is used to easily tag your friends and family in your photos. The popular iPhoneX is already using facial recognition as a digital password. With the boom in personalizing everything — from your shopping experience to advertising, this technology is going to be used more and more for biometric identification. This will continue to rise due to the non-invasive identification and the ease of deployment. Other use cases like payment processing through security checks as well as for law enforcement (in early detection and prevention of crime) are on the rise. These next-generation image recognition technologies can be used for healthcare purposes as well — to follow through clinical trials as well as medical diagnostic procedures. Openwater, one of the forerunners in imaging technologies, is pushing the boundaries of future devices that could read images from our brains!” Source: Swathi Young on Medium

Some U.S. airports have been dabbling with the technology, primarily aimed at international travelers. For example, JetBlue partnered with U.S. Customs for an integrated biometric self-boarding gate for international flights, Delta Air Lines added facial recognition for travelers heading through Hartsfield-Jackson Atlanta International Airport and facial recognition was expanded at Orlando International Airport, making it the first U.S. airport to use the biometric technology on all international travelers, both arriving and departing. Now Panasonic is stepping it up a notch by adding automated facial recognition gates at several airports in Japan. Source: ConnectedThinking

ImageNet: Helping Drive Change Google Wins Best Image Recognition Engine A new report by Perficient Digital has looked to think about some of the key players in the image recognition engine area. Who Has the Best Recognition Engine? – investigated the precision of AWS Rekognition (Amazon), Google Vision, IBM Watson and Microsoft Azure. The outcomes are captivating. Of the picture recognition engines in the analysis, Google performed great and won the survey – particularly across fundamental accuracy and when separating this by confidence level. Source: Analytics Insight Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved

ImageNet is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". The ImageNet project is inspired by a growing sentiment in the image and vision research field – the need for more data. Ever since the birth of the digital era and the availability of web-scale data exchanges, researchers in these fields have been working hard to design more and more sophisticated algorithms to index, retrieve, organize and annotate multimedia data. But good research needs good resources. To tackle these problems in large-scale (think of your growing personal collection of digital images, or videos, or a commercial web search engine’s database), it would be tremendously helpful to researchers if there exists a large-scale image database. (Source: ImageNet.com) Volume 1 Issue 7 - Page Two

Investment Segment Highlight: Connectivity Component: Local and/or Internet Connectivity Defined A traditional definition here is not required as local/Internet connectivity is now on the same plane as paper clips and staplers, at least for the audience reading the ACP Strategic Map. In the context here, though, there are important developments and trends at the connectivity level of the game and how this crucial part of the infrastructure is using the advancements of AI and ML to monitor, optimize and secure the last mile, connection to the cloud and long haul transport of critical data. In a great piece from Network World (AI, machine learning and your access network GT Hill, 2/18/2018), “Today, network managers must wade through volumes of data from Wi-Fi controllers, server logs, wired packet data and application transactions, analyzing and correlating all this data to determine the health of network as well as trends and patterns of network behavior across the stack that impact user performance. Then, they manually apply changes to the network with no real way to definitively determine whether those changes worked or not. Conventional network management and monitoring tools, never designed or developed to deal with these 21st century realities, are ill-equipped to automate this process. Machine learning is useful but only when fed tons of relevant data. On the Enterprise access network, that includes live packets off the wired network, wireless metrics from WLAN controllers, SYSLOG data from different network servers, ad other network data Sources. Machine learning is used to quickly analyze all this different data, correlating it across different network layers. This is something that's not practically possible with people trying to manually correlate it.”

The Azafran Take: The Azafran Capital team has deep experience in telecoms and the world of connectivity and access. This is a highly structured environment (you can’t have typical five nines reliability otherwise), and looking inward to the network, there is an opening now for supervised utilization of ML and AI to improve network performance, increase efficiency and strengthen security. Looking beyond the network itself, our team believes 5G will greatly accelerate the deep tech/learning/ML/AI infrastructure and proliferation of end-user applications. "We see 5G as being the biggest step yet," Qualcomm's Vice President of Marketing Pete Lancia told CNN Business. "3G brought the internet to your phone, and 4G enabled mobile-only companies like Uber and SnapChat to thrive. To say 5G will have a more profound impact than that is huge."

IN THE KNOW The future of image recognition technology is deep learning Dave Wallin, manager of innovations at The Archer Group, offers a high-level explanation of how image technology works along with the deep learning technology that powers it. Much of the innovation in image recognition relies on deep learning technology, an advanced type of machine learning and artificial intelligence. Typical machine learning takes in data, pushes it through algorithms and then makes a prediction, making it appear that the computer is “thinking” and coming to its own conclusions. Deep learning, on the other hand, works by building deep neural networks that simulate the mechanism of the human brain and then interpreting and analyzing data, such as image, video and text. This is especially important for image recognition: “You’d want something like a self-driving car to be able to tell the difference between a signpost and a pedestrian. Deep learning networks do not require human intervention because the nested algorithms run the data through different concepts which eventually learn from their own mistakes.” -

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Source: TechnicallyMedia

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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

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