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Lisa Falzone, CEO of Athena Security – Interview Series

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Athena Security and its CEO Lisa Falzone are improving personal security by reducing the time it takes police and medical teams to arrive at crime scenes. According to Athena Security, it’s driving motto is not to “profile or resell identities or user data,” but to “simply protect the public.”

Detailing her and the company’s work, Ms. Falzone responded to a number of Unite.AI questions.

 

How you came out of retirement to build a life-saving tech business?

After exiting my first co-founded company, the iPad point-of-sale company, Revel Systems, I felt the need to create something again, but this time I wanted to use technology to address a human need and protect life. After the High School Stoneman Douglas shooting happened in 2017 and Congress was still unable to take action to resolve gun violence, I thought I could use technology and computer vision in a proactive way to help prevent these crimes from happening and possibly save lives. That’s what Athena does. Athena’s cameras use computer vision to identify weapons or violent behavior and then alert police or business owners immediately. It’s basically like a fire alarm system for guns.

 

How A.I and Facial Recognition have become interchangeable, and how the media/Hollywood has perpetuated the myth of its misuse?

There’s been a lot of controversy in the media with computer vision and facial recognition systems racially profiling people or invading people’s privacy, but Athena’s cameras are different because they only focus on identifying and flagging weapons or violent behavior.  Athena eliminates bias or racial profiling because our computer vision is programmed to recognize guns, not the face or skin color of the person holding it. We do not do any profiling of people, we simply want to protect people from gun violence and that’s what our cameras do. Our cameras are also proactive, meaning that rather than just record what’s happening they can alert police and medical to reduce the time it takes for first responders to arrive, which can mean the difference between life and death.

 

How does your company Athena advise on-premise computing to clients to avoid the cloud and big brother’s grasp?

Another concern that comes up with computer vision and surveillance is the idea that the government is watching you 24/7, but with Athena’s cameras, we offer on-premise computing, which eliminates the risk of hosting private data on the cloud where it can become vulnerable. Our goal isn’t to spy on people, it’s to save lives by reducing the response time of emergency responders and eliminating human error.

 

Can schools like Archbishop Wood High School and places of worship like Al-Noor Mosque in New Zealand take comfort in having an extra layer of security always on?

We’re constantly hearing about shootings in the news at schools, concerts, or even places of worship and Congress hasn’t done anything to stop them, so we wanted to be part of the solution. Athena has implemented our cameras in schools such as Archbishop Wood High School to allow police to be alerted and respond faster when there’s an active shooter, or even prevent the shooting altogether. We’ve also been able to implement our cameras internationally in the Al-Noor Mosque in New Zealand, which was one of the mosques targeted in the terrorist attack in March. We have the technology and we’re using it to prevent these tragedies from happening.

 

How this unique form of computer vision and object detection was achieved through hiring professional actors to train the A.I. brain to achieve 99%+ accuracy?

It was important for us to figure out how to train Athena’s computer vision brain to detect violent behavior so that it wouldn’t set off false alarms. We brought in trained actors to enact violent situations or wield weapons until the cameras were able to detect this behavior with 99% accuracy. When you have one security guard watching several screens, the chance that they’ll miss something is extremely high, but computer vision removes the human error by being able to watch 100% of the screen at all times. Our cameras also supply police or business owners with real-time information on what is happening at that moment, rather than 24-48 hours later.

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Former diplomat and translator for the UN, currently freelance journalist/writer/researcher, focusing on modern technology, artificial intelligence, and modern culture.

Interviews

Jim McGowan, head of product at ElectrifAi – Interview Series

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Jim McGowan, is the head of product at ElectrifAi, they specialize in extracting massive amounts of disparate data, transforming chaotic structured and unstructured data into actionable business insights.

What is it that attracted you to the world of machine learning and AI?

I first encountered Machine Learning while earning a doctorate for work in cognitive science. AI systems largely consisted of distilling an expert’s experience down to a flow chart. This seemed intuitively to work, but the systems quickly grew too complex and weren’t living up to their promise. Small problems could be solved, but practical solutions to real-world problems were out of reach. You can say that building practical systems was itself impractical. Then Machine Learning came along. That changed everything. Machine Learning unlocked the promise of AI. ElectrifAi fulfills that promise by building solutions to help our clients run their businesses better.

 

ElectrifAi uses something called Practical AI to guide companies to do more with the data that they already have. Can you elaborate on how ElectrifAi defines Practical AI?

We leverage client’s data to provide clear, actionable insights, for real business needs. We help them make better decisions, faster.  Practical AI is solving a real-world business problem with a solution that works well, is based on a clear understanding of the data, has a definitive outcome, fits into existing processes and tools, ships on time, and delivers tremendous business value. We don’t for companies to replace their data systems. We don’t require a specific business model. We don’t take a year to maybe deliver something that’s a compromise on what a client set out to do. We provide a flexible, high-quality solution that is simple to use and does what it supposed to do very, very well. That’s Practical AI.

We make sure with every solution that we achieve the following:

  • Best-in-class time to value
  • Best-in-class data cleansing
  • Best-in-class insights
  • Best-in-class ROIs

 

Could you give some details on how ElectrifAi enables companies to use this Practical AI?

We pull data from all systems—whether they are custom developed databases, highly customized solutions from major vendors, or even a data dump from some legacy application. We clean and understand that data, and find clear, meaningful signals in all that chaos. We then use machine learning to extract valuable insights from those signals, and finally, we indicate how to act on those insights. SpendAi is a great example. We use machine learning to clean the data, and the more machine learning to categorize 98-99% of data from all a client’s procurement systems. We even let the client control that categorization and a granular level, in just seconds, through a drag-and-drop interface. That’s unique, and incredibly powerful. Then we apply another group of machine learning algorithms to give a clear, simple view of where spend is going. We use machine learning to parse contracts and extract key clauses. We then apply still more machine learning to make specific recommendations. For instance, a client may be due a discount from a clause buried in a contract term. Or they may be over-relying on a vendor in a category, who is at financial risk themselves. A client may be under-leveraging their position with a vendor because the vendor operates under multiple names and across multiple divisions of the company. We surface and clean all that, so the client can reduce their spend, increase their working capital, and reduce their risk.

 

Could you discuss PulmoAi CT, and how it may increase efficiency for radiologists and improves radiological outcomes?

PulmoAi CT is an advanced image analytic product designed specifically for pulmonary CTscans. Combining Practical AI, Machine Learning (ML), and image processing technology, PulmoAi CT automatically segments pulmonary scans pixel by pixel, without the blurring or distortion experienced with similar technologies. The result: Crisply rendered 3D imagery— enabling the immediate identification of indications for tumors, nodules, COVID-19, and other anomalies.    With PulmoAi CT, radiologists can easily zoom in on pulmonary details, viewing them side-byside with both clinical analyses and original images. PulmoAi CT quantifies each lung feature with precise metrics, including feature size, and morphological and volumetric extent. This enables the careful monitoring of anomaly progression, even in the presence of multiple morbidities.

PulmoAi CT is a very different technology than any product in the market or even research laboratory. The results are game changing. There’s nothing else like it. It’s not a brute-force approach that requires tens of thousands of samples to work. PulmoAi CT produces results while other Ai solutions are still looking for training data. It’s powerful and it will change what radiologists can do.

 

Another ElectrifAi product is the PulmoAi X-ray which directly addresses the use of X-rays in crisis zones. Could you discuss this technology?

PulmoAi X-ray directly addresses the use of X-rays in crisis zones today. Adapting to the specific challenges of the pandemic, PulmoAi X-ray goes a step further than distinguishing healthy lungs from COVID-19-infected lungs. The cloud-based solution identifies the crucial differences between coronavirus-positive patients sent home who recover safely, and those sent home who return in need of intubation. Pre-trained on pulmonary scans from hospitals in crisis zones, PulmoAi X-ray leverages deep learning neural network technologies to identify critical abnormalities associated with COVID-19. PulmoAi X-ray is unique because it is narrowly tuned to answer the problem that hospitals in crisis zones are trying to answer: will self-quarantine work, or does the patient need hospitalization?

 

Another product is ContractAi which uses practical AI, Machine Learning, and Natural Language Processing (NLP) to automatically read, analyze, and compare contracts across the enterprise. Could you discuss this product and the best use cases for it?

ContractAi is designed for users who interact with contracts in a day-to-day operational role. For example, ContractAi helps people in a procurement group who are analyzing spend against vendor agreements. Recently, with economic shock due to COVID-19, the software is helping companies understand any leverage they may have to exit supplier contracts. When this capability is connected to our SpendAi product, one can immediately understand the financial impact of this leverage. One of the largest advantages of the technology is that it works with contract data in any format—there is no manual entry and no specific format required. Another advantage is that the technology is specifically designed for users who use contracts in an operational role. Many of the existing contract processing technologies are designed for attorneys, who have a different set of concerns.

 

Is there anything else that you would like to share about ElectrifAi?

As a global machine learning company, we have a unique view in to how various markets are developing and using Machine Learning. One advantage of this view is our ability to understand how machine learning (ML) capabilities can be translated from one geography and/or vertical market to another to help solve substantial problems.

For example, we have spent years around the world helping businesses engage their customers using data science. We have now leveraged that expertise to help the US healthcare industry with patient engagement, helping restart healthcare and getting patients back in to the hospitals for critical elective surgeries.

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Phil Duffy, VP of Product, Program & UX Design at Brain Corp – Interview Series

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Phil Duffy,  is the VP of Product, Program & UX Design at Brain Corp a San Diego-based technology company specializing in the development of intelligent, autonomous navigation systems for everyday machines.

The company was co-founded in 2009 by world-renowned computational neuroscientist, Dr. Eugene Izhikevich, and serial tech entrepreneur, Dr. Allen Gruber. Brain Corp’s initial work involved advanced R&D for Qualcomm Inc. and DARPA. The company is now focused on developing advanced machine learning and computer vision systems for the next generation of self-driving robots.

Brain Corp powers the largest fleet of  autonomous mobile robots (AMRs) with over 10,000 robots deployed or enabled worldwide and works with several Fortune 500 customers like Walmart and Kroger.

What attracted you initially to the field of robotics?

My personal interest in developing robots over the last two decades stems from the fact that intelligent robots are one of the two major unfulfilled dreams of the last century—the other dream being flying cars.

Scientists, science-fiction writers, and filmmakers all predicted we would have intelligent robots doing our bidding and helping us in our daily lives a long time ago. As part of fulfilling that vision, I am passionate about developing robots that tackle the repetitive, dull, dirty, and dangerous tasks that robots excel at, but also building solutions that highlight the unique advantages of humans performing creative, complex tasks that robots struggle with. Developing robots that work alongside humans, both empowering each other, ensures we build advanced tools that help us become more efficient and productive.

I am also driven by being part of a fledgling industry that is building the initial stages of the robotics ecosystem. The robotics industry of the future, like the PC or smartphone industry today, will include a wide array of technical and non-technical staff, developing, selling, deploying, monitoring, servicing, and operating robots. I’m excited to see how that industry grows and how decisions we make today impact the industry’s future direction.

 

In 2014, Brain Corp pivoted from performing research and development for Qualcomm, to the development of machine learning and computer-vision systems for autonomous robots. What caused this change?

It was really about seeing a need and opportunity in the robotics space and seizing it. Brain Corp’s founder, Dr. Eugene Izhikevich, was approached by Qualcomm in 2008 to build a computer based on the human nervous system to investigate how mammalian brains process information and how biological architecture could potentially form the building blocks to a new wave of neuromorphic computing. After completing the project, Eugene and a close-knit team of scientists and engineers decided to apply their computational neuroscience and machine learning approaches to autonomy for robots.

While exploring different product directions, the team realized that the robotics industry of the day looked just like the computer industry before Microsoft—dozens of small companies all adding custom software to a recipe of parts from the same hardware manufacturer. Back then, lots of different types of computers existed, but they were all very expensive and did not work well with each other. Two leaders in operating systems emerged, Microsoft and Apple, with two different approaches: while Apple focused on building a self-contained ecosystem of products and services, Microsoft built an operating system that could work with almost any type of computer.

The Brain Corp team saw the value in creating a “Microsoft of robotics” that would unite all of the disparate robot solutions under one cloud-based software platform. Their goal became to help build out the emerging category of autonomous mobile robots (AMRs) by providing autonomy software that others could use to build their robots. The Brain Corp team decided to focus on making a hardware-agnostic operating system for AMRs. The idea was simple: to enable builders of robots, not build the robot intelligence themselves.

 

What was the inspiration for designing an autonomous scrubber versus other autonomous technologies?

Industrial robotic cleaners were the perfect way to enter the market with our technology. The commercial floor cleaning industry was in the midst of a labor shortage when we started out—constant turnover meant many jobs were simply not getting done. Autonomous mobile cleaning robots would not only help fill the labor gap in an essential industry, they would also be scalable—every environment has a floor and that floor probably needs cleaning. Floorcare was therefore a good opportunity for a first application.

Beyond that, retail companies spend about $13B on floorcare labor annually. Most employ cleaning staff who use large machines to scrub store floors, which is rote, boring work. Workers drive around bulky machines for hours when their time could be better spent on tasks that require acuity. An automated floor cleaning solution would fill in for missing workers while optimizing the efficiency and flow of store operations. By automating the mundane, boring task of scrubbing store floors, retail employees would be able to spend more time with customers and have a greater impact on business, ultimately leading to greater job satisfaction.

 

Can you discuss the challenge of designing robots in an environment that often involves tight spaces and humans who may not be paying attention to their surroundings?

It’s an exciting challenge! Retail was the perfect first implementation environment for Brain Corp’s system because they are such complex environments that pose an autonomy challenge, and are ripe with edge cases that allow Brain Corp to collect data that refines the BrainOS navigation platform.

We addressed these challenges of busy and crowded retail environments by building an intelligent system, BrainOS, that uses cameras and advanced LIDAR sensors to map the robot’s environment and navigate routes. The same technology combination also allows the robots to avoid people and obstacles, and find alternate routes if needed. If the robot encounters a problem it cannot resolve, it will call its human operator for help via text message.

The robots learn how to navigate their surroundings through Brain Corp’s proprietary “teach and repeat” methodology. A human first drives the robot along the route manually to teach it the right path, and then the robot is able to repeat that route autonomously moving forward. This means BrainOS-powered robots can navigate complex environments without major infrastructure modifications or relying on GPS.

 

How has the COVID-19 pandemic accelerated the adoption of Autonomous Mobile Robots (AMRs) in public spaces?

We have seen a significant uptick in autonomous usage across the BrainOS-powered fleet as grocers and retailers look to enhance cleaning efficiency and support workers during the health crisis.

During the first four months of the year, usage of BrainOS-powered robotic floor scrubbers in U.S. retail locations rose 18% compared to the same period last year, including a 24% y-o-y increase in April. Of that 18% increase, more than two-thirds (68%) occurred during the daytime, between 6 a.m. and 5:59 p.m. This means we’re seeing retailers expand usage of the robots to daytime hours when customers are in the stores, in addition to evening or night shifts. We expect this increase to continue as the value of automation comes sharply into focus.

 

What are some of the businesses or government entities that are using Brain Corp robots?

Our customers include top Fortune 500 retail companies including Walmart, Kroger, and Simon Property Group. BrainOS-powered robots are also used at several airports, malls, commercial buildings, and other public indoor environments.

 

Do you feel that this will increase the overall comfort of the public around robots in general?

Yes, people’s perception of robots and automation in general is changing as a result of the pandemic. More people (and businesses) realize how robots can support human workers in meaningful ways. As more businesses reopen, cleanliness will need to be an integral part of their brand and image. As people start to leave their homes to shop, work, or travel, they will look to see how businesses maintain cleanliness. Exceptionally good or poor cleanliness may have the power to sway consumer behavior and attitudes.

As we’ve seen in the last months, retailers are already using BrainOS-powered cleaning robots more often during daytime hours, showing their commitment and investment in cleaning to consumers. Now more than ever, businesses need to prove that they’re providing a safe and clean environment for customers and workers. Robots can help them deliver that next level of clean—a consistent, measureable clean that people can count on and trust.

 

Another application by Brain Corp is the autonomous delivery tug. Could you tell us more about what this is and the use cases for it?

The autonomous delivery tug, powered by BrainOS, enables autonomous delivery of stock carts and loose-pack inventory for any indoor point-to-point delivery needs, enhancing efficiency and productivity. The autonomous delivery tug eliminates inefficient back and forth material delivery and works seamlessly alongside human workers while safely navigating complex, dynamic environments such as retail stores, airports, warehouses, and factories.

A major ongoing challenge for retailers—one that has been exacerbated by the COVID-19 health crisis—is maintaining adequate stock levels in the face of soaring demand from consumers, particularly in grocery. Additionally, the process of moving inventory and goods from the back of a truck, to the stockroom, and then out to store shelves, is a laborious and time-consuming process requiring employees to haul heavy, stock-laden carts back and forth multiple times. The autonomous delivery tug aims to help retailers address these restocking challenges, taking the burden off store workers and providing safe and efficient point-to-point delivery of stock without the need for costly or complicated facility retrofitting.

The autonomous delivery application combines sophisticated AI technology with proven manufacturing equipment to create intelligent machines that can support workers by moving up to 1,000 pounds of stock at a time. Based on an in-field pilot program, the autonomous delivery tug will save retail employees 33 miles of back-and-forth travel per week, potentially increasing their productivity by 67%.

 

Is there anything else that you would like to share about Brain Corp?

Brain Corp powers the largest fleet of AMRs operating in dynamic public indoor spaces with over 10,000 floor care robots deployed or enabled worldwide. According to internal network data, AMRs powered by BrainOS are currently collectively providing over 10,000 hours of daily work, freeing up workers so they can focus on other high value tasks during this health crisis, such as disinfecting high-contact surfaces, re-stocking, or supporting customers.

In the long term, robots give businesses the flexibility to address labor challenges, absentee-ism, rising costs, and more. From a societal standpoint, we believe robots will gain consumer favor as they’re seen more frequently operating in stores, hospitals, and health care facilities, or in warehouses providing essential support for workers.

We’re also excited about what the future holds for Brain Corp. Because BrainOS is a cloud-based platform that can essentially turn any mobile vehicle built by any manufacturer into an autonomous mobile robot, there are countless other applications for the technology beyond commercial floor cleaning, shelf scanning, and material delivery. Brain Corp is committed to continuously improving and building out our AI platform for powering advanced robotic equipment. We look forward to further exploring new markets and applications.

Thank you for the amazing interview, readers who wish to learn more should visit Brain Corp.

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Adi Singh, Product Manager in Robotics at Canonical – Interview Series

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Adi Singh, is the Product Manager in Robotics at Canonical.   Canonical specializes in open source software, including Ubuntu, the world’s most popular enterprise Linux from cloud to edge, and they have a global community of 200,000 contributors.

Ubuntu is the most popular Linux distribution for large embedded systems. As autonomous robots mature, innovative tech companies turn to Ubuntu, we discuss advantages of building a robot using open source software and other key considerations.

What sparked your initial interest in robotics?

A few years into software programming, I was dissatisfied with seeing my work only running on a screen. I had an urge to see some physical action, some tangible response, some real-world result of my engineering. Robotics was a natural answer to this urge.

Can you describe your day to day role with Canonical?

I define and lead the product strategy for Robotics and Automotive verticals at Canonical. I am responsible for coordinating product development, executing go-to-market strategies, and engagements with external organizations related to my domain.

Why is building a robot on open source software so important?

Building anything on open source software is usually a wise idea as it allows you to stand on the shoulders of giants. Individuals and companies alike benefit from the volunteer contributions of some of the brightest minds in the world when they decide to build on a foundation of open source software. As a result, popular FOSS repositories are very robustly engineered and very actively maintained; allowing users to focus on their innovation rather than the nuts and bolts of every library going into their product.

Can you describe what the Ubuntu open source platform offers to IoT and robotics developers?

Ubuntu is the platform of choice for developers around the world for frictionless IoT and robotics development. A number of popular frameworks that help with device engineering are built on Ubuntu, so the OS is able to provide several tools for building and deploying products in this area right out of the box. For instance, the most widely used middleware for robotics development – ROS – is almost entirely run on Ubuntu distros (More than 99.5% according to official metrics here: https://metrics.ros.org/packages_linux.html).

What are some of the key considerations that should be analyzed when choosing a robot’s operating system?

Choosing the right operating system is one of the most important decisions to be made when building a new robot, including several development factors. Hardware and software stack compatibility is key as ample time will be spent ensuring components will work well together so as to not hinder progress on developing the robot itself.

Also, prior familiarity of the operating systems by the dev team is a huge factor affecting economics, as previous experience will no doubt help to accelerate the overall robot development process and thereby cut down on the time to market. Ease of system integration and third-party add-ons should also be heavily considered. A robot is rarely a standalone device and often needs to seamlessly interact with other devices. These companion devices may be as simple as a digital twin for hardware-in-the-loop testing, but in general, off-device computation is getting more popular in robotics. Cloud robotics, speech processing and machine learning are all use-cases that can benefit from processing information in a server farm instead of on a resource-constrained robot.

Additionally, robustness and a level of security engineered into the kernel is imperative. Availability of long-term support for the operating system, especially from the community, is another factor. Something to keep in mind is that operating systems are typically only supported for a set amount of time. For example, long-term support (LTS) releases of Android Things are supported for three years, whereas Ubuntu and Ubuntu Core are supported for five years (or for 10 years with Extended Security Maintenance). If the supported lifespan of the operating system is shorter than the anticipated lifespan of the robot in the field, it will eventually stop getting updates and die early.

Thank for for the interview, readers who wish to learn more should visit Ubuntu Robotics.

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