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Emrah Gultekin, CEO and Co-founder of Chooch AI – Interview Series

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Emrah is the co-founder and CEO of Chooch, an end-to-end visual AI solution. Chooch provides fast, accurate facial authentication and object recognition for the media, advertising, banking, medical and security industries. Chooch offers an easy-to-use and deployable API, a dashboard and mobile app SDK.

What was your inspiration for launching Chooch AI?

In our previous entrepreneurial experiences, my co-founder and I saw that there were a multitude of data-driven challenges that needed to be solved in a wide-variety of verticals, so I decided to dive in and solve the ones that I could. I had started companies before, but this was my first true “deep tech” company.

With our broader team, we’ve worked to develop a visual AI product that is sustainable, scalable, robust, and usable for an array of enterprises. The product is now being utilized by companies in the healthcare, public safety, industrial, media and geospatial industries, with uses that range from fraud prevention and decreasing medical errors to deepening the understanding of our world.

 

Can you share with us what Chooch AI does?

Chooch copies human visual intelligence into machines. We train and deploy visual AI for customers in the cloud and on the edge and deliver fast and accurate computer vision for any visual process.

We can do that because Chooch AI is a platform for every step of the visual AI process from data collection, annotation and labeling, to AI training, model deployment, and integration.  Because of the broad range of problems we’ve solved, our team now has deep expertise in scoping and developing computer vision projects that are ready for global scale. This can be everything from cell identification, geospatial image analysis and public safety.

 

What type of imagery can be processed by the computer vision system?

What the human eye can do,  Chooch can do better and at scale.  For example, the human eye cannot process any spectrum from visible to CT scans, but Chooch can detect fevers with IR sensors and process x-rays to detect lung damage. We can do this for video or still imagery, both faster and more accurately than the human eye and have deployed over 2400 models for a variety of applications.

 

Chooch AI connects to the cloud but is also able to run on a local machine, can you elaborate on how this works?

Yes, this is one of our breakthroughs. We launched with the Chooch AI API, which allows companies to use our cloud server to process their images, but our customers wanted to deploy AIoT on the edge in places with no connectivity. So, we created Chooch Edge AI, which is basically a standalone AI container that is generated by our Chooch Cloud AI. For instance, we are able to remotely deploy that AI software on NVIDIA Jetson devices, which are amazing by the way, and we can then remotely update the edge AI as needed from the Chooch Dashboard. Technically, that AI software on the edge is called an inference engine. Chooch is able to connect up to four cameras to the edge devices and the AI can recognize thousands of classes on the edge.  We are able to iterate on models, remove models and train new models on the Edge. This is always improving, because as chip and hardware providers release more powerful devices, we are generating more and more powerful AIoT deployments. We can now run multiple models on the edge with multiple layers of dense classification at very low latency.

 

Is facial recognition technology used?

We don’t do facial recognition as a company policy. We only do facial authentication with liveness detection with the caveat that it will always be consent-based, like providing permission to check in to a location or for a flight with your face instead of a ticket. Chooch AI can be trained with as few a couple of images. Facial authentication files are not stored as pictures of faces. And we do liveness detection to make sure people are not able to spoof the system.

 

Training AI models can be a steep learning curve for the uninitiated, what assistance do you provide for data labelling and annotating?

For the uninitiated, we offer end-to-end training assistance. When companies come to Chooch with a visual problem to solve, our team works in partnership with them to train and deploy AI models. It’s as simple as that. We do labelling and annotation as a service, and generally speaking users supply the data, but we help them organize that. Our training platform can use still images,but with videos, we can generate over 1,000 annotated images per minute, that’s another breakthrough, by the way.  We take on the whole process from planning and consulting on data collection to model creation and testing and support. Our customer relationships become ongoing partnerships.

 

Chooch AI can assist enterprises with COVID-19. Can you detail how it can be of assistance?

Essentially, Chooch AI is supporting public safety with several visual AI models all while working with partners to deploy complete solutions. One such solution detects the presence or absence of masks and another detects fevers with IR cameras, these two solutions can be deployed as a complete solution.  Of note, these AI models do not include any facial recognition features. Additionally, we have a research model that we are providing to researchers for detecting the signs of COVID-19 related pneumonia that looks at x-rays and detects lung injury.

 

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

As a proof point for our technology, our system is live and is being utilized by numerous clients. Our customers are driving real ROI because we can automate literally any visual process at scale, reducing costs and human error.

Thank you for the interview. Readers who wish to learn more should visit Chooch AI.

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Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is also the Co-Founder of Securities.io a news website focusing on digital securities, and is a founding partner of unite.ai

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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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Mike Lahiff, CEO at ZeroEyes – Interview Series

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Mike is the CEO of ZeroEyes a security company powered by AI. Lead by former Navy SEALS, they offer software to monitor camera systems and to detect weapons. They system notifies authorities on the risk of possible active shooters and it reduces response time, with the goal of keep schools and other public spaces safe.

Can you explain what ZeroEyes is, and how implementing this system can save lives?

ZeroEyes is an AI weapons detection platform that helps identify threats at first sight. Founded by a team of Navy SEALs and military veterans dedicated to ending mass shootings, our platform integrates with an organization’s existing IP security cameras to play one component of its overall security process, and provide security personnel and first responders with real-time information needed to keep people safe. ZeroEyes focuses only on the essential information needed to stop a threat, and closes the critical seconds between when a gun could be spotted to when it is fired to save lives.

 

Can you discuss the process for integrating ZeroEyes into an existing video camera infrastructure?

ZeroEyes’ AI weapons detection platform is one component of an organization’s multi-tiered security approach. Our software integrates with an organization’s existing camera systems and video analytics to detect weapons in real time. If ZeroEyes detects a gun, an alert with the image of the weapon goes to the ZeroEyes monitoring team. Once positively identified, an alert is sent to a local emergency dispatch (such as a 911 call center), onsite security staff, police and school administrators (via mobile and desktop). This process takes three to five seconds and bypasses the traditional dispatch process.

ZeroEyes’ software uses AI and computer vision, integrating with existing 3D satellite maps of a building so that as a visible weapon passes a camera, the map lights up. This allows first responders to know the precise location of a threat. By seeing exactly where a shooter(s) is in real time, security personnel can lock doors, move people to safety and enact other aspects of their security process, while first responders can go towards the shooter much faster with the knowledge of how many and what kinds of weapons the person has.

 

How much of a weapon needs to be visible for the system to correctly identify it as a weapon?

This can be dependent on multiple variables such as type of camera, height of camera, angle, lens, field of view, lighting, distance, and type of weapon. If a human eye can detect the gun on a camera feed, our system will detect the same gun.

 

How much of an issue are false positives and how is this minimized?

We are always looking to minimize false positives and are constantly improving our deep learning models based on data collected. In customer installations, we incorporate time upfront to collect data and custom tune the parameters for each camera, which allows us to more effectively filter out false positives. If a false positive happens, an alert gets sent to our team and we vet the threat in real-time. We then respond accordingly and let the customer know that it isn’t a serious threat.

 

Your initial focus was in installing this in schools, what are some other markets that ZeroEyes is targeting?

We sell to a broad list of decision makers, including school resource officers, school district administration, corporate security directors, chief security officers and chief risk officers. Our technology can be used in education (including K-12 schools, universities and training facilities) – our technology is used in Rancocas Valley Regional High School (NJ) and South Pittsburg High School (TN), commercial (including office buildings, malls and stadiums), and military/government installations (force protection). We partner closely with both our customers and local first responders to ensure that they have the additional layer of security to identify and stop threats.

 

Can you discuss the ZeroEyes app and how threat notifications work?

If a true weapon is detected, an alert is sent to ZeroEyes’ security monitoring team. Once positively identified, it is then sent to a local emergency dispatch (such as a 911 call center), onsite security staff, police and school administrators. This process takes three to five seconds and bypasses the traditional dispatch process. We include details such as the location of the camera, bounding box identifying the detected object and detection label.

The image lets first responders know the type of weapon (i.e. pistol or machine gun). This then dictates response tactics and amount of damage a shooter can cause. It also lets us know the total number of shooters and weapons so those responding to the alert are properly informed of the situation.

 

What type of relationship does ZeroEyes have with different law enforcement agencies and how are they set-up to receive dispatch alerts?

ZeroEyes works with local law enforcement to help decrease critical response time to serious threats to public safety like active shooter situations. If a threat is detected and verified, the alert is sent to a local emergency dispatch.

ZeroEyes provides real-time information to help first responders understand the situation at hand, allows security to quickly enact security protocols, and dramatically reduces response time which can mean the difference in saving lives.

 

Facial recognition capabilities are built into the system, but facial redaction is used to protect patrons’ privacy. Can you discuss these capabilities, for example is ZeroEyes able to identify specific individuals such teachers and principals in a school?

We do not use facial recognition, we solely focus on weapons detection. Our technology sits on top of existing IP security cameras, which could also have facial recognition software installed by the organization. We pursued weapons detection because we want to reduce mass shootings and active shooter threats, and security personnel should know where and when weapons are present regardless of who is carrying them.

 

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

Our mission is to detect a threat before it happens. We firmly believe that if this happens, we can reduce the amount of mass shootings and save lives.

Thank you for the interview, readers who wish to learn more should visit ZeroEyes.

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