Deep Science AI made its debut on stage at Disrupt NY 2017, showing in a live demo how its computer vision system could spot a gun or mask in CCTV footage, potentially alerting a store or security provider to an imminent crime. The company has now been acquired in a friendly merger with Defendry, which is looking to deploy the tech more widely.

It’s a great example of a tech-focused company looking to get into the market, and a market-focused company looking for the right tech.

The idea was that if you have a chain of 20 stores, and 3 cameras at each store, and people can only reliably keep an eye on 8-10 feeds at a time, you’re looking at a significant personnel investment just to make sure those cameras aren’t pointless. If instead you used Deep Science AI’s middle layer that highlighted shady situations like guns drawn, one person could conceivably keep an eye on hundreds of feeds. It was a good pitch, though they didn’t take the cup that year.

“The TechCrunch battlefield was a great launching off point as far as getting our name and capabilities out there,” said Deep Science AI co-founder Sean Huver in an interview (thanks for the plug, Sean). “We had some really large names in the retail space request pilots. But we quickly discovered that there wasn’t enough in the infrastructure as far as what actually happens next.”

That is to say, things like automated security dispatch, integration with private company servers and hardware, that sort of thing.

“You really need to build the monitoring around the AI technology rather than the other way around,” Huver admitted.

Meanwhile, Pat Sullivan at Defendry was working on establishing automated workflows for internet of things devices — from adjusting the A/C if the temperature exceeds certain bounds to, Sullivan realized at some point, notifying a company of serious problems like robberies and fires.

“One of the most significant alerts that could take place is someone has a gun and is doing something bad,” he said. “why can’t our workflows kick off an active response to that alert, with notifications and tasks, etc? That led me to search for a weapons and dangerous situations dataset, which led me to Sean.”

Although the company was still only in prototype phase when it was on stage, the success of its live demo with a team member setting off an alert in a live feed (gutsy to attempt this) indicated that it was actually functional — unlike, as Sullivan discovered, many other companies advertising the same thing.

“Everyone said they had the goods, but when you evaluated, they really didn’t,” he opined. “Almost all of them wanted to build it for us — for a million dollars. But when we came across Deep Science we were thrilled to see that they actually could do what they said they could do.”

Ideally, he went on to suggest, the system could be not just an indicator of crimes in progress but crimes about to begin: a person donning a mask or pulling out a gun in a parking lot could trigger exterior doors to lock, for instance. And when a human checks in, either the police could be on their way before the person reaches the entrance, or it could be a false positive and the door could be unlocked before anyone even noticed anything had happened.

Now, one part of the equation that’s mercifully not necessarily relevant here is that of bias in computer vision algorithms. We’ve seen how women and people of color — to start — are disproportionately affected by error, misidentification, and so on. I asked Huver and Sullivan if these issues were something they had to accommodate.

Luckily this tech doesn’t rely on facial analysis or anything like that, they explained.

“We’re really stepping around that issue because we’re focusing on very distinct objects,” said Huver. “There’s behavior and motion analysis, but the accuracy rates just aren’t there to perform at scale for what we need.”

“We’re not keeping a list of criminals or terrorists and trying to match their face to the list,” added Sullivan.

The two companies talked licensing but ultimately decided they’d work best as a single organization, and just a couple weeks ago finalized the paperwork. They declined to detail the financials, which is understandable given the hysteria around AI startups and valuations.

They’re working together with Avinet, a security hardware provider that will essentially be the preferred vendor for setups put together by the Defendry team for clients and has invested an undisclosed amount in the partnership. We’ll be following the progress of this Battlefield success story closely.

Source: TechCrunch

GOVERNMENT USUALLY ISN'T the place to look for innovation in IT or new technologies like artificial intelligence. But Ott Velsberg might change your mind. As Estonia's chief data officer, the 28-year-old graduate student is overseeing the tiny Baltic nation's push to insert artificial intelligence and machine learning into services provided to its 1.3 million citizens.

"We want the government to be as lean as possible," says the wiry, bespectacled Velsberg, an Estonian who is writing his PhD thesis at Sweden’s Umeå University on how to use AI in government services. Estonia's government hired Velsberg last August to run a new project to introduce AI into various ministries to streamline services offered to residents.

Deploying AI is crucial, he says. “Some people worry that if we lower the number of civil employees, the quality of service will suffer. But the AI agent will help us." About 22 percent of Estonians work for the government; that’s about average for European countries, but higher than the 18 percent rate in the US.

Siim Sikkut, Estonia’s chief information officer, began piloting several AI-based projects at agencies in 2017, before hiring Velsberg last year. Velsberg says Estonia has deployed AI or machine learning in 13 places where an algorithm has replaced government workers.


For example, inspectors no longer check on farmers who receive government subsidies to cut their hay fields each summer. Satellite images taken by the European Space Agency each week from May to October are fed into a deep-learning algorithm originally developed by the Tartu Observatory. The images are overlaid onto a map of fields where farmers receive the hay-cutting subsidies to prevent them from turning forests over time.

The algorithm assesses each pixel in the images, determining if the patch of the field has been cut or not. Cattle grazing or partial cutting can throw off the image processing; in those cases, an inspector still drives out to check. Two weeks before the mowing deadline, the automated system notifies farmers via text or email that includes a link to the satellite image of their field. The system saved $1.2 million in its first year because inspectors made fewer site visits and focused on other enforcement actions, according to Velsberg.

In another application, resumes of laid-off workers are fed into a machine learning system that matches their skills with employers. About 72 percent of workers who gain a new job through the system are still on the job after six months, up from 58 percent before the computer-matching system was deployed. In a third case, children born in Estonia are automatically enrolled in local schools at birth, so parents don't have to sign up on waiting lists or call school administrators. That’s because hospital records are automatically shared with local schools. The system doesn’t truly require AI, but it shows how automated services are expanding.


In the most ambitious project to date, the Estonian Ministry of Justice has asked Velsberg and his team to design a “robot judge” that could adjudicate small claims disputes of less than €7,000 (about $8,000). Officials hope the system can clear a backlog of cases for judges and court clerks.

The project is in its early phases and will likely start later this year with a pilot focusing on contract disputes. In concept, the two parties will upload documents and other relevant information, and the AI will issue a decision that can be appealed to a human judge. Many details are still to be worked out. Velsberg says the system might have to be adjusted after feedback from lawyers and judges.

Estonia’s effort isn’t the first to mix AI and the law, though it may be the first to give an algorithm decision-making authority. In the US, algorithms help recommend criminal sentences in some states. The UK-based DoNotPay AI-driven chatbot overturned 160,000 parking tickets in London and New York a few years ago. A Tallinn-based law firm, Eesti Oigusbüroo, provides free legal aid through a chatbot and generates simple legal documents to send to collection agencies. It plans to expand its “Hugo-AI” legal aid service matching clients and lawyers to Warsaw and Los Angeles by the end of the year, said CEO Artur Fjodorov.

The idea of a robot judge might work in Estonia partly because its 1.3 million residents already use a national ID card and are used to an online menu of services such as e-voting and digital tax filing.

Government databases connect with each other through something called the X-road, a digital infrastructure that makes data sharing easier. Estonian residents can also check who has been accessing their information by logging into a government digital portal.

Estonia’s well-documented move to digital government services hasn’t been without at least one glitch. Outside experts revealed a vulnerability in Estonia's ID system in 2017 that led to some embarrassment; it was fixed and the ID cards replaced. But government officials say the country hasn't had a major data breach or theft since it began its digital drive in the early 2000s. In 2016, more than two-thirds of Estonian adults filed government forms on the internet, almost twice the European average.

“The really private and confidential things are not in the hands of government, but banks and telecoms,” says Tanel Tammet, a professor of computer science at Tallinn University of Technology. Tammet is a member of an Estonian government AI task force that will report its findings in May and suggest an additional 35 AI-related demonstration projects by 2020.

Stanford University’s David Engstrom, an expert in digital governance, says Estonian citizens might trust the government's use of their digital data today, but things might change if one of the new AI-based decision-making systems goes awry.


In the US, agencies such as the Social Security Administration are using AI and machine learning algorithms to speed sorting and processing, while the EPA is using AI to determine which factories should be checked for pollution violations. But a coordinated AI effort across the federal government has gone slowly, Engstrom says, mainly because federal databases in each agency are different and aren’t easily shared with other agencies. “We’re not there yet,” he said.

Engstrom and a team of law school and computer science students at Stanford are studying how AI can be better used in US government agencies. They will soon report their findings to the Administrative Conference of the United States, an independent federal agency charged with recommending improvements to administrative processes.

He doesn’t see a AI-driven robo-judge coming to US courtrooms anytime soon. The US has no national ID system and many Americans have an innate fear of Big Government. “We have due process in the Constitution and that has something to say about fully automated decision making by a government agency,” Engstrom said. “Even with a human appeal, there could be a constraint.”


Still, Engstrom foresees a time when AI-driven legal assistants might be presenting judges with case law, precedents, and the background needed to make a decision. “The promise of an AI approach is you get more consistency than we currently have,” he said. “And maybe an AI driven system that is more accurate than human decision making system.”

The flip side is that an AI is only as good as the programming that goes into it. The sentencing algorithms, for example, have been criticized as biased against blacks.

"You also worry about automation bias," Engstrom says. As the machines make more decisions, humans are less likely to inject their own expertise into a system, he says. "That’s one of these creeping things that privacy advocates and good government advocates worry about when the government digitizes in this way."

For now, though, Estonian officials like the idea of an AI robot solving simple disputes, leaving more time for human judges and lawyers to solve tougher problems. Deploying more AI in government services "will allow us to specialize in something the machines can never do,” President Kersti Kaljulaid noted at the recent North Star AI conference in Tallinn. “I want to specialize in being a warm compassionate human being. For that we need the AI to be safe, and demonstrably safe."

Source: Wired

Making the right moral decisions about your AI projects might be just as hard as getting the technology to work.

Like it or not we are all going to get inundated by AI, Robotics and Automation opportunities. As organizations or individuals, we will have to decide how we are going to respond. Since we can no longer count on AI going back into another “AI Winter” because it is not going away. We will have to decide on AI as a trend and each AI encounter we will be facing. Our choices on AI will either make us win or lose in the short term and the long run. What are the responses to AI we can exercise?

Ignore AI

You can take the approach that ignores AI as long as possible. While this might work for a while, eventually you will come face to face with AI in one of your roles in life. The one redeeming benefit to this approach is that by the time you have to deal, AI will be easier, in general, as the human/AI interface will be mature by the time you will face it. The problem is that you may have to catch up with the pack as they have been experienced in leveraging AI. In fact, as an employee, this approach actually has some dangers implied.

Resist AI

Resisting change is a natural human instinct, but it can be dialed up or toned down. This is a delicate stance. Subconsciously, there is a natural resistance to software that can digest large amounts of data and information and maybe be smarter than us. It can tug at our egos, and this is kind of natural and probably will disappear over time. There also could be a conscious resistance that some folks will employ to avoid or just everyday fight AI at all fronts. The motivation might be the learning curve for new skills. We might not be fast enough to not be replaced by AI, so resistance is a tactic.


Verify AI

We all have seen the effects of not verifying and adequately learning how software can affect outcomes. Just look at the recent headlines with the 737 Max 8 planes where the response from authorities is "you should have read the manual."  Not that I would suggest we should read the manual before we step on an airplane, but we need to verify the effect AI and software, in general, l can have on our lives. Had I known there were no specific in-depth training nor specialized simulators for this plane, we could have avoided them. In the same light, we need to verify who has certified and tested the AI we interact with even if it is not life-threatening. What if AI created a custom medicine that incorporated all your meds or you were injected with nanotechnology to monitor aspects of your health, would you verify?

Comply with AI

One can assume that the AI has been tested and is not likely to do anything but help us in various life roles we participate in regularly. This compliance approach exercises faith that the people who applied the AI correctly. I would suspect we will be using AI and not knowing about it anyway. It is evident when you interact with a bot in either a servicing or sales/marketing situations, but AI will be embedded in places that we would hardly detect it. We may or may not have the opportunity to comply as AI will just be there and not be visible.  I would suggest the best AI implementations will behave that way.

Participate with AI

We could also choose to embrace AI actively. One approach would be to get to know the AI implementation early so we could understand the subtleties of interacting an AI component. We could use the implied strengths and weaknesses to our advantage. Another approach would get fully on board to know much about AI and their implementations to take advantage of it for revenue purposes. If AI is as successful as an automation option that all the pundits are projecting, why not get ahead of the curve. This assumes that you like to learn and be at the edge.

Net; Net

I would suggest that most of us will exercise nearly all of these options at some point in the future. You may decide to resist until you see the results. You may decide to ignore AI until it comes to your door and then you execute a downstream decision about AI. The fully embrace approach will be taken by those that like a challenge and don't shrink back from change. Even if you are that kind of individual, it may not be for all the roles that you participate in at the moment.

Source: FORBES

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