Daily Archives: May 23, 2017

In BriefAs automation continues to infiltrate more and more aspects oflife, some are considering the potential of an AI taking over thepresidency. With a higher capacity for unbiased, constructiveproblem solving, an AI president could potentially prove to be abetter leader than our human ones. Robot President

The president. A highly coveted and highly controversial role, one traditionally held by humans. The use of traditionally is a recently added modifier, one necessarywhen discussing a Wired articlesuggesting that the role of president might one day be filled by an artificial intelligence (AI).

Others have pushed the idea of an AI president before, with one group even fighting for Watson, IBMs AI, to run for theposition in 2016. This massive, strange, sci-fi political makeover would completely upend the idea of traditional political discourse, so why are so many consideringit?

One major reason is level-headedness. Humans are vulnerable to emotion, obviously, but an AI can make decisions without prejudice, without anger, without resentment, without impulse, and without ego (a major concern with powerful positions). AI is, by nature, capable of fully considering all aspects of an issue before making up its mind. AI will not make quick decisions based off of a single fact, so in that respect, the machine has humanity beat.

Also, an AI president could not beinfluenced financially. An AI wouldnt have a financial stakein any businesses, so it wouldnt prioritize any over others. It would only calculate what would be best based on the most up-to-date existing facts. For example, an AI would not have any investmentin any energy source, so that wouldfreeit toconsider onlythe facts of the situation when determining action on climate change.

As with any radical idea, the election of an AI president seems impossible on the surface. Could we really ever advance to the point where it would be technologically feasible to entrust the presidency to an AI? Theoretically, yes. AI is advancing rapidly, so if a serious project to build the first presidential AI came to fruition, such a system could probably be built in the not-so-distant future.

The largest issue standing in the way of this becoming a future reality is whether or not we, as a people, will allow it. Would the idea ever be supported by a majority of people? Hard to imagine when you consider that the current political landscape appears more polarized than ever.

Asautomation creeps into more and more aspects of our lives, it is not too out-there to think that we might be willing to one day elect a president unswayed by human shortcomings and programmed to act according to a set of ideals. The president could be programmed to follow conservative or liberal agendas, or instead of electing an AI designed to reflect one political party or another, we could just vote on various issues that would then be considered by our unbiased AI leader.

As AI systems take on tasks traditionally held by humandoctors, lawyers, and even songwriters, considering an AI president isnt so far-fetched. AI can now even be used to create better AI, so it might be time for us to consider a future in which our political leaders are smarter, fairer,and, well, less human.

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Could an AI Ever Be Elected President? - Futurism

CDC

So far the results are promising. Using AI, Ribeiro and her colleagues were able to predict whether someone would attempt suicide within the next two years at about 80 percent accuracy, and within the next week at 92 percent accuracy. Their findings were recently reported

This high level of accuracy was possible because of machine learning, as researchers trained an algorithm by feeding it anonymous health records from 3,200 people who had attempted suicide. The algorithm learns patterns through examining combinations of factors that lead to suicide, from medication use to the number of ER visits over many years. Bizarre factors may pop up as related to suicide, such as acetaminophen use a year prior to an attempt, but that doesn't mean taking acetaminophen can be isolated as a risk factor for suicide.

"As humans, we want to understand what to look for," Ribeiro says. "But this is like asking what's the most important brush stroke in a painting."

With funding from the Department of Defense, Ribeiro aims to create a tool that can be used in clinics and emergency rooms to better find and help high-risk individuals.

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But not everyone who commits suicide has an extensive medical record or has ever even walked into a hospital. So another research team is taking a similar AI-powered approach to suicide prediction by examining data from a more ubiquitous source: smartphones. The DARPA-funded company Cogito has developed a mobile app called Companion that automatically gathers data on someone's communication and movement patterns.

"We don't listen to the conversations, but look at things like how many calls you make, how many miscalls you have," says Skyler Place, a scientist at Cogito. "We are taking hundreds and thousands of data points, combining them in a way that humans can't."

The data is then used to create a risk score that is shown to the clinician. If that score changes over time, the clinician calls the individual to check in and see whether they need additional care. Cogito is currently working with the Department of Veterans Affairs at a suicide prevention center in Denver, Colorado to test the app with veterans who are at high risk for suicide. If the tests are successful, Place imagines more widespread use in a couple of years.

While an AI-powered tool may determine who is at risk of suicide, it cannot predict exactly when they might attempt it. Facebook is trying to solve this problem by

CDC

What happens after someone is found to be at risk is another challenge. Prevention strategies aren't always effective and it can be more difficult to protect high-risk individuals after they leave healthcare settings. To that end, researchers at the University of Vermont and Dartmouth have developed a system for both risk assessment and prevention.

First, the patients use

"They wouldn't have been caught if it were not for this tool," says Bill J. Hudenko, assistant professor of psychiatry at Dartmouth's Geisel School of Medicine.

The risk assessment tool is paired with

If a patient is classified as high risk, a nurse will help them get set up with the Proxi app to create a support system of friends and family, Hudenko adds. "Because we know what is most valuable in suicide prevention is to help them keep connections with others."

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AI May Hold the Key to Stopping Suicide - NBCNews.com

Forbes

Is Cybersecurity A Second Coming For AI? Forbes Artificial intelligence (AI) is all over the technology headlines lately. It seems to be the latest buzzword to take hold, yet the question remains: Will this be a quick fad, or are we actually seeing the second coming of AI? Most recently, there has ...

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Is Cybersecurity A Second Coming For AI? - Forbes

Spectators watch the world's top-ranked Go player, Ke Jie, square off against Google's artificial intelligence program, AlphaGo, during the Future of Go Summit in Wuzhen, China, on Tuesday. The program beat Ke in the first of three planned matches. Peng Peng/AP hide caption

Spectators watch the world's top-ranked Go player, Ke Jie, square off against Google's artificial intelligence program, AlphaGo, during the Future of Go Summit in Wuzhen, China, on Tuesday. The program beat Ke in the first of three planned matches.

In the first of three matches with the world's No. 1 Go player, a Google artificial intelligence program claimed victory Tuesday. It won the round by just a fraction of a point in Wuzhen, China, but the win was enough to leave its grandmaster opponent impressed and thoroughly confounded by the result.

Last year, after Google's AlphaGo dispatched human grandmaster Lee Sedol in the notoriously complex board game for the first time, 19-year-old Ke Jie expressed confidence that he wouldn't share the same fate, according to The New York Times. After all, Ke had defeated Lee several times himself.

By the match's end Tuesday, Ke felt markedly different about his nonhuman competitor.

"Last year, it was still quite humanlike when it played," Ke said. "But this year, it became like a god of Go."

His statement feels rather apt considering that, like a god, AlphaGo is its own greatest instructor. Last year, NPR's Geoff Brumfiel broke down the basics of the program developed by Google's DeepMind lab:

"It started by studying a database of about 100,000 human matches, and then continued by playing against itself millions of times.

"As it went, it reprogrammed itself and improved. This type of self-learning program is known as a neural network, and it's based on theories of how the human brain works.

"AlphaGo consists of two neural networks: The first tries to figure out the best move to play each turn, and the second evaluates who is winning the match overall."

DeepMind has further developed the architecture of the program since it defeated Lee last year.

Also like a god, AlphaGo has been demonstrating its influence far and wide among the game's top players including Ke, who Wired notes adopted some tactics from AlphaGo. The program played many of the world's top players under the pseudonym "Master" in online matches earlier this year, according to the magazine, and its unorthodox playing style affected Ke's.

In a blog post last year, DeepMind CEO and co-founder Demis Hassabis explained what attracted his lab to the ancient game of Go, which has been around since at least the days of Confucius:

"[A]s simple as the rules are, Go is a game of profound complexity. There are 1,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000 possible positions that's more than the number of atoms in the universe, and more than a googol times larger than chess."

It doesn't hurt that the game originated and remains very popular in China, with which Google has a fraught relationship. Roughly seven years ago, the California-based tech giant pulled out of the country, citing discomfort with Chinese censorship requirements.

Although Reuters reports that Eric Schmidt, executive chairman of Google parent Alphabet, watched the match in person in China, The Times notes that "there was no obvious live video of the event" available to viewers on the Chinese mainland. Chinese officials don't allow access to Google-owned YouTube, where the match was livestreamed, or to Google search.

Yet Google continues to have designs on the massive Chinese market, announcing recently that it intends to bring some of its products back to China, according to Reuters.

For now, though, skittish onlookers watching the next two matches between Ke and AlphaGo on Thursday and Saturday might do well to remember Geoff's reassurance: "This program will not lead to a dystopian future in which humanity is enslaved by killer robots.

"At least not for a few more years."

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'Like A God,' Google AI Beats Human Champ Of Notoriously Complex Go Game - NPR

The room where it happened was decked out like a faux studya place where a couple of friends might engage in a friendly game of chess. But the people at the chessboard were professionals, and only one was paid to play chess. One was IBM computer scientist Murray Campbell, whose job it was to move pieces at the instructions of a computer he helped program. He sat with an air of detachment mixed with anticipation, like a passenger on public transit not sure where the bus will stop. The other was world champion chess player Garry Kasparov, whose concentration was intense enough to start a fire in a rainforest. His head hovered over the chessboard as if trying to identify which piece was threatening to betray him. His ankles shook. He was clearly under epic stress. Meanwhile, his putative opponent a supercomputer housed elsewhere on the 35th floor of this midtown skyscrapernot only did not suffer stress, but did not even know what stress was.

I was in that room, for a few minutes at least, taking a turn at occupying one of its eight seats. It was February 1997, and I was covering the Kasparov-Deep Blue matchthe historic contest where IBMs computer would beat the world championfor Newsweek. In my own tribes form of jousting, I had campaigned for the cover, despite the editors declaration that we will never run a cover about chess. I successfully argued that this was not about a game of chess, but rather about a much more epic contest between human and artificial intelligence. What clinched it was the cover line I suggested: The Brains Last Stand. It also helped that no celebrity died that week. So it was that Kasparovs X-ray eyes and ultra-confident visage graced the newsstands of America, at a time when people actually paused at the newsstands to see what the weeklies put on their covers. And that Brains Last Stand line would come to be be invoked to this day. Even Kasparov, in a TED Talk last month, cited it twice.

I stand behind that provocation. Even though chess isnt the toughest thing that computers will tackle for centuries, it stood as a handy symbol for human intelligence. No matter what human-like feat computers perform in the future, the Deep Blue match demands an indelible dot on all timelines of AI progress.

But thats not the only reason why that six-game match in the Equitable Center is still so important. Two decades later, its clear that the significance of that outcome rests as much on how Kasparov was defeated. Though brute force computation and clever algorithms had created the winning positions against him, the champion was shattered by a well-planned psychological attack against him, executed by an IBM effort that leveraged its silicon advantages with human cunning. By the final session of the six-game matchone which began with the two opponents tied in pointsKasparov was a haunted ghost of himself. I knew I didnt have the energy for a complex flight, he writes in his recent book, Deep Thinking, explaining why, early in the game, he made a risky move that effectively ended his chances for winning. The machine had gotten inside the humans head.

And therein lies a parable.

Nine weeks or so before the match, I had lunch with Kasparov and C.J. Tan, the IBM scientist who managed the Deep Blue team. Both of those men maintained a veneer of cordiality that occasionally slipped to reveal the high stakes for each. Looking over the transcript 20 years later, a few things jump out at me. One was the confidence of each man. Tan had earlier remarked to a reporter that IBM was not conducting a scientific experiment anymore, and now he amended that to say, Its part of the experiment to how far the computer will go, and were doing everything we can to win. Kasparov was annoyed that the prospect of an IBM victory was even mentioned. I dont think its an appropriate thing to discuss the situation if I lose, said Kasparov. I never lost in my life.

The other interesting point was our discussion about the psychological aspects of the game. I hope it will be as small as possible, said Kasparov.

Today, those aspects seem to loom larger than the technological achievement of Deep Blue. It turns out that Tans remark about IBM doing everything it could to win included waging psychological warfare against its human opponent.

One tool was the element of surprise. Going into the match, Kasparov was frustrated that IBM had not shared printouts of Deep Blues practice games. He felt at a disadvantage because in a contest with any human, he would have a long history of match performance and would be able to tailor a strategy against that persons tendencies and weaknesses. The best he could do against Deep Blue was to study the chess minds who helped IBM program its systembut the only grandmaster on staff was the American player Joel Benjamin, who was not top-ranked, and to Kasparov, not even worth researching. I have better things to do in my life than study Benjamins games, Kasparov told me. But he did suspect that IBM was secretly working with more experienced grandmasters. I asked Tan directly at our lunch if this was so, and the IBM-er replied, No. Only Benjamin.

But at the match, IBM revealed that formidable grandmaster Miguel Illescas was on its team, as well as two other grandmasters who were working in consulting roles. (In his book, Kasparov says he had known only that Illescas had played training matches against Deep Blue.) Kasparov had no way to prepare, and he was thrown off balance.

That was far from the only trick that IBM would use. Heres a small example Kasparov cites in his book. During a match, human players sometimes will play games with the timing of a move. For instance, they might have a firm plan in mind, and if its going their way, instead of making the next move in the cascade right away they might let some time tick off the clock, to feign uncertainty. IBM actually programmed in the equivalent. In a 2009 interview with a chess publication, Illescas revealed that sometimes when Deep Blue instantly knew its next move, it would wait minutes before acting. When a chess computer stalls like this, it typically signals that the machine is having difficulty, or even has crashed. When Kasparov made his best move, the machine would play immediately, trying to give Kasparov the impression he had fallen into a trap. This has a psychological impact as the machine becomes unpredictable, which was our main goal, said Illescas.

The turning point of the match came in Game Two. Kasparov had won the first game and was feeling pretty good. In the second, the match was close and hard fought. But on the 36th move, the computer did something that shook Kasparov to his bones. In a situation where virtually every top-level chess program would have attacked Kasparovs exposed queen, Deep Blue made a much subtler and ultimately more effective move that shattered Kasparovs image of what a computer was capable of doing. It seemed to Kasparovand frankly, to a lot of observers as wellthat Deep Blue had suddenly stopped playing like a computer (by resisting the catnip of the queen attack) and instead adopted a strategy that only the wisest human master might attempt. By underplaying Deep Blues capabilities to Kasparov, IBM had tricked the human into underestimating it. A few days later, he described it this way: Suddenly [Deep Blue] played like a god for one moment. From that moment Kasparov had no idea whator whohe was playing against. In what he described as a fatalistic depression, he played on, and wound up resigning the game.

After Game Two, Kasparov was not only agitated by his loss but also suspicious at how the computer had made a move that was soun-computer like. It made me question everything, he now writes. Getting the printouts that explained what the computer didand proving that there was no human interventionbecame an obsession for him. Before Game Five, in fact, he implied that he would not show up to play unless IBM submitted printouts, at least to a neutral party who could check that everything was kosher. IBM gave a small piece to a third party, but never shared the complete file.

Kasparov was not the same player after Game Two. He fought to draws in the next three games, but in addition to the added mental pressures of dealing with what he clearly believed was his opponents skullduggery, he was physically wearing down. Though both sides were tied going into the final match, Kasparov approached it with dread. Asked in the press conference after Game Five about a comment Illescas made that he was now afraid of Deep Blue, Kasparov said, Im not afraid to admit Im afraid! Quite a difference from his pre-match confidence.

Indeed, Game Six was a debacle. From where we journalists were sitting, Kasparov seemed disengaged from the start. Afterwards, he claimed that he wasnt in the mood of playing at all. On his seventh move, on what should have been a routine opening-game move, he made a mistake so egregiously awful that there were cries of disbelief in the auditorium where spectators were gathered. It was almost like he was throwing the game. He played in a desultory fashion for a few moves, and then resigned in obvious disgust. In a chaotic post-game press conference, Kasparov alternated between rage and depression.

The master had been mastered.

After the match, I pushed very hard for a one-on-one with Kasparov. We met in a ballroom of the Plaza hotel, where his team had been staying. The space was empty except for a few generic dining chairs, the kind used at banquets. We sat knee-to-kneelike chess players, but of course no board separated us. Kasparov immediately repeated a demand he had made in the press conference: that IBM agree to a rematch, under more favorable conditions.

And of course, he railed about not seeing those full printouts. There is no information, he complained. Im not interested in segments! Im interested in the whole printout! Its their obligation!

But even at that stage, he was clear why he had lost. I never got over Game Two, he said to me. It was sitting in my mind. And then he summed it up: It was a single individual fighting one of the largest corporations in the world.

Indeed, IBMs stock jumped up after the match. The company never agreed to Kasparovs demand for a rematch.

Today, Kasparov is no longer competing for chess titles. He is a political activist squaring off against a more formidable opponent than even IBM: Vladimir Putin. His new book is a departure into a chapter of his life that defines him more than hed like. He now talks about how the future of chess lies in collaborations between human and machine players. In his recent TED Talk, he didnt revert to his complaints about IBM in the Deep Blue match.

In his book, however, he cant help but revisit itthe printouts, the tricks, the misdirection, the grandmasters. He does say that he no longer believes IBM cheated its way to victory. But then he trots out a detailed scenario, rooted in that same Illescas interview, in which IBM might have made changes on the eve of the final game that specifically targeted the move he made that ultimately undid him. He implies, vaguely, that IBM planted Russian-speaking security guards in his private space, which might explain that last-minute shift. Not that they cheated. But still.

I dwell on these suspicions, even ones that may border on paranoia, for a reason. Amazingly, when the Deep Blue match occurred, AI was in its winter peri0d. Now it is flowering. We hear of amazing machine learning accomplishments on a daily basis. But in 2017, we view them differently. We view them as inevitabilities.

The prime example is last years contest, during which DeepMinds AlphaGo program thumped an 18-time world champion in a series of five games. Go is a much more challenging feat for a computer than chess. Yet AlphaGo did not need to resort to any of the tactics that IBM used to distract, deceive, and ultimately destroy Kasparov. The human champion, Lee Sedol, ended with respect for his opponent and awe for how far computer science had come. But though the match deservedly received attention, it was nowhere near as mythic as the Deep Blue match was. The ground has shifted. Given enough time, money, and machine learning, theres no cognitive obstacle that machines will not surmount.

When I covered Kasparov-Deep Blue match, I thought the drama came from a battle between computer and human. But it was really a story of people, with brutal capitalist impulse, teaming up with AI to destroy the confidence and dignity of the greatest champion the world had seen. That leads me to believe its not Skynet that should worry us about AI, but rather the homo sapiens who build, implement, and employ those systems.

Dont get me wrong. Im still on board with the scientists who believe that advances in AI will make life better for all of us. Ultimately, using the power of computation for cognition is a great and historic human enterprise. But may I add a codicil to that declaration?

Always check the printouts.

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We Are All Kasparov - Backchannel

Can AI overhaul the social care system? Thats the pitchunderpinning UK home care providerCeras plans. The startuphas today taken the tiniestbaby steps to launch an AI chatbot that it hopes will, at an unspecified point in future, be able to assist carerswith recommendations forhome care of people with conditions such as dementia. And even potentially steer off medical emergencies via pre-emptive alerts.

The far more basicreality of the chatbot its launching today is that Martha (as its called)will be able to recommend care packages to potential customers. Whichunderlines how the inflated promises of AI really do hingeon data acquisition. In Cerascase its largelyleaningon its social care workersto generate the underlying data to train the AI. These human workerswill be tasked with creating the data points to fill out the care records that will be used to power the chatbots future carerecommendations and alerts.

And while there are plenty of symptom-checker type AIs already out there, Ceras positioning in the social care space sets it apart from other platforms, argues co-founder Ben Maruthappu, given its not aiming for the chatbotto be used directly by its clients (who may not be capable of using a smartphone app, for example), but rather to act as decision support for their carers.

Here AI has the scope to be very impactful, he argues.

The startup, which bills itself as a tech-enabled home care provider launched its social care matching platform last November, and has raised some $3.4 million to date from investors including Kima Ventures and Credo Ventures.

It has hundreds of care workers on its platform at this point,according to Maruthappu, and has delivered tens of thousands of care hours accruing millions of data points, as it couches it.

Maruthappu says Cera isfirstly using technology to accelerate the process ofmatching appropriate care workers to clients, as a route to outmaneuver traditional providers, and also applying tech to squeeze back-end costs so thatit can spend more on front end care and compensation for care workers to try to raise quality standards in an industry that has been beset byscandals.

Ultimately, though, it is also hoping all thosecare-related data points being gathered by care workers onits platform will be able to power an AI that it can deployto augment its future care services with decision support atscale, and provide even more of a differentiator vstraditional care providers.

The chatbot, which is being developed in concert withBloomsbury AI, a machine readingspinout from Londons UCL,will usemachine reading and deeplearning to dispense personalized care advice.

Maruthappu gives theexample of a care worker messagingMartha to say that a patient is feeling a bit hot and the AI then pulling relevant info from their care records noting thepatient had a cough last week, and telling them to check for a temperature and other symptoms in case the patient hasa chest infection.

Were going to use Martha [for] supporting our care workers in providing better quality care. Essentially raising the ceiling on the standard that is delivered, he says of this future plan.

We [also] want Martha to be able to predict if people are going to deteriorate Based on reading previous entries in care records Martha will flag alerts and essentially pre-empt a persons deterioration so that care workers and family members can be adequately alerted and a proactive approach can be taken to their care.

He wont give a time frame for launching the predictive alerts, but decision-support should be coming later this year he says.

Of courseshould Martha actually be in a position to start dispensing care recommendations it would likely need to have been registered as a medical device with the UKs regulatory body, the MHRA. And Maruthappu confirms Cera hasnot currently registered the app, since its merely dispensing sales suggestions to potential customers at this point.

Is the aim to use the AI to effectively upskill care workers with medical training? He says its not to upskill them to the level of trained nurses, for example, but to offer decision-support so they may be better able to identify when escalated care might be required.

Maruthappu also argues that a chatbot interface that can be usedto keep track of individual patients care records canhelp quality of service in instances where a clientmight be seen by multiplecare workers helping to join the dots in theircare over time.

Cera has partnered with ten NHS organizations at this point ten-weeks in, whichMaruthappu says collectively cover a population of around six million people.

We offer a higher quality, more efficient and transparent service, he says, discussing the businesspitch to the healthcare organizations its selling services to. At the moment bed blocking is a tremendous issue in the NHS. This is essentially where a patient whose in a hospital could be discharged home and is medically fit to be discharged to go home but for non-medical reasons they dont go home.

And if you look across winter the number one reason why people werent discharged when they couldhave been is because their home care package was not organizedThis is a massively growing problem for the health service, he adds.

What are the risks ofhaving an automated technology dispensing what amounts to medical advice that may then be actioned by a human? Its fundamentally care advice, and it is decision-support, but these are all things that are within the remit of a high quality care worker. Were simply trying to support and increase consistency in the care, arguesMaruthappu.

As an analogy, if a taxi driver needs to go from A to B and theyre using a maps app, the maps app is supporting them but ultimately its the driver who is driving, who is making decisions about the route and if they need to change the route it will do that accordingly.

Apps are simply an enabler which can potentially improve efficiency and quality, he adds. But ultimately it is up to the person delivering the services to make appropriate decisions and manage that responsibly.

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Cera is building an AI for social care decision support - TechCrunch