Daily Archives: August 15, 2017

For decades, the worlds smartest game-playing humans have been racking up losses to increasingly sophisticated forms of artificial intelligence.

The defeats began in the 1990s when Deep Blue conquered chess master Garry Kasparov. In May, Ke Jie until then the worlds best player of the ancient Chinese board game Go was defeated by a Google computer program.

Now the AIsupergamers havemoved intothe world of e-sports. Last week, an artificial intelligence bot created by the Elon Musk-backed start-up OpenAI defeated some of the worlds most talented players of Dota 2, a fast-paced, highly complex, multiplayer online video game that draws fierce competition from all over the globe.

[Billionaire burn: Musk says Zuckerbergs understanding of AI threat is limited]

OpenAI unveiled itsbot at an annual Dota 2 tournament where players walk away with millions in prize money.It was a pivotal moment in gaming and in AI research largely because of how the bot developed its skills and how long it took to refine them enough to defeat the worlds most talented pros, according to Greg Brockman, co-founder and chief technology officer of OpenAI.

The somewhat frightening reality: It only took the bot two weeks to go from laughable novice to world-class competitor, a period in which Brockman said the bot gathered lifetimes of experience by playing itself.

During that period, players said, the botwent from behaving like a bot to behaving in a way that felt more alive.

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Danylo Dendi Ishutin, one of the games top players, was defeated twice by his AI competition, whichfelt a little like human, but a little like something else, he said, according to the Verge.

Brockman agreed with that perspective:You kind of see that this thing is super fast and no human can execute its moves as well, but it was also strategic, and it kind of knows what youre going to do, he said. When you go off screen, for example, it would predict what you were going to do next. Thats not something we expected.

Brockman said games are a great testing ground for AI because they offer a defined set of rules with baked-in complexity that allow developers to measure a bots changing skill level.He said one of the major revelations of the Dota 2 bots success was that it was achieved via self-play a form of training in which the bot would continuously play against a copy of itself until it amassed more and more knowledge while improving incrementally.

[Was this created by a human or computer? See if you can tell the difference.]

For a game as complicated as Dota 2 which incorporates more than 100 playable roles and thousands of moves self play proved more organic and comprehensive than having a human preprogram the bots behavior.

If youre a novice playing against someone who is awesome playing tennis against Serena Williams, for example youre going to be crushed, and you wont realize there are slightly better techniques or ways of doing something, Brockman said. The magic happens when your opponent is exactly balanced with you so that if you explore and find a slightly better strategy it is then reflected in your performance in the game.

Tesla chief executive Elon Musk hailed the bots achievement in historic fashion on Twitter before going on to once again highlight the risk posed by AI, which he saidposes vastly more risk than North Korea.

Musk unleashed a debate about the danger of AI last month when he tweeted that Facebook chief executive Mark Zuckerbergs understanding of the threat posed by AI is limited.

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'It knew what you were going to do next': AI learns from pro gamers then crushes them - Washington Post

You cant swing a cat these days without hitting some incubator or accelerator, or a startup touting its artificial intelligence chops but for some reason, there are few if any incubators focused just on the AI sector. Seattles Allen Institute for AI is doing just that, with the promise of connecting small classes of startups with the organizations formidable brains (and 250 grand).

AI2, as the Paul Allen-backed nonprofit is more commonly called, already spun off two companies: XNOR.ai, which has made major advances in enabling AI tasks to run on edge devices, is operating independently and licensing its tech to eager customers. And Kitt.ai, a (profitable!) natural language processing platform, was bought by Baidu just last month.

Were two for two, and not in a small way, said Jacob Colker, who has led several Seattle and Bay Area startups and incubators, and is currently the Entrepreneur-in-Residence charged with putting AI2s program on the map. Until now the incubation program has kept a low profile.

Startups will get the expected mentorship and guidance on how to, you know, actually run a company but the draw, Colker emphasized, is the people. A good AI-based startup might get good advice and fancy office space from just about anyone but only AI2, he pointed out, is a major concentration of three core competencies in machine learning, natural language processing, and computer vision.

YOLO in action, from the paper presented at CVPR.

XNOR.ai, still partly run out of the AI2 office, is evidence of that. The companys latest computer vision system, YOLO, performs the rather incredible feat of both detecting and classifying hundreds of object types on the same network, locally and in real time. YOLO scored runner-up for Best Paper at this years CVPR, and thats not the first time its authors have been honored. Id spend more time on the system but its not what this article is about.

There are dozens more PhDs and published researchers; AI2 has plucked (or politely borrowed) high-profile academics from all over, but especially the University of Washington, a longstanding presence at the frontiers of tech. AI2 CEO Oren Etzioni is himself a veteran researcher and is clearly proud of the team hes built.

Obviously AI is hot right now, he told me, but were not jumping on the bandwagon here.

The incubator will have just a handful of companies at a time, he and Colker explained, and the potential investment of up to $250K is more than most such organizations are willing to part with. And as a nonprofit, there are fewer worries about equity terms and ROI.

But the applications of supervised learning are innumerable, and machine learning has become a standard developer tool so ambitious and unique applications of AI are encouraged.

Were not looking for a doohickey, Etzioni said. We want to make big bets and big companies.

AI2 is hoping to get just 2-5 companies for its first batch. Makes it a lot easier for me to keep eyes on them, thats for sure. Interested startups can apply at the AI2 site.

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Allen-backed AI2 incubator aims to connect AI startups with world-class talent - TechCrunch

Xu Lis software scans more faces than maybe any on earth. He has the Chinese police to thank.

Xu runs SenseTime Group Ltd., which makes artificial intelligence software that recognizes objects and faces, and counts Chinas biggest smartphone brands as customers. In July, SenseTime raised $410 million, a sum it said was the largest single round for an AI company to date. That feat may soon be topped, probably by another startup in China.

The nation is betting heavily on AI. Money is pouring in from Chinas investors, big internet companies and its government, driven by a belief that the technology can remake entire sectors of the economy, as well as national security. A similar effort is underway in the U.S., but in this new global arms race, China has three advantages: A vast pool of engineers to write the software, a massive base of 751 million internet users to test it on, and most importantlystaunch government support that includes handing over gobs of citizens data - something that makes Western officials squirm.

Data is key because thats how AI engineers train and test algorithms to adapt and learn new skills without human programmers intervening. SenseTime built its video analysis software using footage from the police force in Guangzhou, a southern city of 14 million. Most Chinese mega-cities have set up institutes for AI that include some data-sharing arrangements, according to Xu. "In China, the population is huge, so its much easier to collect the data for whatever use-scenarios you need," he said. "When we talk about data resources, really the largest data source is the government."

This flood of data will only rise. China just enshrined the pursuit of AI into a kind of national technology constitution. A state plan, issued in July, calls for the nation to become the leader in the industry by 2030. Five years from then, the government claims the AI industry will create 400 billion yuan ($59 billion) in economic activity. Chinas tech titans, particularly Tencent Holdings Ltd. and Baidu Inc., are getting on board. And the science is showing up in unexpected places: Shanghais courts are testing an AI system that scours criminal cases to judge the validity of evidence used by all sides, ostensibly to prevent wrongful prosecutions.

Data access has always been easier in China, but now people in government, organizations and companies have recognized the value of data, said Jiebo Luo,a computer science professor at the University of Rochester who has researched China. As long as they can find someone they trust, they are willing to share it.

The AI-MATHS machine took the math portion of Chinas annual university entrance exam in Chengdu.

Photographer: AFP via Getty Images

Every major U.S. tech company is investing deeply as well. Machine learning -- a type of AI that lets driverless cars see, chatbots speak and machines parse scores of financial information -- demands computers learn from raw data instead of hand-cranked programming. Getting access to that data is a permanent slog. Chinas command-and-control economy, and its thinner privacy concerns, mean that country can dispense video footage, medical records, banking information and other wells of data almost whenever it pleases.

Xu argued this is a global phenomenon. "Theres a trend toward making data more public. For example, NHS and Google recently shared some medical image data," he said. But that example does more to illustrate Chinas edge.

DeepMind, the AI lab of Googles Alphabet Inc., has labored for nearly two years to access medical records from the U.K.s National Health Service for a diagnostics app. The agency began a trial with the company using 1.6 million patient records. Last month, the top U.K. privacy watchdog declared the trial violates British data-protection laws, throwing its future into question.

Go player Lee Se-Dol, right, in a match against Googles AlphaGo, during the DeepMind Challenge Match in March 2016.

Photographer: Google via Getty Images

Contrast that with how officials handled a project in Fuzhou. Government leaders from that southeastern Chinese city of more than seven million people held an event on June 26. Venture capital firm Sequoia Capital helped organize the event, which included representatives from Dell Inc., International Business Machines Corp. and Lenovo Group Ltd.A spokeswoman for Dell characterized the event as the nations first "Healthcare and Medical Big Data Ecology Summit."

The summit involved a vast handover of data. At the press conference, city officials shared 80 exabytes worth of heart ultrasound videos, according to one company that participated. With the massive data set, some of the companies were tasked with building an AI tool that could identify heart disease, ideally at rates above medical experts. They were asked to turn it around by the fall.

"The Chinese AI market is moving fast because people are willing to take risks and adopt new technology more quickly in a fast-growing economy," said Chris Nicholson, co-founder of Skymind Inc., one of the companies involved in the event. "AI needs big data, and Chinese regulators are now on the side of making data accessible to accelerate AI."

Representatives from IBM and Lenovo declined to comment. Last month, Lenovo Chief Executive Officer Yang Yuanqing said he will invest $1 billion into AI research over the next three to four years.

Along with health, finance can be a lucrative business in China. In part, thats because the country has far less stringent privacy regulations and concerns than the West. For decades the government has kept a secret file on nearly everyone in China called a dangan. The records run the gamut from health reports and school marks to personality assessments and club records. This dossier can often decide a citizens future -- whether they can score a promotion or be allowed to reside in the city they work.

U.S. companies that partner in China stress that AI efforts, like those in Fuzhou, are for non-military purposes. Luo, the computer science professor, said most national security research efforts are relegated to select university partners. However, one stated goal of the governments national plan is for a greater integration of civilian, academic and military development of AI.

The government also revealed in 2015 that it was building a nationwide database that would score citizens on their trustworthiness, which in turn would feed into their credit ratings. Last year, China Premier Li Keqiang said 80 percent of the nations data was in public hands and would be opened to the public, with an unspecific pledge to protect privacy. The raging popularity of live video feeds -- where Chinese internet users spend hours watching daily footage caught by surveillance video -- shows the gulf in privacy concerns between the country and the West. Embraced in China, the security cameras also reel in mountains of valuable data.

Some machine-learning researchers dispel the idea that data can be a panacea. Advanced AI operations, like DeepMind, often rely on "simulated" data, co-founder Demis Hassabis explained during a trip to China in May. DeepMind has used Atari video games to train its systems. Engineers building self-driving car software frequently test it this way, simulating stretches of highway or crashes virtually.

"Sure, there might be data sets you could get access to in China that you couldnt in the U.S.," said Oren Etzioni, director of the Allen Institute for Artificial Intelligence. "But that does not put them in a terrific position vis-a-vis AI. Its still a question of the algorithm, the insights and the research."

Historically, the country has been a lightweight in those regards. Its suffered through a "brain drain," a flight of academics and specialists out of the country. "China currently has a talent shortage when it comes to top tier AI experts," said Connie Chan, a partner at venture capital firm Andreessen Horowitz. "While there have been more deep learning papers published in China than the U.S. since 2016, those papers have not been as influential as those from the U.S. and U.K."

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But China is gaining ground. The country is producing more top engineers, who craft AI algorithms for U.S. companies and, increasingly, Chinese ones. Chinese universities and private firms are actively wooing AI researchers from across the globe. Juo, the University of Rochester professor, said top researchers can get offers of $500,000 or more in annual compensation from U.S. tech companies, while Chinese companies will often double that.

Meanwhile, Chinas homegrown talent is starting to shine. A popular benchmark in AI research is the ImageNet competition, an annual challenge to devise a visual recognition system with the lowest error rate. Like last year, this years top winners were dominated by researchers from China, including a team from the Ministry of Public Securitys Third Research Institute.

Relentless pollution in metropolises like Beijing and Shanghai has hurt Chinese companies ability to nab top tech talent. In response, some are opening shop in Silicon Valley. Tencent recently set up an AI research lab in Seattle.

Photographer: David Paul Morris/Bloomberg

Baidu managed to pull a marquee name from that city. The firm recruited Qi Lu, one of Microsofts top executives, to return to China to lead the search giants push into AI. He touted the technologys potential for enhancing Chinas "national strength" and cited a figure that nearly half of the bountiful academic research on the subject globally has ethnically Chinese authors, using the Mandarin term "huaren" -- a term for ethnic Chinese that echoes government rhetoric.

"China has structural advantages, because China can acquire more and better data to power AI development," Lu told the cheering crowd of Chinese developers. "We must have the chance to lead the world!"

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China's Plan for World Domination in AI Isn't So Crazy After All - Bloomberg

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You might not have noticed, but over the weekend a little coup took place. On Friday night, in front of a crowd of thousands, an AI bot beat a professional human player at Dota 2 one of the worlds most popular video games. The human champ, the affable Danil "Dendi" Ishutin, threw in the towel after being killed three times, saying he couldnt beat the unstoppable bot. It feels a little bit like human, said Dendi. But at the same time, its something else.

The bots patron was none other than tech billionaire Elon Musk, who helped found and fund the institution that designed it, OpenAI. Musk wasnt present, but made his feelings known on Twitter, saying: OpenAI first ever to defeat world's best players in competitive eSports. Vastly more complex than traditional board games like chess & Go. Even more exciting, said OpenAI, was that the AI had taught itself everything it knew. It learned purely by playing successive versions of itself, amassing lifetimes of in-game experience over the course of just two weeks.

But how big a deal is all this? Was Friday nights showdown really more impressive than Googles AI victories at the board game Go? The short answer is probably not, but it still represents a significant step forward both for the world of e-sports and the world of artificial intelligence.

First, we need to look at Musks claim that Dota is vastly more complex than traditional board games like chess & Go. This is completely true. Real-time battle and strategy games like Dota and Starcraft II pose major challenges that computers just cant handle yet. Not only do these games demand long-term strategic thinking, but unlike board games they keep vital information hidden from players. You can see everything thats happening on a chess board, but you cant in a video game. This means you have to predict and preempt what your opponent will do. It takes imagination and intuition.

In Dota, this complexity is increased as human players are asked to work together in teams of five, coordinating strategies that will change on the fly based on which characters players choose. To make things even more complex, there are more than 100 different characters in-game, each with their own unique skill set; and characters can be equipped with a number of unique items, each of which can be game-winning if deployed at the right moment. All this means its basically impossible to comprehensively program winning strategies into a Dota bot.

But, the game that OpenAIs bot played was nowhere near as complex as all this. Instead of 5v5, it took on humans at 1v1; and instead of choosing a character, both human and computer were limited to the same hero a fellow named the Shadow Fiend, who has a pretty straightforward set of attacks. My colleague Vlad Savov, a confirmed Dota addict who also wrote up his thoughts on Fridays match, said the 1v1 match represents only a fraction of the complexity of the full team contest. So: probably not as complex as Go.

The second major caveat is knowing what advantages OpenAIs agent had over its human opponents. One of the major points of discussion in the AI community was whether or not the bot had access to Dotas bot API which would let it tap directly into streams of information from the game, like the distances between players. OpenAIs Greg Brockman confirmed to The Verge that the AI did indeed use the API, and that certain techniques were hardcoded in the agent, including the items it should use in the game. It was also taught certain strategies (like one called creep block) using a trial-and-error technique known as reinforcement learning. Basically, it did get a little coaching.

Andreas Theodorou, a games AI researcher at the University of Bath and an experienced Dota player, explains why this makes a difference. One of the main things in Dota is that you need to calculate distances to know how far some [attacks] travel, he says. The API allows bots to have specific indications of range. So you can say, If someone is in 500 meters range, do that, but the human player has to calculate it themselves, learning through trial and error. It really gives them an advantage if they have access to information that a human player does not. This is particularly true in a 1v1 setting with a hero like Shadow Fiend; where players have to focus on timing their attacks correctly, rather than overall strategy.

Brockmans response is that this sort of skill is trivial for an AI to learn, and was never the focus of OpenAIs research. He says the institutes bot could have done without information from the API, but youd just be spending a lot more of your time learning to do vision, which we already know works, so whats the benefit?

So, knowing all this, should we dismiss OpenAIs victory? Not at all, says Brockman. He points out that, perhaps more important than the bots victory, was how it taught itself in the first place. While previous AI champions like AlphaGo have learned how to play games by soaking up past matches by human champions, OpenAIs bot taught itself (nearly) everything it knows.

You have this system that has just played against itself, and it has learned robust enough strategies to beat the top pros. Thats not something you should take for granted, says Brockman. And its a big question for any machine learning system: how does complexity get into the model? Where does it come from?

As OpenAIs Dota bot shows, he says, we dont have to teach computers complexity: they can learn it themselves. And although some of the bots behavior was preprogrammed, it did develop some strategies by itself. For example, it learned how to fake out its opponents by pretending to trigger an attack, only to cancel at the last second, leaving the human player to dodge an attack that never comes exactly like a feint in boxing.

Others, though, are still a little skeptical. AI researcher Denny Britz, who wrote a popular blog post that put the victory in context, tells The Verge that its difficult to judge the scale of this achievement without knowing more technical details. (Brockman says these are forthcoming, but couldnt give an exact time frame.) Its not clear what the technical contribution is at this point before the paper comes out, says Britz.

Theodorou points out that although OpenAIs bot beat Dendi onstage, once players got a good look at its tactics, they were able to outwit it. If you look at the strategies they used, they played outside the box a bit and they won, he says. The players used offbeat strategies the sort that wouldnt faze a human opponent, but which the AI had never seen before. It didnt look like the bot was flexible enough, says Theodorou. (Brockman counters that once the bot learned these strategies, it wouldnt fall for them twice.)

All the experts agree that this was a major achievement, but that the real challenge is yet to come. That will be a 5v5 match, where OpenAIs agents have to manage not just a duel in the middle of the map, but a sprawling, chaotic battlefield, with multiple heroes, dozens of support units, and unexpected twists. Brockman says that OpenAI is currently targeting next years grand Dota tournament in 12 months time to pull this off. Between now and then, theres much more training to be done.

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Did Elon Musk's AI champ destroy humans at video games? It's complicated - The Verge