human experts Archives - Artificial Intelligence

How artificial intelligence beat human experts at poker revealed

aiuniverse — Wed, 20 Dec 2017 05:45:43 +0000

Source – financialexpress.com

Libratus, the artificial intelligence that defeated four top professional poker players earlier this year, uses a three-pronged approach to master a game with more decision points than atoms in the universe, scientists say. In a study published in the journal Science, researchers from the Carnegie Mellon University in the US detailed how their AI was able to achieve superhuman performance by breaking the game into computationally manageable parts and, based on its opponents’ game play, fix potential weaknesses in its strategy during the competition. AI programs have defeated top humans in checkers, chess and Go – all challenging games, but ones in which both players know the exact state of the game at all times. Poker players, by contrast, contend with hidden information – what cards their opponents hold and whether an opponent is bluffing.

In a 20-day competition involving 120,000 hands at Rivers Casino in Pittsburgh in January, Libratus became the first AI to defeat top human players at head’s up no-limit Texas Hold’em Poker – the primary benchmark and long-standing challenge problem for imperfect-information game-solving by AIs. Libratus beat each of the players individually in the two-player game and collectively amassed more than $1.8 million in chips. “The techniques in Libratus do not use expert domain knowledge or human data and are not specific to poker. Thus they apply to a host of imperfect-information games,” researchers said.

Such hidden information is ubiquitous in real-world strategic interactions, including business negotiation, cybersecurity, finance, strategic pricing and military applications. Libratus includes three main modules, the first of which computes an abstraction of the game that is smaller and easier to solve than by considering all possible decision points – about 10 multiplied 161 times – in the game. It then creates its own detailed strategy for the early rounds of Texas Hold’em and a coarse strategy for the later rounds. This strategy is called the blueprint strategy.
In the final rounds of the game, a second module constructs a new, finer-grained abstraction based on the state of play.

It also computes a strategy for this subgame in real-time that balances strategies across different subgames using the blueprint strategy for guidance – something that needs to be done to achieve safe subgame solving. The third module is designed to improve the blueprint strategy as competition proceeds. Typically, AIs use machine learning to find mistakes in the opponent’s strategy and exploit them.

However, that also opens the AI to exploitation if the opponent shifts strategy, Sandholm said. Instead, Libratus’ self-improver module analyses opponents’ bet sizes to detect potential holes in Libratus’ blueprint strategy. Libratus then added these missing decision branches, computes strategies for them, and adds them to the blueprint.

In addition to beating the human pros, Libratus was evaluated against the best prior poker AIs. “The techniques that we developed are largely domain independent and can thus be applied to other strategic imperfect-information interactions, including non-recreational applications,” researchers said. “Due to the ubiquity of hidden information in real-world strategic interactions, we believe the paradigm introduced in Libratus will be critical to the future growth and widespread application of AI,” they said.

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ARTIFICIAL INTELLIGENCE COULD HELP US SEE FARTHER INTO SPACE THAN EVER BEFORE

aiuniverse — Wed, 06 Sep 2017 09:18:34 +0000

Source – digitaltrends.com

Distortions in space-time sound like they’d be more of a concern on an episode of Star Trek than they would in the real world. However, that’s not necessarily true: analyzing images of gravitational waves could help enormously extend both the range and resolution of telescopes like Hubble, and allow us to see farther into the universe than has been possible before.

The good news? Applying an artificial intelligence neural network to this problem turns out to accelerate its solution well beyond previous methods — like 10 million times faster. That means that analysis which could take human experts weeks or even months to complete can now be carried out by neural nets in a fraction of a single second.

Developed by researchers at Stanford University and the SLAC National Accelerator Laboratory, the new neural network is able to analyze images of so-called “gravitational lensing.” This is an effect first hypothesized about by Albert Einstein, who suggested that giant masses such as stars have the effect of curving light around them. This effect is similar to a telescope in that it allows us to examine distant objects with more clarity. However, unlike a telescope, gravitational lenses distort objects into smeared rings and arcs — so making sense of them requires the calculating abilities of a computer.

To train their network, researchers on the project showed it around half a million simulated images of gravitational lenses. After this was done, the neural net was able to spot new lenses and determine their properties — down to how their mass was distributed, and how great the magnification levels of the background galaxy were.

Given that projects like the Large Synoptic Survey Telescope (LSST), a 3.2-gigapixel camera currently under construction at SLAC, is expected to increase the number of known strong gravitational lenses from a few hundred to tens of thousands, this work comes at the perfect time.

“We won’t have enough people to analyze all these data in a timely manner with the traditional methods,” said postdoctoral fellow Laurence Perreault Levasseur, a co-author on the associated Nature research paper. “Neural networks will help us identify interesting objects and analyze them quickly. This will give us more time to ask the right questions about the universe.”

Impressively, the neural network doesn’t even need a supercomputer to run on: one of the tested neural nets was designed to work on an iPhone. Studying the universe in greater detail than ever? Turns out there’s an app for that!

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YouTube removing online terrorism content faster, aided by machine learning

aiuniverse — Thu, 03 Aug 2017 07:32:01 +0000

Source – bgr.com

Nearly a month after YouTube said it was going to combat online terrorist content on its platform, the video-sharing site said the process is going well, thanks to machines and humans alike.

In an August 1 blog post, the Google-owned site said its multi-pronged approach is being aided by machine learning, where computers are detecting and removing the content in a faster manner.

“Our machine learning systems are faster and more effective than ever before,” YouTube said in the post. “Over 75 percent of the videos we’ve removed for violent extremism over the past month were taken down before receiving a single human flag.”

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YouTube added that over the past month, machine learning has helped it remove more than double “both the number of videos we’ve removed for violent extremism, as well as the rate at which we’ve taken this kind of content down.”

Over 400 hours of content are uploaded every minute to the platform, which has more than 1.5 billion logged-in users, according to YouTube CEO Susan Wojcicki.

In addition to using computers, YouTube is utilizing human experts, through its Trusted Flagger program. They’ve added 15 non-governmental organizations including the Anti-Defamation League, the No Hate Speech Movement and the Institute for Strategic Dialogue.

There will also be tougher standards on videos that have been flagged by users as potential violations on hate speech and violent extremism, but may not actually be illegal.

“If we find that these videos don’t violate our policies but contain controversial religious or supremacist content, they will be placed in a limited state,” YouTube said.

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The push comes after several terrorist attacks around the globe, including in the U.K. In June. YouTube said it would redirect people looking for extremist content to videos that confront and discredit the search topics, via the Redirect Method, created by another team at Google and its parent company, Alphabet.

Despite the progress, YouTube said more needs to be done to combat extremist content that lives on its site.

“With the help of new machine learning technology, deep partnerships, ongoing collaborations with other companies through the Global Internet Forum, and our vigilant community we are confident we can continue to make progress against this ever-changing threat,” YouTube wrote. “We look forward to sharing more with you in the months ahead.”

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