AI News, Artificial intelligence: advancements, abilities and limitations

Artificial intelligence: advancements, abilities and limitations

While most of these programs focused on search and learning as the foundation of the newly discovered field, the tricky part was getting AI to solve problems – and AI has gotten pretty good at it over the years.

This may seem overly optimistic, but MIT has tested the software using 3.6 billion log lines of internet activity to come to this conclusion.

Again, these systems do spot cyber attacks successfully, but it’s hard for a human to program scenarios to catch every single attack.

Like most current AI, the software still needs some human intervention to clarify whether the events it discovers are truly suspicious, so it’s not completely autonomous.

This seems like a definite tick in the ability box for AI – anything that can help combat cyber attacks is a positive step forward.

The AI that solved poker Much like one of the first examples of computer programming, an AI system has been successfully created to beat all humans at a game.

If the software were to go from weakly solving the game to actually solving it, it would need to transform 0.000986 to 0.0000000 big blinds per game on expectation.

The team used adaptive software which they fed with every possible situation in a poker game – that’s 316.000.000.000.000.000 different situations in a heads-up game.

The poker world understandably took note of this new bit of AI threatening their beloved game, but most of them reveled in the new piece of tech.

One satirical article even suggested that popular online poker room PokerStars had bought the bot to add it to its roster of pro poker players.

>See also: Google's British AI program defeats Go world champion Lee Sedol in historic match Unlike programs that came before it to beat chess, and Cepheus, AlphaGo doesn’t simply take every possible move from a game of Go and then use computer power to work out the best possible outcome.

The team at Google DeepMind started by taking 150,000 games played by good human Go players and used the artificial neural network to find patterns.

Once this had been established, AlphaGo could combine this valuation approach with a search of all the possible lines of play, targeting its search to plays the policy network thought were more likely, eventually picking the move that forced the most effective board valuation.

AI program beats humans in poker game

Image copyright RIVERS CASINO Image caption Dong Kim was one of the four professional poker players defeated by the AI and won the prize for the best human player A

poker-playing AI has beaten four human players in a marathon match lasting 20 days.

Libratus, an artificial intelligence program developed at Carnegie Mellon University, was trained to play a variant of the game known as no-limit heads-up Texas hold 'em.

'The algorithms can take information and output a strategy in a range of scenarios, including negotiations, finance, medical treatment and cybersecurity.'

Demoralising One of the professional poker players, Jimmy Chou, admitted at the halfway point that the AI was proving a tough opponent.

All four human players shared the $200,000 (£159,000) prize fund, ranked in order of how well they played against the AI.

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