But both the people building commercial bots and those trying to combat them watch the academic work closely.
“Of course a lot of gambling people are worried that it may kill internet gambling for money, because people are worried that bots are going to be so good that they’re going to be had,” said Sandholm.
.. PokerStars, which is owned by the Canadian gaming company Amaya, employs 70 people to combat this kind of fraud. Employees call players and ask them to describe their strategies on certain hands. The company has also sent e-mails to players requiring them to make videos in which users rotate the camera 360 degrees to show their surroundings, then play for over an hour with their hands and keyboards fully visible.
.. To solve checkers, Schaeffer had used a method that essentially attempted to calculate the best move in any relevant situation, without considering what had happened up to that point. But it didn’t make sense to think about each move as an isolated problem in a game like poker, where luck is involved and not everyone has access to all the relevant information.
.. A Nash equilibrium isn’t a single ideal style of play. The key to an equilibrium strategy in poker is to play the strongest potential hands while remaining unpredictable. “When you bet your strong hands, there needs to be some doubt,”
.. Bowling said his research papers are popular on message boards for people building bots. “There’s this whole separate group of people reading these papers and trying to understand them,” he said.
.. Les said he’s trying to figure out how to adapt some of Libratus’s irregular betting behavior to his own game. It’s hard. “We just simply do not have the mental capacity to do it,” he said.
.. Head-to-head matches against professionals are one thing. But there’s no clear path to turn Libratus and DeepStack into players that could be confident of beating a group of flawed humans. That’s because the equilibrium strategy that the AIs use fall apart in multiplayer games, when the point isn’t to play perfectly but to identify and exploit the shortcomings in other people’s games.
The AI Threat Isn’t Skynet. It’s the End of the Middle Class
At a time when the Trump administration is promising to make America great again by restoring old-school manufacturing jobs, AI researchers aren’t taking him too seriously. They know that these jobs are never coming back, thanks in no small part to their own research, which will eliminate so many other kinds of jobs in the years to come
.. At Asilomar, they looked at the real US economy, the real reasons for the “hollowing out” of the middle class. The problem isn’t immigration—far from it. The problem isn’t offshoring or taxes or regulation. It’s technology.
.. the number of manufacturing jobs peaked in 1979 and has steadily decreased ever since. At the same time, manufacturing has steadily increased, with the US now producing more goods than any other country but China.
.. “I am less concerned with Terminator scenarios,” MIT economist Andrew McAfee said on the first day at Asilomar. “If current trends continue, people are going to rise up well before the machines do.”
.. Also on researchers’ minds was regulation—of AI itself. Some fear that after squeezing immigration—which would put a brake on the kind of entrepreneurship McAfee calls for—the White House will move to bottle up automation and artificial intelligence.
Facebook’s PyTorch: deep learning framework that puts Python first.
Tensors and Dynamic neural networks in Python
with strong GPU acceleration.
Have Fun with Machine Learning: A Guide for Beginners
This is a hands-on guide to machine learning for programmers with no background in AI. Using a neural network doesn’t require a PhD, and you don’t need to be the person who makes the next breakthrough in AI in order to use what exists today. What we have now is already breathtaking, and highly usable. I believe that more of us need to play with this stuff like we would any other open source technology, instead of treating it like a research topic.
In this guide our goal will be to write a program that uses machine learning to predict, with a high degree of certainty, whether the images in data/untrained-samples are of dolphins or seahorses using only the images themselves, and without having seen them before. Here are two example images we’ll use: