Five'table ronde' or round table were organised mostly with academics on the different aspects of the societal moves due to Artificial Intelligence (AI or IA in French): It was pointed that some milestone progress on deep learning has been achieved. Machines have surpassed human champions in most intellectually challenging games, including Chess, Scrabble, Othello, even Jeopardy. On March 2016, the Google AlphaGo DeepMind's Artificial Intelligence program beat Lee Sedol, the strongest Go player in the world. Go--a 2,500-year-old game is far more complex than Chess. An exceptional powerful computer had to process more than 30 million moves.

This time, Google's latest machine learning system, DeepMind, has learned to respond to stress with extreme aggression. I dunno about you, but that sounds like we just gave computers a fight or flight response. You may recall DeepMind as the computer that bested human Go players for the first time last years. Now, researchers have been using it to explore the limits of game theory -- a field of psychology that analyzes how people respond to cooperative and competitive opportunities. The team found that when DeepMind suspects that it's about to lose, it will switch to "highly aggressive" tactics to either win or maximize damage to its opponents.

Each of those nine boxes is the PathNet at a different iteration. In this case, PathNet was trained on two different games using a Advantage Actor-critic or A3C. Although Pong and Alien seem very different at first, we observe a positive transfer learning using PathNet (take a look at the score graph). First of all, we need to define the modules. Let L be the number of layers and N be the maximum number of modules per layer (the paper indicates that N is typically 3 or 4).

His present superhuman AI agents can see, learn from experience, simulate our world and orchestrate meta-solutions (General-Purpose AI). The international metric consists of the state-of-the-art OpenAI Gym.

Will artificial intelligence get more aggressive and selfish the more intelligent it becomes? A new report out of Google's DeepMind AI division suggests this is possible based on the outcome of millions of video game sessions it monitored. The results of the two games indicate that as artificial intelligence becomes more complex, it is more likely to take extreme measures to ensure victory, including sabotage and greed. The first game, Gathering, is a simple one that involves gathering digital fruit. Two DeepMind AI agents were pitted against each other after being trained in the ways of deep reinforcement learning.

Top scientists and theoreticians are expressing their concerns about how badly wrong human experimentation with highly advanced artificial intelligence programs could turn out. The renowned physicist Stephen Hawking is one of the most prominent scientists to express his disquiet about the potential of the technology, describing artificial intelligence "the best, or the worst thing, ever to happen to humanity". In the best case scenario, this technology could improve the world in unimaginable ways. But in the worst case scenario, super-intelligent robots capable of thinking for themselves could effectively take over as this planet's most dominant'species', something which might pose an existential threat to humanity itself. While the worst case scenario might seem like the stuff of science-fiction dystopic fantasy, Google's new DeepMind AI system might suggest that this terrifying story might well become a reality.

Each of those nine boxes is the PathNet at a different iteration. In this case, the PathNet was trained on two different games using a Advantage Actor-critic or A3C. Although Pong and Alien seem very different at first, we observe a positive transfer learning using PathNet (take a look at the score graph). First of all, we need to define the modules. Let L be the number of layers and N be the maximum number of modules per layer (the paper indicates that N is typically 3 or 4).

Weak general AI (WGAI) is AI that's capable of learning a wide swath of things, even things it wasn't necessarily programmed to learn. It can then use these learned experiences to come up with creative solutions that can flummox even trained professional humans. Basically, it's as intelligent as a certain creature-- maybe a worm or even a mouse-- but it's nowhere near intelligent enough to enhance itself meaningfully. It may be par-human or even superhuman in some regards, but it's sub-human in others. This is what we see with the likes of DeepMind-- DeepMind's basic algorithm can basically learn to do just about anything, but it's not as intelligent as a human being by far.

The DeepMind researchers believe that as their studies of how AI agents compete become more complex, they could be used to better understand how humans learn to collaborate en masse. "This model also shows that some aspects of human-like behavior emerge as a product of the environment and learning," lead author Joel Weibo told Wired. "Say you want to know what the impact on traffic patterns would be if you installed a traffic light at a specific intersection. You could try out the experiment in the model first and get a reasonable idea of how an agent would adapt."

Each of those nine boxes is the PathNet at a different iteration. In this case, the PathNet was trained on two different games using a Advantage Actor-critic or A3C. Although Pong and Alien seem very different at first, we observe a positive transfer learning using PathNet (take a look at the score graph). First of all, we need to define the modules. Let L be the number of layers and N be the maximum number of modules per layer (the paper indicates that N is typically 3 or 4).