9/24/2019
AI learned to use tools after 500 million games of hide and seek
AI learned to use tools after 500 million games of hide and seek usm systems Sep 24 ¡ 4 min read
In the early days of life on earth, living organisms were very simple. They are microscopic unicellular organisms that lack coordination. Yet billions of years of evolution through competition and natural selection have led to the complex life forms we have today — as well as the complex human intelligence. Researchers at OpenAI, a San Francisco-based nonprofit AI research laboratory, are now testing a hypothesis: If you could emulate that kind of competition in the virtual world, would it also lead to more sophisticated artificial intelligence?
After roughly 25 million rounds of hide-and-seek, the hiders learned to build forts to win the game.
OPEN AI https://medium.com/@usmsystems23/ai-learned-to-use-tools-after-500-million-games-of-hide-and-seek-de0af2537000
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9/24/2019
AI learned to use tools after 500 million games of hide and seek
The experiment draws on two ideas in this field: multi-agent learning, the idea of putting multiple algorithms in competition or coordination to arouse emerging behaviors, and reinforcement learning, the specific machine learning technique, and error that the learner achieves through the trial. OpenAI has now revealed its initial results in a new paper released today. By playing the simple game of hiding and searching hundreds of millions of times, two opposing teams of AI agents seek strategies that involve complex hiding and tool usage and collaboration. This research also provides insight into OpenAI’s dominant research strategy: to dramatically scale existing AI practices to see what features emerge. Six stages of strategy To create the game, the researchers created a virtual environment that enveloped the space with various objects such as blocks, ramps, and mobile and fixed barricades. Agents are controlled by reinforcement-learning algorithms. For each game, the agents were divided into two teams: the Haiders were rewarded or fined for avoiding or failing the explorers; Explorers have been rewarded or fined for failing to find or fail to find the hidden. As in the classic game of Hide and Seek, also have a few seconds of a head start. The investigators gave no other instructions to the agents. Over hundreds of millions of rounds of gameplay, agents have developed a number of strategies and counter-strategies. In the first phase, hides and explorers have developed some very brutal evasion and chasing tactics by moving around the space without changing any objects. But around the 25 million-game mark, the play has become more sophisticated. Hiders learned to move and lock boxes and barricades in the environment to build castles around them, so explorers would never see them. They also developed coordination strategies, such as crossing objects, to speed up the castle construction process. After another 75 million rounds, the explorers invented a counter-strategy, moving the process to the third stage. They learned to move a ramp next to the castle of the hiding and use it to climb the walls. In the fourth stage, after 10 million rounds, the Haiders learned to lock ramps before building their fortress. (No team unlocked the object that the opposing team had locked.) https://medium.com/@usmsystems23/ai-learned-to-use-tools-after-500-million-games-of-hide-and-seek-de0af2537000
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9/24/2019
AI learned to use tools after 500 million games of hide and seek
At first, OpenAI researchers believed this was the end of the gameplay, but eventually, at the 380-million-game mark, two more strategies emerged. Using a locked ramp to climb onto the unlocked box, the explorers once again developed a strategy of entering the Haider’s Castle, and then “surfing” over the box and its walls. In the final stage, the hidden ones learned to lock all the ramps and boxes before building their castle. Emergency Intelligence Researchers hope that these early results show a promising new method for developing more complex AI. Bowen Baker, one of the authors of the paper, said: “We did not tell Haiders or those who wanted to run into a box or interact with it. “But through multiple competitions, they create new things for each other. The other team needs to adapt.” This study reflects OpenAI’s distinctive approach to AI research. Although the laboratory has also invested in developing novel methods compared to other laboratories, it has made a name for itself by dramatically scaling up existing ones. GPT-2, Lab’s infamous linguistic model, for example, has taken algorithmic design heavily from previous language models, including Google BERT; OpenAI’s primary innovation is the mastery of engineering and vast computational resources. In a way, this study reaffirms the value of testing the limitations of current technologies at scale. The team also plans to continue with this strategy. Researchers say the first round of experiments did not come close to reaching the limit of computational resources they could throw at the problem. “We want people to imagine what would happen if you triggered this kind of competition in a more complex environment,” Baker said. “The behaviors they learned could eventually solve some of the problems. We don’t know how to solve them already.” Want to know about Ai services then have a free visit for USM business systems.
https://medium.com/@usmsystems23/ai-learned-to-use-tools-after-500-million-games-of-hide-and-seek-de0af2537000
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9/24/2019
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