skirmish
The Slatest for Nov. 20: Sam Altman's Firing Is Just the First Skirmish in a War Over A.I.
There's been lots of drama over at OpenAI as of late. First, CEO Sam Altman was fired! Then he was … maybe going to be unfired? Then he was hired by Microsoft! To be clear, Nitish Pahwa writes, this saga, with all its twists and turns, was a little stupid--but not as stupid as it looks. He breaks down exactly what's happened and explains why it's best to consider it the first skirmish in a war over A.I. Did you know that more than 30 states in the U.S. have laws that penalize individuals or businesses that boycott Israel over the treatment of Palestinians?
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- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning > Generative AI (0.66)
Stubborn: An Environment for Evaluating Stubbornness between Agents with Aligned Incentives
Rachum, Ram, Nakar, Yonatan, Mirsky, Reuth
Recent research in multi-agent reinforcement learning (MARL) has shown success in learning social behavior and cooperation. Social dilemmas between agents in mixed-sum settings have been studied extensively, but there is little research into social dilemmas in fullycooperative settings, where agents have no prospect of gaining reward at another agent's expense. While fully-aligned interests are conducive to cooperation between agents, they do not guarantee it. We propose a measure of "stubbornness" between agents that aims to capture the human social behavior from which it takes its name: a disagreement that is gradually escalating and potentially disastrous. We would like to promote research into the tendency of agents to be stubborn, the reactions of counterpart agents, and the resulting social dynamics. In this paper we present Stubborn, an environment for evaluating stubbornness between agents with fully-aligned incentives. In our preliminary results, the agents learn to use their partner's stubbornness as a signal for improving the choices that they make in the environment.
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A note on the empirical comparison of RBG and Ludii
Kowalski, Jakub, Mika, Maksymilian, Sutowicz, Jakub, Szykuła, Marek
We present an experimental comparison of the efficiency of three General Game Playing systems in their current versions: Regular Boardgames (RBG 1.0), Ludii~0.3.0, and a Game Description Language (GDL) propnet. We show that in general, RBG is currently the fastest GGP system. For example, for chess, we demonstrate that RBG is about 37 times faster than Ludii, and Ludii is about 3 times slower than a GDL propnet. Referring to the recent comparison [An Empirical Evaluation of Two General Game Systems: Ludii and RBG, CoG 2019], we show evidences that the benchmark presented there contains a number of significant flaws that lead to wrong conclusions.
Fortnite: Battle Royale's New 'Summer Skirmish' Comes With Some Massive Rewards
Millions are at stake in Fortnite's new Summer Skirmish.Credit: Epic Games Epic Games announced today the new'Summer Skirmish' event: 8 weeks of competitive play and $8 million in prize money. That's just a small slice of the esports money the developer and publisher has set aside for the 2018/2019 Fortnite: Battle Royale competitive season. Earlier this year the company announced $100,000,000 in prize money will flush the coffers of the best teams and players in the coming year. The Summer Skirmish series will last 8 weeks and each week will see a new format, keeping us all on our toes from week-to-week. For the inaugural week, competitive Duos competitions will kick things off.
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Google's AI won the game Go by defying millennia of basic human instinct
Lee Sedol had seen all the tricks. He knew all the moves. As one of the world's best and most experienced players of the complex board game Go, it was difficult to surprise him. But halfway through his first match against AlphaGo, the artificially intelligent player developed by Google DeepMind, Lee was already flabbergasted. AlphaGo's moves throughout the competition, which it won earlier this month, four games to one, weren't just notable for their effectiveness.