Humans can extract rich semantic information from minimal visual cues, as demonstrated by point-light displays (PLDs), which consist of sparse sets of dots localized to key joints of the human body. This ability emerges early in development and is largely attributed to human embodied experience. Since PLDs isolate body motion as the sole source of meaning, they represent key stimuli for testing the constraints of action understanding in these systems. Here we introduce ActPLD, the first benchmark to evaluate action processing in MLLMs from human PLDs. Tested models include state-of-the-art proprietary and open-source systems on single-actor and socially interacting PLDs. Our results reveal consistently low performance across models, introducing fundamental gaps in action and spatiotemporal understanding.

In the game of poker, being unpredictable, or bluffing, is an essential skill. When humans play poker, they bluff. However, most works on computer-poker focus on performance metrics such as win rates, while bluffing is overlooked. In this paper we study whether two popular algorithms, DQN (based on reinforcement learning) and CFR (based on game theory), exhibit bluffing behavior in Leduc Hold'em, a simplified version of poker. We designed an experiment where we let the DQN and CFR agent play against each other while we log their actions. We find that both DQN and CFR exhibit bluffing behavior, but they do so in different ways. Although both attempt to perform bluffs at different rates, the percentage of successful bluffs (where the opponent folds) is roughly the same. This suggests that bluffing is an essential aspect of the game, not of the algorithm. Future work should look at different bluffing styles and at the full game of poker.

Scrolling through X--ugh, I deleted the app, so now I use the browser to look at it on my phone--a post from Farhad Manjoo caught my eye. It's a screen cap of a picture of five elderly men dressed like veterans sitting on a plane. Below the photo it says, "The real heroes are not in Hollywood." If you look a little more closely, it screams janky A.I. Which commercial airliner has five seats in a row next to the window? God knows what army they belong to: There are eagles, and stripes, but no stars.

Counterfactual Regret Minimization (CFR) is a popular, iterative algorithm for computing strategies in extensive-form games. The Monte Carlo CFR (MCCFR) variants reduce the per iteration time cost of CFR by traversing a smaller, sampled portion of the tree. The previous most effective instances of MCCFR can still be very slow in games with many player actions since they sample every action for a given player. In this paper, we present a new MCCFR algorithm, Average Strategy Sampling (AS), that samples a subset of the player's actions according to the player's average strategy. Our new algorithm is inspired by a new, tighter bound on the number of iterations required by CFR to converge to a given solution quality. In addition, we prove a similar, tighter bound for AS and other popular MCCFR variants.

Computers are solving problems no human could ever decode -- and in ways that feel distinctly nonhuman to us. Should we embrace or rethink the strange intelligence of machines?In 2019, five of the top poker players in the world sat down in a casino to play poker against a computer. Over the course of the game they lost big -- some $1.7 million (E1.77 million) -- to a poker bot called Pluribus. It was the first time an artificial-intelligence (AI) program beat elite human players at a game of more than two players. In a post-game interview, the players were asked how they felt about losing to a computer.

In 2019, five of the top poker players in the world sat down in a casino to play poker against a computer. Over the course of the game they lost big -- some $1.7 million (€1.77 million) -- to a poker bot called Pluribus. It was the first time an artificial-intelligence (AI) program beat elite human players at a game of more than two players. In a post-game interview, the players were asked how they felt about losing to a computer. Pluribus, they said, ʺbluffed really well.

While robots have taken many of the jobs of the manually skilled, Pluribus and its future generations are coming for the jobs at the other end of the spectrum--the brilliant, the cunning, the creative. Have we reached an artificial intelligence (AI) milestone overload? Are we so jaded about momentous breakthroughs in AI capabilities that we no longer acknowledge them with the appropriate awe AI demands? One would think so after the July performance of Carnegie Mellon and Facebook's Pluribus went virtually unnoticed. You should, because this valedictorian of machine learning is a serious threat to your livelihood.

While robots have taken many of the jobs of the manually skilled, Pluribus and its future generations are coming for the jobs at the other end of the spectrum--the brilliant, the cunning, the creative. Have we reached an artificial intelligence (AI) milestone overload? Are we so jaded about momentous breakthroughs in AI capabilities that we no longer acknowledge them with the appropriate awe AI demands? One would think so after the July performance of Carnegie Mellon and Facebook's Pluribus went virtually unnoticed. You should, because this valedictorian of machine learning is a serious threat to your livelihood.

As Mr. Elias realized, Pluribus knew when to bluff, when to call someone else's bluff and when to vary its behavior so that other players couldn't pinpoint its strategy. "It does all the things the best players in the world do," said Mr. Elias, 32, who has won a record four titles on the World Poker Tour. "And it does a few things humans have a hard time doing." Experts believe the techniques that drive this and similar systems could be used in Wall Street trading, auctions, political negotiations and cybersecurity, activities that, like poker, involve hidden information. "You don't always know the state of the real world," said Noam Brown, the Facebook researcher who oversaw the Pluribus project.

Artificial Intelligence is a common topic nowadays among companies and start-ups. Still, not all the the ones affirming to deal with Ai are actually specialized. This is what shows a study by Mmc, a British investments society. According to the data, 40% of the start-ups affirm to be into AI business just to get more funds, between 15% and 50% more than those not into machine learning field. Using the definition in such inappropriate way, nevertheless, may cause confusion in the companies aiming to innovate, with risks for the real potential of Artificial Intelligence.