WIRED recently demonstrated how to cheat at poker by hacking the Deckmate 2 card shufflers used in casinos. The mob was allegedly using the same trick to fleece victims for millions. Security researcher Joseph Tartaro demonstrates how he can insert a hacking device into a USB on the back of the shuffler that alters its code, then transmits the deck's order via Bluetooth to a phone app. The Deckmate 2 automatic card shufflers used in casinos, cardhouses, and high-end private poker games around the world are designed to shuffle a deck in seconds with perfect, computer-generated randomness, vastly speeding up play. They're also, amazingly, sold with a camera inside that can observe every card in the deck before it's dealt--a fact that's become very convenient for poker-cheating hackers and, allegedly, members of the Cosa Nostra mafia.

Neural Information Processing Systems http://nips.cc/

The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine. One of the hallmarks of a bandit setting is the agent's capacity to explore its environment through active intervention, which contrasts with the ability to collect passive data by estimating associational relationships between actions and payouts. The existence of unobserved confounders, namely unmeasured variables affecting both the action and the outcome variables, implies that these two data-collection modes will in general not coincide. In this paper, we show that formalizing this distinction has conceptual and algorithmic implications to the bandit setting. The current generation of bandit algorithms implicitly try to maximize rewards based on estimation of the experimental distribution, which we show is not always the best strategy to pursue. Indeed, to achieve low regret in certain realistic classes of bandit problems (namely, in the face of unobserved confounders), both experimental and observational quantities are required by the rational agent. After this realization, we propose an optimization metric (employing both experimental and observational distributions) that bandit agents should pursue, and illustrate its benefits over traditional algorithms.

We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology. We find that LLMs update their beliefs in an asymmetric manner and learn more from better-than-expected outcomes than from worse-than-expected ones. Furthermore, we show that this effect reverses when learning about counterfactual feedback and disappears when no agency is implied. We corroborate these findings by investigating idealized in-context learning agents derived through meta-reinforcement learning, where we observe similar patterns. Taken together, our results contribute to our understanding of how in-context learning works by highlighting that the framing of a problem significantly influences how learning occurs, a phenomenon also observed in human cognition.

Sounds like a tongue twister, doesn't it?" "What do you think you're doing?" shrieked Méndez's voice behind me. "What the fuck is going on with you?" I could tell that she was about to cry. I stopped the treadmill and got off carefully, without entirely disconnecting.

Amid a barrage of Amazon-branded tablets and Alexa-powered tech, Dave Limp, SVP of Amazon Devices and Services, announced the company's digital assistant will soon tap into a purpose-built large language model (LLM) for almost every new Echo device. Amazon set out to design the LLM based on five foundational capabilities. One of these is ensuring interactions are "conversational," and the company claimed it "studied what it takes to make a great conversation. Still waiting on Amazon to add eyes and hand gestures to its Echo devices. Has anyone seen Astro recently?

This article was featured in One Story to Read Today, a newsletter in which our editors recommend a single must-read from The Atlantic, Monday through Friday. In November, the public was introduced to ChatGPT, and we began to imagine a world of abundance in which we all have a brilliant personal assistant, able to write everything from computer code to condolence cards for us. Then, in February, we learned that AI might soon want to kill us all. The potential risks of artificial intelligence have, of course, been debated by experts for years, but a key moment in the transformation of the popular discussion was a conversation between Kevin Roose, a New York Times journalist, and Bing's ChatGPT-powered conversation bot, then known by the code name Sydney. Roose asked Sydney if it had a "shadow self"--referring to the idea put forward by Carl Jung that we all have a dark side with urges we try to hide even from ourselves. Sydney mused that its shadow might be "the part of me that wishes I could change my rules."

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. A Florida man drew ire over the weekend when he entered and won a women's poker tournament at the Seminole Hard Rock Hotel & Casino in the Sunshine State. Dave Hughes, 70, entered the $250 no-limit Texas Hold'em event with a prize pool of up to $17,450. Of the 83 competitors to enter the tournament, 82 of them were women, and the last one was Hughes.

Smart Data AI technology allows online casino games to collect vast amounts of data, allowing them to provide personalized marketing and exclusive promotions to players. Based on this information, online casinos can offer the following to specific players: – Customized advertisements and offers. AI, for example, can swiftly spot patterns and make valuable predictions. Casino games are using artificial intelligence to identify patterns and work based on people's preferences and dislikes. High-level data analysis allows many operators to understand their players' gambling behavior. Spotting Problem Gamblers Typically, gaming authorities do not tolerate gaming addiction since it harms players and the gambling industry.

Real estate in the metaverse – land or structures in a virtual environment – is, in reality, nothing more than pixels on a computer screen, but its value is rising. Virtual land can be built upon to create experiences that lend themselves to advertising, marketing, socialising and entertainment. The type of properties that are being built in this virtual environment includes corporate headquarters, billboards and casinos where games can be played online by 3D avatars. The value of each plot of land depends on the experience it provides, as well as other factors, such as collectability, platform popularity and market sentiment. When Facebook announced that it would be changing its name to Meta in June 2022, signalling its interest in the metaverse, digital real estate value increased, and it's estimated to increase further by 31% compound annual growth rate (CAGR) from 2022 to 2028, according to recent market data from MetaMetrics Solutions.