With dozens of image generators on the market that can produce hyper-realistic pictures from text prompts, many developers have started tackling a new challenge: video generation. AI lab Odyssey has launched a video generator that unlocks a new kind of interactive experience. Also: Hume's new EVI 3 model lets you customize AI voices - how to try it On Wednesday, Odyssey launched a research preview of its first interactive video experience, which generates video entirely by AI in real time. Viewing and navigating this interactive video is similar to walking through a video game, using your keyboard, controller, and eventually audio. Introducing AI video you can watch and interact with, in real-time!

Breakthroughs, discoveries, and DIY tips sent every weekday. The world's harbor seals (Phoca vitulina) are masters in seeing through the cloudy coastal waters they call home. Equipped with dexterous whiskers, these pinnipeds use a suite of senses to navigate their surroundings with ease. Harbor seals may also use an important part of their vision to determine which direction they are moving, even with such an opaque view of the world. Now, we might know a bit more about how they can tell which direction they are heading.

In two-player zero-sum extensive-form games, Nash equilibrium prescribes optimal strategies against perfectly rational opponents. However, it does not guarantee rational play in parts of the game tree that can only be reached by the players making mistakes. This can be problematic when operationalizing equilibria in the real world among imperfect players. Trembling-hand refinements are a sound remedy to this issue, and are subsets of Nash equilibria that are designed to handle the possibility that any of the players may make mistakes. In this paper, we initiate the study of equilibrium refinements for settings where one of the players is perfectly rational (the "machine") and the other may make mistakes.

There are an increasing number of domains in which artificial intelligence (AI) systems both surpass human ability and accurately model human behavior. This introduces the possibility of algorithmically-informed teaching in these domains through more relatable AI partners and deeper insights into human decision-making. Critical to achieving this goal, however, is coherently modeling human behavior at various skill levels. Chess is an ideal model system for conducting research into this kind of human-AI alignment, with its rich history as a pivotal testbed for AI research, mature superhuman AI systems like AlphaZero, and precise measurements of skill via chess rating systems. Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools. In this work, we propose a unified modeling approach for human-AI alignment in chess that coherently captures human style across different skill levels and directly captures how people improve. Recognizing the complex, non-linear nature of human learning, we introduce a skill-aware attention mechanism to dynamically integrate players' strengths with encoded chess positions, enabling our model to be sensitive to evolving player skill. Our experimental results demonstrate that this unified framework significantly enhances the alignment between AI and human players across a diverse range of expertise levels, paving the way for deeper insights into human decision-making and AI-guided teaching tools. Our implementation is available here.

We love finding great deals on great laptops, and this 770 HP Victus deal at Best Buy definitely checks both boxes. That's a 400 discount for an entry-level gaming laptop that'll serve you well for some years. You could spend an absolute fortune on a top-of-the-line gaming laptop--even one that's on sale--but you don't really need to if you aren't an upper-rank competitive gamer or someone who chases hundreds of frames per second at Ultra settings. If all you want is reasonable gameplay with Fortnite, World of Warcraft, Minecraft, and the like, then you can get it at an excellent price with this 15.6-inch It ain't the brightest with 250 nits, but you'll be pushing decent graphics at decent frame rates with the RTX 4050 graphics card and Intel Core i7-12650H processor. It'll also be able to keep up with your daily workload with 16GB of RAM, though we do wish the 512GB SSD was more spacious.

Last week, the Guardian spoke to the team behind Lili, a video game retelling of Macbeth, shown at the Cannes film festival. The headline quote from the piece was "Shakespeare would be writing for games today", which I have heard many times, and does make a lot of sense. Shakespeare worked in the Elizabethan theatre, a period in which plays were considered popularist entertainment hardly worthy of analysis or preservation – just like video games today! The authorities were also concerned about the lewd and violent nature of plays and the effect they may have on the impressionable masses – ditto! But if we agree that a 21st-century Shakespeare would be making games, what sort would he be making?

We thank Jiacheng Liu for his work on collecting chess-related data and chess book list. B.1 Universal Chess Interface (UCI) The UCI format is widely used for communication between chess engines and user interfaces. It represents chess moves by combining the starting and ending squares of a piece, such as "e2e4" to indicate moving the pawn from e2 to e4. SAN (Standard Algebraic Notation) is a widely used notation system in the game of chess for recording and describing moves. It provides a standardized and concise representation of moves that is easily understood by chess players and enthusiasts. In SAN, each move is represented by two components: the piece abbreviation and the destination square. The piece abbreviation is a letter that represents the type of piece making the move, such as "K" for king, "Q" for queen, "R" for rook, "B" for bishop, "N" for knight, and no abbreviation for pawns. The destination square is denoted by a combination of a letter (a-h) representing the column and a number (1-8) representing the row on the chessboard. Additional symbols may be used to indicate specific move types. The symbol "+" is used to indicate a check, while "#" denotes a checkmate. Castling moves are represented by "O-O" for kingside castling and "O-O-O" for queenside castling. PGN is a widely adopted format for recording chess games. It includes not only the SAN moves but also additional information like player names, event details, and game results. PGN files are human-readable and can be easily shared and analyzed. FEN is a notation system used to describe the state of a chess game. It represents the positions of pieces on the chessboard, active color, castling rights, en passant targets, and the half-move and full-move counters. The active color is represented by "w" for white or "b" for black.

When solving decision-making tasks, humans typically depend on information from two key sources: (1) Historical policy data, which provides interaction replay from the environment, and (2) Analytical insights in natural language form, exposing the invaluable thought process or strategic considerations. Despite this, the majority of preceding research focuses on only one source: they either use historical replay exclusively to directly learn policy or value functions, or engaged in language model training utilizing mere language corpus. In this paper, we argue that a powerful autonomous agent should cover both sources. Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games. Specifically, we build a large-scale game and language dataset related to chess.

A fundamental challenge in imperfect-information games is that states do not have well-defined values. As a result, depth-limited search algorithms used in singleagent settings and perfect-information games do not apply. This paper introduces a principled way to conduct depth-limited solving in imperfect-information games by allowing the opponent to choose among a number of strategies for the remainder of the game at the depth limit.

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter.