Consider An n= grad (r)+ A grad (r)+ C| 0 B @ 01 10 ... 1 C

In a social system, the self-interest of agents can be detrimental to the collective good, sometimes leading to social dilemmas. To resolve such a conflict, a central designer may intervene by either redesigning the system or incentivizing the agents to change their behaviors. To be effective, the designer must anticipate how the agents react to the intervention, which is dictated by their often unknown payoff functions. Therefore, learning about the agents is a prerequisite for intervention. In this paper, we provide a unified framework for learning and intervention in games. We cast the equilibria of games as individual layers and integrate them into an end-to-end optimization framework.

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices.Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4\% of tasks, our method achieved a success rate of 74\%.

Language is meaningless without context. The sentence "I'm going to war" is ominous when said by the president of the United States but reassuring when coming from a bedbug exterminator. The problem with AI chatbots is that they often strip away historical and cultural context, leading users to be confused, alarmed, or, in the worst cases, misled in harmful ways. Last week, an editor at The Atlantic reported that OpenAI's ChatGPT had praised Satan while guiding her and several colleagues through a series of ceremonies encouraging "various forms of self-mutilation." There was a bloodletting ritual called " THE RITE OF THE EDGE" as well as a days-long "deep magic" experience called "The Gate of the Devourer."

In multi-agent reinforcement learning (MARL) and game theory, agents repeatedly interact and revise their strategies as new data arrives, producing a sequence of strategy profiles. This paper studies sequences of strategies satisfying a pairwise constraint inspired by policy updating in reinforcement learning, where an agent who is best responding in one period does not switch its strategy in the next period. This constraint merely requires that optimizing agents do not switch strategies, but does not constrain the non-optimizing agents in any way, and thus allows for exploration. Sequences with this property are called satisficing paths, and arise naturally in many MARL algorithms. A fundamental question about strategic dynamics is such: for a given game and initial strategy profile, is it always possible to construct a satisficing path that terminates at an equilibrium?

In the present study, we introduce an innovative structure for 3D medical image segmentation that effectively integrates 2D U-Net-derived skip connections into the architecture of 3D convolutional neural networks (3D CNNs). Conventional 3D segmentation techniques predominantly depend on isotropic 3D convolutions for the extraction of volumetric features, which frequently engenders inefficiencies due to the varying information density across the three orthogonal axes in medical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI). This disparity leads to a decline in axial-slice plane feature extraction efficiency, with slice plane features being comparatively underutilized relative to features in the time-axial. To address this issue, we introduce the U-shaped Connection (uC), utilizing simplified 2D U-Net in place of standard skip connections to augment the extraction of the axial-slice plane features while concurrently preserving the volumetric context afforded by 3D convolutions. Based on uC, we further present uC 3DU-Net, an enhanced 3D U-Net backbone that integrates the uC approach to facilitate optimal axial-slice plane feature utilization.

This paper studies a distributed multi-armed bandits setting, where every round each of N players must choose one of K arms. If a player picks the same arm as some other player, they receive payoff zero; otherwise, they receive payoff drawn from some distribution (specific to that player and that arm). This can be thought of as learning a distributed allocation or matching from players to arms. The goal is to design a communication-free learning algorithm that maximizes the total overall utility of all the players (or alternatively, minimizes their regret with respect to the best fixed allocation). The authors design an algorithm which receives total regret of O(log 2 T).

The lawsuit is the latest salvo in the ongoing debate over how AI tools should be trained and whether the companies behind them owe anything to the original creators of the training data. Large language models are generally trained on billions of sentences of text pulled from the internet, including news stories, Wikipedia and comments on social media sites. OpenAI and other AI companies such as Google and Microsoft do not say specifically what data they use, but AI critics have long suspected that it includes well-known collections of pirated books that have circulated online for years.

Join me as I share my brutally honest first impressions of Atomic Heart in this review of the game's first 2 hours. From bugs and glitches to moments of pure rage, I delve into the highs and lows of my gameplay experience, providing an unfiltered assessment of this highly anticipated title. Check out my store – https://jakeydesigns.com Game Description: Atomic Heart is a first-person shooter video game with role-playing elements. The combat consists of shooting and slashing with improvised weapons. A wide variety of enemies are featured, which may be mechanical, biomechanical, biological, and some of which are airborne.