These gains are particularly pronounced on high-resolution tasks, e.g ., 4.2, 11.0, and 4.0 improvements on TextVQA, DocVQA, and InfoVQA compared to LLaV A-1.5-7B, respectively.

Most large multimodal models (LMMs) are implemented by feeding visual tokens as a sequence into the first layer of a large language model (LLM). The resulting architecture is simple but significantly increases computation and memory costs, as it has to handle a large number of additional tokens in its input layer. This paper presents a new architecture *DeepStack* for LMMs. Considering N layers in the language and vision transformer of LMMs, we stack the visual tokens into N groups and feed each group to its aligned transformer layer from bottom to top. Surprisingly, this simple method greatly enhances the power of LMMs to model interactions among visual tokens across layers but with minimal additional cost.

Shall we play a game? A single artificial intelligence can beat human players in chess, Go, poker and other games that require a variety of strategies to win. The AI, called Student of Games, was created by Google DeepMind, which says it is a step towards an artificial general intelligence capable of carrying out any task with superhuman performance. Martin Schmid, who worked at DeepMind on the AI but who is now at a start-up called EquiLibre Technologies, says that the Student of Games (SoG) model can trace its lineage back to two projects. One was DeepStack, the AI created by a team including Schmid at the University of Alberta in Canada and which was the first to beat human professional players at poker.

Chinese scientists have developed an artificial intelligence (AI) program that is quick-minded and on par with professional human players in heads-up no-limit Texas hold'em poker. Named AlphaHoldem, the AI program has achieved the level of sophisticated human players through a 10,000-hand two-player competition after three days of self-training, according to a paper which will be presented in February next year at AAAI 2022 global AI conference in Vancouver, Canada. Texas hold'em is a popular poker game in which players often deceive and bluff. It is more similar to real-world problems than Go or Weiqi and chess since decisions are made with imperfect information. The researchers from the Institute of Automation under the Chinese Academy of Sciences (CAS) reported that AlphaHoldem, a fast learner, used only about three to four milliseconds for each movement, about 1,000 times quicker than that of first-generation AI hold'em players DeepStack and Libratus.

The DeepStack project was conceptualized in mid-2018 with the dream to build a unified, self-contained AI engine that is cross-platform and programming language agnostic. This dream soon became reality when the DeepQuest AI team joined the FBStart Accelerator program same year. DeepStack was initially released in December 2018 as part of a developer Insider Preview program before general availability in March 2019. Since its public release, DeepStack has been used and adopted by multiple IoT and software platforms such Home Assistant IoT platform and the Blue Iris community. DeepStack is supported by a robust documentation, Dev Center, active developer forum and other third party guides and tutorials.

Deep counterfactual value networks combined with continual resolving provide a way to conduct depth-limited search in imperfect-information games. However, since their introduction in the DeepStack poker AI, deep counterfactual value networks have not seen widespread adoption. In this paper we introduce several improvements to deep counterfactual value networks, as well as counterfactual regret minimization, and analyze the effects of each change. We combined these improvements to create the poker AI Supremus. We show that while a reimplementation of DeepStack loses head-to-head against the strong benchmark agent Slumbot, Supremus successfully beats Slumbot by an extremely large margin and also achieves a lower exploitability than DeepStack against a local best response. Together, these results show that with our key improvements, deep counterfactual value networks can achieve state-of-the-art performance.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.

While AI had some success at beating humans at other games such as chess and Go (games that follow predefined rules and aren't random), winning at poker proved to be more challenging because it requires strategy, intuition, and reasoning based on hidden information. Despite the challenges, artificial intelligence can now play--and win--poker. Artificial Intelligence Masters The Game of Poker – What Does That Mean For Humans? Artificial intelligence systems including DeepStack and Libratus paved the way for Pluribus, the AI that beat five other players in six-player Texas Hold'em, the most popular version of poker. This feat goes beyond games. This achievement means that artificial intelligence can now expand to help solve some of the world's most challenging issues.