Summary of Noam Brown: AI vs Humans in Poker and Games of Strategic Negotiation | Lex Fridman Podcast #344

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00:00:00 - 01:00:00

Noam Brown discusses his research on AI and how it can be used to improve poker play and game strategy. He describes how AI can learn by playing against itself and how this process of self-play allows the AI to make better decisions. Brown also discusses the role of AI in poker and games of strategic negotiation, explaining that perfect information makes these games more difficult.

  • 00:00:00 Noam Brown, a research scientist at Facebook's Fair Facebook AI research group, co-created the first AI system to achieve superhuman level performance in No Limit Texas Hold'em poker. Brown also recently created an AI system that can strategically out-negotiate humans using natural language in a popular board game called diplomacy. This is a Lex Friedman podcast to support it, please check out our sponsors in the description and now, dear friends, here's Gnome Proud. You've been a lead on three amazing AI projects.
  • 00:05:00 Noam Brown discusses the advantages of AI playing poker and games of strategic negotiation, as well as the fact that there are always Nash equilibria in these games, even if one player is cheating.
  • 00:10:00 Noam Brown discusses the difference between AI that is designed to win a game and AI that is designed to be fun to play with. He discusses how AI can learn to play a game by playing against itself, and how this process of self-play allows AI to make better decisions in the future.
  • 00:15:00 Noam Brown discusses the concept of counterfactual regret minimization, or "self-play" in relation to poker and other strategic games. Neural networks are used to help the player generalize from past experiences in order to make better decisions in the present.
  • 00:20:00 Noam Brown discusses the role of AI in poker and games of strategic negotiation, explaining that perfect information makes these games more difficult. He compares and contrasts the approaches taken by humans and bots, which aim to be perfectly balanced and unpredictable. Phil Hellmuth is the greatest poker player of all time, due in part to his mastery of the Nash equilibrium.
  • 00:25:00 Noam Brown describes his bot's strategy as being "search based" and explains that this is due to the bot's ability to "in real time try to compute a much better strategy than what [it] had pre-computed by playing against [it]self during self-play." Brown also notes that the bot lost by a "sizable margin" in a competition in 2015, but has since made significant improvements.
  • 00:30:00 Noam Brown discusses how AI can improve poker play by allowing for more strategic planning. By using search algorithms, the AI can find better moves than a human could on their own, leading to more optimal outcomes in the long run.
  • 00:35:00 Noam Brown discusses how search works in poker, describing how AI can outdo pre-computed solutions. He also notes that humans tend to size their bets relative to the size of the pot, but that with Liberatis, players can bet whatever they want. This can lead to humans getting into difficult situations.
  • 00:40:00 Noam Brown discusses how search is essential forAI to compete against top human players in various poker and game of strategic negotiation variants. He also discusses how search is underrated in the AI community, and how this can lead to underperformance.
  • 00:45:00 Noam Brown discusses how AI is not superhuman and how humans are still better at some things, like planning ahead and reasoning across a variety of different scenarios. He argues that this is an important gap in AI, and that we need to find ways to enable computers to do more of this type of reasoning.
  • 00:50:00 Noam Brown describes how his bot for the Liberatis competition was extremely stressful and how the humans were able to find weaknesses in the bot. The bot was not able to compete against humans in a fair game.
  • 00:55:00 Noam Brown discusses how AI can beat humans in poker and other strategic games, and how depth-limited search helped the AI achieve victory in six-player poker.

01:00:00 - 02:00:00

Noam Brown discusses how AI can improve its performance in strategic games by understanding human behavior. He also discusses the difficulty of training AI to play human-like strategies, and the importance of modeling human behavior in order to do so.

  • 01:00:00 Noam Brown discusses the differences between libratus, the computer program that won a recent poker tournament, and pluribus, a program that achieved human-level performance on the six-player version of the game. Brown points out that libratus was much more expensive to build and train, and that pluribus was much cheaper.
  • 01:05:00 Noam Brown discusses how algorithmic improvements can lead to superhuman performance in strategic negotiations, chess, and go. He does not use neural nets in the development of libratus or pluribus, and believes that it is harder to estimate ELO ratings in poker than in other games.
  • 01:10:00 Noam Brown discusses the difference between poker and other games of strategic negotiation, comparing poker to the game of diplomacy. He talks about Daniel Negrinio, one of the few remaining old school strong players that have kept up with the development of AI in poker.
  • 01:15:00 Noam Brown discusses how the game of strategy poker and games of strategic negotiation can be translated into a problem in which an artificial intelligence (AI) tries to win by working with other players to achieve a majority of the map. The social aspect of the game is a key component in making this possible.
  • 01:20:00 Noam Brown discusses the difficulty of AI research into diplomacy, pointing to the complexity of natural language conversations. He also discusses the rule-based approach of research from the 1980s, contrasted with the more recent focus on artificial intelligence learning to make strategic decisions autonomously.
  • 01:25:00 Noam Brown discusses how AI will be able to play poker and other strategic games better than humans due to its ability to understand natural language. He also discusses how humans will need to cooperate with AI in order to be successful, and how the Turing test may be different for strategic games than for other tasks.
  • 01:30:00 Noam Brown, a computer scientist and artificial intelligence researcher, discusses how machine intelligence is performing in poker and other strategic games, and how to measure success. He also discusses the difficulty of achieving human-level performance in diplomacy.
  • 01:35:00 Noam Brown discusses the challenges of incorporating human play data into AI systems for strategic decision-making. He describes how his team trains a language model to generate intent and message based on strategic reasoning, and how this system works surprisingly well in practice. However, there are many ways this system could fail, and it is still an ongoing challenge to create AI systems that are as human compatible as possible.
  • 01:40:00 Noam Brown discusses how his AI plays poker and games of strategic negotiation, which involves deception and lying. Brown also discusses how his AI project Cicero is open sourcing its data and models for research.
  • 01:45:00 Noam Brown's paper "Human-Level Performance by a Machine Learning Poker Bot" shows that a machine learning poker bot can compete with human players. The bot's success is due to its ability to model human behavior and exploit suboptimal behavior.
  • 01:50:00 Noam Brown discusses how AI can improve its strategic poker play, but must rely on human data to do so. He also discusses the difficulty of training AI to play human-likely, and the importance of modeling human behavior in order to do so.
  • 01:55:00 Noam Brown's work investigating how to improve artificial intelligence's performance in strategic negotiations shows that the ceiling for AI performance is much higher than previously thought, and that the work is transferable to chatbots.

02:00:00 - 02:25:00

Noam Brown discusses his work in poker and games of strategic negotiation, and how AI can be used to improve human performance in these areas. He also discusses the ethical considerations related to such technology, and how bots may be able to deceive humans in the future.

  • 02:00:00 Noam Brown talks about how AI can be used to create more compelling NPCs in video games, and how this research is applicable to diplomacy. The anchor policy is a tool used to approximate human behavior, and can be improved by adding planning and reinforcement learning.
  • 02:05:00 Noam Brown discusses the potential for game AI to be used for diplomacy and strategic negotiation, highlighting the importance of data collection and human-like gameplay.
  • 02:10:00 Noam Brown discusses ways in which AI systems can play similarly to humans, but with stronger strategies. He also discusses potential ethical implications of such technology and cyber security challenges.
  • 02:15:00 Noam Brown, a computer scientist and poker player, discusses the potential for artificial intelligence (AI) to improve human performance in various areas of life, including in poker and games of strategic negotiation. He also discusses the ethical considerations related to such technology, and how bots may be able to deceive humans in the future.
  • 02:20:00 Noam Brown gives advice to beginners interested in machine learning, saying that it's important to try something different and not be afraid to explore. He explains that his own path in AI is atypical, and that it has benefits and drawbacks.
  • 02:25:00 Noam Brown discusses his work in poker and games of strategic negotiation, noting that having a diverse range of viewpoints and backgrounds is helpful for tackling difficult challenges. Sun Tzu's ideas on confusing the enemy and achieving a successful outcome through deception are also discussed.

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