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No Priors Ep. 1 | With Noam Brown, Research Scientist at Meta

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💫 Short Summary

The video delves into the speaker's transition from finance to algorithmic trading to AI research, focusing on the intersection of game theory and computer science. It explores the development of AI in games like poker and diplomacy, highlighting advancements, challenges, and the need for improved reasoning capabilities in AI. The discussion also touches on the limitations of scaling AI models, the importance of human-AI cooperation, and the significance of developing AI systems for cooperative games. Ultimately, the video emphasizes the ongoing evolution of AI capabilities and the quest for artificial general intelligence.

✨ Highlights
📊 Transcript
Transition from finance to algorithmic trading to AI and game theory research.
Interest in structuring financial markets led to pursuing a PhD in economics focused on game theory.
Shifted to computer science for faster progress and more exciting opportunities.
Research focus on AI for poker at the intersection of game theory and computer science.
Emphasis on the dynamic and innovative nature of the field.
The speaker's journey into AI research and the evolution of their goals.
Interest in AI sparked by playing poker in high school and college, leading to the creation of a poker bot during undergrad.
Pursued AI research in grad school, recognizing valuable lessons and focusing on personal interest and learning about AI and game theory.
Progress accelerated, leading to a focus on pursuing AGI amidst rapid advancements in the field during grad school.
The impact of AlphaGo on AI development and its contrast with Deep Blue's impact on chess.
AlphaGo showcased superior pattern matching skills, questioning human dominance in this aspect.
Evaluating Go boards is more complex than chess due to the extensive search space.
AlphaGo's success was a wake-up call, showing the potential of AI to outperform humans in certain areas.
Watching the AlphaGo documentary provides further insight into this significant moment in AI history.
AI research shifted focus to diplomacy challenge after success in game-playing AI in 2019.
The goal was to develop AI capable of negotiating and strategizing with humans in natural language, a task considered science fiction at the time.
Despite the risks, aiming high led to the development of a bot that excelled in human competitions.
The success in creating an undetectable bot showcased the potential of AI in diplomacy and strategic interactions.
The choice of diplomacy as the next challenge for AI research led to the development of new techniques and impressive results.
Detecting bots in diplomatic negotiation games is a challenge.
The team concealed the bot's presence in games to avoid detection by players.
The bot went through 40 games without being detected, showcasing progress in language models.
Humans may not be as skilled at detecting bots as previously believed.
The success of the bot in remaining undetected highlights the quality of the language model used.
Relevance of the Turing test in AI technology.
The Turing test is deemed outdated due to advancements in AI technology.
Focus on reasoning capabilities in bots is emphasized in AI development.
Missing elements in the journey towards achieving general intelligence are discussed.
Evolving measures to assess AI capabilities effectively are deemed important.
Research in language models focuses on next word prediction, but lacks reasoning capabilities beyond Chain of Thought.
AI researchers acknowledge the limitation of current bots and the importance of addressing this for true artificial general intelligence.
The Chain of Thought approach, while simple, has proven to be effective in generating longer thought processes for improved conclusions.
Advancements in reasoning methods and data sets are crucial for enhancing AI capabilities.
Discussions on data availability and the potential for large-scale synthetic data creation are prompted by the need for more sophisticated reasoning methods.
Challenges in scaling AI models for performance improvement.
Existing models can cost up to $50 million to train.
Research may lead to improvements in sample efficiency.
Focus on making training more efficient and cost-effective.
Consideration of compute at inference time and balancing upfront costs with long-term benefits.
Challenges in obtaining data for training AI models.
Emphasizes the need for a dataset of 50,000 games with dialogue from a specific website.
Deal made with Web Diplomacy.net to access necessary information.
Utilized a pre-trained language model to fine-tune a bot for the game of diplomacy.
Supervised learning alone may not be enough for optimal gameplay in the strategy aspect.
Importance of self-play in developing strategies for games like chess and go that surpass human performance.
Self-play involves playing against oneself for millions of trajectories to improve strategy.
Transitioning to games like diplomacy, which involve cooperation, requires combining self-play with an understanding of human behavior.
By using data sets to model human behavior, a strategy compatible with how humans play can be developed.
Crucial approach for developing bots that negotiate effectively in games with competitive and cooperative elements.
Importance of AI communication and negotiation with humans.
AI may struggle to cooperate with humans due to lack of understanding of human norms and conventions.
Using human data to build models and adding soft play on top can help AI overcome this challenge.
Recognizing human behavior, including mistakes and non-verbal cues, is crucial for successful AI-human interaction.
AI needs to interact successfully with humans in real-world scenarios, such as self-driving cars navigating roads with human drivers.
AI Progress in Games
Chess has been a traditional benchmark for AI advancement, but exceeding human capabilities in games does not equate to real-world intelligence.
The focus is now on more intricate games like diplomacy, which offer bigger hurdles for AI development.
The trend is towards creating general AI systems that can excel in multiple games and tasks, not just individual gaming scenarios.
AI's potential to surpass humans in tasks and the importance of generality and learning from few samples.
AI has excelled in specific domains like two-player games and negotiation.
Skepticism about AI's ability to outperform humans in complex tasks like writing novels.
The need for AI benchmarks beyond games and humans' advantage in generality and learning across diverse domains.
Emphasis on the importance of human capabilities in comparison to AI.
Challenges of using reinforcement learning for trading in non-stationary environments.
Historical data may not apply due to market responses to world events.
Real-time understanding of the world is crucial for effective trading strategies.
Traditional approaches may not adapt quickly enough to changing conditions.
Incorporating real-time signals and updating model weights are essential for success in financial markets.
Efficiency of AI in financial markets.
AI has shown success in limited ways but is not yet able to fully replace humans.
AI is capable of negotiating with humans in constrained domains like salary negotiations, but more complex tasks such as contract negotiations still require human expertise.
Importance of focusing on neglected research domains with long-term commitment for excellence rather than being distracted by trendy topics.
Importance of planning algorithms in complex reasoning tasks.
Deep learning was a significant factor in the success of Alpha Zero and AlphaGo, but not the only one.
Planning algorithms are crucial for achieving top human performance, especially in games like Go.
Domain-specific nature of algorithms limits their effectiveness in tasks like poker and diplomacy.
Developing more general systems for complex reasoning tasks could lead to advancements in theorem proving and other applications.
The Riemann hypothesis is a key unsolved problem in mathematics with solutions yet to be confirmed.
The focus is on finding general approaches rather than domain-specific ones, similar to the effectiveness of Transformers.
The discussion extends to code generation, emphasizing the need for more generalized techniques.
Scaling up models has worked in the past, but the example of AlphaGo highlights the complexities of achieving expert human performance.
The challenge lies in determining the extent to which scaling up neural net capacity and training is necessary to match existing performance levels.
Limitations of Scaling AI Models to Extreme Levels of Computation Cost.
Shift towards more specialized systems and away from one-size-fits-all architecture.
Questioning the need for a generalizable architecture, comparing to human brain's specialized modules.
Goal of creating AI systems that can succeed across various domains without unique approaches for each.
Discussion on reasoning as a significant domain in AI design, exploring different subtypes and approaches.
Importance of taking risks in research for success.
Emphasizes diverse approaches and thinking outside the box in machine language development.
Encourages researchers to focus on research style over specific areas and take big risks.
Highlights the need for efficiency and better architectures in AI development.
Stresses the multitude of interesting questions that still need to be explored in the field of AI.
Diplomacy is a strategy game created in the 50s to teach diplomacy skills.
Seven player powers negotiate to control the majority of the map.
Players engage in private negotiations, similar to Hunger Games.
Unlike Risk or Settlers of Catan, negotiations are done in private.
Moves are written down and revealed simultaneously to test players' promises.
The game combines risk, poker, and Survivor elements, focusing on trust-building and collaboration.
Research in 2019 utilized deep learning to create Bots for the game, aiming for full natural language diplomacy.
The development of Bots capable of deception and collaboration with humans was a significant breakthrough in AI gaming.
Unlike chess or go, this game emphasizes understanding human elements and cooperation, underscoring the need for AI development in cooperative games.
Importance of AI-human cooperation for real-world utility is highlighted.
Centaur play, involving collaboration between humans and AI, is exemplified through games like chess.
The balance between human and AI contributions in games like Go and diplomacy is discussed.
Potential for AI dominance in the future is considered.
Speaker's research on developing AI for defeating top human players in No Limit Texas Hold'em poker is mentioned.
Development of AI for poker following the success of AlphaGo in chess.
Utilization of k-means clustering to group similar poker hands, reducing complexity from billions to thousands of buckets.
Ability to compute a policy for poker without relying on deep neural nets.
Competition focused on scaling the number of buckets in AI models, leading to significant improvements each year.
AI poker bot won the annual computer poker competition in 2014 and performed well against expert human players.
Implementation of a search planning algorithm in poker competition.
Humans took time to think and strategize, while the bot acted instantly.
Adding the search planning algorithm resulted in a significant performance boost.
Scaling up the search algorithm for future competitions.
Defeating expert poker players convincingly with the improved algorithm.
Development of a poker AI that beats expert players.
AI used a more scalable search technique, searching only a few moves ahead instead of to the end of the game.
Breakthrough attributed to algorithmic advancements rather than just scaling compute power.
AI bot, costing under $150 to train, showed remarkable performance improvements compared to human players.
Bot's ability to make unconventional bets, such as betting $20,000 into a $100 pot, was unprecedented in professional poker play.
Evolution of Poker Strategy in Professional Play
Professional poker players are now using over bets in their strategy, departing from traditional betting sizes.
Bots are being utilized to analyze player performance and enhance decision-making skills.
Poker is likened to high-dimensional chess, requiring players to consider probability distributions over actions.
The Nash equilibrium is seen as the optimal way to play poker, ensuring long-term success and minimizing the role of social cues in live play.
Playing the National Equilibrium in Poker Strategy.
In competitive situations, players need to accept ties in expectation as opponents often make mistakes, leading to profits for skilled players.
Waiting for mistakes and capitalizing on them is a successful strategy in poker, where deviation from the equilibrium is common.
Following the national equilibrium is recommended in poker, with players advised to start with the Nash equilibrium and adjust based on opponents' actions for optimal results.
Adapting to opponents' deviations while playing it safe with the national equilibrium is the conventional wisdom among poker players.