AGI University
  • The AGI Landscape
  • 我们的愿景 Our vision
  • Papers
  • Rationality and intelligence
  • AI safety gridworlds
  • Modeling Friends and Foes
  • Forget-me-not-Process
  • Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
  • Universal Transformers
  • Graph Convolutional Policy Network
  • Thermodynamics as a theory of decision-making with informationprocessing costs
  • Concrete Problems in AI Safety
  • A course in game theory
  • Theory of games and economic behavior
  • Reinforcement learning: An introduction 1e
  • Regret analysis of stochastic and nonstochastic multi-armed bandit problems
  • The nonstochastic multiarmed bandit problem
  • Information theory of decisions and actions
  • Clustering with bregman divergences
  • Quantal Response Equilibria for Normal Form Games
  • The numerics of gans
  • The Mechanics of n-Player Differentiable Games
  • Reactive bandits with attitude
  • Data clustering by markovian relaxation and the information bottleneck method
  • Information bottleneck for Gaussian variables
  • Bounded Rationality, Abstraction, and Hierarchical Decision-Making: An Information-Theoretic Optimal
  • Risk sensitive path integral control
  • Information, utility and bounded rationality
  • Hysteresis effects of changing the parameters of noncooperative games
  • The best of both worlds: stochastic and adversarial bandits
  • One practical algorithm for both stochastic and adversarial bandits
  • An algorithm with nearly optimal pseudo-regret for both stochastic and adversarial bandits
  • Friend-or-Foe Q-Learning in General-Sum Games
  • New criteria and a new algorithm for learning in multi-agent systems
  • Correlated Q-Learning
  • Learning to compete, coordinate, and cooperate in repeated games using reinforcement learning
  • Learning against sequential opponents in repeated stochastic games
  • On the likelihood that one unknown probability exceeds another in view of the evidence of two sample
  • An empirical evaluation of Thompson Sampling
  • What game are we playing? end-to-end learning in normal and extensive form games
  • Intriguing properties of neural networks
    • Untitled
  • Explaining and harnessing adversarial examples
  • go-explore
  • The Landscape of Deep Reinforcement Learning
  • 用因果影响图建模通用人工智能安全框架
  • Papers
    • test
    • Measuring and avoiding side effects using relative reachability
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  • 中文
  • English

我们的愿景 Our vision

中文

通用人工智能大学[^1] [英文: AGI University ]：首家非营利通用人工智能科研教学机构，致力于为拥有通用人工智能理想的人类提供优越环境和丰富资源，研究和开发通用人工智能哲学、算法、框架、平台和应用，分享通用人工智能整体认知和全方位技术进步的阶段性结果，为个人及社会团体提供发展咨询服务，为政府及企事业单位提供通用人工智能方面的政策和治理服务，共同创建人工智能时代人类社会的美好未来。

[^1]:「 大学之道，在明明德，在亲民，在至于至善。」-- 出自《大学》，取其立意而不着相，意味着我们需要追寻的是至善的通用人工智能。

English

AGI University [or Artificial General Intelligence University]: The first non-profit AGI research and education institution, dedicated

  • to provide superior environment and abundant resources for talents dreaming of AGI

  • to research and development of AGI philosophy, algorithms, frameworks, platforms and applications

  • to share the gradual results of AGI and full-aspects technological advancement

  • to provide development consulting services for individuals and social groups and policy and governance services for governments and businesses

  • to create a better future for human society in the era of artificial intelligence together.

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Last updated 5 years ago