AGI University
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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
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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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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