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Conception of Methodology of Cognition on Universal Model of the World is considered, the necessity of which grows in modern Science for the decision of wide spectrum of actual tasks. Nonsystematicness of existent scientific methodologies is shown and the new system of scientific methodologies is qualitative deduced from the higher World concepts that suppose a complete and exact realization. The initial mechanisms of Cognition and principles of their specification are developed. The generalized system of concepts and methodologies of treatment of the World phenomena is got, that applicable in any scientific researches and developments, first all for the effective intermediate theories of decision of the most complex of principles problems, such as AI.

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AIXI model can formally solve them. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI-tl, which is still effectively more intelligent than any other time t and space l bounded agent. The computation time of AIXI-tl is of the order tx2^l. Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.

Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent's estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent's posterior belief that it will survive increases over time.

Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown distribution. We unify both theories and give strong arguments that the resulting universal AIXI model behaves optimal in any computable environment. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI^tl, which is still superior to any other time t and space l bounded agent. The computation time of AIXI^tl is of the order t x 2^l.

.@TonyRobbins recognizes the impact that automation and artificial intelligence will have on millions of Americans and that universal basic income and a more modern notion of work will be necessary. He joins @barackobama @elonmusk @richardbranson as people who see the future. https://t.co/pztrcZJFZ6 Source: https://twitter.com/AndrewYang/status/1086066807264432128 Uploader: Andrew Yang🧢🇺🇸

Marcus Hutter is a senior research scientist at DeepMind and professor at Australian National University. Throughout his career of research, including with Jürgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is a mathematical approach to AGI that incorporates ideas of Kolmogorov complexity, Solomonoff induction, and reinforcement learning. EPISODE LINKS: Hutter Prize: http://prize.hutter1.net Marcus web: http://www.hutter1.net Books mentioned: - Universal AI: https://amzn.to/2waIAuw - AI: A Modern Approach: https://amzn.to/3camxnY - Reinforcement Learning: https://amzn.to/2PoANj9 - Theory of Knowledge: https://amzn.to/3a6Vp7x This conversation

A NEW SOLUTION AND CONCRETE IMPLEMENTATION STEPS THAT CAN REALIZE A TRULY UNIVERSAL ARTIFICIAL INTELLIGENCE 作者： Yongcong，Chen 1 Ting，Zeng 2 Jun, Zhang 3 作者单位： 1. New Millennium Future Technology Co., LTD., Beijing 2. Tsinghua University, BeiJing 3. Shenyang Normal University, Shenyang 通讯作者： Yongcong，Chen Email:[email protected] Ting，Zeng Email:[email protected] 提交时间： 2023-08-15 摘要: At present, the mainstream artificial intelligence generally adopts the technical path of attentionmechanism + deep learning + reinforcement learning. It has made great progress in the field ofAIGC (Artificial Intelligence Generated Content), setting off the technical wave of big models[2][13].But in areas that need to interact with the actual environment, such as elderly care, home nanny,agricultural production, and vehicle driving, trial and error are expensive and a reinforcement learningprocess that requires much trial and error is difficult to achieve. Therefore, in order to achieveArtificial General Intelligence(AGI) that can be applied to any field, we need to use both existingtechnologies and solve the defects of existing technologies, so as to further develop the technologicalwave of artificial intelligence. In this paper, we analyze the limitations of the technical route of largemodels, and by addressing these limitations, we propose solutions, thus solving the inherent defectsof large models. In this paper, we will reveal how to achieve true AGI step by step. artificial general intelligence AGI AIGC reinforcement learning large model LLM ChatGPT 来自： 曾婷 分类： 计算机科学 >> 计算机科学技术其他学科 投稿状态： 未投稿 引用： ChinaXiv:202308.00196 (或此版本 ChinaXiv:202308.00196V1 ) doi:10.12074/202308.00196 CSTR:32003.36.ChinaXiv.202308.00196.V1 推荐引用方式： Yongcong，Chen,Ting，Zeng,Jun, Zhang .(2023).A NEW SOLUTION AND CONCRETE IMPLEMENTATION STEPS THAT CAN REALIZE A TRULY UNIVERSAL ARTIFICIAL INTELLIGENCE.中国科学院科技论文预发布平台.[ChinaXiv:202308.00196] 版本历史 [V1] 2023-08-15 08:51:17 ChinaXiv:202308.00196V1 下载全文

.@TonyRobbins recognizes the impact that automation and artificial intelligence will have on millions of Americans and that universal basic income may be necessary. https://t.co/TMyH0mhRia Source: https://twitter.com/AndrewYang/status/1110929606960390145 Uploader: Andrew Yang🧢🇺🇸

The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI.

The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the award-winning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI.

(As per the writings of Isaac Asimov) Narrated by Peter Fonteece.