A World-Self Model Towards Understanding Intelligence
- URL: http://arxiv.org/abs/2203.13762v1
- Date: Fri, 25 Mar 2022 16:42:23 GMT
- Title: A World-Self Model Towards Understanding Intelligence
- Authors: Yutao Yue
- Abstract summary: We will compare human and artificial intelligence, and propose that a certain aspect of human intelligence is the key to connect perception and cognition.
We will present the broader idea of "concept", the principles and mathematical frameworks of the new model World-Self Model (WSM) of intelligence, and finally an unified general framework of intelligence based on WSM.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Artificial intelligence has achieved tremendous successes in various tasks,
while it is still out of question that there are big gaps between artificial
and human intelligence, and the nature of intelligence is still in darkness. In
this work we will first stress the importance of scope of discussion and
granularity of investigation for this type of research. We will carefully
compare human and artificial intelligence, and propose that a certain aspect
(Aspect 3) of human intelligence is the key to connect perception and
cognition, and the lack of a new model is preventing the understanding and
next-level implementation of intelligence. We will present the broader idea of
"concept", the principles and mathematical frameworks of the new model
World-Self Model (WSM) of intelligence, and finally an unified general
framework of intelligence based on WSM. Rather than focusing on solving a
specific problem or discussing a certain kind of intelligence, our work is
instead towards a better understanding of the nature of the general phenomenon
of intelligence, independent of the kind of task or system of investigation.
Related papers
- Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
We argue that shortcomings stem from one overarching failure: AI systems lack wisdom.
While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems.
We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
arXiv Detail & Related papers (2024-11-04T18:10:10Z) - The Trap of Presumed Equivalence: Artificial General Intelligence Should Not Be Assessed on the Scale of Human Intelligence [0.0]
A traditional approach to assessing emerging intelligence in the theory of intelligent systems is based on the similarity, 'imitation' of human-like actions and behaviors.
We argue that under some natural assumptions, developing intelligent systems will be able to form their own in-tents and objectives.
arXiv Detail & Related papers (2024-10-14T13:39:58Z) - Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions.
In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
arXiv Detail & Related papers (2024-02-28T16:09:56Z) - On a Functional Definition of Intelligence [0.0]
Without an agreed-upon definition of intelligence, asking "is this system intelligent?"" is an untestable question.
Most work on precisely capturing what we mean by "intelligence" has come from the fields of philosophy, psychology, and cognitive science.
We present an argument for a purely functional, black-box definition of intelligence, distinct from how that intelligence is actually achieved.
arXiv Detail & Related papers (2023-12-15T05:46:49Z) - A Theory of Intelligences [0.0]
I develop a framework that applies across all systems from physics, to biology, humans and AI.
I present general equations for intelligence and its components, and a simple expression for the evolution of intelligence traits.
arXiv Detail & Related papers (2023-08-23T20:18:43Z) - Brain in a Vat: On Missing Pieces Towards Artificial General
Intelligence in Large Language Models [83.63242931107638]
We propose four characteristics of generally intelligent agents.
We argue that active engagement with objects in the real world delivers more robust signals for forming conceptual representations.
We conclude by outlining promising future research directions in the field of artificial general intelligence.
arXiv Detail & Related papers (2023-07-07T13:58:16Z) - On the link between conscious function and general intelligence in
humans and machines [0.9176056742068814]
We look at the cognitive abilities associated with three theories of conscious function.
We find that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans.
We propose ways in which insights from each of the three theories may be combined into a unified model.
arXiv Detail & Related papers (2022-03-24T02:22:23Z) - WenLan 2.0: Make AI Imagine via a Multimodal Foundation Model [74.4875156387271]
We develop a novel foundation model pre-trained with huge multimodal (visual and textual) data.
We show that state-of-the-art results can be obtained on a wide range of downstream tasks.
arXiv Detail & Related papers (2021-10-27T12:25:21Z) - Future Trends for Human-AI Collaboration: A Comprehensive Taxonomy of
AI/AGI Using Multiple Intelligences and Learning Styles [95.58955174499371]
We describe various aspects of multiple human intelligences and learning styles, which may impact on a variety of AI problem domains.
Future AI systems will be able not only to communicate with human users and each other, but also to efficiently exchange knowledge and wisdom.
arXiv Detail & Related papers (2020-08-07T21:00:13Z) - Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike
Common Sense [142.53911271465344]
We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks.
We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
arXiv Detail & Related papers (2020-04-20T04:07:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.