The Trap of Presumed Equivalence: Artificial General Intelligence Should Not Be Assessed on the Scale of Human Intelligence
- URL: http://arxiv.org/abs/2410.21296v2
- Date: Mon, 11 Nov 2024 13:43:59 GMT
- Title: The Trap of Presumed Equivalence: Artificial General Intelligence Should Not Be Assessed on the Scale of Human Intelligence
- Authors: Serge Dolgikh,
- Abstract summary: 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 intents and objectives.
- Score: 0.0
- License:
- Abstract: 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, benchmarking the performance of intelligent systems on the scale of human cognitive skills. In this work we attempt to outline the shortcomings of this line of thought, which is based on the implicit presumption of the equivalence and compatibility of the originating and emergent intelligences. We provide arguments to the point that under some natural assumptions, developing intelligent systems will be able to form their own intents and objectives. Then, the difference in the rate of progress of natural and artificial systems that was noted on multiple occasions in the discourse on artificial intelligence can lead to the scenario of a progressive divergence of the intelligences, in their cognitive abilities, functions and resources, values, ethical frameworks, worldviews, intents and existential objectives: the scenario of the AGI evolutionary gap. We discuss evolutionary processes that can guide the development of emergent intelligent systems and attempt to identify the starting point of the progressive divergence scenario.
Related papers
- Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence [0.0]
The pursuit of creating artificial intelligence mirrors our longstanding fascination with understanding our own intelligence.
Recent advances in AI hold promise, but singular approaches often fall short in capturing the essence of intelligence.
This paper explores how fundamental principles from biological computation can guide the design of truly intelligent systems.
arXiv Detail & Related papers (2024-11-22T02:55:39Z) - 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) - 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) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - World Models and Predictive Coding for Cognitive and Developmental
Robotics: Frontiers and Challenges [51.92834011423463]
We focus on the two concepts of world models and predictive coding.
In neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment.
arXiv Detail & Related papers (2023-01-14T06:38:14Z) - Beyond Interpretable Benchmarks: Contextual Learning through Cognitive
and Multimodal Perception [0.0]
This study contends that the Turing Test is misinterpreted as an attempt to anthropomorphize computer systems.
It emphasizes tacit learning as a cornerstone of general-purpose intelligence, despite its lack of overt interpretability.
arXiv Detail & Related papers (2022-12-04T08:30:04Z) - A World-Self Model Towards Understanding Intelligence [0.0]
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.
arXiv Detail & Related papers (2022-03-25T16:42:23Z) - 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) - Modelos din\^amicos aplicados \`a aprendizagem de valores em
intelig\^encia artificial [0.0]
Several researchers in the area have developed a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment.
It is utmost importance that artificial intelligent agents have their values aligned with human values.
Perhaps this difficulty comes from the way we are addressing the problem of expressing values using cognitive methods.
arXiv Detail & Related papers (2020-07-30T00:56:11Z) - Dynamic Cognition Applied to Value Learning in Artificial Intelligence [0.0]
Several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence.
It is of utmost importance that artificial intelligent agents have their values aligned with human values.
A possible approach to this problem would be to use theoretical models such as SED.
arXiv Detail & Related papers (2020-05-12T03:58:52Z) - Neuro-symbolic Architectures for Context Understanding [59.899606495602406]
We propose the use of hybrid AI methodology as a framework for combining the strengths of data-driven and knowledge-driven approaches.
Specifically, we inherit the concept of neuro-symbolism as a way of using knowledge-bases to guide the learning progress of deep neural networks.
arXiv Detail & Related papers (2020-03-09T15:04:07Z)
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.