Cognitive Architecture for Co-Evolutionary Hybrid Intelligence
- URL: http://arxiv.org/abs/2209.12623v1
- Date: Mon, 5 Sep 2022 08:26:16 GMT
- Title: Cognitive Architecture for Co-Evolutionary Hybrid Intelligence
- Authors: Kirill Krinkin and Yulia Shichkina
- Abstract summary: The paper questions the feasibility of a strong (general) data-centric artificial intelligence (AI)
As an alternative, the concept of co-evolutionary hybrid intelligence is proposed.
An architecture seamlessly incorporates a human into the loop of intelligent problem solving is considered.
- Score: 0.17767466724342065
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper questions the feasibility of a strong (general) data-centric
artificial intelligence (AI). The disadvantages of this type of intelligence
are discussed. As an alternative, the concept of co-evolutionary hybrid
intelligence is proposed. It is based on the cognitive interoperability of man
and machine. An analysis of existing approaches to the construction of
cognitive architectures is given. An architecture seamlessly incorporates a
human into the loop of intelligent problem solving is considered. The article
is organized as follows. The first part contains a critique of data-centric
intelligent systems. The reasons why it is impossible to create a strong
artificial intelligence based on this type of intelligence are indicated. The
second part briefly presents the concept of co-evolutionary hybrid intelligence
and shows its advantages. The third part gives an overview and analysis of
existing cognitive architectures. It is concluded that many do not consider
humans part of the intelligent data processing process. The next part discusses
the cognitive architecture for co-evolutionary hybrid intelligence, providing
integration with humans. It finishes with general conclusions about the
feasibility of developing intelligent systems with humans in the
problem-solving loop.
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) - Artificial Human Intelligence: The role of Humans in the Development of Next Generation AI [6.8894258727040665]
We explore the interplay between human and machine intelligence, focusing on the crucial role humans play in developing ethical, responsible, and robust intelligent systems.
We propose future perspectives, capitalizing on the advantages of symbiotic designs to suggest a human-centered direction for next-generation AI development.
arXiv Detail & Related papers (2024-09-24T12:02:20Z) - 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) - 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) - AI for Mathematics: A Cognitive Science Perspective [86.02346372284292]
Mathematics is one of the most powerful conceptual systems developed and used by the human species.
Rapid progress in AI, particularly propelled by advances in large language models (LLMs), has sparked renewed, widespread interest in building such systems.
arXiv Detail & Related papers (2023-10-19T02:00:31Z) - 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) - The Nature of Intelligence [0.0]
The essence of intelligence commonly represented by both humans and AI is unknown.
We show that the nature of intelligence is a series of mathematically functional processes that minimize system entropy.
This essay should be a starting point for a deeper understanding of the universe and us as human beings.
arXiv Detail & Related papers (2023-07-20T23:11:59Z) - Kernel Based Cognitive Architecture for Autonomous Agents [91.3755431537592]
This paper considers an evolutionary approach to creating a cognitive functionality.
We consider a cognitive architecture which ensures the evolution of the agent on the basis of Symbol Emergence Problem solution.
arXiv Detail & Related papers (2022-07-02T12:41:32Z) - Co-evolutionary hybrid intelligence [0.3007949058551534]
The current approach to the development of intelligent systems is data-centric.
The article discusses an alternative approach to the development of artificial intelligence systems based on human-machine hybridization and their co-evolution.
arXiv Detail & Related papers (2021-12-09T08:14:56Z) - Inductive Biases for Deep Learning of Higher-Level Cognition [108.89281493851358]
A fascinating hypothesis is that human and animal intelligence could be explained by a few principles.
This work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing.
The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities.
arXiv Detail & Related papers (2020-11-30T18:29:25Z) - 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)
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.