Brain-inspired and Self-based Artificial Intelligence Paradigm: Understanding the Core of AI Development

Główne pojęcia

The author argues that current artificial intelligence lacks a sense of self, proposing a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm to address this gap by emphasizing the crucial role of the Self in shaping future AI models.

Streszczenie

The content delves into the limitations of current artificial intelligence in understanding and perceiving the world from a subjective perspective. It introduces the BriSe AI paradigm, highlighting the importance of self-awareness in developing advanced AI models. The hierarchical framework of Self is detailed, showcasing how different levels contribute to enhancing cognitive abilities. Various learning strategies are discussed, such as unsupervised learning, supervised learning, reinforcement learning, and more, all aimed at achieving human-level cognition through self-organized coordination.

Statystyki

The Baxter robot achieved an accuracy rate of 94% in a multi-sensory classification task involving 40 distinct objects.
The brain simulation model showed distinct spatial distribution during anesthesia-induced loss of consciousness.
The NeuEvo framework integrates local synaptic dynamics with global network optimization for evolutionary learning.

Cytaty

"The hierarchical framework of Self highlights self-based environment perception, bodily modeling, autonomous interaction with the environment, social collaboration with others." - BriSe AI Paradigm
"Intelligent agents can distinguish themselves from others and understand others’ mental states based on self-experiences." - Social Self
"Empathy for others’ negative emotions becomes an intrinsic drive for altruism." - Affective Empathy

Kluczowe wnioski z

Brain-inspired and Self-based Artificial Intelligence

by Yi Zeng,Feif... o arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18784.pdf

Głębsze pytania

How can integrating self-awareness enhance ethical decision-making in artificial intelligence?

Integrating self-awareness in artificial intelligence can enhance ethical decision-making by enabling AI systems to consider their own actions, intentions, and consequences. With a sense of self, AI models can reflect on the impact of their decisions on themselves and others, leading to more responsible behavior. This self-reflection allows AI to align its actions with moral principles and societal norms, promoting ethical conduct in various scenarios. Additionally, self-aware AI can empathize with stakeholders' perspectives, anticipate potential harm or bias, and make decisions that prioritize fairness and transparency.

What are potential drawbacks or challenges associated with implementing the BriSe AI paradigm?

Implementing the BriSe AI paradigm may face several challenges and drawbacks. One challenge is the complexity of developing a hierarchical framework for Self that accurately mirrors human cognition. Designing cognitive architectures that encompass Perception & Learning, Bodily Self, Autonomous Self, Social Self, and Conceptual Self requires significant computational resources and expertise. Another drawback could be the interpretability of such advanced AI models - understanding how these systems arrive at decisions based on multi-modal learning strategies might be challenging for users or regulators. Moreover, ensuring the seamless integration of different cognitive functions within a unified framework poses technical hurdles.

How might advancements in brain-inspired cognitive functions impact other fields beyond artificial intelligence?

Advancements in brain-inspired cognitive functions have the potential to revolutionize various fields beyond artificial intelligence. In healthcare, these advancements could lead to improved diagnostic tools based on neural network models that mimic human brain processes for pattern recognition or disease detection. In robotics and automation industries, developments in spiking neural networks could enhance robot dexterity and adaptability for complex tasks requiring real-time decision-making capabilities similar to humans'. Furthermore,
these innovations could influence neuroscience research by providing insights into how biological brains process information through computational modeling approaches inspired by neural mechanisms.