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AGI via "Selves and Conversations all the Way Up"

#artificialintelligence

The oddest thing about Artificial Neural Networks is that they actually work despite being based on a completely false model of a biological neuron. Why Artificial Neural Networks (ANN) work remains a mystery. Understanding that "Why" can inform us why real biological neurons might work. We can make progress by identifying universal characteristics between the biological and the synthetic. One universality that we can be certain of is that both biological and artificial neurons are pattern-matching machines.


Intelligence as information processing: brains, swarms, and computers

arXiv.org Artificial Intelligence

There is no agreed definition of intelligence, so it is problematic to simply ask whether brains, swarms, computers, or other systems are intelligent or not. To compare the potential intelligence exhibited by different cognitive systems, I use the common approach used by artificial intelligence and artificial life: Instead of studying the substrate of systems, let us focus on their organization. This organization can be measured with information. Thus, I apply an informationist epistemology to describe cognitive systems, including brains and computers. This allows me to frame the usefulness and limitations of the brain-computer analogy in different contexts. I also use this perspective to discuss the evolution and ecology of intelligence.


Toward the quantification of cognition

arXiv.org Artificial Intelligence

The machinery of the human brain - analog, probabilistic, embodied - can be characterized computationally, but what machinery confers what computational powers? Any such system can be abstractly cast in terms of two computational components: a finite state machine carrying out computational steps, whether via currents, chemistry, or mechanics; plus a set of allowable memory operations, typically formulated in terms of an information store that can be read from and written to, whether via synaptic change, state transition, or recurrent activity. Probing these mechanisms for their information content, we can capture the difference in computational power that various systems are capable of. Most human cognitive abilities, from perception to action to memory, are shared with other species; we seek to characterize those (few) capabilities that are ubiquitously present among humans and absent from other species. Three realms of formidable constraints --- a) measurable human cognitive abilities, b) measurable allometric anatomic brain characteristics, and c) measurable features of specific automata and formal grammars --- illustrate remarkably sharp restrictions on human abilities, unexpectedly confining human cognition to a specific class of automata ("nested stack"), which are markedly below Turing machines.


Morphological Computation and Learning to Learn In Natural Intelligent Systems And AI

arXiv.org Artificial Intelligence

At present, artificial intelligence in the form of machine learning is making impressive progress, especially the field of deep learning (DL) [1]. Deep learning algorithms have been inspired from the beginning by nature, specifically by the human brain, in spite of our incomplete knowledge about its brain function. Learning from nature is a two-way process as discussed in [2][3][4], computing is learning from neuroscience, while neuroscience is quickly adopting information processing models. The question is, what can the inspiration from computational nature at this stage of the development contribute to deep learning and how much models and experiments in machine learning can motivate, justify and lead research in neuroscience and cognitive science and to practical applications of artificial intelligence.


Foundations of Intelligence in Natural and Artificial Systems: A Workshop Report

arXiv.org Artificial Intelligence

In March of 2021, the Santa Fe Institute hosted a workshop as part of its Foundations of Intelligence in Natural and Artificial Systems project. This project seeks to advance the field of artificial intelligence by promoting interdisciplinary research on the nature of intelligence. During the workshop, speakers from diverse disciplines gathered to develop a taxonomy of intelligence, articulating their own understanding of intelligence and how their research has furthered that understanding. In this report, we summarize the insights offered by each speaker and identify the themes that emerged during the talks and subsequent discussions.