Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

Yanofsky, Noson S.

doi:10.1215/0961754x-2872666

Cited by 9 publications

(6 citation statements)

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“…In other words, the visualization looks for a structured high dimensional manifold on which the data is relatively uniformly distributed, and then, in this high dimensional space, attempts to construct membership classes using a 'fuzzy' membership criteria. This 'fuzzy' criteria is somewhat different than a 'probability,' instead being, in the terminology of ecorithms [Val13,HGE13], more concerned with 'possibilities' in as much as they are concerned with systems inherently too complex to fully discretize. The resulting graph in figure 13 not only captures separation along the different classification tasks (RHT, FHT, and FT), but is able to characterize data points that exist 'in between' the more differentiated fuzzy boundaries, e.g.…”

Section: Resultsmentioning

confidence: 99%

Dual stream neural networks for brain signal classification

Kuang¹,

Michoski²

2021

J. Neural Eng.

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Objective. The primary objective of this work is to develop a neural nework classifier for arbitrary collections of functional neuroimaging signals to be used in brain–computer interfaces (BCIs). Approach. We propose a dual stream neural network (DSNN) for the classification problem. The first stream is an end-to-end classifier taking raw time-dependent signals as input and generating feature identification signatures from them. The second stream enhances the identified features from the first stream by adjoining a dynamic functional connectivity matrix aimed at incorporating nuanced multi-channel information during specified BCI tasks. Main results. The proposed DSNN classifier is benchmarked against three publicly available datasets, where the classifier demonstrates performance comparable to, or better than the state-of-art in each instance. An information theoretic examination of the trained network is also performed, utilizing various tools, to demonstrate how to glean interpretive insight into how the hidden layers of the network parse the underlying biological signals. Significance . The resulting DSNN is a subject-independent classifier that works for any collection of 1D functional neuroimaging signals, with the option of integrating domain specific information in the design.

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Section: Resultsmentioning

confidence: 99%

Dual stream neural networks for brain signal classification

Kuang¹,

Michoski²

2021

J. Neural Eng.

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“…It has often been noted that reinforcement learning and evolution by natural selection are closely analogous (Skinner 1981, Watson andSzathmary 2016), and indeed, the replicator equation (an abstraction of biological evolution under natural selection) and Bayesian updating (a learning optimisation process) have been shown to be formally equivalent (Harper 2009, Shalizi 2009. See also (Campbell 1983, Frank 2009, Valiant 2013, Chastain, Livnat et al 2014, Kouvaris, Clune et al 2017, Vanchurin, Wolf et al 2021 for the relationship between learning and evolution. These works expand and deepen our understanding of the adaptation provided by natural selection.…”

Section: The Relationship Between Natural Induction and Natural Selec...mentioning

confidence: 99%

Natural Induction: Spontaneous adaptive organisation without natural selection

Buckley,

Lewens,

Levin

et al. 2024

Preprint

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Evolution by natural selection is believed to be the only possible source of spontaneous adaptive organisation in the natural world. This places strict limits on the kinds of systems that can exhibit adaptation spontaneously, i.e. without design. Physical systems can show some properties relevant to adaptation without natural selection or design. 1) The relaxation, or local energy minimisation, of a physical system constitutes a natural form of optimisation insomuch as it finds locally optimal solutions to the frustrated forces acting on it or between its components. 2) When internal structure 'gives way' or accommodates to a pattern of forcing on a system this constitutes learning insomuch as it can store, recall and generalise past configurations. Both these effects are quite natural and general, but in themselves insufficient to constitute non-trivial adaptation. However, here we show that the recurrent interaction of physical optimisation and physical learning together results in significant spontaneous adaptive organisation. We call this adaptation by natural induction. The effect occurs in dynamical systems described by a network of viscoelastic connections subject to occasional disturbances. When the internal structure of such a system accommodates slowly across many disturbances and relaxations, it spontaneously learns to preferentially visit solutions of increasingly greater quality (exceptionally low energy). We show that adaptation by natural induction thus produces network organisations that improve problem-solving competency with experience. We note that the conditions for adaptation by natural induction, and its adaptive competency, are different from those of natural selection. We therefore suggest that natural selection is not the only possible source of spontaneous adaptive organisation in the natural world.

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“…The link between evolution and simple types of learning has often been noted (Skinner, 1981;Watson and Szathmáry, 2016) but sometimes interpreted in an uninteresting way: learning is simply a form of random variation and selection (Campbell, 1956;Skinner, 1981;Watson and Szathmáry, 2016). However, the formal equivalence between evolution and learning (Campbell, 2016;Frank, 2009;Harper, 2009;Shalizi, 2009) also has a much more interesting implication, namely: Evolution is more intelligent than we realised (Chastain et al, 2014;Parter et al, 2008;Valiant, 2013;Watson and Szathmáry, 2016). Connectionist models of conventional learning, familiar in artificial neural networks, greatly expand this perspective (Watson et al, , 2022Watson and Szathmáry, 2016).…”

Section: Cognition Learning and Problem-solving In Biological Network...mentioning

confidence: 99%

The collective intelligence of evolution and development

Watson

Levin

2023

Collective Intelligence

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Collective intelligence and individual intelligence are usually considered to be fundamentally different. Individual intelligence is uncontroversial. It occurs in organisms with special neural machinery, evolved by natural selection to enable cognitive and learning functions that serve the fitness benefit of the organism, and then trained through lifetime experience to maximise individual rewards. Whilst the mechanisms of individual intelligence are not fully understood, good models exist for many aspects of individual cognition and learning. Collective intelligence, in contrast, is a much more ambiguous idea. What exactly constitutes collective intelligence is often vague, and the mechanisms that might enable it are frequently domain-specific. These cannot be mechanisms selected specifically for the purpose of collective intelligence because collectives are not (except in special circumstances) evolutionary units, and it is not clear that collectives can learn the way individual intelligences do since they are not a singular locus of rewards and benefits. Here, we use examples from evolution and developmental morphogenesis to argue that these apparent distinctions are not as categorical as they appear. Breaking down such distinctions enables us to borrow from and expand existing models of individual cognition and learning as a framework for collective intelligence, in particular connectionist models familiar in the context of neural networks. We discuss how specific features of these models inform the necessary and sufficient conditions for collective intelligence, and identify current knowledge gaps as opportunities for future research.

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Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World

Cited by 9 publications

References 0 publications

Dual stream neural networks for brain signal classification

Dual stream neural networks for brain signal classification

Natural Induction: Spontaneous adaptive organisation without natural selection

The collective intelligence of evolution and development

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