Tag: artificial-intelligence
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Linking fast and slow
“Linking fast and slow: The case for generative models” A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain…
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Robust Decision-Making Via Free Energy Minimization
“Robust Decision-Making Via Free Energy Minimization” html, pdf, video, Nature Communications Despite their groundbreaking performance, state-of-the-art autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirable behaviors or even catastrophic failures. Robustness towards these training/environment ambiguities is a core requirement for intelligent agents and its fulfillment is a…
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The ‘made-up mind’.
“The ‘made-up mind’. Deriving new hypotheses on delusions from general psychological models of belief maintenance” Highlights Contemporary definitions of delusions highlight their resistance to conflicting evidence as the core feature, but there has been little progress in understanding why even explicit confrontation with contradicting evidence seldom leads to belief revision. This review aims to generate…
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Fast, slow, & metacognitive
“Fast, slow, and metacognitive thinking in AI” Inspired by the ”thinking fast and slow” cognitive theory of human decision making, we propose a multi-agent cognitive architecture (SOFAI) that is based on ”fast”/”slow” solvers and a metacognitive module. We then present experimental results on the behavior of an instance of this architecture for AI systems that…
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Towards embodied intelligence
“Intelligent soft matter: towards embodied intelligence” Intelligent soft matter lies at the intersection of materials science, physics, and cognitive science, promising to change how we design and interact with materials. This transformative field aims to create materials with life-like capabilities, such as perception, learning, memory, and adaptive behavior. Unlike traditional materials, which typically perform static…
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Known and Unknown Biases
“Known and Unknown Biases: A Framework for Contextualising and Identifying Bias in Animal Behaviour Research“ (This article discusses the bias in animal behaviour research, but – as known to most readers, I hope – humanes too are members of the animal kingdom 🙂 Biases in animal behaviour research are inevitable consequences of our societal and…
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Pathfinding: a neurodynamical account of intuition
Pathfinding: a neurodynamical account of intuition We examine the neurobiology of intuition, a term often inconsistently defined in scientific literature. While researchers generally agree that intuition represents “an experienced-based process resulting in a spontaneous tendency toward a hunch or hypothesis,” we establish a firmer neurobiological foundation by framing intuition evolutionarily as a pathfinding mechanism emerging…
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Complexity data science
“Complexity data science: A spin-off from digital twins “ Digital twins offer a new and exciting framework that has recently attracted significant interest in fields such as oncology, immunology, and cardiology. The basic idea of a digital twin is to combine simulation and learning to create a virtual model of a physical object. In this paper,…
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Resilience phenotypes derived from an active inference account of allostasis
“Resilience phenotypes derived from an active inference account of allostasis“:Within a theoretical framework of enactive allostasis, we explore active inference strategies for minimizing surprise to achieve resilience in dynamic environments. While individual differences and extrinsic protective factors traditionally account for variability in resilience trajectories following stressor exposure, the enactive model emphasizes the importance of the…
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The power of mathematical models for better policy decisions
“Harnessing the power of mathematical models for better policy decisions” sets out four practical recommendations to help policymakers across a wide range of policy areas effectively capitalise on, and sidestep pitfalls of, using mathematical models for decision-making. Decision-makers are often keen to “follow the science” in highly-charged contexts such as climate policy, pandemic response, economic…
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Humans rationally balance abstract world models
This work adds to a growing body of research showing that the brain arbitrates between approximate decision strategies. The current study extends these ideas from simple habits into usage of more sophisticated approximate predictive models, and demonstrates that individuals dynamically adapt these in response to the predictability of their environment. How do people model the…
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Defining intelligence: Bridging the gap
“Defining intelligence: Bridging the gap between human and artificial perspectives“ Achieving a widely accepted definition of human intelligence has been challenging, a situation mirrored by the diverse definitions of artificial intelligence in computer science. By critically examining published definitions, highlighting both consistencies and inconsistencies, this paper proposes a refined nomenclature that harmonizes conceptualizations across the two disciplines.…
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Bayesian Models of Cognition
“Bayesian Models of Cognition Reverse Engineering the Mind” is a new MIT-press Open Access book available for online reading. The definitive introduction to Bayesian cognitive science, written by pioneers of the field. How does human intelligence work, in engineering terms? How do our minds get so much from so little? Bayesian models of cognition provide…
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regulation of motivated behavior
in “A unified theoretical framework underlying the regulation of motivated behavior“, Yu-Been Kim, Young Hee Lee, Shee-June Park and Hyung Jin Choi explain that multiple psychological components have evolved in order to orchestrate behaviors for survival. Despite several theories regarding behavior regulation, these theories do not clearly distinguish distinct components and do not explain the…
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The Edge of Sentience
“The Edge of Sentience: Risk and Precaution in Humans, Other Animals, and AI.” by Jonathan Birch Can octopuses feel pain and pleasure? What about crabs, shrimps, insects, or spiders? How do we tell whether a person unresponsive after severe brain injury might be suffering? When does a fetus in the womb start to have conscious experiences?…
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Why the simplest explanation isn’t always the best
Eva L. Dyer and Konrad Kording discuss in a commentary article “Why the simplest explanation isn’t always the best” an essential learning related to the article Phantom oscillations in principal component analysis (also available on BioRXiv) Dimensionality reduction simplifies high-dimensional data into a small number of representative patterns. One dimensionality reduction method, principal component analysis (PCA), often selects oscillatory…
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