Tag: data-science

• 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…

  walterstiers

  2025-02-10

  Biology of Information, Complexity, Decision Intelligence, Life Ideas, Science

  #DecisionIntelligence, #ProblemSolvingMind, #TheInformationLens, AI, artificial-intelligence, data-science, machine-learning, technology

• Is Ockham’s razor losing its edge?

  Is Ockham’s razor losing its edge? New perspectives on the principle of model parsimony The preference for simple explanations, known as the parsimony principle, has long guided the development of scientific theories, hypotheses, and models. Yet recent years have seen a number of successes in employing highly complex models for scientific inquiry (e.g., for 3D…

  walterstiers

  2025-01-28

  #Sensemaking, #Wicked, Complexity, Decision Intelligence, Information Technology, Science

  #ActiveInference, #DecisionIntelligence, #TheInformationLens, #Uncertainty, AI, data-science, machine-learning, philosophy, Science

• 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…

  walterstiers

  2024-11-19

  #sense-making, Active Inference, AI, Biology of Information, Complexity, Decision Intelligence, Neurobiology/psychology, Science

  #ActiveInference, #DecisionIntelligence, #HumanAI, #TheInformationLens, AI, artificial-intelligence, data-science, machine-learning, statistics

• 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…

  walterstiers

  2024-04-18

  Biology of Information, Complexity, Information Technology, Neurobiology/psychology, Science

  #DecisionIntelligence, #TheInformationLens, AI, artificial-intelligence, data-science, machine-learning, unsupervised-learning

• the problem of algorithmic recommendations

  I just want to point out an MIT Technology Review article on three new books warn against turning into the person the algorithm thinks you are “A machine-learning algorithm walks into a bar. The bartender asks: ‘What’ll you have?’ The algorithm says: ‘What’s everyone else having?’” Chet Haase (Google) Software engineer Chet Haase’s joke sums…

  walterstiers

  2024-03-12

  #sense-making, AI, Decision Intelligence, Information Technology, Policy, Social-Technical

  #DecisionIntelligence, #HumanAI, #TheInformationLens, AI, artificial-intelligence, data-science, machine-learning, technology

• The past as a stochastic process

  The concept of history unfolding stochastically is not new; in the context of the history of life on Earth, Stephen J. Gould famously asked what would happen if we could “replay the tape”, which implicitly supposes that an underlying stochastic process generated that tape. Similarly, stochastic process modeling of environmental dynamics has been used to…

  walterstiers

  2024-02-09

  #Sensemaking, #Wicked, Complexity, Science, Social-Technical

  #DecisionIntelligence, #TheInformationLens, #Wicked, AI, artificial-intelligence, data-science, machine-learning, technology