Tag: #HumanAI
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How Occam’s razor guides human decision-making
A rather complex but very interesting article was published @PennLibraries and (somewhat more recent) @bioRXiv But for those who want to understand by a lecture, I can recommend the Simons Faoundation lecture from Joshua Gold (also available on Youtube: How Occam’s Razor Guides Human and Machine Decision-Making) Occam’s razor is the principle stating that, all…
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As a human it would be quite easy to spot
Man beats machine at Go in human victory over AI A human player has comprehensively defeated a top-ranked AI system at the board game Go, in a surprise reversal of the 2016 computer victory that was seen as a milestone in the rise of artificial intelligence. Kellin Pelrine beat the machine by taking advantage of…
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Cognitive Computational Neuroscience
Cognitive science has developed computational models that decompose cognition into functional components. Computational neuroscience has modeled how interacting neurons can implement elementary components of cognition. It is time to assemble the pieces of the puzzle of brain computation and to better integrate these separate disciplines. Modern technologies enable us to measure and manipulate brain activity…
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Prediction: multi-scale pattern completion of the future
The notion of the brain as a prediction machine has been extremely influential and productive in cognitive sciences.One prominent framework is of a “Bayesian brain” that explicitly generates predictions and uses resultant errors to guide adaptation. The prediction-generation component of this framework may involve little more than a pattern completion process. Brain-like systems can get…
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Designing Ecosystems of Intelligence from First Principles
Karl Friston joins VERSES as Chief Scientist to Lead New Era in Artificial Intelligence.VERSES published its research paper to arxiv.org to explore the applications and implications of Active Inference on the future of Artificial Intelligence. “Designing Ecosystems of Intelligence from First Principles” lays out a vision of research and development in the field of artificial intelligence…
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Technology readiness levels for machine learning systems
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards…