Category: Biology of Information
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“active learning conditions” stimulate common processes that become part of the representations
There is an emerging consensus on the virtues of active learning methods for improving student performance. Such learning methods can be any instruction or technique that requires students to actively engage in the learning process, as compared to more traditional, passive ways of learning.One form of active learning is retrieval practice (RP), where the activity…
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Binary “Space-Time decisions” accumulate
There was this great article in PNAS, recently: The geometry of decision-making in individuals and collectives. Luis M. Rocha posted a perfect summary on twitter: In biology, complex dynamics so often lead to binary (thresholded/critical) decision: “we predict that the brain repeatedly breaks multichoice decisions into a series of binary decisions in space–time”.
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World Model – “Free Energy” Selections of Perception & Policy
During their lives humans constantly interact with the physical environment, as well as with themselves and others.World model learning and inference are crucial concepts in brain and cognitive science, as well as in AI and robotics. The outstanding challenges of building a generalpurpose AI needs world modelling and probabilistic inference, needed to realise a brain-like…
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Designing for Human-AI interaction is hard. (So steal like an artist :-)
Following is and interesting article/blog , and just “stolen like an artist” from https://www.simonoregan.com/short-thoughts/the-design-difficulties-of-human-ai-interaction Designing for Human-AI interaction is hard. Here Yang et al. catalog where designers run into problems when applying the traditional 4Ds process to designing AI systems. These difficulties can be broadly attributed to two sources: This uncertainty and complexity combination then…
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VVUQ your model and twin – should we trust ?
The world is moving towards digital twins. I recently came across a insightfull article: A probabilistic graphical model foundation for enabling predictive digital twins at scale, available at Arxiv – & published Nature The digital twin is a set of coupled computational models that evolve over time to persistently represent the structure, behavior, and context…
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Learning: Brain vs xNN
The neural and cognitive architecture for learning from a small sample is a nice article I would like to recommend. It highlights how human learners avoid generalization issues found in machine learning, proposes a general model explaining how the brain may simplify complex problems. Synergy between cognitive functions and reinforcement learning allows simplification.Recurrent loops between…
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Less = More, “Lets Kill”
One of the great readings recently was the article from Adams, G.S., Converse, B.A., Hales, A.H. et al. in Nature: People systematically overlook subtractive changes. The summary video, clearly marks the point: Experiments show that people default to adding as a solution in various situations. It appears to be an uncommon insight. When solving problems, people prefer adding…
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