Category: AI
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AI can distort human beliefs
Without a zone of uncertainty plus other troubling features, generative AI is poised to amplify bias and falsehoods, distort human perception. Individual humans form their beliefs by sampling a small subset of the available data in the world. Once those beliefs are formed with high certainty, they can become stubborn to revise. Fabrication and bias…
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Resemblance to reAIlity …
Questioner: How much will LLM impact human activity?Answer: Hmmm, the oncoming singularity?Q: YesA: Try … Tudor’s Graph. Q: What’s the data behind this?A: Seriously? Ok, let me make it clearer …
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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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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…
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Beslissen: FEP, AI, Bayes
“We sample the world to ensure our predictions become a self-fulfilling prophecy.” Karl Friston De beslissingswetenschappen en neurowetenschappen werden recent verrijkt door het principe van vrije energie (Free Energy Principle / FEP) van Karl Friston. FEP is misschien wel het meest allesomvattende idee sinds de theorie van natuurlijke selectie van Charles Darwin. Samenvattend is het…
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Rethinking Computational Approaches to the Mind
Rethinking Computational Approaches to the Mind Fundamental Challenges and Future Perspectives One-day Online Symposium21st October 2022 REGISTER HERE This one-day online event will bring together researchers with expertise in various areas such as complexity science, machine learning & artificial intelligence, information theory & data science, as well as computational/theoretical neuroscience & philosophy to explore different computational approaches…
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Begin with the decision-maker
I enjoyed the article “The first step in AI might surprise you” on AI, ML, Data Science from Cassie so much, I decided to steal some quotes: Leaders, figure out who’s calling the shots. If it’s you, then let’s designate you “The Decision-Maker“ for this project. Otherwise, delegate the position to someone else and ask them to read the…
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Will it ever happen?
Evolution of Brains and Computers: The Roads Not Taken – Can machines ever achieve true intelligence? , is a perspective article in entropy by Ricard Solé and Luís F. Seoane, has a great discussion on intelligence. When computers started to become a dominant part of technology around the 1950s, fundamental questions about reliable designs and…
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Why Can the Brain (And Not a Computer) Make Sense of the Liar Paradox?
Ordinary computing machines prohibit self-reference because it leads to logical inconsistencies and undecidability. In contrast, the human mind can understand self-referential statements without necessitating physically impossible brain states. Why can the brain make sense of self-reference? This paper addresses this question by defining the Strange Loop Model, which features causal feedback between two brain modules,…
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What Does It Mean for AI to Understand?
Melanie Mitchell has a great article, she just announced at Complexity Digest: Language models can generate uncannily humanlike prose (and poetry!) and seemingly perform sophisticated linguistic reasoning. How can we test if these machines actually understand what they’re doing? Read the full article at: www.quantamagazine.orghttps://www.quantamagazine.org/what-does-it-mean-for-ai-to-understand-20211216/# Understanding language requires understanding the world, and a machine exposed only…
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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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Ready, Steady, Go AI
AI is a potential solution to the challenge of turning big phenomics data into insights. Ready, Steady, Go AI is an interactive tutorial for disease classification that has been of significance as the foundation for achieving digital phenomics. It is designed as an open-source, freely available code via virtual lab notebooks to empower not only…
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