An M.I.T. Report Warns A.I. Is Causing ‘Cognitive Surrender.’ www.nytimes.com/2026/09/15/u...
Who's Who of AI
Earl K. Miller
Why they matter
Researcher with public evidence across AI research, AI business.
- AI signals
- 3 past 30d
- Sources
- 3 distinct domains
- Discusiones
- 1 past 30d
- Latest signal
- 4h ago
Picower Professor of Neuroscience @ MIT
Cognitive neuroscience, executive brain functions, consciousness, and bass guitar. You know, the good stuff.
ekmillerlab.mit.edu
Co-founder, Neuroblox
https://www.neuroblox.ai/
What they're sharing
nytimes.com
Grounded world models in biological organisms and future embodied AI arxiv.org
Predictive Coding with Bayesian Priors via Proximal Gradients arxiv.org
Scientific Machine Learning of Chaotic Systems Learns Reduced-Order Equations for Neural Populations arxiv.org
Slashdot m.slashdot.org
Cognitive outsourcing 3/3: analogue asymmetry - The Cynefin Co thecynefin.co
Articles & links
Today's AI systems learn mostly by passively absorbing data, but the brain builds intelligence in the reverse order. Grounded world models in biological organisms and future embodied AI arxiv.org/abs/2607.13560 #neuroscience
AI Weekly's analysis
→
- A group of computational neuroscientists led by Giovanni Pezzulo argues current AI leans on passive linguistic training rather than grounded environmental interaction.
- The paper points to five neural circuit families including navigation, affordance perception, active exploration, allostatic control, and self-versus-external distinction.
- The authors say embodied AI is missing intrinsic dynamics, action-centered learning, autonomous open-ended learning, and social grounding aligned with human norms.
Read full analysis →
The brain continuously optimizing its predictions. Local "leaky" circuits correct errors, while hierarchies link these computations using nonlinearities shaped by prior expectations. Predictive Coding with Bayesian Priors via Proximal Gradients arxiv.org/abs/2606.08374 #neuros…
It's a struggle to figure out the math behind chaotic systems, especially when the data is limited and noisy. PEM-UDE can recover interpretable equations from messy, chaotic data, and it yields testable predictions about neural networks. arxiv.org/abs/2507.03631 #neuroscience
This seems like a bold claim, given that we still don’t understand how consciousness works and that current evidence suggests the brain operates very differently from today’s AI. A Silent Workspace In Claude Mirrors Key Features of Human Consciousness m.slashdot.org/story/4561…
A discussion of what analog cognition and consciousness imply for AI. Spoiler: the brain operates fundamentally differently from today’s AI. thecynefin.co/cognitive-ou... #neuroscience
Their network
In Earl K. Miller's orbit
Center = Earl K. Miller. Left = members they follow (green edges). Right = members who follow them (blue edges). Top = mutual follows (orange edges, slightly larger). Drag any node to reposition; click to open that profile.
Are you Earl K. Miller? Show it.
Add the Who’s Who of AI badge to your site or bio. It links back to this profile.
Markdown: [](https://aiweekly.co/whos-who/person/earlkmiller-bsky-social)