My Ground Truths post was about the unanticipated, remarkable AI- enabled detection of thymus health in people of advanced age. Today @Carolynyjohnson wrote about it here @PostHealthSci gift link
RESEARCH
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Top AI Papers of the Week: Agents, MAS, and Agentic Models
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The Top AI Papers of the Week (April 26 – May 3) – Latent Agents
– RecursiveMAS
– OneManCompany
– AgenticQwen-30B-A3B
– Agentic World Modeling
– Agentic Harness Engineering
– From Skill Text to Skill Structure Read on for more: -
MIT Researcher Maps Path From Worm to Digital Human Brain
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A former MIT researcher just mapped the path from a worm to a digital human brain. The plan scales from a 302-neuron worm to 86 billion neurons. Three technologies are making this tractable. 1. High-resolution imaging now maps neurons at scale
2. Functional scans capture -
Little Evidence LLMs Improve Patient or Doctor Health Outcomes
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“In summary, there is very little evidence for LLMs benefiting patients or doctors for health outcomes” – Dr. @EricTopol Read his full review here: https://
open.substack.com/pub/erictopol/
p/the-paradox-of-medical-ai-implementation?selection=0babca4b-a31e-4e24-897e-37b939fd3b92&r=8tdk6&utm_medium=ios
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AI Cell Modeling Could Accelerate Medical Cures
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If #AI Can Model Cells, Science Can Deliver Cures
by Priscilla Chan @time Learn more: https://
bit.ly/4ta5RDA #ArtificialIntelligence #ML #MachineLearning -
Open Source Models Still Trail Frontier Models in Capabilities
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Los modelos OSS ya tienen muy buenas capacidades para resolver muchas tareas, peeeeero aún están atrás de los modelos frontera.
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ASCII Emoticons Can Trick LLMs Into Generating Harmful Code
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Can smiley faces break AI? Researchers from Xi’an Jiaotong University, NTU, and UMass Amherst reveal a new LLM vulnerability: emoticon semantic confusion. ASCII emoticons like 🙂 can trick models into misinterpreting intent, leading to harmful code generation. Their study,
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Domain-Specific Neurosymbolic AI Offers More Hope Than LLMs for Science
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Agreed with the first part, but I think the nuanced part is what you mean by “this technology”. AI may help all of this, but pure, domain-general LLMs per se probably won’t. Domain-specific neurosymbolic hybrids offer more hope for science than chatbots do. See my October NYT
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Diverse Simulated Personas Boost ReviewerToo Performance
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Oh, and in case that's interesting: one data point supporting the value of diversity in point of view from our work on ReviewerToo is that we get the best results when we pool more simulated reviewing personas in our system
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Open vs Closed AI Models: Beyond Benchmark Gaps
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This is a good explanation of why the gap between open and closed models is larger than it appears in benchmarks. I would add in that current open models are also more fragile than closed: they handle out-of-distribution problems far less well & have lower emergent capabilities.