Riddhiman Das

The next frontier of AI isn't just about choosing the right model. It's about building moats through implementation. Across the Emergence Capital portfolio, companies like Bland and Federato are scaling faster by embedding Forward-Deployed Engineers. These engineers bridge the gap between AI and reality, ensuring real results in production. Read more from my colleagues Gordon and Kyle on why FDEs are the key to unlocking AI's full value. https://lnkd.in/gcsRRGX2

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“Nature doesn’t try to abstract itself in a human-understandable form.”   At the core of my conversation with Peter Lee was the fact that nature is staggeringly complex. It’s evolved over billions of years, meaning it has a billions-of-years head start on any human attempts to understand it.    But now, AI is giving humans a tool that can process and decipher much more of nature’s complexity. And in doing so, it is totally transforming biomedical research.   The coming together of human, nature, and machine intelligences will create what I call #polyintelligence. It will lead to new medicines, treatments, and technologies that promise to improve the lives of millions of people around the world. It will also challenge humans to become comfortable working alongside AI on problems and solutions that we don’t necessarily understand.    Thanks to Peter and Microsoft Research for having me on to talk about how AI is changing the way we do research and about how we are building a polyintelligent future at Flagship Pioneering with ProFound Therapeutics, Quotient Therapeutics, and Lila Sciences, among others.

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mpathic is becoming THE partner to the AI industry as they work to safeguard users from adverse mental health and safety outcomes As millions of users — including large numbers of young people — increasingly turn to AI chatbots as their first-line “counselors” and confidants, Seattle-based startup mpathic is stepping in to ensure those digital agents don’t provide dangerous advice when it matters most.

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LLMs are getting commoditized. The pace of core model breakthroughs has clearly slowed. We are no longer seeing step-function jumps from “just a bigger model”. For most real systems today: • architecture matters more than the base LLM, • planning, orchestration, memory, and verification dominate quality, • the model itself is increasingly a replaceable component. In my own code factory, I can swap GPT, Claude, or Gemini with minimal impact. There are differences in latency and edge cases, but they no longer define the system. The next real breakthroughs will not come from GPT-6. They will come from new architectures built around models, not inside them. LLMs are becoming infrastructure. Systems are becoming the product.

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At Logical Health, we’re combining AI with declarative, logic-based programming to deliver highly accurate results in healthcare. Super cool to see MIT exploring a similar approach: their new paper Teaching LLMs to Plan shows how step-by-step logical reasoning boosted planning accuracy from 28% to 94%. The future of AI isn’t just bigger models. It’s smarter, more reliable ones that align with real-world needs. Arxiv Link: https://lnkd.in/gnN-XYGs

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🧠 "Zoom in. Zoom in a little more..." — Imagine performing surgery and having to talk someone through moving your images mid-procedure. Sherry Chang, CEO and co-founder of Neural Lab, shares how touchless gesture control is eliminating this chaos in the OR—enabling sterile, precise interaction in high-stakes environments. This isn’t a cool demo—it’s a life-saving transformation. Watch the full episode here: - youtube: https://lnkd.in/gbkU_XQJ - spotify: https://lnkd.in/gMZkt5ws #healthtech #visualintelligence #unlearnpodcast

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"To be fair, if humanity were to use less compute, we would reduce carbon emissions. But If we are going to use more, data centers are the cleanest way to do it; and computation produces dramatically less carbon than alternatives. Google had estimated that a single web search query produces 0.2 grams of CO2 emissions. In contrast, driving from my home to the local library to look up a fact would generate about 400 grams. Google also recently estimated that the median Gemini LLM app query produces a surprisingly low 0.03 grams of CO2 emissions), and uses less energy than watching 9 seconds of television. AI is remarkably efficient per query — its aggregate impact comes from sheer volume. Major cloud companies continue to push efficiency gains, and the trajectory is promising." https://lnkd.in/g2_xvGS5

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In healthcare, almost all of the LLM performance evaluation research has been dedicated to studying clinical correctness. But that leaves another important domain out the picture: interoperability performance. At FHIR DevDays Flexpa launched an LLM Eval for FHIR, where we focused on key interoperability tasks like generating and transforming FHIR resources. LLMs are, perhaps surprisingly to some, extremely adept at understanding and manipulating FHIR, separate from the question of terminology accuracy. With access to the $validator operation, an agent can generate de novo FHIR resources from short natural language descriptions of healthcare events.

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The authors present LERD, a novel Bayesian neural dynamical framework designed to improve diagnosis and monitoring of neurodegenerative diseases like Alzheimer’s using multichannel EEG data. Traditional machine learning approaches in this domain often treat classifiers as “black boxes” and fail to explicitly model the underlying neural dynamics generating the observed signals. In contrast, LERD infers latent neural events and their relational structure directly from EEG without requiring external event or interaction labels, combining a continuous-time event inference module with a stochastic event generation process. An electrophysiology-inspired dynamical prior guides the learning process, promoting physiologically meaningful representations. The paper also contributes a theoretical analysis that provides a tractable training bound and stability guarantees for the inferred latent relational dynamics. Extensive experiments on synthetic data and two real Alzheimer’s EEG cohorts show LERD consistently outperforms strong baseline methods, while yielding latent summaries that align with known physiological characteristics of the disease. These results suggest that modeling latent event interactions and dynamics offers a more interpretable and accurate approach for EEG-based neurodegenerative classification than traditional black-box classifiers. https://lnkd.in/g-nH6tyT

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OpenAI has teamed up with Retro Biosciences to create GPT-4b micro, a biological AI model trained specifically on biology and protein engineering data. https://buff.ly/8iwZRZD

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