brandonb

Sure! Here’s a description: https://www.empirical.health/blog/wearable-foundation-model-...

LeCun's technical approach with AMI will likely be based on JEPA, which is also a very different approach than most US-based or Chinese AI labs are taking.

If you're looking to learn about JEPA, LeCun's vision document "A Path Towards Autonomous Machine Intelligence" is long but sketches out a very comprehensive vision of AI research: https://openreview.net/pdf?id=BZ5a1r-kVsf

Training JEPA models within reach, even for startups. For example, we're a 3-person startup who trained a health timeseries JEPA. There are JEPA models for computer vision and (even) for LLMs.

You don't need a $1B seed round to do interesting things here. We need more interesting, orthogonal ideas in AI. So I think it's good we're going to have a heavyweight lab in Europe alongside the US and China.

Fiber is the ultimate nutritional power tool.

Not only does fiber reduce cardiovascular mortality by 26% (by cutting cholesterol), surprisingly enough, fiber even reduces your risk of cancer by 22%: https://www.empirical.health/blog/dietary-fiber-reduces-all-...

(Oatmeal is high in fiber, among other things, which I think is part of what's going on here.)

We've done a little research in this area, attempting to approximate blood-based biomarkers on wearable data. You can get some of them: https://www.empirical.health/blog/wearable-foundation-model-...

A YC company, Athelas, started by developing an at-home blood test analyzer (they've since pivoted).

(OP) This was fun to write.

The most surprising thing I learned that coconut milk is a lot less healthy than I would have guessed (high in lauric acid).

I had always heard that dark chocolate was heart healthy, in spite of being high in saturated fat. In addition to the effect of antioxidants, the stearic acid in dark chocolate quickly converted by your liver into oleic acid (a monounsaturated fat).

Also worth noting: women experience heart attack symptoms differently. For example, pressure, tightness, or burning instead of crushing pain.

This post summarizes four government programs that launched in the last 60 days:

* ARPA-H ADVOCATE: autonomous AI cardiologist.

* CMS ACCESS: pays for outcomes, not visits, with reimbursement set that you have to use AI to scale.

* FDA TEMPO: lets AI devices deploy before full clearance

* Utah: AI can prescribe medication (refills at first).

I've been working on AI in medicine for 10 years. I'm honestly surprised at how fast the regulations are evolving.

Cool! What do you think Mimir's biggest advantage is over, say, ChatPRD?

The main result is:

> HDHP status was associated with worse OS (hazard ratio [HR], 1.46; 95% CI, 1.19-1.79) and CSS (HR, 1.34; 95% CI, 1.01-1.77) among cancer survivors.

"OS" is overall survival and "CSS" is cancer-specific survival.

About 20% of people who have normal LDL cholesterol will have normal ApoB. So they agree a lot, but disagree sometimes.

This post was inspired by Hims' Superbowl ad, which claims that rich people do live longer. The claim is true, but it's partly explained by regional differences rather than just straight income.

Original source is here: https://news.ycombinator.com/item?id=46916402

Archive link: https://archive.is/G2lLb#selection-1233.0-1233.65

Sad to see what's become of Heroku. I've used Render as a good modern day alternative and have been pretty happy with it.

Part of the gap is that men develop heart disease 7 years earlier: https://www.empirical.health/blog/men-vs-women-heart-disease...

I think your second sentence is the strongest -- it's specific about what you actually help with.

Right now, the title and preview text on mobile probably look like "Quick scheduling question" / "Hi [name], I've followed [company] for...". This looks like a lot of other emails in my inbox that are requesting a meeting time. So I think a lot of people who quickly scan their inbox would pass this by and not even open the full email.

I'd also drop the "re: " part unless it's genuinely a reply. It breaks trust.

Every now and then, I think it's useful to look at what great work looked like in its first form.

In retrospect, this result was the first step toward the LLMs that are now the dominant approach in AI.

In 2017, this was a very modest result and probably didn't seem very promising. It took real courage and tenacity on OpenAI's part to keep at it for the next 5 years.

This is the first new paper from Alec Radford since leaving OpenAI. Token-level data filtering is kind of a simple idea, but so are many effective ideas in LLMs.

One advantage is that this type of safety guardrail can't be undone by an adversary in post-training, so it's a good fit for open source models.

The experiments are all done in preventing models from acquiring medical capabilities, while preserving related capabilities like e.g., biology.

> My doctor said she’s seeing a huge number of patients coming back with excessively high Vitamin D levels after taking supplement doses recommended by influencers. It happened to me, too, with what I though was a conservative dose of Vitamin D (5K IU, not even taken every day)

IMO that’s part of what’s interesting about this study design — they tested vitamin D blood levels and adjusted the supplement dose based on that. This seems like a much better approach than taking a high dose blindly.

I think the headline is accurate. The 52% number is from the experiment arm (participants who received a vitamin D supplement, with the quantity guided by blood testing). While it’s technically possible for the supplement dosage to be calculated as zero, 85% of participants were deficient at baseline, so this isn’t the main effect.

The 365º view of your heart health was supposed to be a joke. Although not necessarily a good one. :)

The study was presented at the AHA scientific sessions; full manuscript isn't out yet. It's in the caveats section in the article.

Cool! Is this the one? https://pubmed.ncbi.nlm.nih.gov/37302737/

Can you paste an example title and email here? I'm a founder and get 100's of emails per day. I often reply to cold emails, but honestly, with 90% of them, I can't really tell if they will solve one of my top 3 problems.

This was a write-up of a new study (TARGET-D) that used vitamin D supplements -- with the supplement amount guided by blood testing -- to reduce heart attack risk.

I've been working in heart health in 10 years and I was surprised at the magnitude of the effect here.

I hope it holds up as they move toward the final publication. Vitamin D supplementation is cheap and this could have a huge benefit.

NASA got its support in much the same way during the space race. Spreading the jobs widely is a good way to get political support.

One source: https://www.escardio.org/communities/councils/cardiology-pra...

Definitely shows the comparative power of medications. Statins, ezetimibe, and PCSK9 inhibitors can reduce LDL or ApoB by 85-95%.

The lack of self-consistency does seem like a sign of a deeper issue with reliability. In most fields of machine learning robustness to noise is something you need to "bake in" (often through data augmentation using knowledge of the domain) rather than get for free in training.

FWIW, Apple has published validation data showing the Apple Watch's estimate is within 1.2 ml/kg/min of a lab-measured Vo2Max.

Behind the scenes, it's using a pretty cool algorithm that combines deep learning with physiological ODEs: https://www.empirical.health/blog/how-apple-watch-cardio-fit...

We trained a foundation model specifically for wearable data: https://www.empirical.health/blog/wearable-foundation-model-...

The basic idea was to adapt JEPA (Yann LeCun's Joint-Embedding Predictive Architecture) to multivariate time series, in order to learn a latent space of human health from purely unlabeled data. Then, we tested the model using supervised fine tuning and evaluation on on a bunch of downstream tasks, such as predicting a diagnosis of hypertension (~87% accuracy). In theory, this model could be also aligned to the latent space of an LLM--similar to how CLIP aligns a vision model to an LLM.

IMO, this shows that accuracy in consumer health will require specialized models alongside standard LLMs.

Archive link: https://archive.is/mJSEh