They'll likely make it available at some point, but for now one can use OpenEvolve [0] which is not quite as good but should be a good start to use the same LLM-driven evolutionary framework.
I used those two in combination to fix pain after 3x surgeries to repair a torn pec + infection. They work and helped me heal from being at a 3/10 constant pain down to baseline.
Not something I would do at any point for fun. But anecdotally, it's materially better than other alternatives offered/available.
You don't need a startup. Millions of people have an effective tax rate that is 0% and they have a net tax rate that is negative. They do this simply by having no meaningful skills or knowledge.
- additional modalities
- Faster FPS (inferences per second)
- Reaction time tuning (latency vs quality tradeoff) for visual and audio inputs/outputs
- built-in planning modules in the architecture (think premotor frontal lobe)
- time awareness during inference (towards an always inferring / always learning architecture)
Steve here, one of the co-authors. Totally valid on OpenBio. I will say that comparison numbers for this paper were such a challenge, in part because we found that a lot of the LLMs on the Medical LLM leaderboard struggled to follow even slight changes in instructions. On one hand it felt inaccurate to just print '[something very low]% Accuracy' on structuring/abstraction tasks and call it a day, but it also seemed like the amount of engineering effort needed to get non-trivial results from those LLMs was saying something important about how they worked.
I think that's especially true when you look at how well GPT-4o worked out of the box -- it makes clear what you get from the battle-hardening that's done to the big commercial models. For the numbers we did include, the thought was that was the most meaningful signal was that going from 8B to 70B with Llama3 actually gives you a lot in terms of mitigating that brittleness. That goes a step towards explaining the story of what we're seeing, moreso than showing a bunch of comparison LLMs fall over out of the box.
In the end, we presented those models that did best with light tuning and optimization (say a week's worth of iteration or so). I anticipate that we'll have to expand these results to include OpenBio as we work through the conference reviewer gauntlet. Any others you think we definitely should work to include? Would definitely be helpful!
We're excited to share pitchpilot with the HN community. Our beta users have found the embedded audio particularly useful for enterprise sharing. We're keen to keep improving, and our mission is to make communication easier.
In the roadmap is adding video export, digital twin presentations, and real-time presentations. We don't wrap a public LLM, so we don't share any data.
Given that Generative AI can now read brain scans [1] and this, I wonder how far away we are from "you thought negatively about something, the authorities are on their way".
Well we’re not infinitely far away from it, which is why we need to build political and legal systems that can respect human dignity even in the presence of such technologies.
1. First, models will predict pollution. The outcomes will help shape urban policy. But these won't solve crime or stop people from driving.
2. Second, models will predict individual behavior and track person level emissions. The outcomes will force behavior changes, mostly freedom limiting.
3. Third, and finally, models will predict thoughts. The the thought of driving instead of walking might trigger a response.
It's a slippery slope and we need to draw a line between prediction and policy.
Even allowing for the ridiculously massive technical leap from 1 to 2 and then 2 to 3, it doesn't make much sense.
For one thing, if states are determined to enforce individual emissions limits, they can do it today with legislation. You don't need a predictive model. What does the model add?
Also, the only difference between 2 and 3 is whether a person acts on a thought.
So are you suggesting with #3 that predicted thoughts (e.g. not literal mind reading) which a person doesn't act upon will prompt state action?
Using the unqualified word "freedom" has an ambiguity that political actors exploit. Freedom to do something is entirely separate to "free to live in a world where ___".
To be honest, I feel the latter sense of the word is a bit of a stretch - semantically, not politically.
But you see it because "freedom" is a powerful word in politics, and rather than argue against "freedom", pundits go up the ladder of abstraction and argue the definition instead.
Good question! eHealth Exchange (eHEX) is one of 3 national HIEs that we connect to (currently through Carequality). eHEX is mainly focused on connecting to state-level regional HIEs, which cover a different portion of providers than CommonWell, or Carequality do.
For example, Cerner is a major EHR vendor (used by the VA and others) whose data can only be accessed through CommonWell, since they don't participate in other HIEs.
> that have many years of experience
Relatively speaking, modern HIEs are a relatively new concept (Carequality was founded in 2014) - so extra years of experience doesn't necessarily add any value, and usually just results in more legacy tech to deal with!
TL;DR just to get started it's going to cost you $20k + some months to integrate, $12.5k/yr as the base membership fee (up to $400k if you make a lot of money!), and they charge a per-query price.
The caveat here is per-query in eHEX, isn't what a query is in Metriport. They literally mean every single query (remember the HTTP requests to thousands of endpoints to find patient records, each one of those would be a query). So, if you want to integrate with eHEX only to get limited, messy C-CDA data, then you're looking at paying ~$0.80 per full record retrieval for a patient with 2k documents.
How do I access AlphaEvolve?