> Let's also imagine for the purpose of conversation that one of the things that can be gotten at an extremely high skill level through increased intelligence is the power of persuasion.
That feels like a bait and switch slyly introducing a whole new dimension. The discussion started with establishing that LLMs are extremely helpful with AI research. What is the evidence to justify rapidly improving persuasion skills? Even the scenario you've outlined hinges on a human being stupid enough to take LLM advice wholesale -- and every conversation that hypes up AI takes us one step closer to a world where some susceptible human feels amenable to that.
No bait-and-switch here. The discussion started with the idea that LLMs are helpful at improving LLMs. The goal of improving an LLM is to make the LLM more "intelligent" (whatever humans think that means). If they're helpful at that, they have to keep becoming more "intelligent".
I didn't present any evidence that more-intelligent means more-persuasive. If you don't think it does, then there is nothing to think about in the point I made, as no matter how "smart" the models get they will likely remain subject to human wills.
Nice work putting this together! But also, we're entering an era where folks might easily be able to scratch their own itch for many of these needs :-)
The striking detail here is that the patient's post-shift numbers wouldn't have tripped the static alarms we currently use; a resting heart rate moving from 44 to 49 bpm is still "athletic" by population standards. What mattered was the synchronized shift in several metrics relative to his personal baseline.
Perhaps because statistics education attunes us to average over individual differences, any mention of "data" unfortunately makes people instinctively jump to this 'cross-sectional' corner... textbooks explain how the typical human body behaves, and standard education and medical practice respectively emphasize averaging and population-level guidelines.
I think this approach is limited in some fundamental ways, and forces a dilemma: either accept wide and unactionable bands for what counts as normal, or use narrow bands that over-pathologize most individuals as diseased. The natural consequence of this is to start "fixing" deviations, and this is the territory of dangerous over-steering. This is why the medical community is afraid of "incidentalomas" and over-intervention. Tests which are point-in-time and compare one human to others are problematic in this respect. This is especially acute when the body is measured along many axes: like a full body scan. Much like the US Air Force Study of pilots discovered that there is no single pilot compatible with the "average" pilot measurements, comparing metrics from an individual's full body scan to a cohort average is likely to drastically overestimate problems.
In some ways, learned systems (like our physiology which is constantly adapting to our individual environment and experiences) follow the opposite of the Anna Karenina principle: every healthy individual is healthy in their own way. So the real diagnostic leverage isn't comparing an individual to an aggregate human template, but measuring change against their own homeostasis, and this requires high temporal resolution, not wider population comparisons.
I'm currently exploring how to bring this kind of longitudinal tracking to higher-fidelity modalities like wearable imaging. If any clinicians or researchers here are thinking about how to operationalize personal baselines in practice, I'd love to chat (email in profile).
I was a surgical trainee not a cardiologist, but there is something interesting and unusual about the step change here. In the context of structural heart disease, this to me implies a cardiac event ?chorda tendinae rupture or possibly ?ischaemia that suddenly worsened the competency of the heart valves, reducing cardiac output and leading to the change in resting heart rate etc. This later put the patient at risk for sudden cardiac death.
I guess the authors' argument here is that the change in wearable data could alert clinicians to the need for further imaging and/or change management. But the sudden step change here may not be typical or generalisable across all causes of sudden cardiac death - they obviously state this is n=1, but I think it's worth emphasising that the change may be quite specific to this case.
We do generalise in medicine as it speeds up decision making, but we always see vitals in the patient context. Longitudinal data may be able to build a picture of what is normal for a patient - but I would say healthy physiology is fairly consistent. Even with individualised data, the problem will still be signal to noise, and working out when to alert the clinician or patient.
One of the reasons doctors don't like unnecessary scans is they have knock on effects - radiation, unnecessary procedures / biopsies, patient anxiety and extra strain on healthcare systems. A larger problem is reaching patients that are less health-conscious, that are unlikely to wear wearables or access the diagnostic tests they need.
What kind of wearable imaging are you thinking of?
Is the development experience compatible with modern AI-assisted workflows? Coding agents have drastically reduced the entry barrier for playing with software projects, and I can think of some fun ideas for Smalltalk things I've been curious about.
I saw a comment saying it is, and having a coding agent able to modify a running environment is a game changer, but I haven't wrapped my head around that yet.
AI isn't my personal cup of tea, but anyway. There is a monthly "show your cool stuff" online meetup, and the most recent one was directly about one dude using AI to build his own quasi-gnome-desktop-like morphic world within cuis.
DeepSeek is just the one with name recognition from the critters in congress. Remember when Nvidia stock had it's so-called "deepseek moment"? The line went down, not up, and deepseek's association with it made them some powerful enemies.
Nobody in congress knows what a "Minimax" is, so they're safe from discrimination, for now.
People in government talk to each other. Congress are the ones with power. DHS was created by an act of Congress, and continues to exist at the pleasure of Congress.
It's perfectly fine and accurate to say scoping the action here to DeepSeek reflects poor knowledge from DHS and the administration. DHS has broad supply chain authorities granted to it by Congress in 2018, and those authorities are what empowers this decision, but the specifics of the policy are downstream from the administration, not Congress. This administration doesn't care at all about what Congress thinks.
It is THE competitor right now when it comes to price and intelligence. Also, just banning it plants fear in other sponsors and investors. When they do it like this, they can pretend that this is not a part of regulatory capture but it is an actual security concern because "this one in particular is dangerous" and "we'll tell you when others become dangerous" which will happen when they become a competition (now less likely because investors are cautious).
Reinforcement learning can definitely (in principle) leave this envelope, but it depends crucially on whether the feedback generates new information from reality, rather than just repackaging what humans have already discovered in the form of RL environments and densified rewards. So the question is not about RL per se, but what is the information source, what is the bandwidth of information it supplies, and then only finally the effectiveness of RL in distilling information that into models.
> Unfortunately AI excels at such problems because it oversees a much larger slice of our knowledge than most individual humans. I've always said that cross domain application of knowledge is the easiest way to make progress and I haven't really seen anything conflicting with that to date.
What is unfortunate about this? I suspect we concur in not buying into the AGI/ASI/RSI/<jargon of the day> hype, but this isn't this kind of breakthrough extremely valuable regardless?
Maybe it's not expanding the convex hull of what is possible, but the fact is that human progress is extremely jagged even within that convex hull -- for myriad reasons limitations related to funding, cross-domain expertise, number of scientists, etc.
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If you're worried about what it takes for a novice setting out in the field, it might be useful to see how specific "mature" subfields have evolved in their training structures. Eg. I have a background in theoretical physics and it is common for grad students to do courses for a couple of more years and take a few more years to reach the same productivity compared to grad students in (say) CS/AI.
Masked emails are infinitely shareable eg. a spammer can harvest that email address and then reach you. You don't typically have a setting to block anyone else (other than the original intended counter-party) from reaching you -- unless you set up some specific filtering rule (if that's even supported).
That feels like a bait and switch slyly introducing a whole new dimension. The discussion started with establishing that LLMs are extremely helpful with AI research. What is the evidence to justify rapidly improving persuasion skills? Even the scenario you've outlined hinges on a human being stupid enough to take LLM advice wholesale -- and every conversation that hypes up AI takes us one step closer to a world where some susceptible human feels amenable to that.
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