HedonicEscalator
Paul Conyngham’s cancer vaccine is an example of AI behaving as a normal technology
Thanks for the response!
Can you explain to me what your counterfactual vision for “the world if non-sunburn exposure causes cancer” looks like? For example, do you expect that oncologists would track whether melanoma patients are in the subgroup of “white outdoor-working sunscreen-abstainers [without any sunburn history]”, and report those statistics? Please give some specific examples of what data you’d expect to see, and how we’d be able to gather and verify it.
Can you also explain to me what background you have on carcinogenesis? For example, are you familiar with initiators vs. promoters, the two-hit/multi-hit model, common oncogenes and tumor suppressor genes, etc.? It would be helpful to understand what your internal model of how cancer develops looks like currently before I write any more about the topic.
It’s not controversial. I don’t think anyone knowledgeable in oncology would dispute that sub-sunburn UV exposure significantly increases risk of skin cancer. My aim here was to explain why we can be confident of this belief in as straightforward of a way as I can.
I haven’t looked at your links in detail, but the first and second cover the same ground as the first and second points I made (observable DNA damage + cancer from indoor tanning). As for the third, I wanted to avoid resting any points solely on cohort studies because I didn’t want to get into too much of the controversy there. Though I ended up getting pulled into it on Reddit anyways.
Contra Byrnes on UV & cancer
Of course that cuts both ways, since if it were a pure win to be protected from the sun, we would all be dark skinned.
Not necessarily.
We know that humans need sunlight for optimal Vitamin D production, so this does happen to be true. But your logic here doesn’t hold up. Synthesizing melanin is metabolically costly. Cave creatures, which have no use for pigmentation but also incur no obvious direct disadvantages from it, often evolve albinism because of the benefits of repurposing melanin precursors for other needs.
Humans aren’t cavefish, but the principle applies. If you don’t need melanin, making it is a waste of energy, and benefits from sunlight aren’t necessary to explain the evolutionary pressure against pigmentation.
Cohort studies claiming benefits from UV exposure are not credible. It is impossible to separate UV exposure from positive traits correlated with UV exposure, such as exercising or having a social life, and no, you can’t just control for it.
It is true that your risk of dying from melanoma is low, and not worth freaking out too much about.
You should do a blinded test of your sun exposure with identical placebo sunscreen to see if the UV exposure itself is necessary for positive effects on your mood, for science.
IQ is normalized to mean 100, standard deviation 15 from a sample population of test takers, usually matched by age. The mean is set to 100, by design.
Technically, since widely used norming populations are usually drawn from developed countries and exclude people with severe disabilities, the “actual” mean IQ, if you were to test everyone in the world, is lower than 100.
Nanopore basecalling is a great example of a technology that genuinely depends on deep learning! (It also fits my characterization of problems suitable for deep learning: predicting bases from electric signals is highly complex, and you have massive amounts of labeled training data).
When it comes down to it, “usually preferred” is hard to quantify. What I’m trying to get at is that when I read the methods section of (post-2020) papers in this field, I almost always find that they only mention the use of traditional algorithms, not deep learning ones. Even fewer discoveries are dependent on deep learning. Meanwhile, there are scores of papers floating around enthusiastically presenting DL algorithms which don’t ever see use in the lab. It’s easy to overestimate the role of “AI” by skimming through abstracts.
It looks like DeepConsensus is being used, but it’s not obvious to me how much better it is than traditional methods. Is it more of an incremental improvement useful in specific situations, or more of a paradigm shift no analyst can ignore? I’d love to hear someone working in a related field explain how often deep learning tools are used in practice.