For example, it helps me install academic software, debug things. It helps me take a large dataset and write scripts to ask questions. It helps me go through experiment drafts to see if I'm missing things. It helps me remember obscure formulas I use every 6 months. It has not, at least in my experience, come up with anything truly novel.
A concrete example: AlphaFold is great...to come up with a starting model for a chimeric fusion or something. What would have taken me 1-2 hours fumbling around in PDB or CIF files is now a quick prompt.
is it all the work? no, but it's a part that's early on and have high perceived impact.
then, as you progress, that tool actually gets in the way and a new feature that would take 2 hours, now is around 2 days.
Though in some areas where I can sustain interest, AI is helping me go deeper. For instance, I've been putting myself to sleep at night by just asking it questions about expectation maximization and Bayesian statistics. This has seriously boosted my understanding of cryo-EM alignment algorithms in a way I couldn't do in grad school because there was no professor that understood enough to help me when I got stuck reading literature.
So it's a double edged sword for sure.
A) no education
B) no resources
C) not smart enough to be a self-taught bio-hacker
Everyone hears "AI is going to cure disease" and pictures some cure-all pill from a bio lab which is what I feel this paper is hinting at is missingb but that's the top of the funnel; I'm at the bottom where patients live and that is where AI is already quietly working. Its just not being benchmarked.
I built https://crohns.ai. I set out to make an AI-native clinical-trial manager with a feedback loop (DDP) and ended up somewhere completely different: instead of chasing a new "drug" which is totally out of my grasp; financially, intellectually etc... I used it to codify a care protocol that helped me avoid a flare after I got laid off, lost my insurance, and lost access to Skyrizi.
I think that was originally linked but got changed to the £30 to Elsevier version for some reason.
It's difficult to calibrate statements made by other scientists unless you're well embedded within a field: Is this someone whose opinions matter? Are they the subject matter expert they make themselves out to be? Is this research itself truly impactful? Is it really 5 years until it will be realized outside of academic labs? Etc...
It's difficult to decipher questions around credibility because they rely on real-world interactions and associations that extend beyond the physical tokens of paper counts, publication venues, citations, and author lists that typically lag behind the front of human knowledge which is generated from real-world interactions. It can be simple things, like the insightful question a grad student, with minimal publication history, asks in a seminar.
Of course, the paywall is also unhelpful too, but a good, brief commentary by an appropriate commentator is a better link for 99% of prospective readers compared to most "peer reviewed" (scare quotes because that's a real question nowadays) articles.
Only for values of 'all' that exclude well-connected members of the billionaire class and their select associates.
Wheres my follicles dammit?
its slow-release oral minoxidil formulation called MINX. AI helped with the formulation [1].
its in in similar category as VDPHL01. Hundreds of millions if not a billion dollars has been invested into Veradermics, and their main product is VDPHL01 (also an extended-release oral formulation).
I've been taking it without significant side effects for ~15 years, so I'm not worried, although at this point it's losing its main effect as well.
The lack of comparable data and testability really does seem to be a challenge. I wonder if people would be more willing to collect and share lots of health data if the collecting company was a non-profit dedicated to anonymizing it.
A National Treasure, on the other hand - they enrich life without being a vector for tribalism (eg:Michael Kramer/Kate Reading).
Ketamin should have huge impacts on neuro/brain plasticity when used properly (i.e. in therapy)
In therapy as well.
Please. Please let some people with power and influence understand this lesson sooner rather than later. I understand the reasons that's unlikely to occur, but usually the impact isn't quite so drastic and expensive as this is. Just because something is new and shiny doesn't mean that it'll produce the outcomes you need at the other end, and until it's shown that capability your approach to it should be MODERATE.
It's also worth mentioning that drug development timelines typically exceed the interval in which these technologies have been available (or at least effective). Measuring impact will take a long time.