I actually do this for a living within pharma now! There's a TON of work that goes into drug discovery before we even call it a program. The odds of success are low, so we put in months of work evaluating a candidate before even have a hunch of a program.
Yes, the science advances, previously high-hanging fruits become low-hanging become high-hanging again [1], but the tooling also advances: we now have databases like OpenTargets which let us more easily evaluate potential drug targets. Failure is so much more than that, though: a program can fail after you've shown efficacy in animals, sometimes it just doesn't happen in the human subjects. Or you fail to find the right measurement (endpoint). A million ways to die.
[1] Gene editing is an example: impossible, then very possible, but now the blocker is public perception which in turn blocks investment.
Doesn't that happen with any first-year psychology or med-student, too? You have some basic knowledge of what could happen, which therefore means that it does happen, right now, here. It normally goes away with more experience and exposure, and yeah, some personal growth.
I used to co-supervise a PhD student who suffered from severe anxiety, she was good but her anxiety stood in her way almost all of the time. You could see that she used up 3/4 of her brain just on being anxious, there wasn't much power left to do the actual work to any standard. It's a horrible disease. (and yeah, she was on medication and diagnosed).
It wouldn't surprise me if Japan has its own market for train assets. There's a big community of train simulators! Go to the Kyoto or Tokyo train museums, they have dozens where you step into a replica of a train cab and then drive a photorealistic simulation (sometimes also just film) - the ex-keyboarder for Casiopeia runs a train simulation game company that makes those since the 90s (https://en.wikipedia.org/wiki/Minoru_Mukaiya). There are some Nintendo Switch train simulators like Densha de GO that are only available in Japan.
I'm sure there's a treasure trove of already-built high-quality assets of Japanese trains.
They are pretty much the same, yes! Those eDNA providers usually use a wider range of primers (12S, 16S, COI etc.) to capture a range of invertebrates, vertebrates, plants etc. For Covid, since it's only that one virus, you can target only the one gene so it's a bit easier.
Many billionaires are starting up ocean expedition places, surely they'll want IT. Off the top of my head I can think of OceanX (Ray Dalio), Inkfish (Gabe Newell), RevOcean (Kjell Inge Rokke out of Norway). They're all building or already have massive ships with sophisticated IT and research equipment.
>In user experience (UX) design, personas are fictional characters representing the different types of users who might use a product or service. These personas are based on user research and are designed to help designers and other stakeholders understand the needs, goals, and behaviors of the different types of users.
KPGM et al. are used as political ammo to push through internal changes. Those in power rely on consultancies underlying their decisions (painful redundancies, firings, etc.). Acknowledging that the arguments for these painful decisions was hallucinated will lead to many problems for powerful people, so for now it's best to just try and sweep it all under the rug.
I'd go even further: what happens in biology is antithetical to the way software people think.
The HN/YC crowd generally has software brain: https://www.theverge.com/podcast/917029/software-brain-ai-ba..., "when you see the whole world as a series of databases that can be controlled with the structured language of software code". Biology doesn't work like that most of the time, it's squishy and weird and unpredictable, and the models we have of biology (including genomics!) are faulty at best, misleading at worst. I've supervised PhD-students and it takes some time for people's brains to be comfortable with that squishiness, that random behaviour, that 'putting A into the system only rarely produces B and we don't really know why but we do it anyway' view of the world. Software engineers struggle, even abhor that kind of world, which is why you rarely see them being interested in it; and if they work in it, outcomes are sometimes amazing and Nobel Prize worthy, more often nonsense that silently disappears.
Modeling protein-protein binding is still a massively unsolved problem, mainly because we don't really have the data. Alphafold2 was great but didn't actually 'solve' protein-folding as all input data is from single 'state' X-ray crystallography of the proteins, not 'really' how these proteins behave in the wild. So it's still very, very had to predict what binds to what, which of course is a multi-billion-dollar industry.
I work in a pharma-field and I wish we could easily design molecular binders. We still spend millions every year finding targets that could 'smuggle' our drugs into cells.
Some other players in this field are Boltz Lab and Isomorphic Labs (the Alphafold Google spinoff led by Hasabi). None of them can predict anything complex or 'big', everything is peptide-level. OP's work is another step towards something better.
The most interesting part in the preprint is that they find no matches for their designed binders in the world-write protein database. An open question with protein-designers is whether they just regurgitate training material, which is far easier to test with English-language models.
At my previous work, I was collating somewhat random unconfirmed animal sightings. I also had a separate database of animal occurrence probabilities (species distribution maps). I'm not a statistician but that sounded like a clear job for Bayes theorem: given a sighting and the overall probability of that sighting in that area (species distribution map), and some other assumptions about the noise of the sighting, what is the probability that the sighting actually included that species?
Claude asked me three questions and then wrote a beautiful Python implementation that queries the map and spits out a table of adjusted probabilities. Felt immensely powerful - I can do this 'on my own' now, I don't need to wait to find the right people or learn the right thing first.
Ah yes, there is a gap between what our regulator wants and what the reality is. I have no qualms that they'll hover out the data if they want to, we know that since Snowden. But I have to comply with the regulator, not with reality.
3. from my opportunity - For many (not all) LLMs, Bedrock gives you control over which country the data stays in. You have no control over that with the Claude API, for example. We do not work in the US and have strong requirements for the data to stay in our country, which Bedrock gives us control over.
Yes, the science advances, previously high-hanging fruits become low-hanging become high-hanging again [1], but the tooling also advances: we now have databases like OpenTargets which let us more easily evaluate potential drug targets. Failure is so much more than that, though: a program can fail after you've shown efficacy in animals, sometimes it just doesn't happen in the human subjects. Or you fail to find the right measurement (endpoint). A million ways to die.
[1] Gene editing is an example: impossible, then very possible, but now the blocker is public perception which in turn blocks investment.