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Not OP, but OP did not imply any of the things you mentioned at all.

The entire class of "biologics" drugs only came about in the past 15 years thanks to advances in sequencing and biotech. They are the mainstays of treatment for dozens of serious dermatologic, rheumatologic, and GI diseases, not to mention they directly cured multiple cancers.

I think you may be misunderstanding how the tool is used (at least the version I used).

The doctor talks to the patient, does an exam, then formulates and discusses the plan with the patient. The whole conversation is recorded and converted to a note after the patient has left the room.

The diagnosis and plan was already worked out while talking to the patient. The ai has to convert that conversation into a note. The ai cant influence the plan because the plan was already discussed and the patient is gone.

As a medical student, I used the dragon dictation software (no AI) to write notes in the ED and more recently I used a pilot of this ai version to write clinic notes.

Overall, I was quite impressed. It definitely made writing notes much faster, which all doctors hate to do. While it had some problems with where to put key pieces of information (like putting details from the physical exam back in the history), it only took 5 mins of rearrangement after the visit to complete the note.

For simple diagnoses, it does a decent job coming up with the assessment and plan, probably because all the simple diagnoses were in the training set. For more complex ones though, it needs to be exactly dictated by the doctor. I can see this being used very well in primary care.

Edit: When I said “coming up with an assessment and plan” I mean documenting the assessment and plan based on the ai’s recorded conversation with the patient. The conversation with the patient is meant to be understandable. The “assessment and plan” documentation on the other hand is jargony and meant to be read by other physicians.

The Emperor of all Maladies: A Biography of Cancer by Siddhartha Mukherjee.

This book describes how hundreds of people tried (and failed) to cure cancer over *millennia*. It spends a lot of time talking about how the modern approach, which works surprisingly well, was developed by Sidney Farber and others through great effort and a lot of good science.

My feeling when reading this book was similar to reading about the making of the atomic bomb- what happens when you put a bunch of smart people in a box and tell them to solve a problem. However, this time it didn't work nearly as well, because as we found out, curing cancer is an order of magnitude harder than building an atomic bomb. Building the bomb required new engineering, while curing cancer requires new science and new engineering.

I’ve gotten it working with small cherry tomatoes, but you have to cut it into quarters to get the right wavelength.

I suppose if the grape is in a puddle of electrolyte, it can short out the quarter wave antenna. So drying probably only matters if your grape is very juicy!

For people who want to try making plasma from grapes in a microwave, here are the steps that work for me every time (I’ve done it for friends and highschoolers about 20 times)

1. Cut a large grape (1.5cm diameter) partially in half, leaving just a sliver of skin connecting the two halves.

2. Dry the cut sides by dabbing with a paper towel.

3. Place the grape halves cut side down on a Pyrex dish. Keep the turntable in the microwave (important, since microwaves have hotspots). Place the dish with the grape so that the grape orbits inside the microwave.

4. Microwave on high for 30s.

If you don’t hear a hum and see sparks within 10 seconds, you may have too large a grape. In that case you can try splitting the grape into two quarters, connected by a thin sliver of skin. Don’t forget to dry all the cut sides!

I would not be surprised by this number given the sheer volume of crap journals that publish anything. However I would expect that the proportion of fakery in papers with >20 citations is much lower than 1 in 7, which should be somewhat reassuring. Remember that a p-value of 0.05 means that 1 in 20 studies cannot be replicated by random chance alone without any malicious intent.

Side note: the “1 in 7” claim from this paper is based on a straw-poll of N=12 existing forensic metascientific papers. I find it somewhat ironic that the author makes some strong conclusions based on this number which to me, though very plausible, is itself lacking scientific rigor.

Shockingly enough it is actually possible to do decent ray casting and much more on the GBA despite the incredibly minimal compute budget.

Joshua Barretto has been working on a GBA port of Super Mario 64 with entire 3D levels, characters, and movements. In my opinion just incredible work:

https://youtu.be/9mUsgJ-HiDM

From Gizmodo article, a reporter asked:

Scoble can be heard in the video asking one of the robots, “Hey Optimus, how much of you is AI?”

The robot, or whoever was controlling it, seemed to scramble for an answer, saying “I can’t disclose just how much. That’s something you’ll have to find out later.”

“But some or none?” Scoble asked with a laugh.

“I would say, it might be some. I’m not going to confirm, but it might be some,” the robot responded.

It’s pretty normal for heart rate to increase with a drop in blood pressure. It’s part of a normal reflex called the baroreceptor reflex that your body evolved to keep you alive.

To answer your question, there have been an abundance of epidemiological studies showing that the drop in blood pressure is worth the slightly increased heart rate (assuming you’ve been diagnosed with hypertension). The main benefit is the drop in stroke risk, atherosclerosis, and kidney damage, even despite the fact that your heart has to beat faster.

Surprisingly, yes the predicted structures from AlphaFold had functional groups that fit with experimental data of binding partners and homologues. While I don't know whether it matched with the actual crystallization, it did match with those orthogonal experiments (these were cell biology, genetics, and molecular biology labs, not protein structure labs, so they didn't try to actually crystalize the proteins themselves).

Well I'm sure one could look at number of published papers etc, but that metric is a lot to do with hype and I see it as a lagging indicator.

A better one is seeing my grad-school friends with zero background in comp-sci or math, presenting their cell-biology results with AlphaFold in conferences and at lab meetings. They are not protein folding people either- just molecular biologists trying to present more evidence of docking partners, functional groups in their pathway of interest.

It reminds me of when Crispr came out. There were ways to edit DNA before Crispr, but its was tough to do right and required specialized knowledge. After Crispr came out, even non-specialists like me in tangential fields could get started.

I think I disagree with most of the comments here stating it’s premature to give the Nobel to AlphaFold.

I’m in biotech academia and it has changed things already. Yes the protein folding problem isn’t “solved” but no problem in biology ever is. Comparing to previous bio/chem Nobel winners like Crispr, touch receptors, quantum dots, click chemistry, I do think AlphaFold already has reached sufficient level of impact.

I’ll add to this excellent comment by mentioning that embedded+RF is another area that’s very lucrative and specialized. Lots of wearable communications stuff from low-end IOT to cars, industrial machinery, to high end phones use embedded and RF.

People used to mention medical devices as another specialization but I think nowadays the embedded design side of things is not the “hard part” and so it’s less lucrative.

A key skill for any scientist is to differentiate quality work and science that can be easily faked.

The Alzheimer's and Parkinson's fields are too easy to fake, and too difficult to replicate. The new ideas are only ~20 years old. Big pharma companies are understandably wary of published papers.

When people say "trust the science", they often refer to things like masks, and antibiotics, and vaccines. That science is hundreds of years old and have been replicated thousands of times.

TL;DR: Some science should absolutely be trusted, some shouldn't. It's not surprising that you can't make blanket statements on a superfield ranging from germ theory to cold fusion.

The problem with putting the onus on the journals is there is no incentive for them to reward replications. Journals don't make money on replicated results. Customers don't buy the replication paper they just read the abstract to see if it worked or not.

I do like the idea of institutions giving tenure to people with results that have stood the test of time, but again, there is no incentive to do so. Institutions want superstar faculty, they care less about whether the results are true.

The only real incentive that I think can be targeted is still grant money, but I would love to be proved wrong.

This sort of behavior is only going to worsen in the coming decades as academics become more desperate. It's a prisoner's dilemma: if everyone is exaggerating their results you have to as well or you will be fired. It's even more dire for the thousands of visa students.

The situation is similar to the "Market for lemons" in cars: if the market is polluted with lemons (fake papers), you are disincentivized to publish a plum (real results), since no one can tell it's not faked. You are instead incentivized to take a plum straight to industry and not disseminate it at all. Pharma companies are already known to closely guard their most promising data/results.

Similar to the lemon market in cars, I think the only solution is government regulation. In fact, it would be a lot easier than passing lemon laws since most labs already get their funding from the government! Prior retractions should have significant negative impact on grant scores. This would not only incentivize labs, but would also incentivize institutions to hire clean scientists since they have higher grant earning potential.

HN has always had limitations to all posts- see second paragraph of the FAQ:

> Off-Topic: Most stories about politics, or crime, or sports, or celebrities, unless they're evidence of some interesting new phenomenon. Videos of pratfalls or disasters, or cute animal pictures. If they'd cover it on TV news, it's probably off-topic.

I personally appreciate that political news is limited on HN. It's reasonable to create communities where things stay technical, just like many people don't discuss politics in the workplace.

> In 2025, creators will also be able to use Dream Screen for generating 6 second standalone video clips for their Shorts, like a cinematic underwater reveal of the Golden Gate Bridge.

While this is clearly an announcement for investors (see how they bring up the Transformers paper again), I fail to understand the value add for Youtube content.

Just like scrolling through AI generated Facebook photos is not engaging, so too will be the glut of AI generated Youtube shorts.

Super excited about this! I hope more people will pick it up in the hobbyist space now that Fusion costs money.

I'm not sure what the popularity of these different CAD softwares are. I've seen quite a few hobbyists use OnShape recently, and a few people use OpenScad. I don't think I've seen another FreeCad user in real life though.

Beautiful, this person can do some really good web design.

Cool visualization!

It would've been nice to not assume only one lottery winner. People tend to pick numbers that are meaningful for them: birthdays, favorite numbers, lucky numbers. Thus it actually significantly increases your EV if you pick unusual numbers, which is not reflected here.

Exactly. If the proteins suggested in this paper are very similar to known good binders in PDB then I am much less impressed by the results. You could argue they are generating a structure from the training set.

I want more info about how novel these proteins are.

Maybe this is in the supplement of the whitepaper [0], but I would have loved to see more analysis of how novel the designed proteins really are.

In the whitepaper they mention that they are novel compared to other in silico design techniques, but to my knowledge other binders to VEGF and Covid spike protein exist and would already be found in the PDB database that Deepmind trained the model on.

This is not to minimize the results- if the history of ML is anything to go by, even if AlphaProteo does not currently beat the best affinity found by in vitro screens, I do not doubt that it soon will!

[0] - https://storage.googleapis.com/deepmind-media/DeepMind.com/B...

This essay resonated with me, speaking about the "mediocrity" of our future relationship with computer assistants.

I loved the paragraph about feeling "scammed" though I would've called it being "faked". The AI doctor can never use the stethoscope around her neck. She is hijacking totems of professionalism to appear more comforting, without the capabilities to back them up. The fake veterinarian can suggest a diagnosis but can't actually treat anyone. The real-estate chatbot cannot try to help a domestic violence victim.

Maybe that's why I interact with AI assistants the same way I interact with psycopaths. I'm comfortable interacting with them in jobs where the law will incentivize them to behave well. But for things like teaching, or medicine, or personal matters, I prefer someone with empathy.

In case anyone is looking for the youtube video, it's pretty impressive:

https://youtu.be/abi84lnjNV4

You don’t need to force the halves together quickly to start a chain reaction, but you do need to put them together fast to create a bomb. If it’s not fast enough you will get a “Fizzle” [0] where some chain reaction is occurring but not over a small enough timespan to make a bomb or to stop the material from disintegrating itself. A similar slow chain reaction process is used to control energy release in nuclear power plants.

[0] - https://en.m.wikipedia.org/wiki/Nuclear_chain_reaction#Prede...