https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
Is this just some performative grousing or do you really think what has been developed to date is “artificial intelligence”?
These comments all conveniently fail to define their author’s goalposts that apparently have been reached or surpassed. What were yours?
Notice how nobody is talking about the Turing Test anymore now that it's either already been passed or is very damn close? We can argue back and forth about whether the real stupidity was the earlier expectation that the Turing Test was a useful threshold for AI, but it's impossible to claim that it wasn't a somewhat common and well-known one, so that goalpost really has been moved in a very dramatic way (or rather, removed altogether and replaced with nothing in particular other than a vague "I'll know it when I see it", in most cases).
But it is also true that numerous ground techniques are issue of the field of AI and generally called AI as they come out. It makes for good press. And that too was silly.
For actually already done: Actually believable chat-bots? Summarizers and question answerers? Generative text and graphics actually usable for generation of text, graphics and (mostly) photo-realistic renderings? Architecture brainstorming? (And logos, etc.) Kinda working self-driving cars? New Go playing strategies? A super-human Go champion? AI is on a roll these days.
That's not counting the more proprietary and discreet applications being already used all over the place. I fully expect there are already several.
It's like arguing that when a child rides a bike with stabilisers that they have "learned to ride a bike" and then complaining anytime someone suggests the child learns to ride without stabilisers, because they "already learned to ride a bike" and now that's moving the goalposts - but you still want them to learn without stabilisers, presumably, you're just complaining about the term used to describe it, for ... apparently no reason or benefit whatsoever?
- Passing the Turing test (for some measure of that) is a huge achievement - Poopoo-ing it is unuseful.
- Moving the bar from chat-bot Turing test to wwwaaayyy over there at super-human intelligence is unuseful. There are lots of valuable steps in between.
- There are many valuable steps before super-human intelligence.
- Most humans are nowhere near super-human intelligence. They are still "intelligent" enough for significant effects on the world as well as day-to-day grind.
- You can get plenty of sci-fi-level results without super-human intelligence
- We have already achieved AGI - Artificial General Intelligence because plenty of humans operate "just fine" in the world with bog-standard intelligence and for the ones limited to a keyboard roughly equivalently to an LLM-based chatbot. Top of the line AGI OR top of the line intelligence is not necessary to massively change the world.
- Incremental and bonus goals are a great thing! You are right!
- Many humans will fight hard to reserve the term intelligence to wet stuff. That's unuseful.
- Just because "it's done" doesn't mean all of a sudden that Turing test was not a good test.
About the only point of arguing it is if you personally developed Deep Blue to beat Gary Kasparov at chess, when "beating a human grandmaster at chess" would definitely(tm) be AI, and now you feel hacked off that your personal or team recognition has been trivialised by moving goalposts and you've missed out on fame and a place in history. But I'm thinking it can't possibly be that all the people rambling about moving goalposts could be in that position and not mention it.
Why does it feel like people think this is a useful or interesting whine? OK you brute-force solved tic-tac-toe, you built an AI. Congratulations, everyone recognises the dawn of Artificial Intelligence - and truly, enumerating all states of tic-tac-toe is all we ever dreamed of, all we could want, it's really all there is to intelligence. The term "AI" will never ever be used to mean anything else.
The term "AI" means (solving the first problem that was ever suggested to be AI decades ago) - how is that a better state of the world? What has anyone gained from "not moving the goalpoasts"?
These things are... physically possible, but have WBE and uploads as a hard requirement. Those are going to affect a hell of a lot of things more than the drug industry!
Amusingly, machine-phase nanotechology and blood nanobots would be easier to evaluate, since simple cell-level mechanical interventions (reading surface proteins on cancer cells and chopping them up, say) will have fewer interactions than a small molecule that diffuses into every cell in the body.
Humans aren't capable of doing this, but still make useful drug discoveries. AI can be empowered to conduct research in the real world, it doesn't need to simulate everything.
This is how the human doctors who have cured things in the past have done it, is it?
The way this is going to work, when it happens, is that you'll ask the AI for a cure and it will give you a hundred candidates. A human doctor will look at the list and throw half of them out because they're toxic, several of the remainder will be excluded by animal trials, the few remaining will proceed to human clinical trials and one of them will actually work.
The rate limiting step isn't "thinking up molecules." The University of Bern enumerated all possible molecules composed only of hydrogen, carbon, nitrogen, oxygen, sulfur and chlorine, up to 17 atoms. That produced 166 billion molecules. https://pubs.acs.org/doi/10.1021/ci300415d There are commercial drugs considerably larger than that. We've got molecular structures out the nose. There is no shortage of molecules.
The problem is the clinical trial. Putting drugs in humans and seeing what they do. That's the part that takes years and tens of millions of dollars. Using AI for anything else is like saying Microsoft Powerpoint accelerated drug development. Sure, it made presentations easier, but did it do anything for the problem of putting chemicals in people?
Which is useless, because you can't run 166 billion clinical trials.
But you could run half a dozen if there's a strong chance one of them will be a success. Filtering the list down to 100 molecules from 166 billion, some of which can be further eliminated by human evaluation without the expense of clinical trials, is actually useful.
You still ultimately have to do the clinical trial, because there is no substitute for empiricism.
> That's the part that takes years and tens of millions of dollars.
It doesn't matter if it takes tens of millions of dollars if the result is a billion dollar drug.
Clinical trials only start after about five years of research and development. While they do represent a large part of the budget (even in the hundreds of millions of dollars), there are countless of other necessary steps before, during, and after trials to ensure that drugs are both safe and effective. The problem is that we still don't understand how most of these molecules behave in the body, and how we can produce them reliably and efficiently enough, which brings me to the next point:
> [...] but did it do anything for the problem of putting chemicals in people?
Yes, there are plenty of problems that AI and computational chemistry already help with in the pharmaceutical industry, including predicting solubility, stability, crystallization, granulation, toxicity, pharmacokinetics, developing the formulation, optimizing and scaling up both the synthesis and production process, developing appropriate techniques for quality control, and so on.
In all these cases and more, AI can help reduce the amount of experiments that need to be done in the lab, which require highly specialized equipment, personnel, and a lot of time. Oh and design of experiments is also a very important topic, again aiming at reducing the amount of lab time needed.
Admittedly, most of these things aim at ensuring that we do not put the wrong chemical in people, but they do represent most of the R&D effort spent in pharma, and reducing everything to clinical trials is not correct. There is a very wide gap between "AI will design drugs entirely on its own" and "AI is useless".
Google is already doing something like this: https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-...
This is great if you want to use well understood pathways or make new drugs that you can then patent and mark up.
New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first: https://globalnews.ca/news/9793403/ozempic-canada-scientist-...
Sure, but a lot of the ground work has already been done, or is susceptible to simulation. They're getting a lot of results out of simulating protein folding and things like that.
AlphaFold was a nice surprise when it happened, too.
Also, while not medicine focused, Google’s GNOME project results announced a few weeks ago was pretty remarkable. They discovered more theoretical new materials using their ML approach than the rest of human history combined, and they are already confirming many of the results in laboratory settings. That has the potential to be a revolution in limitless scientific and engineering applications.