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Comment by mcdonji | Hacker News Reader

I think the effort in the testing of the thousands of drugs was to help create the AI model. Then they used that model and it seems to have identified a promising antibiotic. The next time they go through the process they would not need to train the model again right? So get another big list of chemicals and run them through the model.

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   I think the effort in the testing of the thousands of drugs was to help create the AI model.

This gets to the crux of my skepticism around the big claims around the pace of AI advancement. At a fundamental level the upper limit of AI advancement, in any area, is "the speed of information". For some areas, like pharmaceutical/drug development, the information comes from the real world, human/biological processes (e.g. clinical drug trials), which take time. At the extreme, the outcomes of interest could be long-term (i.e. years or decades). AI surely advances analytically capabilities, but ultimately models can only be developed or refined with new data/information, which unfolds at a rate that may be independent of computational speeds. AI models that are highly predictive and valuable by definition necessitates a feedback loop that is tied back to real-world outcomes/timescales.

I'm no expert on AI, but I get this sense that the exponential improvements that many believe will lead to the singularity may in fact reach an inflection point where the curve flattens out becomes linear or asymptotic, as the rate of improvement is governed by the rate of new information in the real world.

Even for existing information, there remains an enormous amount of contextual / cultural / insider knowledge about the world that is not documented in any digestible way by an AI.

For the moment. Things like gpt-4 are already multimodal, but not widely deployed in that fashion. Your data may just be a smart Webcam on wheels away from being ingested.

No, let's be real. The most basic human tasks have yet to be automated (like housekeeping or grocery cart parking lot fetching) because there is a long tail of edge cases and even novel scenarios that occur in even every day situations. It's why we don't have self driving cars at scale without remote or onsite human intervention capabilities, despite well-defined, algorithmic rules of the road. Most jobs are not well-defined / algorithmic and there is no amount of reading that can prepare you for the embodied, dynamic experience of performing those tasks.

sorry, AI is designed for replacing people who work in office.

Truth. Things that require dexterity outside of very controlled conditions (ie. huge factories) are mostly safe from this wave of AI advancement. Things that require human interaction are also safe until uncanny valley is crossed. Even things that require application of domain knowledge - that the AI can have - in the real world are mostly safe. Your plumber won't get automated any time soon. Many desk jobs, however, will become redundant quickly: perhaps 1 in 10 will keep their job, but the job will change into AI supervision and management. At least I hope it will; giving the AI any kind of uncontrolled agency currently seems like a pretty dumb thing to do... Not that people won't try, though.

>he most basic human tasks have yet to be automated (like housekeeping or grocery cart parking lot fetching)

Maybe because they don't pay shit, and can be done by the massive amounts of unskilled labor that exist? Really hard to develop a robot cheap enough for the dexterity needed.

But, even then it's a mistake to think this isn't going to be a massive problem. If everything 'expensive' gets automated then that can lead to a huge pool of labor fighting for low paid jobs that can't actually pay for any assets like houses, education, stocks, etc.

> Most jobs are not well-defined / algorithmic and there is no amount of reading that can prepare you for the embodied, dynamic experience of performing those tasks.

Yea, there is, building an embodied robot and feeding it virtual situations based on real situations. As we get closer and closer to AGI the 'general' functioning of the robot is more and more covered and less and less human intervention is needed.

You hit the nail on the head, and I train transformers for a living. This pervasive axiom that intelligence can just scale exponentially at a rapid pace is rarely questioned or even stated as an assumption. It's far from clear that this is possible, and what you've outlined is a plausible alternative.

It depends on the scaling nature of the problem being researched. If it's one like the '9 months to make a baby' issue, then you can't really reduce the minimum time. On the other hand if it's studying bacteria with a fast breeding rate, then expanding to hundreds of thousands of AI Petrie dishes is apt to rapidly accelerate the study of the problem.

OK, but for a rapidly emerging super intelligence to occur, it seems like all of the relevant problems would need to be of that second type, and that's far from obviously true, and I would argue is much more likely to not be true.

It's possible that no new information is needed, just better analysis.

That's not how science works though. You generate a hypothesis about how something works, and then you execute an experiment that's designed to directly test the hypothesis as much as possible. If we had to just rely on slicing existing data, we wouldn't get very far. You can find data to confirm or deny about anything. Predicting the results before the experiment, and then confirming it works out that way is the much harder, and more valuable, part.

Reading the study's abstract, there will not be a "next time" because the dataset they created, and the model they trained, was specific to Acinetobacter baumannii:

Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii.

https://www.nature.com/articles/s41589-023-01349-8

Not only were both the dataset they created, and the model they trained on it, specific to one organism, the drug they discovered also only works on that one organism ("narrow spectrum activity against A. baumannii"). If they wanted to discover drugs that work on other organisms, like Staphylococcus aureus and Pseudomonas aeruginosa that the BBC article mentions, they'd have to start all over again.

So, not an approach that looks very practical at this time. Maybe in the future, when the sample efficiency and generalisation ability of neural nets has significantly improved it will be useful in practice.

Study:

https://www.nature.com/articles/s41589-023-01349-8

Reading "in silico" never gets old for me.

Counterpoint, the specialization training seems to have been fast enough to be worthwhile; they might need to find multiple antibiotics for the same organism; a narrow antibiotic might be really good because it doesn't mess with your gut bacteria as much as something that broadly destroys everything.

If we had a model that could predict the next working antibiotic for MRSA that would be amazing. And you'd probably need it multiple times as MRSA keeps evolving new defenses.

Even narrowing down the list of substances to test by 10x it's amazing.

This looks very practical to me.

Thanks for the counter!

>was specific to Acinetobacter baumannii

We can reasonably expect the bacteria to mutate against the new antibiotic if/once it's used. It's one shifty opponent. This may make the model obsolete, but maybe not - there'd cause to try the model. Actually, it would have been preferable to get more than one result at first...

[EDIT: Then again, would they have another candidate list? This model doesn't do toxicology. The second list was created by using existing proven-safe meds. Do they have another couple thousand materials good to go? If not, they won't be able to run a second time. ]

The EDIT is an important consideration. Like you say, the trained model doesn't do toxicology. Someone else (even some other model) must first synthesise all the candidate drugs.

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