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.
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.
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.
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.
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.
>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.
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.
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.
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.
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.
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 is a fun project, and I respect implementing anything birthed from XKCD, but I agree and don't think I'd use this sort of thing to generate my passwords. ( Or I'll just stick to Password123 )
That is a brilliant demo. I get the impression we could scale that out to many dimensions and the quantum computing would be really fast. Could you give some indication of the time to run and cost to run on a quantum computer as of now and the rate of change on those? I would like to have a feel of when this would be both possible and economic. I.E. How big is my problem to give Entropica Labs a call?
:-) we would love to get a call to solve problems.
But quantum computers are still in their infancy, the most powerful ones have ~50 qubits, 50 unstable and noisy qubits.
Meaning that we can only perform 10 to 20 operations per qubit before the noise starts blurring everything.
With the method described here, on an actual 50-qubit computer we can process a feature vector of at most maybe 500 dimensions. This is a small problem for classical machine learning...
However, the industry strongly believes that in fews years we will have quantum computers with thousands of qubits. And things have been improving steadily.
This is a brilliant idea. I really like the idea of bringing modelling into everyday thinking. I do not understand the idea of hiding the hard stuff. Why can't I have access to and change the code underlying the model too? Rich Hickey talked about code as a violin for the master or a one button instrument for the beginner. Perhaps we could have both?
Unfortunate that academics have reasonable solutions (or at least something reasonable to try) to many of the troubles we are encountering but we are unable to hear and internalize them. This poor guy has been singing this song for a long time.
http://timjackson.org.uk/ecological-economics/pwg/
I'm surprised this isn't getting more attention. I have used this metaphor of code as a living entity before and it resonates with those that have lived through such growth. The article beautifully articulates this idea. I think this idea extends to ideas or memes in the
https://edge.org/conversation/richard_dawkins-this-is-my-vis...
dawkins view of battling evolving concepts. The code swarm is a visualization of a one such concept. I think in the enterprise world I see this lots with business process but there is no concept of even writing the business process down let alone visualizing the process over time and the idea of culturing a business process is not even on the radar.
Do the same thing you did for the Gittorrent. A GUI client that runs a torrent of a php file that connects to a torrent of a database file. You just need to always be connecting to the latest and greatest.
