[geph2021]

   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.

[agentofoblivion]

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.

[pixl97]

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.

[agentofoblivion]

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.

[ajuc]

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

[agentofoblivion]

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.

[chrisco255]

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.

[pixl97]

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.

[chrisco255]

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.

[pixl97]

>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.

[melagonster]

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

[klibertp]

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.