I think the most succinct argument I can make as to why I bank on “AI as the solution moving forward” is I just took a break from my day job. And without revealing any proprietary information, to do just basic tasks and analyze video frames in real time, at low resolution, for objects of interest (aka resnet-50, etc) requires teraflops. We trivially talk about how many “TOPs” a given workload is, aka trillion operations per second. And it takes hundreds to do anything semi-good enough to be useful.
That simply didn’t exist during past hype days of AI. It was flat out impossible. There was no future world where the researchers looking into AI in the 1960s could have gotten the results we get today. (which I am sure you will point out are still mediocre, autonomous cars now drive themselves but only when all the conditions are just right). Or in 2010.
So I am just going to take any past hype as the ‘rantings of a uncredible madman’, regardless of which Ivy League lab they were working out of, and go with recent results as my barometer for when AI is going to really takeoff. Which are getting rather good.
Anyways the other piece of this is all your trends you are linking are observing a process where:
a. technology keeps getting more complicated
b. the human beings trying to improve it are not getting smarter very fast (if at all), and they live finite lives.
So it’s perfectly reasonable for real progress to slow over time, except in fields where the technology can help you develop itself, which in some domains of computers has clearly happened. (succinct example: frameworks have made highly sophisticated apps and websites, that would have required an entire studio months of effort 20 years ago to create, doable by one person in a week).
No doubt very significant advances in AI have occurred within the past decade or so. AlphaFold practically “solving” the problem of protein folding for example, is a hopeful glitter of technological progress and the promise of artificial intelligence.
However, it remains an open question how far AI will advance before it runs out of track. Because does appear to be approaching a wall. OpenAI observes that the rate at which added computational power is being supplied to create ever more advanced AI models is far outstripping Moore’s Law. It doubles every 3.4 months. This can’t be sustained for much longer.
Meanwhile, many of the advancements in the quality of the actual algorithms utilized seem to be ephemeral. Numerousstudies have discovered that many of the actual models used by AI today aren’t objectively better than those which already existed years ago.
Given that progress in the quality of models seems to be progressing relatively slowly and the brute force method of adding more computational power isn’t sustainable, another AI winter is well within the cards.
To drag civilization out of a technological stagnation, AI doesn’t need to reach human levels, but it also needs to be able to do much more than it can today. Enabling level 5 autonomous vehicles would probably be a feat on the same scale as the triumphs of the 20th century, but so far AI has continued to fail to deliver complete self driving and it isn’t guaranteed that it manages to before hitting the winter.
Solving protein folding and related problems might be enough to create a new era of progress. Look at the issues we have with vaccines. If we could run computer simulations that tell us what kind of antibodies the body creates when given different antigens we would gain a lot in vaccine design. That means we could make a lot of progress on a universal flu vaccine and an AIDS vaccine.
Designing protein to catalyse various chemical progresses is also a huge win.
If AlphaFold 2 is as accurate as its creators have claimed it undoubtedly represents an enormous technical leap. However, it remains to be seen how regulatory constraints and IP laws will erode its value. mRNA vaccines also represent a huge advancement, but were it not for COVID-19 lowering normal regulatory barriers (and providing a deluge of capital), they would still be a decade away, despite most of the fundamental technology already being ready.
With or without advanced protein folding simulations, so long as we remain in an environment where it costs billions to get a novel medical treatment to mass market, there’s little doubt the full potential of this breakthrough will not be realized anytime soon. The question is, how much progress will remain possible working within these constraints. I still expect it will aid in numerous future medical breakthroughs but I dunno about it unilaterally ushering in a new era of progress.
I don’t think the regulatory barrier against mRNA vaccines were completely unreasonable. If we for example at a recent paper that describes the problems with PEGylated anything (including mRNA) from 2019:
The administration of PEGylated drugs can lead to the production of anti-PEG antibodies(anti-PEG immunoglobulin M (IgM)) and immune response (Figure 1) [30]. Due to these phenomena,the PEG-conjugation of drugs/NPs often only provides a biological advantage during the first dose of a treatment course. By the second dose, the PEGylated agents have been recognized by themononuclear phagocyte system in the spleen and liver and are rapidly cleared from circulation.
We now have a way to deliever mRNA a few times per person to people but outside of pandemic conditions it’s unclear why you want to make the few times that you can effectively give mRNA to a person a vaccine that likely could be made is well via established methods at the cost of maybe not being able to give the person later an mRNA cancer treatment because of too much PEG immunogenicity.
The side effects of the second dose of an mRNA vaccines we see currently are higher then the side effects we see from our well tested vaccine formulations.
I think the most succinct argument I can make as to why I bank on “AI as the solution moving forward” is I just took a break from my day job. And without revealing any proprietary information, to do just basic tasks and analyze video frames in real time, at low resolution, for objects of interest (aka resnet-50, etc) requires teraflops. We trivially talk about how many “TOPs” a given workload is, aka trillion operations per second. And it takes hundreds to do anything semi-good enough to be useful.
That simply didn’t exist during past hype days of AI. It was flat out impossible. There was no future world where the researchers looking into AI in the 1960s could have gotten the results we get today. (which I am sure you will point out are still mediocre, autonomous cars now drive themselves but only when all the conditions are just right). Or in 2010.
So I am just going to take any past hype as the ‘rantings of a uncredible madman’, regardless of which Ivy League lab they were working out of, and go with recent results as my barometer for when AI is going to really takeoff. Which are getting rather good.
Anyways the other piece of this is all your trends you are linking are observing a process where:
a. technology keeps getting more complicated
b. the human beings trying to improve it are not getting smarter very fast (if at all), and they live finite lives.
So it’s perfectly reasonable for real progress to slow over time, except in fields where the technology can help you develop itself, which in some domains of computers has clearly happened. (succinct example: frameworks have made highly sophisticated apps and websites, that would have required an entire studio months of effort 20 years ago to create, doable by one person in a week).
No doubt very significant advances in AI have occurred within the past decade or so. AlphaFold practically “solving” the problem of protein folding for example, is a hopeful glitter of technological progress and the promise of artificial intelligence.
However, it remains an open question how far AI will advance before it runs out of track. Because does appear to be approaching a wall. OpenAI observes that the rate at which added computational power is being supplied to create ever more advanced AI models is far outstripping Moore’s Law. It doubles every 3.4 months. This can’t be sustained for much longer.
Meanwhile, many of the advancements in the quality of the actual algorithms utilized seem to be ephemeral. Numerous studies have discovered that many of the actual models used by AI today aren’t objectively better than those which already existed years ago.
Given that progress in the quality of models seems to be progressing relatively slowly and the brute force method of adding more computational power isn’t sustainable, another AI winter is well within the cards.
To drag civilization out of a technological stagnation, AI doesn’t need to reach human levels, but it also needs to be able to do much more than it can today. Enabling level 5 autonomous vehicles would probably be a feat on the same scale as the triumphs of the 20th century, but so far AI has continued to fail to deliver complete self driving and it isn’t guaranteed that it manages to before hitting the winter.
Solving protein folding and related problems might be enough to create a new era of progress. Look at the issues we have with vaccines. If we could run computer simulations that tell us what kind of antibodies the body creates when given different antigens we would gain a lot in vaccine design. That means we could make a lot of progress on a universal flu vaccine and an AIDS vaccine.
Designing protein to catalyse various chemical progresses is also a huge win.
If AlphaFold 2 is as accurate as its creators have claimed it undoubtedly represents an enormous technical leap. However, it remains to be seen how regulatory constraints and IP laws will erode its value. mRNA vaccines also represent a huge advancement, but were it not for COVID-19 lowering normal regulatory barriers (and providing a deluge of capital), they would still be a decade away, despite most of the fundamental technology already being ready.
With or without advanced protein folding simulations, so long as we remain in an environment where it costs billions to get a novel medical treatment to mass market, there’s little doubt the full potential of this breakthrough will not be realized anytime soon. The question is, how much progress will remain possible working within these constraints. I still expect it will aid in numerous future medical breakthroughs but I dunno about it unilaterally ushering in a new era of progress.
I don’t think the regulatory barrier against mRNA vaccines were completely unreasonable. If we for example at a recent paper that describes the problems with PEGylated anything (including mRNA) from 2019:
We now have a way to deliever mRNA a few times per person to people but outside of pandemic conditions it’s unclear why you want to make the few times that you can effectively give mRNA to a person a vaccine that likely could be made is well via established methods at the cost of maybe not being able to give the person later an mRNA cancer treatment because of too much PEG immunogenicity.
The side effects of the second dose of an mRNA vaccines we see currently are higher then the side effects we see from our well tested vaccine formulations.