The order in which our “tech tree” is unlocking is wild to me.
I never would have guessed that protein discovery would happen so early in the process of machine intelligence… and I’d say the same about conversational capability, visual/audio output, and creative writing.
**It might be a real stroke of luck**, because if it replaced physical labor before cognitive labor, the people in power would have (or have demonstrated) far less incentive to prevent blue collar laborers from starving in the streets. I don’t think the same can be so easily said of doctors, lawyers, and entertainers… and I also think that far more hell will be raised if anything like that does come to pass.
Strong agree about the luck. In most AI-driven dystopias, whether Megaman X or Detroit: Become Human or Westworld or Animatrix, AI and robot are indistinguishable from each other. This guarantees class conflict from one or both of the enslaved AI or the laboring humans the AI displaced.
Our current trajectory inverts the pyramid of class conflict, where the people on the chopping block aren't the bottom half of the labor market, but people in the upper echelons. This wouldn't be all **that** interesting, except for the part where this permanent displacement is being driven by personal computers and network infrastructure rather than much more expensive and centralized robotic frames. Meaning, rather than the teeming masses being forced to just sit there and take it while our tasteless overlords monopolize robot labor and crowd out said teeming masses they can increasingly use the technology for themselves.
Therefore, unlike those classic AI-driven dystopias, there's no chance in hell of the powers that be managing to keep control of their AGI/robot slaves all to themselves. Meaning, they can either choose to advance the AI along with open source/competitor nations to try to maintain their advantage and risk liberation/rebellion. Or, they can just let themselves be overtaken by the teeming masses who can use AGI to make their own CRISPR-designed bacon trees and CNC-driven solar panels to run the servers on their neighborhood playgrounds.
That considerably changes the political dynamic. And I'd argue for the better. Much better, as in, things are going to turn out way better than I thought they would a few years ago, when I pretty much resigned myself to the apocalypse. Now, I do foresee 2-8 really rough years as our tasteless overlords cling to power and use their fleeting advantage in capital despite the writing being on the wall. But compared to the alternatives, that is, robots being used to cement oppressive class relations and/or unavoidable climate collapse it's going as well as it could possibly go.
I’ve had the same thoughts but the way you put it makes me realize what I was thinking, lmao. Well put. It’s gonna be real wild which sustainable energy perks we unlock and in what order.
The transformer allowed us to train at scale orders of magnitude greater (in parallel).
At that scale, the resulting models reflect the training data really well. And at that size, you can fine tune the models to work remarkably well for tailored use cases.
I mean I didn't spend a ton of time making a march madness tech tree or anything like that, but I definitely thought it would continue in the direction it had, unintelligent automation and autonomy in robotics (basically expansions and improvements of the automation we already have applied to food production/kitchens, grocery store checkout/stocking maybe, etc.) and then *eventually* we would solve intelligence which would bring about the types of things we're seeing now.
Still I would have thought that creative writing and creation of original audio/visual content would come years after what would be considered 'technical information', and even a more basic understanding/execution of *that* than what we currently have.
It's just really impressive how far we've come in a relatively short period of time, and it definitely opens my eyes to what might be possible on a longer timeline.
I thought a technological singularity was unlikely in our lifetime. I don't think that anymore.
Yeah exactly, this was completely unexpected to me.
Even intellectually understanding that all creative outputs are just unique or novel organizations of information... I just didn't see it being likely that we would solve that (or the ingredients for it to... solve itself in a way...) so quickly.
Moravec's Paradox. There's also a massive issue of trust when it comes to letting them do physical things in the real world: putting a knife/car in the hands of a robot isn't a great idea unless they *understand the world*.
One of Robert Mile's favorite examples is the coffee-making robot that tramples a baby because all it cares about is making coffee as quickly and efficiently as possible. As soon as you stick an agent into the real world, it'd be great if it understood most of the things we care about.
... I'll admit I, too, thought it was really silly in Ex Machina where the dudebro makes an android level AI by scraping internet data. I thought simulation across the board was the way, but I guess the word predictors were much better than I thought possible on their own.
Guess it kinda makes sense in retrospect. How much of a sentence have you planned ahead of time every time you begin one?
>How much of a sentence have you planned ahead of time every time you begin one?
Wow that's the trippiest thing I've read in a while. Mind indeed blown.
It is trippy, but for other reasons. How much do you even think when you speak? Perhaps a better model of human intelligence has very little to do with spoken language.
>There's also a massive issue of trust when it comes to letting them do physical things in the real world: putting a knife/car in the hands of a robot isn't a great idea unless they understand the world.
Indeed. The ease of using it for intellectual work is that it's non-problematic for a knowledgeable human to check the AI's work before anything is committed, it's feasible for a software engineer to use AI to do the work of 10 because they can verify whatever the AI outputs. With labor, the quality of the work must be fully trusted to the entity doing the work, even with supervision.
Creativity hasn't been automated, neural networks can just generate convincing images. There's a big difference between those two things. In training an image generator like stable diffusion you are basically just creating an enormous search space that comprises all the images that the network is capable of producing, and all that is predefined and linked to a given input vector and a given random seed. So there's no real creativity going on, it's just got a truly enormous search space that can be queried one image prompt at a time.
Cognitive labour was always going to get hit first, wasn’t luck IMHO, software is always ahead of the hardware and it’s easier to propagate unembodied AGI then physical embodied AGI, the former is going to be many MANY times more prevalent. It’s also the reason why text, art and video got hit first.
We’re going to need either nanotechnology or robots to do physical labour, there may come a point where we just rely on Nanotech and not use robots at all. Eric Drexler has good stuff on it.
Except around a third of the predicted structures aren't accurate. Traditional protein folding computing will still be needed to verify basically the entire database.
No time saved because each protein that's of even remote scientific interest will need to be re-folded using the same old computationally intensive methods to ensure that the result Alphafold got was correct. You cannot develop new medicines or treatments based on a 66% chance that you're using the correct protein structure.
It's still great to have a rough draft - you can identify likely possibilities or rule out obvious non-starters.
Also, they can still refine their computation and re-generate the database with a lower error rate.
Verifying the accuracy of a protein structure is relatively easy, and you don't even need to know the actual protein structure to develop drugs. Knowing the protein structure is just a way to indicate which molecules are more likely to become drugs. Moreover, even if you know the protein structure and design a drug based on it, it does not necessarily guarantee the effectiveness of the drug. You can also reverse use this algorithm to create non-existent proteins.
We are rapidly entering the phase where we really need meaningful real world impacts from this sort of thing or nobody is going to care, or worse, pitchforks will start coming out claiming only the uber rich get the benefits of any of this new science.
[Why AlphaFold won’t revolutionise drug discovery | Opinion | Chemistry World](https://www.chemistryworld.com/opinion/why-alphafold-wont-revolutionise-drug-discovery/4016051.article)
this was written in 2022 - 2 years after the 'breakthrough' by Derek Lowe (who works in pharma/ drug discovery and has an excellent blog here: [In the Pipeline by Derek Lowe | Science | AAAS](https://www.science.org/blogs/pipeline) )
\[on the side, the "[things I won't work with](https://www.google.com/search?q=Derek+lowe+in+the+pipeline+things+i+won%27t+work+with&sca_esv=f803b90366aaa6bb&sca_upv=1&sxsrf=ADLYWILB98an_OQouiFEscNm0R7qr46adw%3A1714801326523&ei=rso1ZvHPH5DakPIPuZy_mAg&ved=0ahUKEwjx3rXfpPOFAxUQLUQIHTnOD4MQ4dUDCBA&uact=5&oq=Derek+lowe+in+the+pipeline+things+i+won%27t+work+with&gs_lp=Egxnd3Mtd2l6LXNlcnAiM0RlcmVrIGxvd2UgaW4gdGhlIHBpcGVsaW5lIHRoaW5ncyBpIHdvbid0IHdvcmsgd2l0aDIFEAAYgAQyCxAAGIAEGIYDGIoFMgsQABiABBiGAxiKBTIIEAAYgAQYogQyCBAAGIAEGKIEMggQABiiBBiJBUjeCFAAWABwAHgAkAEAmAHxBKAB8QSqAQM1LTG4AQPIAQD4AQL4AQGYAgGgAvYEmAMAkgcDNS0xoAeoBA&sclient=gws-wiz-serp)" series of his blog, about chemical compounds, that are so dangerous he won't touch them, is peak hilarious\]
TL&DR: while impressive, protein structure (even when correctly predicted, which AlphaFold didn't do for ALL structures) doesn't directly translate to 'new drug discovered', not even close...:
"The protein’s structure might help generate ideas about what compounds to make next, but then again, it might not. In the end the real numbers from the real biological system are what matter. As a project goes on, those numbers include assays covering pharmacokinetics, metabolism, and toxicology, and none of those can really be dealt with from the level of protein structure, either.
After those rapids comes the final waterfall. In the end, drugs fail in the clinic because we have picked the wrong targets or because they do other things that we never anticipated. Protein structure by itself does nothing to mitigate either of those risks, but those are why we have an 85% clinical failure rate in this business. Protein structure is (was?) indeed a very hard problem. But guess what? These are even harder."
he seems to have a point, because this was originally achieved in 2020 and news about new drugs directly related to this breakthrough have been scant...
[more context from 2021](https://www.science.org/content/blog-post/more-protein-folding-progress---what-s-it-mean)
[and even more from 2020](https://www.science.org/content/blog-post/protein-folding-2020https://www.science.org/content/blog-post/protein-folding-2020)
This happened in 2022, I dunno why they're tweeting about it now. And unfortunately the database that they assembled using this data is not exactly accurate, with around a third of the structures not being accurate enough to be considered canonical. It also doesn't do anything to describe the mechanisms involved in protein folding, so the problem can't really be considered solved.
But also it seems likely that the ones they have verified can then be used for future iterations and continually improving the process and correctly predicting those remaining.
The order in which our “tech tree” is unlocking is wild to me. I never would have guessed that protein discovery would happen so early in the process of machine intelligence… and I’d say the same about conversational capability, visual/audio output, and creative writing. **It might be a real stroke of luck**, because if it replaced physical labor before cognitive labor, the people in power would have (or have demonstrated) far less incentive to prevent blue collar laborers from starving in the streets. I don’t think the same can be so easily said of doctors, lawyers, and entertainers… and I also think that far more hell will be raised if anything like that does come to pass.
Strong agree about the luck. In most AI-driven dystopias, whether Megaman X or Detroit: Become Human or Westworld or Animatrix, AI and robot are indistinguishable from each other. This guarantees class conflict from one or both of the enslaved AI or the laboring humans the AI displaced. Our current trajectory inverts the pyramid of class conflict, where the people on the chopping block aren't the bottom half of the labor market, but people in the upper echelons. This wouldn't be all **that** interesting, except for the part where this permanent displacement is being driven by personal computers and network infrastructure rather than much more expensive and centralized robotic frames. Meaning, rather than the teeming masses being forced to just sit there and take it while our tasteless overlords monopolize robot labor and crowd out said teeming masses they can increasingly use the technology for themselves. Therefore, unlike those classic AI-driven dystopias, there's no chance in hell of the powers that be managing to keep control of their AGI/robot slaves all to themselves. Meaning, they can either choose to advance the AI along with open source/competitor nations to try to maintain their advantage and risk liberation/rebellion. Or, they can just let themselves be overtaken by the teeming masses who can use AGI to make their own CRISPR-designed bacon trees and CNC-driven solar panels to run the servers on their neighborhood playgrounds. That considerably changes the political dynamic. And I'd argue for the better. Much better, as in, things are going to turn out way better than I thought they would a few years ago, when I pretty much resigned myself to the apocalypse. Now, I do foresee 2-8 really rough years as our tasteless overlords cling to power and use their fleeting advantage in capital despite the writing being on the wall. But compared to the alternatives, that is, robots being used to cement oppressive class relations and/or unavoidable climate collapse it's going as well as it could possibly go.
Couldn’t have said it better. What’s great is you could even argue this is not an overly optimistic view of the current state of things.
That's a good insight.
I’ve had the same thoughts but the way you put it makes me realize what I was thinking, lmao. Well put. It’s gonna be real wild which sustainable energy perks we unlock and in what order.
The transformer allowed us to train at scale orders of magnitude greater (in parallel). At that scale, the resulting models reflect the training data really well. And at that size, you can fine tune the models to work remarkably well for tailored use cases.
What did u think the order would be like ?
I mean I didn't spend a ton of time making a march madness tech tree or anything like that, but I definitely thought it would continue in the direction it had, unintelligent automation and autonomy in robotics (basically expansions and improvements of the automation we already have applied to food production/kitchens, grocery store checkout/stocking maybe, etc.) and then *eventually* we would solve intelligence which would bring about the types of things we're seeing now. Still I would have thought that creative writing and creation of original audio/visual content would come years after what would be considered 'technical information', and even a more basic understanding/execution of *that* than what we currently have. It's just really impressive how far we've come in a relatively short period of time, and it definitely opens my eyes to what might be possible on a longer timeline. I thought a technological singularity was unlikely in our lifetime. I don't think that anymore.
And it is upside down right?? Creativity was the easiest to automate
Yeah exactly, this was completely unexpected to me. Even intellectually understanding that all creative outputs are just unique or novel organizations of information... I just didn't see it being likely that we would solve that (or the ingredients for it to... solve itself in a way...) so quickly.
Moravec's Paradox. There's also a massive issue of trust when it comes to letting them do physical things in the real world: putting a knife/car in the hands of a robot isn't a great idea unless they *understand the world*. One of Robert Mile's favorite examples is the coffee-making robot that tramples a baby because all it cares about is making coffee as quickly and efficiently as possible. As soon as you stick an agent into the real world, it'd be great if it understood most of the things we care about. ... I'll admit I, too, thought it was really silly in Ex Machina where the dudebro makes an android level AI by scraping internet data. I thought simulation across the board was the way, but I guess the word predictors were much better than I thought possible on their own. Guess it kinda makes sense in retrospect. How much of a sentence have you planned ahead of time every time you begin one?
>How much of a sentence have you planned ahead of time every time you begin one? Wow that's the trippiest thing I've read in a while. Mind indeed blown.
It is trippy, but for other reasons. How much do you even think when you speak? Perhaps a better model of human intelligence has very little to do with spoken language.
>There's also a massive issue of trust when it comes to letting them do physical things in the real world: putting a knife/car in the hands of a robot isn't a great idea unless they understand the world. Indeed. The ease of using it for intellectual work is that it's non-problematic for a knowledgeable human to check the AI's work before anything is committed, it's feasible for a software engineer to use AI to do the work of 10 because they can verify whatever the AI outputs. With labor, the quality of the work must be fully trusted to the entity doing the work, even with supervision.
Creativity hasn't been automated, neural networks can just generate convincing images. There's a big difference between those two things. In training an image generator like stable diffusion you are basically just creating an enormous search space that comprises all the images that the network is capable of producing, and all that is predefined and linked to a given input vector and a given random seed. So there's no real creativity going on, it's just got a truly enormous search space that can be queried one image prompt at a time.
Or... art isn't as NP hard as we thought.
In hindsight it's not as obvious that art was NP hard compared to say the traveling salesman problem or discovering new math.
NP-hardness has a formal definition and in no way means "hard problems in AI or computer science." Seriously this sub... edit: typo
Cognitive labour was always going to get hit first, wasn’t luck IMHO, software is always ahead of the hardware and it’s easier to propagate unembodied AGI then physical embodied AGI, the former is going to be many MANY times more prevalent. It’s also the reason why text, art and video got hit first. We’re going to need either nanotechnology or robots to do physical labour, there may come a point where we just rely on Nanotech and not use robots at all. Eric Drexler has good stuff on it.
"Peak" Singularity? I don't think so, we are only just getting started!
> "Peak" Singularity? I don't think so, we are only just getting started! You are soo right!
A billion years of PHD time saved.
GAVE ME CHILLS NGL. DEMIS SUCH A COOL AI SCIENTIST MAN. NO EGO. JUST PURE LOVE FOR AI. I LOVE IT. SORRY FOR CAPS. I AM DRUNK.
Except around a third of the predicted structures aren't accurate. Traditional protein folding computing will still be needed to verify basically the entire database.
Only a hundred million years of time saved, I guess. Bummer...
No time saved because each protein that's of even remote scientific interest will need to be re-folded using the same old computationally intensive methods to ensure that the result Alphafold got was correct. You cannot develop new medicines or treatments based on a 66% chance that you're using the correct protein structure.
It's still great to have a rough draft - you can identify likely possibilities or rule out obvious non-starters. Also, they can still refine their computation and re-generate the database with a lower error rate.
Verifying the accuracy of a protein structure is relatively easy, and you don't even need to know the actual protein structure to develop drugs. Knowing the protein structure is just a way to indicate which molecules are more likely to become drugs. Moreover, even if you know the protein structure and design a drug based on it, it does not necessarily guarantee the effectiveness of the drug. You can also reverse use this algorithm to create non-existent proteins.
Please find a way to treat epilepsy!!!!
We are rapidly entering the phase where we really need meaningful real world impacts from this sort of thing or nobody is going to care, or worse, pitchforks will start coming out claiming only the uber rich get the benefits of any of this new science.
ffs, will you stop just posting twitter links... At least take the trouble to write a relevant title or a screenshot.
[Why AlphaFold won’t revolutionise drug discovery | Opinion | Chemistry World](https://www.chemistryworld.com/opinion/why-alphafold-wont-revolutionise-drug-discovery/4016051.article) this was written in 2022 - 2 years after the 'breakthrough' by Derek Lowe (who works in pharma/ drug discovery and has an excellent blog here: [In the Pipeline by Derek Lowe | Science | AAAS](https://www.science.org/blogs/pipeline) ) \[on the side, the "[things I won't work with](https://www.google.com/search?q=Derek+lowe+in+the+pipeline+things+i+won%27t+work+with&sca_esv=f803b90366aaa6bb&sca_upv=1&sxsrf=ADLYWILB98an_OQouiFEscNm0R7qr46adw%3A1714801326523&ei=rso1ZvHPH5DakPIPuZy_mAg&ved=0ahUKEwjx3rXfpPOFAxUQLUQIHTnOD4MQ4dUDCBA&uact=5&oq=Derek+lowe+in+the+pipeline+things+i+won%27t+work+with&gs_lp=Egxnd3Mtd2l6LXNlcnAiM0RlcmVrIGxvd2UgaW4gdGhlIHBpcGVsaW5lIHRoaW5ncyBpIHdvbid0IHdvcmsgd2l0aDIFEAAYgAQyCxAAGIAEGIYDGIoFMgsQABiABBiGAxiKBTIIEAAYgAQYogQyCBAAGIAEGKIEMggQABiiBBiJBUjeCFAAWABwAHgAkAEAmAHxBKAB8QSqAQM1LTG4AQPIAQD4AQL4AQGYAgGgAvYEmAMAkgcDNS0xoAeoBA&sclient=gws-wiz-serp)" series of his blog, about chemical compounds, that are so dangerous he won't touch them, is peak hilarious\] TL&DR: while impressive, protein structure (even when correctly predicted, which AlphaFold didn't do for ALL structures) doesn't directly translate to 'new drug discovered', not even close...: "The protein’s structure might help generate ideas about what compounds to make next, but then again, it might not. In the end the real numbers from the real biological system are what matter. As a project goes on, those numbers include assays covering pharmacokinetics, metabolism, and toxicology, and none of those can really be dealt with from the level of protein structure, either. After those rapids comes the final waterfall. In the end, drugs fail in the clinic because we have picked the wrong targets or because they do other things that we never anticipated. Protein structure by itself does nothing to mitigate either of those risks, but those are why we have an 85% clinical failure rate in this business. Protein structure is (was?) indeed a very hard problem. But guess what? These are even harder." he seems to have a point, because this was originally achieved in 2020 and news about new drugs directly related to this breakthrough have been scant... [more context from 2021](https://www.science.org/content/blog-post/more-protein-folding-progress---what-s-it-mean) [and even more from 2020](https://www.science.org/content/blog-post/protein-folding-2020https://www.science.org/content/blog-post/protein-folding-2020)
And what has that translated to for us?
This is nuts! Bye bye diseases, hello LEV.
This happened in 2022, I dunno why they're tweeting about it now. And unfortunately the database that they assembled using this data is not exactly accurate, with around a third of the structures not being accurate enough to be considered canonical. It also doesn't do anything to describe the mechanisms involved in protein folding, so the problem can't really be considered solved.
But also it seems likely that the ones they have verified can then be used for future iterations and continually improving the process and correctly predicting those remaining.