8 ms·

I had a feeling it had to be something like massive waste due to a misguided feature of the algorithms that shouldn't have been there in the first place. Once

I had a feeling it had to be something like massive waste due to a misguided feature of the algorithms that shouldn't have been there in the first place. Once the "math is done" quite likely it would have paid off better than most investments for the top people to have spent a few short years working with grossly underpowered hardware until they could come up with amazing results there before scaling up. Rather than grossly overpowered hardware before there was even deep understanding of the underlying processes. When you think about it, what we have seen from the latest ultra-high-powered "thinking" machines is truly so impressive. But if you are trying to fool somebody into believing that it's a real person it's still not "quite" there. Maybe a good benchmark would be to take a regular PC, and without reliance on AI just pull out all the stops and put all the effort into fakery itself. No holds barred, any trick you can think of. See what the electronics is capable of this way. There are some smart engineers, this would only take a few years but looks like it would have been a lot more affordable. Then with the same hardware if an AI alternative is not as convincing, something has got to be wrong. It's good to find out this type of thing before you go overboard. Regardless of speed or power, I never could have gotten an 8-bit computer to match the output of a 32-bit floating-point algorithm by using floating-point myself. Integers all the way and place the decimal where it's supposed to be when you're done. Once it's really figured out, how do you think it would feel being the one paying the electric bills up until now?

jimmaswell 2y ago
Faster progress was absolutely worth it. Spending years agonizing over theory to save a bit of electric would have been a massive disservice to the world.
- Forgeties79 2y ago
  “A bit”?
  - bartread 2y ago
    Yes, a large amount for - in the grand scheme of things - a short period of time (i.e., a quantity of energy usage in an intense spike that will be dwarfed by energy usage over time) can accurately be described as “a bit”. Of course, the impact is that AI will continue to become cheaper to use, and induced demand will continue the feedback loop driving the market as a result.
- rossjudson 2y ago
  You're sort of presuming that LLMs are going to be a massive service to the world there, aren't you? I think the jury is still out on that one.
  - jimmaswell 2y ago
    They already have been. Even just in programming, even just Copilot has been a life changing productivity booster.
    - wruza 2y ago
      Are you sure it’s a life changing productivity booster? Sometimes I look at my projects and wonder how would I explain it to an LLM what this code should have done if it didn’t exist yet. Must be a shitton of boilerplate programming for copilot to be a life-changing experience.
      - AYBABTME 2y ago
        You haven't used them enough. Everytime an LLM reduces my search from 1min to 5s, the LLM pays. Just summary features: save me 20min of reading a transcript, turn it into 20s. That's a huge enabler.
        mecsred 2y ago
        If 20 mins of informations can legitimately be condensed into 20 seconds, it sounds like the original wasn't worth reading in the first place. Could have skipped the llm entirely.
        bostik 2y ago
        I upvoted you, because I think you have a valid point. The tone is unnecessarily aggressive though. Effective and information-dense communication is really hard. That doesn't mean we should just accept the useless fluff surrounding the actual information and/or analysis. People could learn a lot from the Ignoble Prize ceremony's 24/7 presentation model. Sadly, it seems we are heading towards a future where you may need an LLM to distill the relevant information out of a sea of noise.
pcl 2y ago
Isn’t this paper pretty much about spending a few short years to improve the performance? Or are you arguing that the same people who made breakthroughs over the last few years should have also done the optimization work?
- fuzzfactor 2y ago
  >the same people who made breakthroughs over the last few years should have also done the optimization work I never thought it would be ideal if it was otherwise, so I guess so. When I first considered neural nets from state-of-the art vendors to assist with some non-linguistic situations over 30 years ago, it wasn't quite ready for prime time and I could accept that. I just don't have generic situations all the time which would benefit me, so it's clearly my problems that have the deficiencies ;\ What's being done now with all the resources being thrown at it is highly impressive, and gaining all the time, no doubt about it. It's nice to know there are people that can afford it. I truly look forward to more progress, and this may be the previously unreached milestone I have been detecting that might be a big one. Still not good enough for what I need yet so far though. And I can accept that as easily as ever. That's why I put up my estimation that not all of those 30+ years has been spent without agonizing over something ;)
Scene_Cast2 2y ago
This is a bit like recommending to skip vacuum tubes, think hard and invent transistors.
- fuzzfactor 2y ago
  This is kind of thought-provoking. That is a good correlation when you think about how much more energy-efficient transistors are than vacuum tubes. Vacuum tube computers were a thing for a while, but it was more out of desperation than systematic intellectual progress. OTOH you could look at the present accomplishments like it was throwing more vacuum tubes at a problem that can not be adequately addressed that way. What turned out to be a solid-state solution was a completely different approach from the ground up. To the extent a more power-saving technique using the same hardware is only a matter of different software approaches, that would be something that realistically could have been accomplished before so much energy was expended. Even though I've always thought application-specific circuits would be what really helps ML and AI a lot, and that would end up not being the exact same hardware at all. If power is truly being wasted enough to start rearing its ugly head, somebody should be able to figure out how to fix it before it gets out-of-hand. Ironically enough with my experience using vacuum tubes, I've felt that there were some serious losses in technology when the research momentum involved was so rapidly abandoned in favor of "solid-state everything" at any cost. Maybe it is a good idea to abandon the energy-intensive approaches, as soon as anything completely different that's the least bit promising can barely be seen by a gifted visionary to have a glimmer of potential.
michaelmrose 2y ago
This comment lives in a fictional world where there is a singular group that could have collectively acted counterfactually. In the real world any actor that individually went this route would have gone bankrupt while the others collected money by showing actual results even if ineffeciently earned.
- newyankee 2y ago
  Also it is likely that the rise of LLMs gave many researchers in allied fields the impetus to tackle with the problems that are relevant to making it more efficient and people stumbled upon a solution hiding there. The momentum with LLMs and allied technology may last till it keeps on improving even by a few percentage points and keeps shattering human created new benchmarks every few months
VagabundoP 2y ago
That's just not how progress works. Its iteritive, there are plenty of cul-de-sacs and failures. You can't really optimise until you have something that works and its a messy process that is inefficient. You're looking at this with hindsight.

jimmaswell 2y ago

Faster progress was absolutely worth it. Spending years agonizing over theory to save a bit of electric would have been a massive disservice to the world.

Forgeties79 2y ago
“A bit”?
- bartread 2y ago
  Yes, a large amount for - in the grand scheme of things - a short period of time (i.e., a quantity of energy usage in an intense spike that will be dwarfed by energy usage over time) can accurately be described as “a bit”. Of course, the impact is that AI will continue to become cheaper to use, and induced demand will continue the feedback loop driving the market as a result.
rossjudson 2y ago
You're sort of presuming that LLMs are going to be a massive service to the world there, aren't you? I think the jury is still out on that one.
- jimmaswell 2y ago
  They already have been. Even just in programming, even just Copilot has been a life changing productivity booster.
  - wruza 2y ago
    Are you sure it’s a life changing productivity booster? Sometimes I look at my projects and wonder how would I explain it to an LLM what this code should have done if it didn’t exist yet. Must be a shitton of boilerplate programming for copilot to be a life-changing experience.
    - AYBABTME 2y ago
      You haven't used them enough. Everytime an LLM reduces my search from 1min to 5s, the LLM pays. Just summary features: save me 20min of reading a transcript, turn it into 20s. That's a huge enabler.
      - mecsred 2y ago
        If 20 mins of informations can legitimately be condensed into 20 seconds, it sounds like the original wasn't worth reading in the first place. Could have skipped the llm entirely.
        bostik 2y ago
        I upvoted you, because I think you have a valid point. The tone is unnecessarily aggressive though. Effective and information-dense communication is really hard. That doesn't mean we should just accept the useless fluff surrounding the actual information and/or analysis. People could learn a lot from the Ignoble Prize ceremony's 24/7 presentation model. Sadly, it seems we are heading towards a future where you may need an LLM to distill the relevant information out of a sea of noise.

pcl 2y ago

Isn’t this paper pretty much about spending a few short years to improve the performance? Or are you arguing that the same people who made breakthroughs over the last few years should have also done the optimization work?

fuzzfactor 2y ago
>the same people who made breakthroughs over the last few years should have also done the optimization work I never thought it would be ideal if it was otherwise, so I guess so. When I first considered neural nets from state-of-the art vendors to assist with some non-linguistic situations over 30 years ago, it wasn't quite ready for prime time and I could accept that. I just don't have generic situations all the time which would benefit me, so it's clearly my problems that have the deficiencies ;\ What's being done now with all the resources being thrown at it is highly impressive, and gaining all the time, no doubt about it. It's nice to know there are people that can afford it. I truly look forward to more progress, and this may be the previously unreached milestone I have been detecting that might be a big one. Still not good enough for what I need yet so far though. And I can accept that as easily as ever. That's why I put up my estimation that not all of those 30+ years has been spent without agonizing over something ;)

Scene_Cast2 2y ago

This is a bit like recommending to skip vacuum tubes, think hard and invent transistors.

fuzzfactor 2y ago
This is kind of thought-provoking. That is a good correlation when you think about how much more energy-efficient transistors are than vacuum tubes. Vacuum tube computers were a thing for a while, but it was more out of desperation than systematic intellectual progress. OTOH you could look at the present accomplishments like it was throwing more vacuum tubes at a problem that can not be adequately addressed that way. What turned out to be a solid-state solution was a completely different approach from the ground up. To the extent a more power-saving technique using the same hardware is only a matter of different software approaches, that would be something that realistically could have been accomplished before so much energy was expended. Even though I've always thought application-specific circuits would be what really helps ML and AI a lot, and that would end up not being the exact same hardware at all. If power is truly being wasted enough to start rearing its ugly head, somebody should be able to figure out how to fix it before it gets out-of-hand. Ironically enough with my experience using vacuum tubes, I've felt that there were some serious losses in technology when the research momentum involved was so rapidly abandoned in favor of "solid-state everything" at any cost. Maybe it is a good idea to abandon the energy-intensive approaches, as soon as anything completely different that's the least bit promising can barely be seen by a gifted visionary to have a glimmer of potential.

michaelmrose 2y ago

This comment lives in a fictional world where there is a singular group that could have collectively acted counterfactually. In the real world any actor that individually went this route would have gone bankrupt while the others collected money by showing actual results even if ineffeciently earned.

newyankee 2y ago
Also it is likely that the rise of LLMs gave many researchers in allied fields the impetus to tackle with the problems that are relevant to making it more efficient and people stumbled upon a solution hiding there. The momentum with LLMs and allied technology may last till it keeps on improving even by a few percentage points and keeps shattering human created new benchmarks every few months

VagabundoP 2y ago

That's just not how progress works. Its iteritive, there are plenty of cul-de-sacs and failures. You can't really optimise until you have something that works and its a messy process that is inefficient. You're looking at this with hindsight.