3 ms·

Godot Isn't Making It

13 points by rcarmo 2y ago

armchairhacker 2y ago
This isn't about the game engine Godot, it's about current AI not living up to expectations.
- deleted 2y ago
  [deleted]
- Terr_ 2y ago
  I assume it's a reference to the play Waiting for Godot, where the titular character-- like future promises of AI--never actually arrives. But yeah, not a very useful title to display all on its own.
ablation 2y ago
I used to find Ed Zitron's writing quite engaging, but I dunno... he's really trying to carve this niche out for himself as a 'caustic AI skeptic' isn't he? It's all a bit repetitive now. I'm also a little uncertain of his AI background and experience, given he runs a PR company.
- fortyseven 2y ago
  I dunno, I stopped reading after I got interrupted twice by popup crap. (The opening bit, linking to all of their prior articles in a "told you so" manner helped me not fight it and just close the page.)
- surgical_fire 2y ago
  In a sense, the AI hype itself has been quite repetitive too. It's small wonder that articles debunking it would be as well.
Ukv 2y ago
> [...] and likely charging nowhere near enough to cover their costs. As I wrote in the Subprime AI Crisis in September, in the event that these companies start charging what they actually need to, I hypothesize it will multiply the costs of their customers to the point that they can't afford to run their businesses [...] Free offerings are loss-leaders and will likely be enshittified within the next few years, but businesses relying on the models will instead be using a cloud GPU host or API, the price of which (based on the models we know the details of) appears to cover inference plus a profit margin. Plausible that releases of better models will slow down due to training/R&D expenses if investment decreases, but I think if you're paying for GPT-4 currently then from this point on there'll always be at least some equivalent model available. > [...] diminishing returns [...]. The entire generative AI movement lives and dies by the idea that more compute power and more training data makes these things better, and if that's no longer the case [...] what's the point? Diminishing returns means going from 100 GPUs to 101 GPUs does not give the same improvement as going from 1 GPU to 2 GPUs - not that it gives no improvement. The scaling laws predict this, and I believe it's likely true of any approach to intelligence rather than a limitation specific to deep learning. Computer graphics also has diminishing returns from compute, for instance. > The constant refrain I hear from VCs and AI fantasists is that "chips will bring down the cost of inference," yet I don't see any proof of that happening Surely it already has? Try running some recent LLM on 2010-2012 hardware from around when modern AI was taking off. > It cost $100 million to train GPT-4o [...] Beyonce's home cost $200 million, and advertising for the Monopoly Go mobile game cost $500 million. It's undoubtedly a lot of money, but also relatively high-impact and I wouldn't necessarily bet against it eventually going a couple of order of magnitudes higher.
scotty79 2y ago
> limitations of probabilistic models that hallucinate, where they authoritatively state things that aren't true I really don't understand why people think it's some insurmountable wall. People do hallucinate in the same way llm-s do. If you talk to a baby that just acquired language it spews all sorts of nonsense, but also some sensible things. So far we made a baby, granted, with huge vocabulary and shallow knowledge of many things. But now we need to raise it. The same way we raise humans. With better training. By exposing it to new knowlege but also reducing input of garbage. By making it hear the words that have more and more complex concepts behind them and telling it when it generates outputs that are erroneous. It's a monumental task, but we are not raising an only child. We need to figure out how to make children cooperate and teach from each other. Synthetic data will be a huge part of this process. Gold and shovels ... AI might be search for gold, but creating variety of synthetic data from deep reasoning will be shovels factory. It's weird that people suddenly expect 4 year old to accurately reproduce encyclopedia just because it can talk now.
- dartos 2y ago
  > I really don't understand why people think it's some insurmountable wall. People do hallucinate in the same way llm-s do. Go talk to some real people. A person making a mistake is not similar to a statistical model’s prediction not matching reality. It’s weird that you think probabilistic a text generator is even nearly comparable to an organic brain (let alone a human) It’s like comparing a wax statue with flesh and blood. They may look similar, they last a lot longer, but they’re not comparable. You’ve fallen totally into personification bias. I hope you find your way out.
  - snapcaster 2y ago
    Nothing in your comment argues against it being surmountable
    - dartos 2y ago
      Nothing in my comment argues against the existence of bees either. I have some opinions on the matter, but arguments about unfalsifiable traits aren’t really useful, especially since you’re not going to be convinced, nor I. I don’t personally believe that we’ll get much further with regards to general intelligence with statistical models, specifically. I think a lot of the purported progress towards “intelligence” is personification bias and a little bit of p-hacking. Many of the papers that are published and spread aren’t peer-reviewed or all that reproducible. We’ll probably see some very useful models made soon which focus on narrower text generation tasks and achieve pretty good results.

13 points by rcarmo 2y ago

armchairhacker 2y ago

This isn't about the game engine Godot, it's about current AI not living up to expectations.

deleted 2y ago
[deleted]
Terr_ 2y ago
I assume it's a reference to the play Waiting for Godot, where the titular character-- like future promises of AI--never actually arrives. But yeah, not a very useful title to display all on its own.

ablation 2y ago

I used to find Ed Zitron's writing quite engaging, but I dunno... he's really trying to carve this niche out for himself as a 'caustic AI skeptic' isn't he? It's all a bit repetitive now. I'm also a little uncertain of his AI background and experience, given he runs a PR company.

fortyseven 2y ago
I dunno, I stopped reading after I got interrupted twice by popup crap. (The opening bit, linking to all of their prior articles in a "told you so" manner helped me not fight it and just close the page.)
surgical_fire 2y ago
In a sense, the AI hype itself has been quite repetitive too. It's small wonder that articles debunking it would be as well.

Ukv 2y ago

> [...] and likely charging nowhere near enough to cover their costs. As I wrote in the Subprime AI Crisis in September, in the event that these companies start charging what they actually need to, I hypothesize it will multiply the costs of their customers to the point that they can't afford to run their businesses [...] Free offerings are loss-leaders and will likely be enshittified within the next few years, but businesses relying on the models will instead be using a cloud GPU host or API, the price of which (based on the models we know the details of) appears to cover inference plus a profit margin. Plausible that releases of better models will slow down due to training/R&D expenses if investment decreases, but I think if you're paying for GPT-4 currently then from this point on there'll always be at least some equivalent model available. > [...] diminishing returns [...]. The entire generative AI movement lives and dies by the idea that more compute power and more training data makes these things better, and if that's no longer the case [...] what's the point? Diminishing returns means going from 100 GPUs to 101 GPUs does not give the same improvement as going from 1 GPU to 2 GPUs - not that it gives no improvement. The scaling laws predict this, and I believe it's likely true of any approach to intelligence rather than a limitation specific to deep learning. Computer graphics also has diminishing returns from compute, for instance. > The constant refrain I hear from VCs and AI fantasists is that "chips will bring down the cost of inference," yet I don't see any proof of that happening Surely it already has? Try running some recent LLM on 2010-2012 hardware from around when modern AI was taking off. > It cost $100 million to train GPT-4o [...] Beyonce's home cost $200 million, and advertising for the Monopoly Go mobile game cost $500 million. It's undoubtedly a lot of money, but also relatively high-impact and I wouldn't necessarily bet against it eventually going a couple of order of magnitudes higher.

scotty79 2y ago

> limitations of probabilistic models that hallucinate, where they authoritatively state things that aren't true I really don't understand why people think it's some insurmountable wall. People do hallucinate in the same way llm-s do. If you talk to a baby that just acquired language it spews all sorts of nonsense, but also some sensible things. So far we made a baby, granted, with huge vocabulary and shallow knowledge of many things. But now we need to raise it. The same way we raise humans. With better training. By exposing it to new knowlege but also reducing input of garbage. By making it hear the words that have more and more complex concepts behind them and telling it when it generates outputs that are erroneous. It's a monumental task, but we are not raising an only child. We need to figure out how to make children cooperate and teach from each other. Synthetic data will be a huge part of this process. Gold and shovels ... AI might be search for gold, but creating variety of synthetic data from deep reasoning will be shovels factory. It's weird that people suddenly expect 4 year old to accurately reproduce encyclopedia just because it can talk now.

dartos 2y ago
> I really don't understand why people think it's some insurmountable wall. People do hallucinate in the same way llm-s do. Go talk to some real people. A person making a mistake is not similar to a statistical model’s prediction not matching reality. It’s weird that you think probabilistic a text generator is even nearly comparable to an organic brain (let alone a human) It’s like comparing a wax statue with flesh and blood. They may look similar, they last a lot longer, but they’re not comparable. You’ve fallen totally into personification bias. I hope you find your way out.
- snapcaster 2y ago
  Nothing in your comment argues against it being surmountable
  - dartos 2y ago
    Nothing in my comment argues against the existence of bees either. I have some opinions on the matter, but arguments about unfalsifiable traits aren’t really useful, especially since you’re not going to be convinced, nor I. I don’t personally believe that we’ll get much further with regards to general intelligence with statistical models, specifically. I think a lot of the purported progress towards “intelligence” is personification bias and a little bit of p-hacking. Many of the papers that are published and spread aren’t peer-reviewed or all that reproducible. We’ll probably see some very useful models made soon which focus on narrower text generation tasks and achieve pretty good results.