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IBM Watson was more than a couple years back. The Jeopardy event was in 2011. It's currently 2024. As for cars, I don't know what you're referring to specifical

by Jach 2y ago

IBM Watson was more than a couple years back. The Jeopardy event was in 2011. It's currently 2024. As for cars, I don't know what you're referring to specifically, and the hype is still ongoing as far as I can tell. It has taken 10+ years to get to present day, from the start of the "deep learning revolution" around 2010. I vaguely recall Uber promising self-driving pickups somewhere around 8-10 years ago. A main difference between current AI systems and the systems behind the cyclical hype cycles ongoing since the 1950s is that these systems are actually delivering impressive and useful results, increasingly so, to a much larger amount of people. Waymo alone services tens of thousands of autonomous rides per month (edit: see sibling comment, I was out of date, it's currently hundreds of thousands of rides per month -- but see, increasingly), and LLMs are waaaaay beyond the grandparent's flippant characterization of "plausible-looking but incorrect sentences". That's markov chains territory.

aithrowawaycomm 2y ago
> Waymo alone services tens of thousands of autonomous rides per month (edit: see sibling comment, I was out of date, it's currently hundreds of thousands of rides per month -- but see, increasingly) But they aren't particularly autonomous, there's a fleet of humans watching the Waymos carefully and frequently intervening for the case where every 10-20 miles or so the system makes a stupid decision that needs human intervention: https://www.nytimes.com/interactive/2024/09/03/technology/zoox-self-driving-cars-remote-control.html https://www.nytimes.com/interactive/2024/09/03/technology/zo... I think Waymo only releases the "critical" intervention rate, which is quite low. But for Cruise the non-critical interventions was every 5 miles and I suspect Waymos are similar. It appears that Waymos are way too easily confused and left to their own devices make awful decisions about passing emergency vehicles, etc. Which is in fact consistent with what self-driving skeptics were saying all the way back in 2010: deep learning could get you 95% of the way there but it will take many decades - probably centuries! - before we actually have real self-driving cars. The remote human operators will work for robotaxis and buses but not for Teslas. (Not to mention the problems that will start when robotaxis get old and in need of automotive maintenance, but the system didn't have any transmission problem scenarios in its training data. At no time in my life has my human intelligence been more taxed than when I had a tire blowout on the interstate while driving an overloaded truck.)
- lupusreal 2y ago
  Centuries seems like quite a stretch, we haven't even been doing this computer stuff for one century yet.
  - aithrowawaycomm 2y ago
    The problem is not "computers," it's intelligence itself. We still don't know how even the simplest neurons actually work, nor the simplest brains. And we're barely any closer to scientific definitions of "intelligence," "consciousness," etc than we were in the 1800s. There are many decades of experiments left to do, regardless of how fancy computers might be. I suspect it will take centuries before we make dog-level AI because it will take centuries to understand how dogs are able to reason.
- anon291 2y ago
  > Which is in fact consistent with what self-driving skeptics were saying all the way back in 2010: deep learning could get you 95% of the way there but it will take many decades - probably centuries! - before we actually have real self-driving cars. The remote human operators will work for robotaxis and buses but not for Teslas. If this is the end result, this is already a substantial business savings.
- Jach 2y ago
  The link you gave does not support your claims about Waymo, it's just speculation. What "critical" intervention rate are you talking about? What network magically supports the required low latencies to remotely respond to an imminent accident? How does your theory square with events like https://www.sfchronicle.com/sf/article/s-f-waymo-robotaxis-freeway-19410999.php https://www.sfchronicle.com/sf/article/s-f-waymo-robotaxis-f... that required a service team to physically go and deal with the stuck cars, rather than just dealing with them via some giant remotely intervening team that's managed to scale to 10x rides in a year? (Hundreds of thousands per month absolutely.) Sure, there's no doubt a lot of human oversight going on still, probably "remote interventions" of all sorts (but not tele-operating) that include things like humans marking off areas of a map to avoid and pushing out the update for the fleet, the company is run by humans... But to say they aren't particularly autonomous is deeply wrong. I would be interested if you can dig up some old skeptics, plural, saying probably centuries. May take centuries, sure, I've seen such takes, they were usually backed by an assumption that getting all the way there requires full AGI and that'll take who knows how long. It's worth noticing that a lot of such tasks assumed to be "AGI-complete" have been falling lately. It's helpful to be focused on capabilities, not vague "what even is intelligence" philosophizing. Your parenthetical seems pretty irrelevant. First, models work outside their training sets. Second, these companies test such scenarios all the time. You'll even note in the link I shared that Waymo cars were at the time programmed to not enter the freeway without a human behind the wheel, because they were still doing testing. And it's not like "live test on the freeway with a human backup" is the first step in testing strategy, either.
anon291 2y ago
Yeah I have no idea what these people are talking about. The current gen of AI is qualitiatively different than previous attempts. For one, GPT et al are already useful without any kind of special prompting. I'd also like to challenge people to actually consider how often humans are correct. In my experience, it's actually very rare to find a human that speaks factually correctly. Many professionals, including doctors (!), will happily and confidently deliver factually incorrect lies that sound correct. Even after obvious correction they will continue to spout them. Think how long it takes to correct basic myths that have established themselves in the culture. And we expect these models, which are just getting off the ground, to do better? The claim is they process information more similarly to how humans do. If that's true, then the fact they hallucinate is honestly a point in their favor. Because... in my experience, they hallucinate exactly the way I expect humans to. Please try it, ask a few experts something and I guarantee you that further investigation into the topic will reveal that one or more of them are flat out incorrect. Humans often simply ignore this and go based on what we believe to be correct. A lot of people do it silently. Those who don't are often labeled know-it-alls.
- skydhash 2y ago
  Yon don't ask a neurosurgeon how to build an house, just like you don't ask a plane pilot how to drill a tunnel. Expertise is localized. And the most important thing is that humans learn.
  - anon291 2y ago
    > And the most important thing is that humans learn. Implementation detail that will be solved as the price of AI training decreases. Right now only inference is feasible at scale. Transformers are excellent here since they show great promise at 'one shot' learning meaning they can be 'trained' for the same cost as inference. Hence the sudden boom in AI . We finally have a taste of what could be should we be able to not only inference but also train models at scale.
  - neevans 2y ago
    Humans learn from seeing I don't think we are the stage of training models with videos / images dataset. We only reached the plateau with text dataset to train with.

by Jach 2y ago

aithrowawaycomm 2y ago

> Waymo alone services tens of thousands of autonomous rides per month (edit: see sibling comment, I was out of date, it's currently hundreds of thousands of rides per month -- but see, increasingly) But they aren't particularly autonomous, there's a fleet of humans watching the Waymos carefully and frequently intervening for the case where every 10-20 miles or so the system makes a stupid decision that needs human intervention: https://www.nytimes.com/interactive/2024/09/03/technology/zoox-self-driving-cars-remote-control.html https://www.nytimes.com/interactive/2024/09/03/technology/zo... I think Waymo only releases the "critical" intervention rate, which is quite low. But for Cruise the non-critical interventions was every 5 miles and I suspect Waymos are similar. It appears that Waymos are way too easily confused and left to their own devices make awful decisions about passing emergency vehicles, etc. Which is in fact consistent with what self-driving skeptics were saying all the way back in 2010: deep learning could get you 95% of the way there but it will take many decades - probably centuries! - before we actually have real self-driving cars. The remote human operators will work for robotaxis and buses but not for Teslas. (Not to mention the problems that will start when robotaxis get old and in need of automotive maintenance, but the system didn't have any transmission problem scenarios in its training data. At no time in my life has my human intelligence been more taxed than when I had a tire blowout on the interstate while driving an overloaded truck.)

lupusreal 2y ago
Centuries seems like quite a stretch, we haven't even been doing this computer stuff for one century yet.
- aithrowawaycomm 2y ago
  The problem is not "computers," it's intelligence itself. We still don't know how even the simplest neurons actually work, nor the simplest brains. And we're barely any closer to scientific definitions of "intelligence," "consciousness," etc than we were in the 1800s. There are many decades of experiments left to do, regardless of how fancy computers might be. I suspect it will take centuries before we make dog-level AI because it will take centuries to understand how dogs are able to reason.
anon291 2y ago
> Which is in fact consistent with what self-driving skeptics were saying all the way back in 2010: deep learning could get you 95% of the way there but it will take many decades - probably centuries! - before we actually have real self-driving cars. The remote human operators will work for robotaxis and buses but not for Teslas. If this is the end result, this is already a substantial business savings.
Jach 2y ago
The link you gave does not support your claims about Waymo, it's just speculation. What "critical" intervention rate are you talking about? What network magically supports the required low latencies to remotely respond to an imminent accident? How does your theory square with events like https://www.sfchronicle.com/sf/article/s-f-waymo-robotaxis-freeway-19410999.php https://www.sfchronicle.com/sf/article/s-f-waymo-robotaxis-f... that required a service team to physically go and deal with the stuck cars, rather than just dealing with them via some giant remotely intervening team that's managed to scale to 10x rides in a year? (Hundreds of thousands per month absolutely.) Sure, there's no doubt a lot of human oversight going on still, probably "remote interventions" of all sorts (but not tele-operating) that include things like humans marking off areas of a map to avoid and pushing out the update for the fleet, the company is run by humans... But to say they aren't particularly autonomous is deeply wrong. I would be interested if you can dig up some old skeptics, plural, saying probably centuries. May take centuries, sure, I've seen such takes, they were usually backed by an assumption that getting all the way there requires full AGI and that'll take who knows how long. It's worth noticing that a lot of such tasks assumed to be "AGI-complete" have been falling lately. It's helpful to be focused on capabilities, not vague "what even is intelligence" philosophizing. Your parenthetical seems pretty irrelevant. First, models work outside their training sets. Second, these companies test such scenarios all the time. You'll even note in the link I shared that Waymo cars were at the time programmed to not enter the freeway without a human behind the wheel, because they were still doing testing. And it's not like "live test on the freeway with a human backup" is the first step in testing strategy, either.

anon291 2y ago

Yeah I have no idea what these people are talking about. The current gen of AI is qualitiatively different than previous attempts. For one, GPT et al are already useful without any kind of special prompting. I'd also like to challenge people to actually consider how often humans are correct. In my experience, it's actually very rare to find a human that speaks factually correctly. Many professionals, including doctors (!), will happily and confidently deliver factually incorrect lies that sound correct. Even after obvious correction they will continue to spout them. Think how long it takes to correct basic myths that have established themselves in the culture. And we expect these models, which are just getting off the ground, to do better? The claim is they process information more similarly to how humans do. If that's true, then the fact they hallucinate is honestly a point in their favor. Because... in my experience, they hallucinate exactly the way I expect humans to. Please try it, ask a few experts something and I guarantee you that further investigation into the topic will reveal that one or more of them are flat out incorrect. Humans often simply ignore this and go based on what we believe to be correct. A lot of people do it silently. Those who don't are often labeled know-it-alls.

skydhash 2y ago
Yon don't ask a neurosurgeon how to build an house, just like you don't ask a plane pilot how to drill a tunnel. Expertise is localized. And the most important thing is that humans learn.
- anon291 2y ago
  > And the most important thing is that humans learn. Implementation detail that will be solved as the price of AI training decreases. Right now only inference is feasible at scale. Transformers are excellent here since they show great promise at 'one shot' learning meaning they can be 'trained' for the same cost as inference. Hence the sudden boom in AI . We finally have a taste of what could be should we be able to not only inference but also train models at scale.
- neevans 2y ago
  Humans learn from seeing I don't think we are the stage of training models with videos / images dataset. We only reached the plateau with text dataset to train with.