3 ms·

Get a job that requires a human physical presence. Nurse, plumber, etc.

deleted 2y ago
[deleted]
ben_w 2y ago
Humanoid robots are a thing. I assume we're currently seeing a lot of smoke and mirrors on the AI controlling those robots, but the mere physical form factor isn't enough any more. Nurses may have longer than plumbers because many of us will want the human connection, and servos behind rubber masks are currently deeply in uncanny valley — at least, they are for me, but I've noticed from the breadth of responses to GenAI that uncanny valley is in a different place for different people.
- HarHarVeryFunny 2y ago
  We've had human shaped robots for decades, but so what ? Robots that not only have AGI (running locally, under battery power?!) but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off, and would of course require something a lot more advanced that today's LLM based AI - would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft.
  - ben_w 2y ago
    > We've had human shaped robots for decades, but so what ? They've improved a lot since the ones decades ago. When ASIMO was state of the art, that meant being able to walk on a flat surface and climb stairs. Now, it's more like this: https://www.youtube.com/watch?v=WlUFoZstcWg https://www.youtube.com/watch?v=WlUFoZstcWg (Though again, as this is a sales video, I expect it to be implying more than it can actually do, in the same vein as the Tesla Optimus robot bartenders but not necessarily the same specifics) > Robots that not only have AGI (running locally, under battery power?!) Assuming batteries are a must and we can't e.g. use mains, that means the gap between AI reaching the power-performance envelope needed for level 5 self driving cars and the one needed for such robots would be about 5 years. Get the former in x years from now, the latter in x+5 years. > but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off Single humanoid hand, solving a Rubik's Cube, real time video from 5 years ago: https://www.youtube.com/watch?v=kVmp0uGtShk https://www.youtube.com/watch?v=kVmp0uGtShk > and would of course require something a lot more advanced that today's LLM based AI The future existence of a sufficiently more advanced AI is baked into the assumptions in the question at the top of this thread. It's the goal and driving purpose of the field. Dunno when it comes; if I did, that would make it easier to prepare for. > would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft. How confident are you about what specifically they need and don't have, and why? I've seen people confidently say AI would need brain-like capabilities to play Chess, then when Deep Blue beat Kasparov they were saying it about Go, then when AlphaGo beat Lee Sedol they were saying it about natural language and creating pictures, then ChatGPT happened and various Transformer and Diffusion models got so good that collectively they've become a fraud risk. Based on the observation that LLMs could get up to the level of interns or mid-university students, I suspect we've already got AI capable of learning a craft to that (mediocre) level just by training on all the YouTube DIY videos. Based on all the sim2real work, I think it's at least plausible (not certain, only plausible) that some mediocre plumber AI bootstrapped by YouTube would also be able to improve substantially in this fashion: https://arxiv.org/search/?query=sim2real&searchtype=all&source=header https://arxiv.org/search/?query=sim2real&searchtype=all&sour... I don't think we need to solve, e.g., the question of why humans seem to be able to learn from so much less data than our AI require to reach any given skill level. Likewise, we do we don't need to agree on what "consciousness" is, nor do we need to then give that to the machines.
    - HarHarVeryFunny 2y ago
      I think one of the major things missing from "LLMs as AGI" is the ability to learn continuously and incrementally, which is needed to learn a new skill (as well as to reason in the general case where you are learning as you go when facing a novel task). If we're relying on pre-training for skill/knowledge acquisition, then the training set would need to include robot-POV training data for every task and scenario is was expected to succeed at, which seems essentially impossible even given a potential world simulator in which to train them. Without continual learning, or being pre-trained for every task, past or future, it'd be perpetual groundhog day where you coach your robo-plumber to do a task one day, and have to coach it again every time the same task comes up. Of course most consumers aren't expert plumbers, so there is really no alternative than have robo-plumber come pre-trained for all eventualities or be able to learn on the job the same way an inexperienced plumber would do. There are many other things needed to replicate animal intelligence other than continual learning (e.g. traits like curiosity & boredom, in order to drive an autonomous system to experiment and learn), but continuous learning is a big one. We've been stuck on "whole dataset SGD-train, then deploy" since the advent of neural networks, despite many smart folk like Hinton trying to find something better. As far as things like flexibility, the bar is pretty high. The robo-plumber needs to be able to lie on it's back in a pool or spray of water while contorting itself in the cabinet under your kitchen sink to fix a leak when the water couldn't be shut off 100% ... the real world is infinitely more messy and challenging than any simulation is going to be, and the simulation isn't going to prepare the robot for the wet/greasy/slippery/etc physical environment in which it'd be working. Never mind figuring how to build human level AGI (with learning, etc), having it operate in real time and be battery powered is a massive challenge. A car at least has the battery being charged continuously by the engine/alternator. Real-time response by a multi-modal LLM currently requires multiple H100's or similar - probably a few kilo-watts. There's no reason to suppose that even in theory it's possible to build a compact battery (or super-capacitor) technology capable of delivering that sort of power output for an 8-hour shift. There's more hope that future cognitive architectures, and realizations (dataflow vs synchronous?) might reduce power needed to what's available from a battery, but that'd be many decades away.

deleted 2y ago

[deleted]

Humanoid robots are a thing. I assume we're currently seeing a lot of smoke and mirrors on the AI controlling those robots, but the mere physical form factor isn't enough any more. Nurses may have longer than plumbers because many of us will want the human connection, and servos behind rubber masks are currently deeply in uncanny valley — at least, they are for me, but I've noticed from the breadth of responses to GenAI that uncanny valley is in a different place for different people.

HarHarVeryFunny 2y ago
We've had human shaped robots for decades, but so what ? Robots that not only have AGI (running locally, under battery power?!) but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off, and would of course require something a lot more advanced that today's LLM based AI - would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft.
- ben_w 2y ago
  > We've had human shaped robots for decades, but so what ? They've improved a lot since the ones decades ago. When ASIMO was state of the art, that meant being able to walk on a flat surface and climb stairs. Now, it's more like this: https://www.youtube.com/watch?v=WlUFoZstcWg https://www.youtube.com/watch?v=WlUFoZstcWg (Though again, as this is a sales video, I expect it to be implying more than it can actually do, in the same vein as the Tesla Optimus robot bartenders but not necessarily the same specifics) > Robots that not only have AGI (running locally, under battery power?!) Assuming batteries are a must and we can't e.g. use mains, that means the gap between AI reaching the power-performance envelope needed for level 5 self driving cars and the one needed for such robots would be about 5 years. Get the former in x years from now, the latter in x+5 years. > but also the dexterity, flexibility, etc to match that of a plumber seem a long, long way off Single humanoid hand, solving a Rubik's Cube, real time video from 5 years ago: https://www.youtube.com/watch?v=kVmp0uGtShk https://www.youtube.com/watch?v=kVmp0uGtShk > and would of course require something a lot more advanced that today's LLM based AI The future existence of a sufficiently more advanced AI is baked into the assumptions in the question at the top of this thread. It's the goal and driving purpose of the field. Dunno when it comes; if I did, that would make it easier to prepare for. > would need to have full blown brain-like capabilities, obviously including the mechanisms to learn a craft. How confident are you about what specifically they need and don't have, and why? I've seen people confidently say AI would need brain-like capabilities to play Chess, then when Deep Blue beat Kasparov they were saying it about Go, then when AlphaGo beat Lee Sedol they were saying it about natural language and creating pictures, then ChatGPT happened and various Transformer and Diffusion models got so good that collectively they've become a fraud risk. Based on the observation that LLMs could get up to the level of interns or mid-university students, I suspect we've already got AI capable of learning a craft to that (mediocre) level just by training on all the YouTube DIY videos. Based on all the sim2real work, I think it's at least plausible (not certain, only plausible) that some mediocre plumber AI bootstrapped by YouTube would also be able to improve substantially in this fashion: https://arxiv.org/search/?query=sim2real&searchtype=all&source=header https://arxiv.org/search/?query=sim2real&searchtype=all&sour... I don't think we need to solve, e.g., the question of why humans seem to be able to learn from so much less data than our AI require to reach any given skill level. Likewise, we do we don't need to agree on what "consciousness" is, nor do we need to then give that to the machines.
  - HarHarVeryFunny 2y ago
    I think one of the major things missing from "LLMs as AGI" is the ability to learn continuously and incrementally, which is needed to learn a new skill (as well as to reason in the general case where you are learning as you go when facing a novel task). If we're relying on pre-training for skill/knowledge acquisition, then the training set would need to include robot-POV training data for every task and scenario is was expected to succeed at, which seems essentially impossible even given a potential world simulator in which to train them. Without continual learning, or being pre-trained for every task, past or future, it'd be perpetual groundhog day where you coach your robo-plumber to do a task one day, and have to coach it again every time the same task comes up. Of course most consumers aren't expert plumbers, so there is really no alternative than have robo-plumber come pre-trained for all eventualities or be able to learn on the job the same way an inexperienced plumber would do. There are many other things needed to replicate animal intelligence other than continual learning (e.g. traits like curiosity & boredom, in order to drive an autonomous system to experiment and learn), but continuous learning is a big one. We've been stuck on "whole dataset SGD-train, then deploy" since the advent of neural networks, despite many smart folk like Hinton trying to find something better. As far as things like flexibility, the bar is pretty high. The robo-plumber needs to be able to lie on it's back in a pool or spray of water while contorting itself in the cabinet under your kitchen sink to fix a leak when the water couldn't be shut off 100% ... the real world is infinitely more messy and challenging than any simulation is going to be, and the simulation isn't going to prepare the robot for the wet/greasy/slippery/etc physical environment in which it'd be working. Never mind figuring how to build human level AGI (with learning, etc), having it operate in real time and be battery powered is a massive challenge. A car at least has the battery being charged continuously by the engine/alternator. Real-time response by a multi-modal LLM currently requires multiple H100's or similar - probably a few kilo-watts. There's no reason to suppose that even in theory it's possible to build a compact battery (or super-capacitor) technology capable of delivering that sort of power output for an 8-hour shift. There's more hope that future cognitive architectures, and realizations (dataflow vs synchronous?) might reduce power needed to what's available from a battery, but that'd be many decades away.