10 ms·

From the notes: "A reality check for people that think full embodied AGI is right around the corner is to ask your dancing humanoid robot to pick up a joystick

From the notes: "A reality check for people that think full embodied AGI is right around the corner is to ask your dancing humanoid robot to pick up a joystick and learn how to play an obscure video game."

throw_nbvc1234 1y ago
This sounds like a problem that could be solved around the corner with a caveat. Games generally are solvable for AI because they have feedback loops and a clear success or failure criteria. If the "picking up a Joystick" part is the limiting factor, sure. But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage. I'm sure if you compared a monkey and a dolphins performance using a joystick you'd get results that aren't really correlated with their intelligence. I would guess that if you gave robots an R2D2 like port to jack into and play a game, that problem could be solved relatively quickly.
- mellosouls 1y ago
  The point isn't about learning video games its about learning tasks unrelated to its specific competency generally.
- johnb231 1y ago
  No, the joystick part is really not the limiting factor. They’ve already done this with a direct software interface. Physical interface is a new challenge. But overall you are missing the point.
- xnickb 1y ago
  Just like OpenAI early on promised us an AGI and showed us how it "solved" Dota 2. They also claimed it "learned" to play by playing itself only however it was clear that most of the advanced techniques were borrowed from existing AI and by observing humans. No surprise they gave up on that project completely and I doubt they'll ever engage in anything like that again. Money better spent on different marketing platforms.
  - jsheard 1y ago
    It also wasn't even remotely close to learning Dota 2 proper. They ran a massively simplified version of the game where the AI and humans alternated between playing one of two pre-defined team compositions, meaning >90% of the games characters and >99.999999% of the possible compositions and matchups weren't even on the table, plus other standard mechanics were also changed or disabled altogether for the sake of the AI team. Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.
    - xnickb 1y ago
      Exactly. What I find surprising in this story though is not the OpenAI. It's investors not seeing through these blatant.. lets call them exaggerations of the reality and still trusting the company with their money. I know I wouldn't have. But then again, maybe that's why I'm poor.
      - deleted 1y ago
        [deleted]
      - ryandrake 1y ago
        In their hearts, startup investors are like Agent Mulder: they Want To Believe. Especially after they’ve already invested a little. They are willing to overlook obvious exaggerations up to and including fraud, because the alternative is admitting their judgment is not sound. Look at how long Theranos went on! Miraculous product. Attractive young founder with all the right pedigree, credentials, and contacts, dressed in black trurtlenecks. Hell, she even talked like Steve Jobs! Investors never had a chance.
      - jdross 1y ago
        They already have 400 million daily users and a billion people using the product, with billions of consumer subscription revenue, faster than any company ever. They are also aggregating R&D talent at a density never before seen in Silicon Valley That is what investors see. You seem to treat this as a purity contest where you define purity
- jappgar 1y ago
  A human would learn it faster, and could immediately teach other humans. AI clearly isn't at human level and it's OK to admit it.
- jandrese 1y ago
  > But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage. Elon's response to this is that if we want these androids to replace human jobs then the lowest friction alternative is for the android to be able to do anything a human can do in a human amount of space. A specialized machine is faster and more efficient, but comes with engineering and integration costs that create a barrier to entry. Elon learned this lesson the hard way when he was building out the gigafactories and ended up having to hire a lot of people to do the work while they sorted out the issues with the robots. To someone like Elon a payroll is an ever growing parasite on a companies bottom line, far better if the entire thing is automated.
ferguess_k 1y ago
We don't really need AGI. We need better specialized AIs. Throw in a few specialized AIs and they will leave some impact in the society. That might not be that far away.
- bluGill 1y ago
  Specialized AIs have been making an impact on society since at least the 1960s. AI has long suffered from every time they come up with something new it gets renamed and becomes important (where it makes sense) without giving AI credit. From what I can tell most in AI are currently hoping LLMs reach that point quick just because the hype is not helping AI at all.
  - ferguess_k 1y ago
    Yeah I agree with it. There is a lot of hype, but there is some potentials there.
  - Forgeties79 1y ago
    Yeah “AI” tools (such a loose term but largely applicable) have been involved in audio production for a very long time. They have actually made huge strides with noise removal/voice isolation, auto transcription/captioning, and “enhancement” in the last five years in particular. I hate Adobe, I don’t like to give them credit for anything. But their audio enhance tool is actual sorcery. Every competitor isn’t even close. You can take garbage zoom audio and make it sound like it was borderline recorded in a treated room/studio. I’ve been in production for almost 15 years and it would take me half a day or more of tweaking a voice track with multiple tools that cost me hundreds of dollars to get it 50% as good as what they accomplish in a minute with the click of a button.
  - danielbln 1y ago
    Bitter lesson applies here as well though. Generalized models will beat specialized models given enough time and compute. How much bespoke NLP is there anymore? Generalized foundational models will subsume all of it eventually.
AndrewKemendo 1y ago
This debate is exhausting because there's no coherent definition of AGI that people agree on. I made a google form question for collecting AGI definitions cause I don't see anyone else doing it and I find it infinitely frustrating the range of definitions for this concept: https://docs.google.com/forms/d/e/1FAIpQLScDF5_CMSjHZDDexHkcSYAiD176g5r-8EosFKU5tOmH6pK72Q/viewform https://docs.google.com/forms/d/e/1FAIpQLScDF5_CMSjHZDDexHkc... My concern is that people never get focused enough to care to define it - seems like the most likely case.
- mvkel 1y ago
  It doesn't really seem like there's much utility in defining it. It's like defining "heaven." It's an ideal that some people believe in, and we're perpetually marching towards it
  - theptip 1y ago
    No, it’s never going to be precise but it’s important to have a good rough definition. Can we just use Morris et al and move on with our lives? Position: Levels of AGI for Operationalizing Progress on the Path to AGI: https://arxiv.org/html/2311.02462v4 https://arxiv.org/html/2311.02462v4 There are generational policy and societal shifts that need to be addressed somewhere around true Competent AGI (50% of knowledge work tasks automatable). Just like climate change, we need a shared lexicon to refer to this continuum. You can argue for different values of X but the crucial point is if X% of knowledge work is automated within a decade, then there are obvious risks we need to think about. So much of the discourse is stuck at “we will never get to X=99” when we could agree to disagree on that and move on to considering the x=25 case. Or predict our timelines for X and then actually be held accountable for our falsifiable predictions, instead of the current vide based discussions.
    - mvkel 1y ago
      This is a great reply, thank you. For me, I just zoom out a little further and say: at the rate AGI is approaching, what is the utility in trying to regulate it ahead of time? Seems like advancement is slow enough that society can/will naturally regulate it based on what feels comfortable. And it's a global phenomenon that can't have rules applied at the protocol level like the internet, because it's so culturally subjective. Precedents need to be set first, and I think we'll only be able to call them when we see them.
vonneumannstan 1y ago
Is this supposed to be a gotcha? We know these systems are typically trained using RL and they are exceedingly good at learning games...
- johnb231 1y ago
  No it is not a “gotcha” and I don’t understand how you got that impression. Carmack believes AGI systems should be able to learn new tasks in realtime alongside humans in the real world.

throw_nbvc1234 1y ago

This sounds like a problem that could be solved around the corner with a caveat. Games generally are solvable for AI because they have feedback loops and a clear success or failure criteria. If the "picking up a Joystick" part is the limiting factor, sure. But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage. I'm sure if you compared a monkey and a dolphins performance using a joystick you'd get results that aren't really correlated with their intelligence. I would guess that if you gave robots an R2D2 like port to jack into and play a game, that problem could be solved relatively quickly.

mellosouls 1y ago
The point isn't about learning video games its about learning tasks unrelated to its specific competency generally.
johnb231 1y ago
No, the joystick part is really not the limiting factor. They’ve already done this with a direct software interface. Physical interface is a new challenge. But overall you are missing the point.
xnickb 1y ago
Just like OpenAI early on promised us an AGI and showed us how it "solved" Dota 2. They also claimed it "learned" to play by playing itself only however it was clear that most of the advanced techniques were borrowed from existing AI and by observing humans. No surprise they gave up on that project completely and I doubt they'll ever engage in anything like that again. Money better spent on different marketing platforms.
- jsheard 1y ago
  It also wasn't even remotely close to learning Dota 2 proper. They ran a massively simplified version of the game where the AI and humans alternated between playing one of two pre-defined team compositions, meaning >90% of the games characters and >99.999999% of the possible compositions and matchups weren't even on the table, plus other standard mechanics were also changed or disabled altogether for the sake of the AI team. Saying you've solved Dota after stripping out nearly all of its complexity is like saying you've solved Chess, but on a version where the back row is all Bishops.
  - xnickb 1y ago
    Exactly. What I find surprising in this story though is not the OpenAI. It's investors not seeing through these blatant.. lets call them exaggerations of the reality and still trusting the company with their money. I know I wouldn't have. But then again, maybe that's why I'm poor.
    - deleted 1y ago
      [deleted]
    - ryandrake 1y ago
      In their hearts, startup investors are like Agent Mulder: they Want To Believe. Especially after they’ve already invested a little. They are willing to overlook obvious exaggerations up to and including fraud, because the alternative is admitting their judgment is not sound. Look at how long Theranos went on! Miraculous product. Attractive young founder with all the right pedigree, credentials, and contacts, dressed in black trurtlenecks. Hell, she even talked like Steve Jobs! Investors never had a chance.
    - jdross 1y ago
      They already have 400 million daily users and a billion people using the product, with billions of consumer subscription revenue, faster than any company ever. They are also aggregating R&D talent at a density never before seen in Silicon Valley That is what investors see. You seem to treat this as a purity contest where you define purity
jappgar 1y ago
A human would learn it faster, and could immediately teach other humans. AI clearly isn't at human level and it's OK to admit it.
jandrese 1y ago
> But why would we want robots to use an interface (especially a modern controller) heavily optimized for human hands; that seems like the definition of a horseless carriage. Elon's response to this is that if we want these androids to replace human jobs then the lowest friction alternative is for the android to be able to do anything a human can do in a human amount of space. A specialized machine is faster and more efficient, but comes with engineering and integration costs that create a barrier to entry. Elon learned this lesson the hard way when he was building out the gigafactories and ended up having to hire a lot of people to do the work while they sorted out the issues with the robots. To someone like Elon a payroll is an ever growing parasite on a companies bottom line, far better if the entire thing is automated.

ferguess_k 1y ago

We don't really need AGI. We need better specialized AIs. Throw in a few specialized AIs and they will leave some impact in the society. That might not be that far away.

bluGill 1y ago
Specialized AIs have been making an impact on society since at least the 1960s. AI has long suffered from every time they come up with something new it gets renamed and becomes important (where it makes sense) without giving AI credit. From what I can tell most in AI are currently hoping LLMs reach that point quick just because the hype is not helping AI at all.
- ferguess_k 1y ago
  Yeah I agree with it. There is a lot of hype, but there is some potentials there.
- Forgeties79 1y ago
  Yeah “AI” tools (such a loose term but largely applicable) have been involved in audio production for a very long time. They have actually made huge strides with noise removal/voice isolation, auto transcription/captioning, and “enhancement” in the last five years in particular. I hate Adobe, I don’t like to give them credit for anything. But their audio enhance tool is actual sorcery. Every competitor isn’t even close. You can take garbage zoom audio and make it sound like it was borderline recorded in a treated room/studio. I’ve been in production for almost 15 years and it would take me half a day or more of tweaking a voice track with multiple tools that cost me hundreds of dollars to get it 50% as good as what they accomplish in a minute with the click of a button.
- danielbln 1y ago
  Bitter lesson applies here as well though. Generalized models will beat specialized models given enough time and compute. How much bespoke NLP is there anymore? Generalized foundational models will subsume all of it eventually.

AndrewKemendo 1y ago

This debate is exhausting because there's no coherent definition of AGI that people agree on. I made a google form question for collecting AGI definitions cause I don't see anyone else doing it and I find it infinitely frustrating the range of definitions for this concept: https://docs.google.com/forms/d/e/1FAIpQLScDF5_CMSjHZDDexHkcSYAiD176g5r-8EosFKU5tOmH6pK72Q/viewform https://docs.google.com/forms/d/e/1FAIpQLScDF5_CMSjHZDDexHkc... My concern is that people never get focused enough to care to define it - seems like the most likely case.

mvkel 1y ago
It doesn't really seem like there's much utility in defining it. It's like defining "heaven." It's an ideal that some people believe in, and we're perpetually marching towards it
- theptip 1y ago
  No, it’s never going to be precise but it’s important to have a good rough definition. Can we just use Morris et al and move on with our lives? Position: Levels of AGI for Operationalizing Progress on the Path to AGI: https://arxiv.org/html/2311.02462v4 https://arxiv.org/html/2311.02462v4 There are generational policy and societal shifts that need to be addressed somewhere around true Competent AGI (50% of knowledge work tasks automatable). Just like climate change, we need a shared lexicon to refer to this continuum. You can argue for different values of X but the crucial point is if X% of knowledge work is automated within a decade, then there are obvious risks we need to think about. So much of the discourse is stuck at “we will never get to X=99” when we could agree to disagree on that and move on to considering the x=25 case. Or predict our timelines for X and then actually be held accountable for our falsifiable predictions, instead of the current vide based discussions.
  - mvkel 1y ago
    This is a great reply, thank you. For me, I just zoom out a little further and say: at the rate AGI is approaching, what is the utility in trying to regulate it ahead of time? Seems like advancement is slow enough that society can/will naturally regulate it based on what feels comfortable. And it's a global phenomenon that can't have rules applied at the protocol level like the internet, because it's so culturally subjective. Precedents need to be set first, and I think we'll only be able to call them when we see them.

vonneumannstan 1y ago

Is this supposed to be a gotcha? We know these systems are typically trained using RL and they are exceedingly good at learning games...

johnb231 1y ago
No it is not a “gotcha” and I don’t understand how you got that impression. Carmack believes AGI systems should be able to learn new tasks in realtime alongside humans in the real world.