5 ms·

Why do you think we're not? It's weird how people don't find it weird to just give assertions like this without any supporting argument.

whimsicalism 5y ago
There are just so many unsolved problems. Taking sequences of actions, having a general model of the world beyond just language, memory, etc. are all large ones off the top of my head.
- hackinthebochs 5y ago
  Sure, there are problems that remain. But how can we say there are "so many" of them? We've seen large leaps in capabilities from architectures based on transformers. We've yet to see the limit of transformers as the scaling curves haven't plateaued. It seems to me that we don't know if we're two innovations or two thousand away from AGI. What we do know is that progress has been faster than anyone would have predicted 10 years ago.
  - whimsicalism 5y ago
    Look, I'm actually a massive optimist on this topic and think we can get very far with large language models. Memory I think will actually not be that difficult to solve, optimizing for long series of actions will be a hard one though, as will making an AI that can reason in a representationally invariant and transferrable way (ie. something that can reason just as well about data in the form of images as text). I'm not saying we're not seeing continuous improvements on performance on benchmarks from scaling up - we absolutely are. But "the end of the benchmarks" is not AGI - even if we have a tool AI/oracle AI that can perform at a superhuman level on benchmark tasks (which I think is absolutely doable within the current paradigm), we do not have a general AI.
    - FeepingCreature 5y ago
      Hooking up transformers into an agent AI does not seem like a daunting task given the ability to generate snippets of reasoning on demand.
  - astrange 5y ago
    The thing we’re progressing towards isn’t AGI, if by that you mean is conscious and sentient. I’m not sure this is actually possible, ie I don’t think we know “general intelligence” exists. The existence of your mind is a trick your brain plays on itself after all. You don’t exist independently of your body.
    - FeepingCreature 5y ago
      AGI is AI that is as good as humans on all skills. It has nothing to do with consciousness or sentience.
      - visarga 5y ago
        I think reaching AGI has to do with the 4 E's: embodied, enacted, embedded and extended. These models have no embodiment, no complex external environment, and there is no goal to attain other than language games. But they could be embodied, and some experiments have shown for example how a LM can guide an agent in a 3D virtual home to accomplish tasks. (*) Maybe AIs can be educated like children after they reach a certain threshold, by giving them robotic bodies and immersing them in the human society which is the most complex environment for intelligence. (*) Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents - https://arxiv.org/pdf/2201.07207.pdf https://arxiv.org/pdf/2201.07207.pdf
        sullyj3 5y ago
        Here's an example of an extended language model operating in a real world environment: https://www.youtube.com/watch?v=ysFav0b472w https://www.youtube.com/watch?v=ysFav0b472w
        FeepingCreature 5y ago
        They have a complex external environment just as much as we do: the physical world generates their inputs. Just because their inputs are prefiltered through human language doesn't mean they're not exposed to its full complexity just as much as we are, after having it prefiltered through our eyeball and optic nerve. And predicting human statements implicitly requires modeling agents with goals - see the joke explanations.
- FeepingCreature 5y ago
  The chain-of-thought prompting demonstrates that the output tape can act as procedural memory.
  - whimsicalism 5y ago
    Well it is unfortunately an output that scales at O(n^2) its length, which is not super great once you get to sequences of 1000s of words
    - FeepingCreature 5y ago
      Sparse transformers are a thing. Dictionary lookup is a thing. The transformer can probably be trained to store long chains of information in a dedicated memory system, retrieving it using keywords. These are the things that came to mind in ten seconds. This is not going to be the problem that meaningfully delays AGI.
      - whimsicalism 5y ago
        As I said in another comment, I think memory will be the easiest of these challenges to solve. That said, it hasn't really been solved yet. If we can scale up S4-style architectures to this size, maybe it will be solved.
- nl 5y ago
  > Taking sequences of actions, having a general model of the world beyond just language, memory, etc. are all large ones off the top of my head. Google: "Robots ground large language models in reality by acting as their eyes and hands while LLMs help robots execute long, abstract language instructions" https://say-can.github.io/ https://say-can.github.io/ Released today. An example: Given the task "I spilled my coke, can you bring me something to clean it up?", SayCan successfully planned and executed the following steps 1. Find a sponge 2. Pick up the sponge 3. Bring it to you 4.
- mountainriver 5y ago
  Yeah the multimodal work is already making progress and will likely be the future. That problem seems solvable soonish. Sequence of actions is much harder but with their new Chain of Thought research and whats happened in RL, I wouldn't be surprised if this was mostly solved in < 10 years. Retrieval transformers are the most promising on memory.
  - whimsicalism 5y ago
    I think multimodal work is still very much in the early stages. That said, I don't see any reason why the exact same approach we are using here wouldn't work ultimately for multi-modal settings as well. Memory also seems easy to solve. I am still very skeptical of our current approach in relation to sequences of actions, reasoning, discrete choices, and interaction with complex environments. RL is at baby level compared to the advances we have made in language modeling.
qeternity 5y ago
Have you played with GPT3? The amount of effort that goes into the prompts is huge for these examples (and likely cherry picked at that). The results are unbelievable. But they require a huge amount of nudging. I know it seems like we’ve made huge progress but we’re further away from AGI than we are closer to it.
- visarga 5y ago
  Have you tried the instruct series of GPT3? It's much better at following instructions than the original.

whimsicalism 5y ago

There are just so many unsolved problems. Taking sequences of actions, having a general model of the world beyond just language, memory, etc. are all large ones off the top of my head.

hackinthebochs 5y ago
Sure, there are problems that remain. But how can we say there are "so many" of them? We've seen large leaps in capabilities from architectures based on transformers. We've yet to see the limit of transformers as the scaling curves haven't plateaued. It seems to me that we don't know if we're two innovations or two thousand away from AGI. What we do know is that progress has been faster than anyone would have predicted 10 years ago.
- whimsicalism 5y ago
  Look, I'm actually a massive optimist on this topic and think we can get very far with large language models. Memory I think will actually not be that difficult to solve, optimizing for long series of actions will be a hard one though, as will making an AI that can reason in a representationally invariant and transferrable way (ie. something that can reason just as well about data in the form of images as text). I'm not saying we're not seeing continuous improvements on performance on benchmarks from scaling up - we absolutely are. But "the end of the benchmarks" is not AGI - even if we have a tool AI/oracle AI that can perform at a superhuman level on benchmark tasks (which I think is absolutely doable within the current paradigm), we do not have a general AI.
  - FeepingCreature 5y ago
    Hooking up transformers into an agent AI does not seem like a daunting task given the ability to generate snippets of reasoning on demand.
- astrange 5y ago
  The thing we’re progressing towards isn’t AGI, if by that you mean is conscious and sentient. I’m not sure this is actually possible, ie I don’t think we know “general intelligence” exists. The existence of your mind is a trick your brain plays on itself after all. You don’t exist independently of your body.
  - FeepingCreature 5y ago
    AGI is AI that is as good as humans on all skills. It has nothing to do with consciousness or sentience.
    - visarga 5y ago
      I think reaching AGI has to do with the 4 E's: embodied, enacted, embedded and extended. These models have no embodiment, no complex external environment, and there is no goal to attain other than language games. But they could be embodied, and some experiments have shown for example how a LM can guide an agent in a 3D virtual home to accomplish tasks. (*) Maybe AIs can be educated like children after they reach a certain threshold, by giving them robotic bodies and immersing them in the human society which is the most complex environment for intelligence. (*) Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents - https://arxiv.org/pdf/2201.07207.pdf https://arxiv.org/pdf/2201.07207.pdf
      - sullyj3 5y ago
        Here's an example of an extended language model operating in a real world environment: https://www.youtube.com/watch?v=ysFav0b472w https://www.youtube.com/watch?v=ysFav0b472w
      - FeepingCreature 5y ago
        They have a complex external environment just as much as we do: the physical world generates their inputs. Just because their inputs are prefiltered through human language doesn't mean they're not exposed to its full complexity just as much as we are, after having it prefiltered through our eyeball and optic nerve. And predicting human statements implicitly requires modeling agents with goals - see the joke explanations.
FeepingCreature 5y ago
The chain-of-thought prompting demonstrates that the output tape can act as procedural memory.
- whimsicalism 5y ago
  Well it is unfortunately an output that scales at O(n^2) its length, which is not super great once you get to sequences of 1000s of words
  - FeepingCreature 5y ago
    Sparse transformers are a thing. Dictionary lookup is a thing. The transformer can probably be trained to store long chains of information in a dedicated memory system, retrieving it using keywords. These are the things that came to mind in ten seconds. This is not going to be the problem that meaningfully delays AGI.
    - whimsicalism 5y ago
      As I said in another comment, I think memory will be the easiest of these challenges to solve. That said, it hasn't really been solved yet. If we can scale up S4-style architectures to this size, maybe it will be solved.
nl 5y ago
> Taking sequences of actions, having a general model of the world beyond just language, memory, etc. are all large ones off the top of my head. Google: "Robots ground large language models in reality by acting as their eyes and hands while LLMs help robots execute long, abstract language instructions" https://say-can.github.io/ https://say-can.github.io/ Released today. An example: Given the task "I spilled my coke, can you bring me something to clean it up?", SayCan successfully planned and executed the following steps 1. Find a sponge 2. Pick up the sponge 3. Bring it to you 4.
mountainriver 5y ago
Yeah the multimodal work is already making progress and will likely be the future. That problem seems solvable soonish. Sequence of actions is much harder but with their new Chain of Thought research and whats happened in RL, I wouldn't be surprised if this was mostly solved in < 10 years. Retrieval transformers are the most promising on memory.
- whimsicalism 5y ago
  I think multimodal work is still very much in the early stages. That said, I don't see any reason why the exact same approach we are using here wouldn't work ultimately for multi-modal settings as well. Memory also seems easy to solve. I am still very skeptical of our current approach in relation to sequences of actions, reasoning, discrete choices, and interaction with complex environments. RL is at baby level compared to the advances we have made in language modeling.

qeternity 5y ago

Have you played with GPT3? The amount of effort that goes into the prompts is huge for these examples (and likely cherry picked at that). The results are unbelievable. But they require a huge amount of nudging. I know it seems like we’ve made huge progress but we’re further away from AGI than we are closer to it.

visarga 5y ago
Have you tried the instruct series of GPT3? It's much better at following instructions than the original.