3 ms·

Imho, in order to reach AGI you have to get out of the LLM space. It has to be something else. Something close to biological plausability.

bob1029 3y ago
I think big parts of the answer include time domain, multi-agent and iterative concepts. Language is about communication of information between parties. One instance of an LLM doing one-shot inference is not leveraging much of this. Only first-order semantics can really be explored. There is a limit to what can be communicated in a context of any size if you only get one shot at it. Change over time is a critical part of our reality. Imagine if your agent could determine that it has been thinking about something for too long and adapt strategy automatically. Increase to higher param model, adapt the context, etc. Perhaps we aren't seeking total AGI/ASI either (aka inventing new physics). From a business standpoint, it seems like we mostly have what we need now. The next ~3 months are going to be a hurricane in our shop.
hackinthebochs 3y ago
LLMs as we currently understand them won't reach AGI. But AGI will very likely have an LLM as a component. What is language but a way to represent arbitrary structure? Of course that's relevant to AGI.
valine 3y ago
Covering an airplane in feathers isn't going to make it fly faster. Biological plausibility is a red haring imho.
- foooorsyth 3y ago
  The training space is more important. I don’t think a general intelligence will spawn from text corpuses. A person only able to consume text to learn would be considered severely disabled. A significant part of intelligence comes from existence in meatspace and the ability to manipulate and observe that meatspace. A two year old learns much faster with much less data than any LLM.
  - valine 3y ago
    We already have multimodal models that take both images and text as input. The bulk of the training for these models was in text, not images. This shouldn’t be surprising. Text is a great way of abstractly and efficiently representing reality. Of course those patterns are useful for making sense of other modalities. Beyond modeling the world, text is also a great way to model human thought and reason. People like to explain their thought process in writing. LLMs already pick up on and mimic chain of thought well. Contained within large datasets is crystallized thought, and efficient descriptions of reality that have proven useful for processing modalities beyond text. To me that seems like a great foundation for AGI.
    - foooorsyth 3y ago
      Text and 2D images are a tiny subset of physical reality as perceived by an able-bodied human. Even our best approximation (3D VR headset with Spatial Audio) is a poor representation. We don’t even bother to simulate touch, temperature, equilibrio-sense, etc. And the more detailed you get, the less data you have. These senses can be described via text, but I’m highly skeptical that the learning outcomes will be the same.
      - valine 3y ago
        >> Text and 2D images are a tiny subset of physical reality as perceived by an able-bodied human. Even our best approximation is a poor representation. This is wrong. There’s nothing magical about human perception. You see the world because a 2D image is projected onto your retina. GPT-4 was trained on text and generalized the ability to output 2D images. There’s absolutely nothing to suggest text can’t generalize further to new modalities. GPT4 is forced to serialize images as SVGs to output them (a crazy emergent ability btw), but that demonstrates an inherent spatial reasoning capability baked into the model. GPT4V was created with a transfer learning step where image embeddings are passed as input in place of text. That’s further evidence of models ability to generalize to new modalities. Everything you need to do multimodal input and output is already trained in, GPT-4V I’m sure is just the start.
orbital-decay 3y ago
Definitions, again. OpenAI defines AGI as highly autonomous agents that can replace humans in most of the economically important jobs. Those don't need to look or function like humans.
deleted 3y ago
[deleted]

bob1029 3y ago

I think big parts of the answer include time domain, multi-agent and iterative concepts. Language is about communication of information between parties. One instance of an LLM doing one-shot inference is not leveraging much of this. Only first-order semantics can really be explored. There is a limit to what can be communicated in a context of any size if you only get one shot at it. Change over time is a critical part of our reality. Imagine if your agent could determine that it has been thinking about something for too long and adapt strategy automatically. Increase to higher param model, adapt the context, etc. Perhaps we aren't seeking total AGI/ASI either (aka inventing new physics). From a business standpoint, it seems like we mostly have what we need now. The next ~3 months are going to be a hurricane in our shop.

hackinthebochs 3y ago

LLMs as we currently understand them won't reach AGI. But AGI will very likely have an LLM as a component. What is language but a way to represent arbitrary structure? Of course that's relevant to AGI.

valine 3y ago

Covering an airplane in feathers isn't going to make it fly faster. Biological plausibility is a red haring imho.

foooorsyth 3y ago
The training space is more important. I don’t think a general intelligence will spawn from text corpuses. A person only able to consume text to learn would be considered severely disabled. A significant part of intelligence comes from existence in meatspace and the ability to manipulate and observe that meatspace. A two year old learns much faster with much less data than any LLM.
- valine 3y ago
  We already have multimodal models that take both images and text as input. The bulk of the training for these models was in text, not images. This shouldn’t be surprising. Text is a great way of abstractly and efficiently representing reality. Of course those patterns are useful for making sense of other modalities. Beyond modeling the world, text is also a great way to model human thought and reason. People like to explain their thought process in writing. LLMs already pick up on and mimic chain of thought well. Contained within large datasets is crystallized thought, and efficient descriptions of reality that have proven useful for processing modalities beyond text. To me that seems like a great foundation for AGI.
  - foooorsyth 3y ago
    Text and 2D images are a tiny subset of physical reality as perceived by an able-bodied human. Even our best approximation (3D VR headset with Spatial Audio) is a poor representation. We don’t even bother to simulate touch, temperature, equilibrio-sense, etc. And the more detailed you get, the less data you have. These senses can be described via text, but I’m highly skeptical that the learning outcomes will be the same.
    - valine 3y ago
      >> Text and 2D images are a tiny subset of physical reality as perceived by an able-bodied human. Even our best approximation is a poor representation. This is wrong. There’s nothing magical about human perception. You see the world because a 2D image is projected onto your retina. GPT-4 was trained on text and generalized the ability to output 2D images. There’s absolutely nothing to suggest text can’t generalize further to new modalities. GPT4 is forced to serialize images as SVGs to output them (a crazy emergent ability btw), but that demonstrates an inherent spatial reasoning capability baked into the model. GPT4V was created with a transfer learning step where image embeddings are passed as input in place of text. That’s further evidence of models ability to generalize to new modalities. Everything you need to do multimodal input and output is already trained in, GPT-4V I’m sure is just the start.

orbital-decay 3y ago

Definitions, again. OpenAI defines AGI as highly autonomous agents that can replace humans in most of the economically important jobs. Those don't need to look or function like humans.

deleted 3y ago

[deleted]