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.
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.
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.
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.
It's only one part, predicting text is relatively straightforward because it doesn't require predicting complex sequences like 'a S23mz s.zawsds'. Based on statistical analysis, there is a limited number of word combinations that humans use. With hundreds of billions of parameters, significant compression is possible. Mathematics is different as it requires actual reasoning, an area where LLMs often struggle significantly because they lack the capability for genuine reasoning.
These senses can be described via text, but I’m highly skeptical that the learning outcomes will be the same.
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.
And it shows. It has a poor grasp of reality. It does a poor job with complex tasks. It cannot be trusted with specialized tasks typically done by expert humans. It is certainly an amazing technical achievement that does a decent job with simple tasks requiring cursory knowledge, but that’s all it is at this time.
>There’s absolutely nothing to suggest text can’t generalized further to new modalities
Inversion of burden of proof.
Nope. OpenAI has already demonstrated the ability to generalize GPT4 to a new modality. Your claim that text models can only generalize to images and not other modalities is utterly unconvincing. Explain to me why vision is so much different than say audio?
>> And it shows. It has a poor grasp of reality. It does a poor job with complex tasks.
GPT4 is a proof of concept more than anything. I’m excited to see how much reliability improves over time. It’s grasp of reality isn’t prefect, but at least it understands how burden of proof works.
Hilarious walk-back. “Text can generalize anything” —-> “It’s just a demo, bro” in the same post.
Lmao
I don’t understand why some people have a such hard time envisioning the potential of new technologies without a polished end product in their hands. Imagine if AI researchers had the same attitude.
Technology can be both real and unpolished at the same time. Those two things are not contradictory.