More details on these findings in this blog post https://www.assemblyai.com/blog/ai-trends-graph-neural-networks/

Could you kindly give me a dumbed down explanation about what the difference between the diffusion and GNN models?

I looked up both definitions but I don't understand them.

Cheers.

Not on their own. We know the human brain has different processing centers, and I think AGI is going to require activation and routing networks to invoke specific functional networks, ie image processing, language processing, etc. So I could see graph networks to work out simulated thought processing of inputs that produces probabilistic routes through those functional networks, with a sort of reality filter or expectation filter -- maybe a Boltzmann type of energy activation -- to choose from those results.

They're not getting much attention right now since Deepmind stays pretty quiet these days an LLM's have the spotlight. But to me they're the most exciting because they seem to hold the most potential for scientific applications. The best performing AI will probably be a mix of many different architectures though if I had to guess.

Personally, I think the future of AI is some currently unknown architecture that will be invented by AI.

you'll probably get the best description from chatgpt

LLMs are the future. How do you think? Through graphs, or through texts? So why build an AI model that isnt a substitute for how we think?

I do see the great potential, like wolfram alpha is an amazing software paired with GPT it can produce amazing results. And I think in the future AI will utilize the models as tool, just like it already is with ChatGPT plug-ins. We gave AI a voice, we let AI see and now AI can use tools.

LLMs are the base, mimic how we think. We use language to think nothing else the rest is addons, like the minds eye and so on.

For Diffusion models, I'd point you to the introduction in this excellent blog post: https://www.assemblyai.com/blog/diffusion-models-for-machine-learning-introduction/

Complex cognition exists independent of language structures and LLMs mimic language structures, not cognition. You can destroy the language centers of the brain and general intelligence, ie cognition and self recognition, remain intact. Meanwhile ChatGPT isn't thinking or even imitating thought, it's imitating language by computing a latent space for emergent words based on prior language input. Math.

Meanwhile a baby can act on knowledge learned by observing the world long before language emerges. AGI requires more than language, more than memory. It requires the ability to model reality and learn language from raw sensory input alone, and to synthesize information and observation into new ideas, and motives to act on that information, the ability to predict an outcome and a value scale to weigh one potential outcome over another. A baby can do that but ChatGPT doesn't even know when it's spouting utter nonsense and stable diffusion doesn't know how many fingers a human has.

We have no ways of modeling unobserved information. A LLM cannot add a new word to it's model. It will never talk about anything that was invented after its training. Yes, they are impressive. On the level of parlor tricks and street magic.

Thank you kindly. I now understand diffusion models.