Pinterest improved it's recommendation system by 150%.

Google Maps improved its ETA predictions by up to 50%.

MIT discovered a novel antibiotic, Halicin.

Baker Lab invented a protein design paradigm that solves 100% more benchmark problems than its predecessor.

All of these seemingly disparate advances have one thing in common - Graph Neural Networks.

GNNs are type of neural network that have been quietly making rapid progress for several years, driven by a relatively small team of dedicated researchers.

While Diffusion Models, like those which have been powering DALL-E 2 and Stable Diffusion, have been in the limelight, GNNs have quietly become the dark horse behind a wealth of exciting discoveries and innovations.

Could GNNs be the future of AI?

Comments

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DauntingPrawn t1_jddtbgn wrote on March 23, 2023 at 5:58 PM

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.

DragonForg t1_jdgwsgg wrote on March 24, 2023 at 9:19 AM

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.

DauntingPrawn t1_jdhi42a wrote on March 24, 2023 at 1:07 PM

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.

Dziadzios t1_jdew6li wrote on March 23, 2023 at 10:05 PM

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

mrx-ai OP t1_jdcdcnl wrote on March 23, 2023 at 11:58 AM

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

DragonForg t1_jdgwpva wrote on March 24, 2023 at 9:18 AM

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.

Semifreak t1_jdd38uw wrote on March 23, 2023 at 3:13 PM

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.

zenzukai t1_jdf0256 wrote on March 23, 2023 at 10:32 PM

you'll probably get the best description from chatgpt

mrx-ai OP t1_jdh809f wrote on March 24, 2023 at 11:37 AM

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/

Semifreak t1_jdi167y wrote on March 24, 2023 at 3:19 PM

Thank you kindly. I now understand diffusion models.

94746382926 t1_jddue88 wrote on March 23, 2023 at 6:04 PM

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.