81–90 of 125 posts
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What if I told you I have a machine that takes 1 20k-dimensional vector and turns it into another meaningful 20k-dimensional vector, but the machine is made of a bunch of proteins and fats and liquids and gels? Would you be willing to call it sentient now?
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Is there room for generative AI in science? I am experimenting with this a lot at https://atomictessellator.com , As a computational chemist I found it difficult just to stay on top of all of the papers that are released, I thought it would be cool to have generative AI attempt to reproduce the experiments using simulation tech. Here's a few cool insights I have uncovered while working on this: - Developer tools are…
We are only a few years from next generation multimodal modeling. The future contains protein embeddings, genome embeddings, medical image embeddings, and chatbot decoders to discuss them with you.
Imagine prompting an image-conditioned decoder with questions like "Q: Why do you think this brain MRI indicates the person will get Parkinson's?" These are things that models can currently do, but we have basically no understanding of what they are looking at.
Can't wait for most of these companies to disappear once the hype dies down and the money dries up. Generative AI in its current form is a huge letdown.
Typical HN pessimism, but I strongly disagree with anyone characterizing it as a huge letdown. Even if you personally don't get much use out of it (honestly hard for me to believe if you give it a fair try), there are tons of uses that will be impacting you significantly within the next year. Contrast that with crypto.
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I think it absolutely has. Certainly in developer tools, but you can see it in other tools like Hubspot, Gong, etc. Not to mention the enterprise organizations who are building their own features that use LLMs. Yes, we'll see some "enterprises" adopt their first LLM in 10 years because some move as slow as molasses. That's fine; there's companies who are tasting Cloud for the first time right now, but nobody is sayin…
Are there any examples of companies turning a GAAP profit using generative models that aren’t selling the shovels, but actually using them to dig?
I forget how far from industry HN can be sometimes.
Is there room for generative AI in science? I am experimenting with this a lot at https://atomictessellator.com , As a computational chemist I found it difficult just to stay on top of all of the papers that are released, I thought it would be cool to have generative AI attempt to reproduce the experiments using simulation tech. Here's a few cool insights I have uncovered while working on this: - Developer tools are…
> Is there room for generative AI in science? I would love a system like ChatGPT but targeted specifically at exploring existing literature. A system that can recommend papers to read, that you can chat with about your problem and can recommend approaches that have worked for others and tell you why. That you can prompt and refine and go into detail with while it helps you figure out what do next based on previous wo…
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Are there any examples of companies turning a GAAP profit using generative models that aren’t selling the shovels, but actually using them to dig?
Uh... everywhere? I forget how far from industry HN can be sometimes.
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Isn't the takeaway : "holy shit, these things are advanced enough to make people like Blake Lemoine believe they are sentient?"
HFRL is literally just training the AI to be convincing. That's what these systems are optimized for.
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But that's not how it works at all? If I watch 100s of artists cell shade something and build a normal piece of software that mimics the results I owe them absolutely nothing. I've not seen an explanation as to why this is different.
People are fine with AI learning stuff from others. People are not fine with AI trained on everyone's recorded work and then replacing everyone to make massive profits for just that company. Private owned GAI is a dystopia, shared GAI is utopia like star trek. The difference might seem tiny today, but people really don't want to go down the dystopia route.
“Four decades of the internet (accelerated by COVID) has given us trillions of tokens’ worth of training data.” What’s up with the “accelerated by COVID”? It feels completely out of place. Would we have not had enough training data if COVID didn’t happen? Blessings in disguise, I guess.