dandi8

But let’s just say for the sake of argument that you’re 100% correct, and that Evo is not an LLM.

Why only assume? I cited Wikipedia. You cited nothing.

That is why I absolutely reject your disrespectful framing that by defending a technology, and NOT it’s worst uses, I’m somehow opposite to “care(ing) for humanity.”

There you go putting words in my mouth again.

The actual technology behind Evo and ChatGPT is structurally the same.

…Except that it isn’t. It’s not a Generative Pre-Trained Transformer. It uses a Transformer-like architecture. You cannot use Evo’s architecture to make a chatbot. It’s a GLM.

The methods of training a model on genomic data or weather patterns is indistinguishable from training it on stolen media.

Go ahead and train a StripedHyena2 model to be a chatbot, then. I’m sure that will work great.

For someone who’s such a stickler for making 100% correct and unambiguous statements, you’re sure keen on asserting equality where there is merely similarity.

Nobody is claiming these technologies don’t share some (or even a lot of) DNA. Being upset that people correctly use the definition of LLMs as outlined by Wikipedia, where even Evo’s own Github page doesn’t claim it’s an LLM, is just derailing the conversation away from people’s righteous objections.

Nobody is campaigning against using non-chatbot AI for science.

Read the room. Pay more attention to the context.

You don’t understand what LLMs are, because LLMs ARE deep learning models, and instead of taking the time to actually learn about the technology you’re responding to my corrections with hostility.

You’re claiming that I don’t understand technology while seemingly claiming that because LLMs are a type of deep learning, then all deep learning models are LLMs.

Evo was trained on genomic sequences, not human text. Per Wikipedia:

A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation.

Genomic sequences are not natural language. Ergo, “definitionally” Evo 2 is not an LLM.

While its StripedHyena2 architecture is very similar to LLMs, it does not use the same Generative Pretrained Transformer architecture associated with LLMs (per: https://docs.nvidia.com/bionemo-recipes/2.6.3/interactives/illustrated-evo2/index.html ).

My hate of LLMs is certainly not misinformed. It’s only tribe-based in that I care for humanity.

if you think LLMs can only be chat-bots, and can only be corporate, and can only be trained unethically

Don’t put words in my mouth.

1 and Evo 2 for example “Speak” genome sequences. There are other models that have been used to improve weather modeling, and animal behaviour analysis.

Evo 1 and 2 are deep learning models (I think Genomic Language Models would be the correct term), but not Large Language Models. I’m willing to bet neither are the “other” models you’re mentioning. They’re unlikely to be trained on vast amounts of human text for purposes of natural language interaction.

Besides, none of those “other models” are in any way applicable to either the OP or the critique of using LLMs as a source of information.

Obviously I’m using AI here as a shorthand for (all) LLMs, like the author of the article. The term AI is, on its own, so broad and meaningless, as to be entirely useless without a proper context to scope it.

No LLM (corporate or not) can be a reliable source of information due to the architectural limitations of LLMs.

Just like all those developers from the study who thought they were 19% faster but were, in fact, 10% slower?

Fair enough, it makes Jensen Huang’s life better, I suppose.

Or maybe just don’t use AI at all? It’s not a reliable source of information, it’s unsustainable in terms of resources consumed, it’s unethically trained and it makes everyone’s lives worse.

A slap to the face? AI isn’t a reliable source of information.