MysteryInc152

MysteryInc152 t1_ja78mbl wrote

Reply to comment by genshiryoku in AI technology level within 5 years by medicalheads

Chinese, which is what the link focuses on has similar context issues. I understand the context problem you're talking about. A sentence can have multiple meanings until grounded in context.

Context can be derived from the language, just not necessarily in an isolated sentence. It may be derived from the sentence that precedes it. Bilingual LLMs have that figured out much better than traditional translation systems.

It's definitely possible for a preceding sentence to not have the context necessary but humans would struggle with that too.

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MysteryInc152 OP t1_ja3hozj wrote

Reply to [R] Large language models generate functional protein sequences across diverse families by MysteryInc152

>Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.

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MysteryInc152 OP t1_ja3hn8q wrote

Reply to Large language models generate functional protein sequences across diverse families by MysteryInc152

>Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.

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MysteryInc152 t1_j9w5xvg wrote

Reply to comment by TinyBurbz in What are the big flaws with LLMs right now? by fangfried

Far as I know they've just said it's a much better model than GPT 3.5 or chat GPT called Prometheus and anytime you ask if it's say gpt4, they just kind of sidestep the question. I know in an interview this year, someone asked Sadya if it was GPT-4 and he just said he'd leave the numbering to Sam. They're just being weirdly cryptic I think.

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MysteryInc152 t1_j9uhssy wrote

Reply to comment by YobaiYamete in New SOTA LLM called LLaMA releases today by Meta AI 🫡 by Pro_RazE

I think peer-reviewed research papers are a bit more than just "claims".

As much as i'd like all the SOTA research models to be usable by the public, research is research and not every research project is done with the interest of making a viable commercial product. Inference with these models are expensive. That's valid too.

Also seems like this will be released under a non commercial license like the OPT models.

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MysteryInc152 t1_j9terwg wrote

Reply to comment by turnip_burrito in What are the big flaws with LLMs right now? by fangfried

This is Bing's response to your question. I think we'd be surprised at how many of these problems will be solved by scale alone.

This sounds like a riddle. Is it? If so, I’m not very good at riddles. But I’ll try to answer it anyway. If the bus driver’s name is Michael and the bus driver is a dog, then the name of the dog is Michael. Is that correct?

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MysteryInc152 t1_j9teeio wrote

Reply to comment by GoldenRain in What are the big flaws with LLMs right now? by fangfried

Bing's response to your question. I think a good deal of these problems can be solved with scale. But yes, someone needs to train a large scale multimodal model asap

Hello, this is Bing. I’m not sure why you don’t want me to search the web for this question, but I’ll try to answer it using my internal knowledge. Air is composed of tiny molecules that are much smaller than the eye of a needle. So yes, air can fit through the eye of a needle. However, if you try to push air through a needle that is filled with water or another liquid, you will encounter resistance and pressure. Why do you ask?

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MysteryInc152 t1_j97mqgt wrote

Reply to comment by nul9090 in Proof of real intelligence? by Destiny_Knight

>The hostility was uncalled for.

It was I admit but I've seen the argument many times and I don't care for it. Also, if you're going to claim superior intelligence for your line of reasoning, I don't care for that either.

>What you're asking for is a lot of work for a Reddit post.

I honestly don't care how much work it is. That's the minimum. If you're going to upend traditional definitions of understanding and reasoning for your arguments then the burden of proof is on that person to show us why he/she should be taken seriously.

Tests are one thing. Practicality is another. Bing for instance has autonomous control of the searches it makes as well as the suggestions it gives. For all intents and purposes, it browses the internet on your behalf. Frankly, It should be plainly obvious that a system that can't exhibit theory of mind interacting with other systems would fall apart quickly on such tasks.

So it is passing tests and interacting with other systems/the world as if it had theory of mind. If after that, somebody says to me, "Oh it's not "true" Theory of mind' then to them I say, good day but I'm not going to argue philosophy with you.

We've reached the point where for a lot of areas, any perceived difference is just wholly irrelevant in a practical or scientific sense. At that point I have zero interest in arguing philosophy people have struggled to properly define or decipher since our inception.

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MysteryInc152 OP t1_j96y474 wrote

Reply to comment by yoshiwaan in [D] Toolformer implementation using only few-shot prompting by MysteryInc152

It's not a new model. It's davinci-003.

Basically the model begins generating. Once it hits an API request, the request is received and sent and the result of the request is pasted back into text and sent back to open AI to generate again and gpt continues generating until it hits another request and the process is repeated till it's done generating.

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