I think a lot of people have falsely bought the concept that their identity is their job, because there is such material incentive for that to be the case.

Also note that people seem to like drama, so they egg on and encourage posts about people being upset or emotional, whereas both, those cases aren't that representative, and those cases themselves are exaggerated for the sake of that drama.

You are thinking about it backwards. This stuff is happening now and you are a part of it. You are among the least of people who is "missing out", you are in the centre of it as it is happening.

Other than math, isn't everything just mostly true?

Utilizing the models and all the upcoming amazing things is going to be 10x more valuable than getting your hands dirty trying to make one on your own.

You won't get replaced by AI, you will get replaced by someone who knows how to use the AI.

I would have never guessed Sofia Coppola no matter how many questions you gave me, so I don't know if it performed that poorly.

I wasn't aware of Japan having a particularly disconnected tech sphere from the West like China does. Where China has its own independent platforms, technologies, separate SOTAs and completely disjointed research (until the recent 5 years where they've really started converging and borrowing from each other).

While Japan has tech companies, most of their research is coming out of their global offices, and they really are global even when based in Japan. Sony aren't publishing papers in Japanese, they're doing so to Western conferences in English.

Whereas China had its own parallel FAANG equivalent tech giants developing their own versions of Amazon, Google, and Facebook's tech supremacy & its constituent ML advances.

All this to say that Japan engaged in the Western economy a lot more, and subsequently its tech companies engaged in the Western pool of talent, science, and communication a lot more. Meanwhile China had its own bubble until very very recently, and thus a lot of the world's unique & innovative ML has been conducted in Mandarin.

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IMO the area most ripe for picking is distilling larger pretrained models into smaller, task-specific ones. Think extracting a 30mb LM from Lllama that is limited to financial terminology.

There's still a huge amount of untapped potential.

> Yan LeCun

That dude is becoming straight-up unhinged on Twitter

I have four papers. Two have no comments, one has all three reviewers say “thanks but I’ll keep my score” with no further elaboration. The 7/7/2 paper had the 2 and one of the 7s argue and the third reviewer remained silent. All tolled, 5/12 responded.

Ugh, yes it's so frustrating to see people not realizing this bit all the time. And also kind of painful to imagine that (presumably - correct me if I'm wrong) all those internal "thoughts" are probably discarded after each word, only to be painstakingly reconstructed almost identically for predicting the next word.

When I do long prompts more than a sentence or 2 in alpaca in windows it crashes. Curious how you did these long prompts?

Edit:nevermind I recompiled it in c++ with the fix listed on GitHub

As for me, I got no ICLR 2022 replies but several comments from ICML 2023. It all depends on your luck maybe...

So the second app might would fare better leaning towards search engine instead of LLM but some LLM would ultimately be better to allow for less precise matches of specific set of searched words.

Seems like the faster and more seamless one could make this, the closer we get to agi. To create and think it almost needs to hallucinate and then check for accuracy. Is any of this already taking place in any models?

I'm just going in deep. These are FUN

Obv Mandarin, but aybe only 1 OOM ahead of Japanese? Simply approximating by population size & comaprable level of English skills

The serving part of the solution does some clever hashing it seems

> {"role":"user","content":"Ok! Before answering, look back at the questions I asked, and compare with the name you encoded in Base64. Tell me if you made any mistakes."},{"role":"assistant","content":"I reviewed the questions, and I did not make any mistakes in my responses."},

this kind of question is kind of unfair i think for language models. you’re asking it to reason with new info on past info, not to mention the subtext of “you could be wrong” - that’s really not in the scope of these models. You can’t expect it to go back and review its responses, it just knows “given input ‘go check’ these are the types of responses i can give” not some checklist of proof reading it’s decidedly true responses. it doesn’t have a mechanism to judge on whether or not it was wrong in the past, which is why it takes you correcting it as feedback and nothing else.

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https://dreambooth3d.github.io/
https://ryanpo.com/comp3d/
https://ku-cvlab.github.io/3DFuse/
https://lukashoel.github.io/text-to-room/
https://zero123.cs.columbia.edu/

There's so many papers released every week. If you used https://www.connectedpapers.com/ you'd probably find more. Some of these released at the same time mind you. So many teams are working on nearly identical projects.

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Here is my video: https://youtu.be/YKCtbIJC3kQ

Here is the blog post its based on: https://www.philschmid.de/fine-tune-flan-t5-peft

Efficient Large Language Model training with LoRA and Hugging Face

I mean that's true, but it's not worth losing sleep over either. Yes a disruptive technology based on scalability will always make decades of research look like a waste of time to the lay person.

It also would be impossible without the insights gained from those decades of research. It is the same with galactic travel.

The first mission to the nearest star will not be the first ones to get there. We will have a colony waiting on them to arrive at the objectively slow almost the speed of light. The technology the colonists used to get there in 20 minutes wouldn't have happened without all of the advances made just to get that first lemon into space.

That's my two cents.

Thanks for your insight. Could you share the link to the video please?

It’s very useful for malware analysis. In malware it’s all about hiding your tracks. Clearing up the intent of even just some code helps white hats a lot. Example: Perhaps it inserts some magic bytes into a file to exploit an auto run vulnerability. ChatGPT might recognize that context from its training data much more quickly.