Worked with .ac.uk e-mail for me.

boomer

I'm not sure if bash has autocomplete capabilities like that (like asking for a query under the current command line)

My Uni doesn't use .edu (German Uni), I wonder if that's a factor

As a large language model I can't comment, but my friends say it works :D

ALL the data

You're thinking of that other library, Keraskeras Cola.

Looks great, definitely going to try it out. Pity it works only with zsh though and not with bash

Nice. Now I have a paper to read. :) Thank you.

Regexes are definitely the bane of my existence.

[deleted]

Especially given the compute used on OpenAI end. This isn’t sustainable

The ffmpeg example is worth it alone

Does that link "actually" works? 😂

If you know and remember the command - yes, if you need to google or read the man first this could be faster. Or sometimes faster for complex commands with subshells and regexes.

The Winograd Schema is a test of commonsense reasoning. It's hard because it requires not just knowledge of english, but also knowledge of the real world.

But as you found, it's pretty much solved now. As of 2019 LLMs could complete it with better than 90% accuracy, which means it was actually already solved when Tom Scott made his video.

laughs in magnet link

Impressive!

This is definitely impressive but it also feels like more work than to just bang out the commands.

Fantastic reply, it's great to see all those concrete advances thst made it intro prod. Thanks for sharing.

I tried the same questions in separate chats as in the edited post. ChatGPT gave incorrect/unsatisfying answers this time. May be without context from previous Q&A, it can only infer using grammar rule? What do you think?

> I mean... why were they not doing this already? They would have to code it but it seems like low hanging fruit

GPT-3 came out in 2020 (they had their initial price then a modest price drop early on).

Flash attention is June of 2022.

Quantization we've only figured out how to do it fairly lossless recently (especially int4). Tim Dettmers LLM int8 is from August 2022.

https://arxiv.org/abs/2208.07339

> That seems large, which paper has that?

See

https://github.com/HazyResearch/flash-attention/raw/main/assets/flashattn_memory.jpg

>We show memory savings in this graph (note that memory footprint is the same no matter if you use dropout or masking). Memory savings are proportional to sequence length -- since standard attention has memory quadratic in sequence length, whereas FlashAttention has memory linear in sequence length. We see 10X memory savings at sequence length 2K, and 20X at 4K. As a result, FlashAttention can scale to much longer sequence lengths.

https://github.com/HazyResearch/flash-attention

Personally, I think the answer is existing bias in the training data.

I don’t know enough about chatgpt to state this as fact, but I think it’s safe to assume that chatgpt understands or adheres to grammar rules. I.e. nowhere in the code does it state “antecedent pronouns should refer to the subject of a sentence”

Instead I assume chatgpt grammar comes repeated convention in the training data. Enough data in which the antecedent refers to something other then the sentence object means that the “they” can refer to any of the preceding nouns. In that case “councilmen fear voilence” is a far more common sentence in the training than “protesters fear violence”

Then again your example was passive tense, so I dunno 🤷‍♀️.

Wow this is awesome. Can’t wait to tinker around with it

It's a leak, but seems to be legitimate.

https://twitter.com/transitive_bs/status/1628118163874516992