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Better Language Models and Their Implications

blog.openai.com

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Re: Better Language Models and Their Implications

#91

Post by rfdearborn »

Sure, not releasing the full trained model probably delays it, but sooner or later a bad actor will do their own scraping and train their own model and share it around and the genie will be out of the bottle. Then what?

I think we need to be conducting AI research (and building software generally) under the assumption that all of it will eventually be repurposed by bad actors. How would our practices be different if we consistently and cautiously did this?

Here's a thought experiment: how would the Manhattan project have been different if it were carried out in the open and its products were instantaneously and infinitely reproducible? What is the MAD equilibrium of AI research? I think the impact potential is similar even before AGI.

Re: Better Language Models and Their Implications

#92

Post by [deleted] »

resters wrote: post #13

Just as many pesticides mimic the hormonal and chemical signals of pests to drive certain behaviors that lead to eradication, this work mimics the linguistic signals of humans. I think viewing it metaphorically as the most sophisticated humanicide discovered to date is probably appropriate. Consider that conventional munitions make an effective pesticide but are not used due to their side effects. Instead, chemicals…

[deleted]

Re: Better Language Models and Their Implications

#93

Post by mlb_hn »

rfdearborn wrote: post #91

Sure, not releasing the full trained model probably delays it, but sooner or later a bad actor will do their own scraping and train their own model and share it around and the genie will be out of the bottle. Then what? I think we need to be conducting AI research (and building software generally) under the assumption that all of it will eventually be repurposed by bad actors. How would our practices be different if…

A lot of the advancements that made the Manhattan project were published by the Germans. On hearing about Hiroshima, Otto Hahn was "'shattered,' and went on to say that he felt 'personally responsible for the deaths of hundreds of thousands of people,' believing that his discovery had made the bomb possible." (https://www.washingtonpost.com/archive/opinions/1992/03/01/t...)

Re: Better Language Models and Their Implications

#94

Post by amayne »

wuthefwasthat wrote: post #3
legatus wrote: post #2

I was honestly surprised by the quality of the generated text. While I can't say I've been following the state of the art in the last months, this seems like a pretty important step forward. Furthermore, at the end of the post they note that the samples are somewhat representative of their results. Maybe they should consider releasing a text file with some more (not hand-chosen) samples? Whatever the case, fantastic…

Thank you! We've released 500 random unconditional samples from GPT-2 at https://github.com/openai/gpt-2/blob/master/gpt2-samples.txt

Reading through these is amazing. It’s like an alternate reality...

There’s a whole article about whales attacking boats and posing a threat to shipping.

My days as a novelist are numbered.

Re: Better Language Models and Their Implications

#95

Post by schoen »

xiphias2 wrote: post #90
schoen wrote: post #73

Earlier quoted context omitted.

Wow, some of these really go off the rails but those that only kinda go off the rails are absolutely hilarious and/or bizarre. A few summaries of ones that I looked at which appeared to be more or less staying on a single topic: Sample 1: An Austin nonvegetarian vegetarian restaurant encounters a series of difficulties in opening, as its nonexistent but extensive menu depicts a wide range of food options and the rest…

I'm looking forward to computers generating short clips from these stories. I wouldn't be surprised if some of them get viral (especially if A/B testing would be incorporated by looking at when the users stop watching the videos).

Compare Sunspring (2016).

https://arstechnica.com/gaming/2016/06/an-ai-wrote-this-movi...

Re: Better Language Models and Their Implications

#96

Post by defen »

sanxiyn wrote: post #63
modeless wrote: post #48

Earlier quoted context omitted.

Agreed that "simply" scaling up with more compute will result in progress and useful systems, and work in that direction is interesting and valuable. But, while we may not need new architectures or training objectives to make progress, we do need them to approach human level sample complexity. Humans don't need to read through 40 GB of text multiple times to learn to write.

40 GB is surprisingly close. I estimate I already read at least 4 GB of text so far. That's just 10 times more samples. I probably write better than GPT-2, but certainly not faster.

4GB of English? 500 words per single-spaced page, 5 letters on average per English word, so 2500 bytes/page.

4 gigs then would be 1,600,000 pages.

That's 219 single-spaced pages per day every single day for 20 years straight. High, but I guess not outside the realm of possibility depending on the complexity of the text.

Re: Better Language Models and Their Implications

#98

Post by Cybiote »

wuthefwasthat wrote: post #85
Cybiote wrote: post #51

It's becoming ever more certain that the transformer architecture is one of the largest contributions to AI (not merely machine learning, but AI), often beating LSTMs despite LSTMs being expressive enough to capture Turing Equivalence (at least in theory). Its main ideas are three: shorter paths help gradient flow, the training setup and the final key aspect, unhelpfully called self-attention. Self-attention is bette…

Yeah, there are definitely still places the samples fall short! Keep in mind we're still using very naive sampling techniques. RE Winograd: WNLI is different, see https://arxiv.org/pdf/1804.07461.pdf

Amazing results, how excited are you? :)

You're right, I noted too that the comparison isn't direct but then, I wasn't justified in calling out the gap claim as wrong, so sorry for that. I think it'd be nice however, to have it undergo an external or more neutral test of performance. I say this without at all doubting the quality of the results.

Re: Better Language Models and Their Implications

#100

Post by a-dub »

rfdearborn wrote: post #91

Sure, not releasing the full trained model probably delays it, but sooner or later a bad actor will do their own scraping and train their own model and share it around and the genie will be out of the bottle. Then what? I think we need to be conducting AI research (and building software generally) under the assumption that all of it will eventually be repurposed by bad actors. How would our practices be different if…

wasn't that the point of this whole openai thing? they didn't like the idea of there being a club with just google in it that had access to resources and funding to collect and train on massive datasets so they were going to be the "bad actors" who would do their own scraping, train their own models and share them around?

isn't it supposed to be called OPENai?

they don't want to share the data because they don't want to throw away the edge they've gained by collecting it. :)

computer programs that generate human like text aren't dangerous, the internet is full of human like text that is mostly bullshit anyway.

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