Discover and read the best of Twitter Threads about #gpt2

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I've been playing with the GPT-2 neural net text generators, and they produce interesting results when primed with two disparate themes, e.g. secure C++ coding meets trashy romance novel:
Most of the time GPT-2 will veer towards a single theme and forget the other. However occasionally it produces absolute gold. e.g. in this alternative universe Brent ignores Sylvia's advice and takes a risk:
Two alternative takes on the Rust language vs. the zombie apocalypse:
Read 8 tweets
First line of famous poems continued by the @openAI GPT-2 example model from "Language Models are Unsupervised Multitask Learners" #gpt2poetry #GPT2
It little profits that an idle king,
who loves his throne for a moment to enjoy a good meal,
might, if he was not in the right position,
become the subject of a great banquet.
But as the royal household will do,
so too shall their subjects. #gpt2poetry #tennyson
That is no country for old men.
They are old men of our land, of old times.
Nowhere is this more true than in these two cases,
as it now goes: the United States of America,
the oldest people on this Earth, are all in a state of rebellion. #gpt2poetry #yeats
Read 9 tweets
#GPT2 is published and suddenly every ML researcher is an expert on infosec. so much interest in dual-use issues _literally_ overnight is awesome! let's not confuse knowing the limitations of language modeling with knowing how threat actors operate (1/n)
I'm seeing a lot of anecdotal and hypothetical arguments from people with zero background in this kind of work (on both sides of the weights' non-release debate). for a field that treasures empirical study, this is perplexing.
when the media, marketeers, and scientists from other fields mischaracterize our work, we get upset. it's dismissive and damaging to everything we work for. it causes stakeholders and decision makers to make misinformed judgments. why would we risk doing similar to infosec?
Read 9 tweets
I'd like to weigh in on the #GPT2 discussion. The decision not to release the trained model was carefully considered and important for norm-forming. Serving the public good requires us to draw lines on release somewhere: better long before catastrophe than after.
Disclaimer before I dive in: I work at @OpenAI. I was not involved in this research project. This thread represents my personal opinions and not OpenAI's. Now that that's out of the way:
I've seen criticism fall into a few camps: 1) claims that OpenAI should have released everything for reproducibility's sake, 2) claims that OpenAI is feeding a harmful hype cycle, 3) claims that this was the wrong point for drawing the line, and sadly, 4) derision and mockery.
Read 25 tweets

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