Christopher Manning (@chrmanning) / X

Christopher Manning

2,933 posts

Christopher Manning

@chrmanning

Founder @stanfordnlp & cs224n—Senior Fellow @StanfordHAI—Prof. CS & Linguistics @Stanford—GP @aixventureshq—MTS @moonlake—Australian🇦🇺—Do #NLProc & #AI 👋

Palo Alto

nlp.stanford.edu/~manning/

Joined September 2014

Christopher Manning
@chrmanning
Jan 27, 2025
Re: “Every major breakthrough in AI has been American”: America does itself no favors when it overestimates its specialness. Yes, the center of the AI industry is the US (California!), but many of the breakthroughs of (neural, gradient-based) AI happened elsewhere: • LSTMs,
Alexandr Wang
@alexandr_wang
Jan 26, 2025
DeepSeek is a wake up call for America, but it doesn’t change the strategy: - USA must out-innovate &race faster, as we have done in the entire history of AI - Tighten export controls on chips so that we can maintain future leads Every major breakthrough in AI has been American
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Christopher Manning
@chrmanning
Mar 14, 2024
I do not believe human-level AI (artificial superintelligence, or the commonest sense of #AGI) is close at hand. AI has made breakthroughs, but the claim of AGI by 2030 is as laughable as claims of AGI by 1980 are in retrospect. Look how similar the rhetoric was in @LIFE in 1970!
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Christopher Manning
@chrmanning
Dec 11, 2017
Machine Learning just ate Algorithms in one large bite, thx to @tim_kraska, @alexbeutel, @edchi, @JeffDean & Polyzotis at @Google—faster, smaller trees, hashes, bloom filters arxiv-vanity.com/papers/1712.01…
Christopher Manning
@chrmanning
Nov 20, 2023
I’ve kept quiet on the @OpenAI fiasco, since I also don’t know what’s going on, 🤷 but I can’t possibly support today’s interim CEO—the below in a thread on “50/50 everyone gets paperclipped & dies”—or a residue board that believes in these EA-infused fantasy lands. HT @vkhosla.
Emmett Shear
@eshear
Jun 1, 2023
Replying to @BarbettiJames @ApriiSR and @BellaRudd1
The Nazis were very evil, but I'd rather the actual literal Nazis take over the world forever than flip a coin on the end of all value.
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Christopher Manning
@chrmanning
Mar 26, 2024
“The fact that [transformer neural nets] model language is probably one of the biggest discoveries in history. That you can learn language by just predicting the next word with a Markov chain—that’s just shocking to me,” Mikhail Belkin says. By @strwbilly.
Large language models can do jaw-dropping things. But nobody knows exactly why.
From technologyreview.com
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Christopher Manning
@chrmanning
Jun 9, 2019
This paper gives some really nice insights and mathematical depth to what had previously (for us) been “the mystery of squared distance” in revealing the representation of parse trees in deep contextual representations (BERT, ELMo, etc.). Great to read!
Martin Wattenberg
@wattenberg
Jun 7, 2019
How does a neural net represent language? See the visualizations and geometry in this PAIR team paper arxiv.org/abs/1906.02715 and blog post pair-code.github.io/interpretabili…
Christopher Manning
@chrmanning
Mar 9, 2023
This is truly an opinion piece. Not even a cursory attempt is made to check easily refutable claims (“they may well predict, incorrectly”). Melodramatic claims of inadequacy are made not of specific current models but any possible machine learning approach
nytimes.com
Opinion | Noam Chomsky: The False Promise of ChatGPT (Gift Article)
The most prominent strain of A.I. encodes a flawed conception of language and knowledge.
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Christopher Manning
@chrmanning
Oct 26, 2020
Artificial Intelligence Definitions: This (northern) summer, I spent more time than I’d like to admit coming up with a handout defining key terms in AI in 1 page, trying to be informative and suitable for non-specialists – let me know if you like them! hai.stanford.edu/sites/default/…
Christopher Manning
@chrmanning
Sep 6, 2022
Meanwhile at @Stanford, we just encourage all students to take as many CS courses as they would like …
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Christopher Manning
@chrmanning
May 27, 2024
There are 2 mistakes you can make about LLMs: ① Thinking everything LLMs say is correct, they can reason, and with a bit more scale they’ll get us to superintelligence ② Thinking LLMs are good for almost nothing—they are FAR better at all #NLProc tasks than previous methods
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Christopher Manning
@chrmanning
May 31, 2023
Replying to @chrmanning
But most AI people work in the quiet middle: We see huge benefits from people using AI in healthcare, education, …, and we see serious AI risks & harms but believe we can minimize them with careful engineering & regulation, just as happened with electricity, cars, planes, ….
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Christopher Manning
@chrmanning
Dec 2, 2022
Dear @emilymbender—and @Abebab—you need to keep “reminding” people of your viewpoint because it is not an argument that is convincing to all or a self-evident truth. It is a particular academic position, which lots of people support but a good number of others disagree with. 1/8
@emilymbender.bsky.social
@emilymbender
Dec 1, 2022
Yes, exactly this. I wish we didn't need to keep reminding people, and @Abebab is commendable for being gentle about it! For the long form of this argument, see Bender & @alkoller 2020: aclanthology.org/2020.acl-main.…
Christopher Manning
@chrmanning
Aug 2, 2023
Reflecting again on how knowing all the architecture & equations of the Transformer model is really of no use at all in convincingly explaining to someone how an LLM like ChatGPT can write paragraphs of lucid text in response to a prompt. I guess I’m saying “Beware reductionism”.
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Christopher Manning
@chrmanning
Mar 14, 2024
Replying to @chrmanning
LLMs and other generative AI are enormously powerful, because they soak up, abstract, and can mashup the work of millions of humans. But they are only a bit more intelligent than an encyclopedia. Central to intelligence is the ability to learn, adapt, and act in novel situations.
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