David Poole

282 posts

David Poole

David Poole

@davpoole

Scientist, Educator, Artificial Intelligence Researcher

Vancouver BC Katılım Ağustos 2013
256 Takip Edilen652 Takipçiler
David Poole retweetledi
UBC Computer Science
UBC Computer Science@UBC_CS·
UBC Computer Science Professors Emeriti Alan Mackworth and David Poole were awarded the AAAI/EAAI Patrick Henry Winston Outstanding Educator Award for developing free online resources to learn foundations of AI. Congratulations! Read more: cs.ubc.ca/news/2026/02/m…
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David Poole
David Poole@davpoole·
@RichardSSutton "Discriminative models are usually better than generative models" is true when the test is distributed the same as the training. Generally we want to predict the future from the past. Assuming these are distributed the same is a big stretch, I'm surprised anyone would make!
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Richard Sutton
Richard Sutton@RichardSSutton·
Rich's slogans for AI research (revised 2006): 1. Approximate the solution, not the problem (no special cases) 2. Drive from the problem 3. Take the agent’s point of view 4. Don’t ask the agent to achieve what it can’t measure 5. Don't ask the agent to know what it can't verify 6. Set measurable goals for subparts of the agent 7. Discriminative models are usually better than generative models 8. Work by orthogonal dimensions. Work issue by issue 9. Work on ideas, not software 10. Experience is the data of AI incompleteideas.net/rlai.cs.ualber…
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Sebastian Thrun
Sebastian Thrun@SebastianThrun·
I really wonder why so many AI companies seem to blindly compete for building the same thing: the best foundational models, as measured by some abstract set of benchmarks. It feels like there is now an entire ecosystems of companies that want to outperform GPT.x. Why?
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@emilymbender.bsky.social
@emilymbender.bsky.social@emilymbender·
PSA: Every time you cite the arXiv version of something instead of the peer reviewed version, you're lending legitimacy to nonsense like this: Source: #S6" target="_blank" rel="nofollow noopener">arxiv.org/html/2404.0722…
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neil turkewitz
neil turkewitz@neilturkewitz·
“The profound anthropomorphisms that characterize today’s AI discourse—conflating predictive analytics w/INTELLIGENCE & massive datasets with KNOWLEDGE & EXPERIENCE—are primarily the result of marketing hype, technological obscurantism & public ignorance.” publicbooks.org/now-the-humani…
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Yann LeCun
Yann LeCun@ylecun·
AI is not some sort of natural phenomenon that will just emerge and become dangerous. *WE* design it and *WE* build it. I can imagine thousands of scenarios where a turbojet goes terribly wrong. Yet we managed to make turbojets insanely reliable before deploying them widely. The question is similar for AI: "do we think there exists at least one design of an AI system that is simultaneously safe/controllable, and can fulfill objectives in more intelligent ways than humans ?" If the answer is yes, we'll be fine. If the answer is no, we won't build it. Right now, we don't even have a hint of a design of a human-level intelligent system. So it's too early to worry about it. And it's way too early to regulate it to prevent "existential risk."
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David Poole
David Poole@davpoole·
@GaryMarcus @randomwalker Geoff is probably right about machine learning; we just need lots of homogenous data and lots of compute. However, for lots of problems there is inherently little data. And machine learning is only part of AI, which is far from being solved.
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Gary Marcus
Gary Marcus@GaryMarcus·
Hinton is completely wrong about this, and that will become increasingly clear over the next year. (See my recent essay on diminishing returns, @randomwalker’s recent thread, etc) Hinton’s intutions about LLMs are surprisingly far off, between this and his nonsense about them “just storing weights” [“They don't pastiche together text that they've read on the web, because they're not storing any text. They're storing these weights.”] Then again in 2016 he thought deep learning would replace radiologists by 2021 or so, too.
Joel Hellermark@joelhellermark

Spoke to @geoffreyhinton about OpenAI co-founder @ilyasut's intuition for scaling laws👇. "Ilya was always preaching that you just make it bigger and it'll work better. And I always thought that was a bit of a cop-out, that you're going to have to have new ideas too. It turns out Ilya was basically right." Link to full interview in 🧵.

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Subbarao Kambhampati (కంభంపాటి సుబ్బారావు)
Like Astrology (and unlike boring Science), CoT apparently works for you only if you *really* believe in it..
Subbarao Kambhampati (కంభంపాటి సుబ్బారావు)@rao2z

📢Thanks to @karthikv792 and @kayastechly's tireless efforts, here is the paper analyzing the (in)effectiveness of Chain of Thought prompting. The good news is that everything I said here and in my talks about CoT delusions still holds. The better news is that Karthik and Kaya have done more extensive experiments both with GPT4 and Claude 3 Opus. 👉 arxiv.org/abs/2405.04776 tldr; LLMs may well be smarter than that dog in the Farside cartoon (although I am sure @ylecun will pushback vociferously😋), but there is little reason to believe that we can advise them the way we advise our friends--and expect them to operationalize that advise..

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Melanie Mitchell
Melanie Mitchell@MelMitchell1·
The journal Nature: "AI now beats humans at basic tasks". Me: Really? New blog post. See link to "More thoughts" in my bio.
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Sriraam Natarajan
Sriraam Natarajan@Sriraam_UTD·
While intellectual independence and scholarly pursuits are the key reasons why many of us choose to be in academia, arguably, the most satisfaction comes from teaching the next generation of great minds!
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Lenka Zdeborova
Lenka Zdeborova@zdeborova·
Another class of ML papers that collect good review scores largely overclaims the implications and generality of what is actually proven and sweeps the underlying assumptions under the carpet. See below what I ask the referees for those:
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David Myers
David Myers@DavidGMyers·
A psych science perspective (writing as President Biden’s age-mate) on the challenges and strengths of octogenarian life.... in tomorrow's print NY Times:
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@emilymbender.bsky.social
@emilymbender.bsky.social@emilymbender·
Wow -- what if we could use those funds to build: alternative energy capacity, improved electricity grid, public transit, modernization of public buildings (schools, hospitals; think ventilation, energy efficiency) instead of data centers for churning out synthetic media?
Karen Hao@_KarenHao

But we don't know how this is adding up with the massive genAI acceleration. Only that Microsoft is now funneling >$10B into infrastructure expansions every *quarter* to support this growth. @dylan522p calls this “the largest infrastructure buildout that humanity has ever seen.”

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@emilymbender.bsky.social
@emilymbender.bsky.social@emilymbender·
It seems like there are just endless bad ideas about how to use "AI". Here are some new ones courtesy of the UK government. ... and a short thread because there is so much awfulness in this one article. /1 ft.com/content/f2ae55…
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