Ted Pavlic (he/him/his)

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Ted Pavlic (he/him/his)

Ted Pavlic (he/him/his)

@TedPavlic

Assoc. Prof in @SCAI_ASU/@ASUSOLS. Autonomous decision making in living and artificial sys. @[email protected] @tedpavlic.bsky.social @TEDx: https://t.co/MntGxda3fO

Tempe, AZ, United States Katılım Aralık 2008
4.6K Takip Edilen2.7K Takipçiler
Ted Pavlic (he/him/his)
I get the urge to editorialize about consequences of data center power use, but you can't just start plugging in unrelated problems and pretending it's good enough to "manifest" a connection.
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Ted Pavlic (he/him/his)
That "The Resistance of a Cow" @RadioLab episode is pretty disappointing. They didn't even bring up that Denmark uses a TT grounding scheme (totally different from North America), and the nutrient deficiency hypothesis *from a vet* is given about 10 secs. radiolab.org/podcast/the-re…
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Ted Pavlic (he/him/his)
@mbeisen Also, are the only plotting those in the upper half? Because it would be suspicious if all scores were >=0 (with some being very close to 0). A weird kind of Lake Woebegon effect. Lake Woembryon?
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Ted Pavlic (he/him/his)
@LucaAmb @francoisfleuret I feel like it's closer to AI+Human > Human (alone) > AI (alone) And this is only considering performance at inference. When you add cost, then it's multi-objective criteria space that cannot be ordered. AI can't work alone, and it's too costly to only have 1 human and all AI.
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Luca Ambrogioni
Luca Ambrogioni@LucaAmb·
The idea that things in the real word can be well ordered that way is so naive that your all argument collapses to the ground instantly saying 'AI > humans' is like saying 'pear > apple' and concluding that apple argiculture will desappear as a logical consequence So I'd say that rationality so far is on their side more than on yours
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François Fleuret
François Fleuret@francoisfleuret·
Serious take: The optimistic "AI will not replace humans for sophisticated reasonning problems, the best will be collaboration" has no rationale of any sort. If AI > Human, then \forall alpha > 0, AI > (1-alpha)*AI + alpha*Human
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Ted Pavlic (he/him/his) retweetledi
Raphaël Doan
Raphaël Doan@raphaeldoan·
Le mathématicien Daniel Litt sur le problème d'Erdős récemment résolu par GPT-5.5 : "Une autre explication est que la solution exigeait des idées venues de domaines que la plupart des chercheurs travaillant sur ce problème ne connaissaient pas. Si elles sont justes, ces explications devraient nous mettre mal à l’aise. Elles suggèrent que les incitations à la spécialisation et au cloisonnement, si compréhensibles soient-elles, nous ont privés de travaux scientifiques de grande qualité." Le retour des polymathes à la Leibniz à travers les LLM ? Ce serait une bonne nouvelle.
Daniel Litt@littmath

(What I wrote is screenshotted below.)

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Ted Pavlic (he/him/his)
The era of the AI subsidy coming to an end? A natural check on the growth of AI as the market finds its equilibrium?
Hedgie@HedgieMarkets

🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products. My Take The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested. This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown. Hedgie🤗

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Michael 英泉 Eisen
I looked carefully back at Andrew Wakefield's paper connecting autism and the MMR vaccine, and confirmed that all of the cited papers exist, and that Wakefield and co-authors appear to have read the papers in question, so I'm having trouble understanding why the paper was retracted.
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Ted Pavlic (he/him/his)
You don't say?! -- "Based on our findings, we cannot conclude that dogs make altruistic or prosocial choices; rather, they appear motivated by selfish interests." "Reward expectation drives dogs’ choices in a prosocial test" by Piotti et al. (2026) link.springer.com/article/10.100…
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Ted Pavlic (he/him/his)
Why are we still talking about Turing tests? Rather than concluding that we have achieved "human-equivalent intelligence", can't we just admit that LLM's have shown us that the Turning test is maybe a flawed construct? (and we should maybe just move on to other approaches)
Eric Topol@EricTopol

A paper published @PNASNews today: "three current AI systems achieve a pass rate of at least 50% in a standard Turing test" The systems were GPT-4o, LLaMa-3.1, and GPT-4.5 All over 1-2 years old. pnas.org/doi/full/10.10…

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Ted Pavlic (he/him/his)
I feel like there's a major homogenization of new web interfaces now that AI makes it so easy to build very nice frontends. On one hand, the UI is great! On the other hand, all of the styling and elements look so similar to other completely unrelated projects from other people.
Leandro von Werra@lvwerra

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used: When modelling DNA sequences a lot of the performance comes down to tokenizing the sequences in a smart way. BPE tokenizer struggle because there are no whitespaces and character (called base in DNA) level tokenizers waste a lot of compute on too many tokens. Carbon is built with a unique tokenizer: we split sequences in chunks of 6 bases, but during both training and inference we can work with single base resolution. That's similar to having word tokens but resolving them at the character level. All possible thanks to the DNA tokens unique structure. The architecture combined with the tokenizer makes the model 275x faster than the previous SoTA (Evo2) at this size. We built an interactive demo so you can explore how the model can generate DNA sequences, investigate the structure of genes, predict the effect of mutations, generate and fold proteins and even reconstruct parts of the tree of life. huggingface.co/spaces/Hugging…

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Ted Pavlic (he/him/his)
The software article (Aygün et al., 2026) is much more promising as it's an approach firmly in augmented intelligence (not AI). The article about hypothesis generation is a but more troubling and could lead to lazy enquiry with narrow focus.
Eric Topol@EricTopol

A big day for multi-agent AI to accelerate biomedical discovery, hypothesis generation, designing experiments with proof points of new candidate drugs (cancer, fibrosis, macular degeneration, antimicrobial resistance, and more) 2 @Nature reports @GoogleDeepMind @FutureHouseSF nature.com/articles/s4158… nature.com/articles/s4158…

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Ted Pavlic (he/him/his)
"America’s Cybersecurity & Infrastructure Agency (CISA) has had a large store of plaintext passwords, SSH private keys, tokens, and “other sensitive CISA assets” exposed in a public GitHub repo since at least November 2025" arstechnica.com/information-te…
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Ted Pavlic (he/him/his)
This is a really cool result, and it's SO REFRESHING that the author posts about it so humbly (as if it's just another day) without prefixing it with "REWRITE THE TEXTBOOKS" or anything like that.
Peter Ly@PeterLyLab

Excited to share our latest paper, out today @CellCellPress. We found that large pieces of the human genome can transfer between cells upon direct contact, endowing recipient cells with heritable phenotypic changes. (1/7) cell.com/cell/fulltext/…

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Ted Pavlic (he/him/his) retweetledi
Michael 英泉 Eisen
A Nobel laureate widely celebrated for his collegiality and integrity once, when asked why his Nature paper didn’t cite earlier work that made the same “discovery” his paper asserted replied by saying “I don’t read other people’s work so that I don’t get in trouble for not citing it.
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Ted Pavlic (he/him/his)
Ted Pavlic (he/him/his)@TedPavlic·
@LucaAmb @scottnarmstrong It was my bad for trying to be as dramatic as the pro-arXiv side (to make a point). I'm grateful you didn't immediately block me, as the (still not formally written) arXiv policy might suggest! :-)
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Ted Pavlic (he/him/his)
Ted Pavlic (he/him/his)@TedPavlic·
No one on the "let's discuss the details of the arXiv policy" side has ever said anything about letting LLM's run amok. It's heartbreaking that people on the "ban coauthors for life" side has shown no willingness to practice the cautious deliberation they use in their research.
Krista K. Thomason@kkthomason

One of the painful lessons that LLMs have taught me is that apparently many researchers have very little integrity. They do not hold themselves to professional ethical standards as scholars. My bad, as it turns out.

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