

Ted Pavlic (he/him/his)
33.2K posts

@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



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…


PSA: Google Apps Scripts can work with gmail directly. Eg can pull out every e-mail from a person and take every attachment in those e-mails and put them onto drive. And other things. With Gemini-3 "advice" this took me 3 min to implement. Holy Ada Lovelace.


A friend of mine had her embryos screened by Herasight and they found one with an IQ score in the 99.99th percentile



Mathematicians are having a Lee Sedol moment.

(What I wrote is screenshotted below.)




Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946. For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids. An OpenAI model has now disproved that belief, discovering an entirely new family of constructions that performs better. This marks the first time AI has autonomously solved a prominent open problem central to a field of mathematics.


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…

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…

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…


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/…



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.