Showing posts with label tweet. Show all posts
Showing posts with label tweet. Show all posts

Saturday, September 5, 2026

Those with programming skills are best at vibe coding

From the tweet:

The hype told us that learning to code is dead because language is all you need.

The data just proved the opposite.

To truly master the vibe, you still need to understand how the machine thinks.

Here's the paper, Computer Science Achievement and Writing Skills Predict Vibe Coding Proficiency.

Saturday, August 29, 2026

AIs are not very good at long horizon tasks

Tuesday, August 25, 2026

How LLMs work [in pictures]

Thursday, August 13, 2026

Skateboarding the eclipse

Tuesday, August 11, 2026

Where “the will to live” resides in the brain

Monday, August 10, 2026

Mongolian Girl Has a Laugh with her Camel.

Sunday, August 9, 2026

Séb Krier has 3 meta observations about the state of AI

Written out in full:

1. AI will continue to improve, get better integrated, and produce lots of value. This is true even if you think there's a bubble-y dynamic or an imminent correction. Lots of people genuinely believe the transformative prospects, but also lots of people have strong incentives to believe so AND for others to believe so too. So a lot of bulls are *honestly* bullish, whilst at the same time self-selecting into, and being driven by, discourse that happens to align well with their own interests.

2. Not exactly a revolutionary insight, but the very same facts will lead some people to think the exact opposite of what another group believes. The shape of recent progress will make some people think we are close to some sort of 'recursive self-improvement' dynamic (sometimes with unstated accompanying beliefs about speed of societal transformation). But another group, looking at the same results but indexing on other variables, will conclude we're seeing diminishing returns, jaggedness, and real but incremental progress (sometimes with unstated accompanying beliefs about the criticality of temporary failures).

3. A lot of public discussions on AI feel like they rest on a scaffold of leaky and highly imperfect abstractions. Too much is being written about models with reference to parables, metaphors, analogies, and stylized stories. Ofc this is somewhat unavoidable, but many jump to easy pattern matching and reason probabilistically *within* a particular causal story without adequately representing uncertainty over the story itself. There's so much noise that the correlations seem more explanatory than they actually are. Because the underlying understanding is itself so murky and uncertainty is uncomfortable, people go for easy familiar abstractions and are too quick to trust the data generating process itself. As a result of the above, the experts themselves are often more confused than one might expect, and so proper division of labour and deferral to authority is much harder in AI than in other established fields.

* * * * *

A comment from me: Also, much of the discourse about AI is based on concepts that arose before, say, the ChatGPT breakthrough to the public or the AlexNet breakthrough within the industry. It's discourse based on possibility not proved actuality.

Saturday, August 8, 2026

Norway has a museum dedicated to whales

Friday, August 7, 2026

François Chollet sees Large Reasoning Models (LRMs) in the future

What are large reasoning models?

Red flower

Thursday, August 6, 2026

What we’ve got in frontier models is now neurosymbolic

Tuesday, August 4, 2026

Why no child prodigies in biology?

Monday, August 3, 2026

Looks like DeepMind just ran into ontological dependencies in LLMs

Rewiring the brain, neuroplasticity

Sunday, August 2, 2026

Illusions and delusions about the power of AI

What I think is that, OTOH lots of people commenting on AI have not given much systematic thought to method, theirs or anyone else’s. OTOH they’ve also (uncritically) absorbed the idea that math and theoretical physics are at the top of some intellectual pyramid. Therefor, they conclude, AI is going to clear the board real soon now.

Saturday, August 1, 2026

Information-theoretic Limits on Programmatic Specification of Biological Systems

Abstract of the post linked in the tweet:

The central problem of biology is the origin of biological organization. We show, using informa- tion theory, that an organism does not contain enough organism-specific information to specify its own fully functioning microscopic organization. The organized machinery of life is therefore not the execution of a fully prewritten organism-specific program under favorable conditions. Rather, it is the compilation of a coarse organism-specific specification by a shared physical background that is constitutive of biological organization. We formalize this as a coarse-graining information threshold on biological specification, with two complementary entropy faces — a Shannon face controlling stochastic generation and a Hartley face controlling zero-error deterministic addressability. Above the threshold, organism-controlled information is sufficient to specify structural and functional ensembles; below it, programmed microstate determinism is impossible: deterministic addressability fails by pigeonhole, and any algorithm producing sub- threshold outputs must consume runtime randomness proportional to the information deficit. The threshold follows from two information-theoretic constraints — finite specification capacity and causal locality — supplemented by a mixing lemma showing that initial-condition information decays exponentially under thermal dynamics. We establish the threshold as a family of maximal capacity-compatible coarse-grainings, distinguish the proven impossibility below the threshold from the empirically realized coarse mappings above it, and locate the threshold empirically through worked cases of protein folding, E. coli, Drosophila early development, and C. elegans, together with computational verification using AlphaFold-2, the JCVI-syn3A 4D whole-cell simulation, and canonical stochastic gene network models. We further show that no known naturally realized environmental channel can close the gap. The result rules out programmed microstate determinism while leaving physical determinism untouched, reframes the genome as a generator specification rather than a trajectory program, and unifies gene-centric, developmental, and field-theoretic (bioelectric, morphogenetic, and related continuum) views of biological specification under a single coarse-graining framework.

Sabine: “You can brute-force counterexamples by just trying a lot of guesses quickly...”

Ethan Mollick: “AI has blurred lines between jobs.”

Wednesday, July 29, 2026

Logic and language in the brain

The abstract of the linked article:

Humans are endowed with a powerful capacity for inductive and deductive logical thought: we easily form generalizations based on a few examples and draw conclusions from known premises. Humans also arguably have the most sophisticated communication system in the animal kingdom: natural language allows us to express complex and structured meanings. Some have therefore argued for a tight relationship between complex thought and language, postulating that reasoning, including logical reasoning, relies on linguistic representations. We systematically investigated the relationship between logical reasoning and language using two complementary approaches. First, we used noninvasive brain imaging (fMRI) to examine neural activity as healthy adults engaged in logical reasoning tasks. And second, we behaviorally evaluated logical abilities in individuals with extensive lesions to the language brain areas and consequent severe linguistic impairment. Our findings reveal that the language brain network is not engaged during logical reasoning, and patients with severe aphasia exhibit intact performance on logic tasks. Instead, inductive reasoning recruits the domain-general multiple demand network implicated broadly in goal-directed behaviors, whereas deductive reasoning draws on brain regions that are distinct from both the language and the multiple demand networks. Together, these results indicate that linguistic representations are neither utilized nor required for inductive or deductive logical reasoning.

Why Adam Hunt has “flipped from being bullish to being bearish about AI.”