Max Little

Depends on how you spin it I guess (screenshots from 2 different articles).

People working at universities are pushed so incredibly hard to ensure that every study is a breakthrough that they just...lie. All the time. Probably without even realizing it.

www.standard.co.uk/news/tech/im...

Every Jan 1 I post a scorecard on predictions I made, with dates, on Jan 1, 2018 on cars (self-driving), robots, AI, & ML, and on human spaceflight. Besides telling which turned out right and which wrong in the last year I also talk a lot of smack about these topics. rodneybrooks.com/predictions-...

Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics Do large language models (LLMs) solve reasoning tasks by learning robust generalizable algorithms, or do they memorize training data? To investigate this question, we use arithmetic reasoning as a rep...

LLMs can be also seen as big bags of heuristics: arxiv.org/abs/2410.21272

U.S. science funding agencies roll out policies on free access to journal articles NIH and DOE are first to act, with implementation by all set to begin by end of 2025

www.science.org/content/arti...

𝗢𝟯 𝘄𝗮𝘀 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝟳𝟱% 𝗼𝗳 𝘁𝗵𝗲 𝗽𝘂𝗯𝗹𝗶𝗰 𝘀𝗲𝘁 𝗳𝗼𝗿 𝗔𝗥𝗖-𝗔𝗚𝗜.

OpenAI did not disclose this in the video. Sam said they didn’t target the test.

Never trust a staged demo.
Never trust a product you haven’t tried.
Never trust OpenAI.

o3, AGI, the art of the demo, and what you can expect in 2025 OpenAI’s new model was revealed yesterday; its most fervent believers think AGI has already arrived. Here’s what you should pay attention to in the coming year.

o3, AGI, and the art of the demo. Long read on what OpenAI didn’t tell you yesterday. garymarcus.substack.com/p/o3-agi-the...

Likewise, a simple adversarial strategy beats "superhuman" Go-playing algorithms: goattack.far.ai It's wise to remember that there is no scientific consensus on what "intelligence", actually is.

Just for those who don't know: the vast majority of open problems in maths, are not numerical in nature.

The questions have numerical answers, so it is easy to check whether it gets them right.

How many times do we have to see this same movie, where an AI beats some benchmark and influencers gleefully shout “It’s So Over” without even trying out the AI and then on careful inspection the AI turns out to not be robust or reliable?

Thousands?

(It’s already been hundreds.)

It seems that OpenAI's latest model, o3, can solve 25% of problems on a database called FrontierMath, created by EpochAI, where previous LLMs could only solve 2%. On Twitter I am quoted as saying, "Getting even one question right would be well beyond what we can do now, let alone saturating them."

Scholars Are Supposed to Say When They Use AI. Do They? Journals have policies about disclosing ChatGPT writing, but enforcing them is another matter, according to a new study.

It's widely agreed that scholars are supposed to say when they use ChatGPT. Yet phrases like "I am an AI language model"—with no disclosure—are popping up in papers.

I wrote about how journals seemingly aren't enforcing their AI policies, according to a new study: www.chronicle.com/article/scho...

Is AI progress slowing down? Making sense of recent technology trends and claims

This seems like a pretty balanced commentary. They certainly get this right: "connection between capability improvements & AI’s social or economic impacts is extremely weak. The bottlenecks for impact are the pace of product development and the rate of adoption" www.aisnakeoil.com/p/is-ai-prog...

Good reporting here, but sadly, these tragedies were predictable. Those of us who actually work on machine learning know that deep-learning based computer vision simply isn't reliable enough for safety-critical applications such as self-driving cars. @garymarcus.bsky.social @filippie509.bsky.social

When does generative AI qualify for fair use?

The late Suchir Balaji’s blog post on AI, copyright and fair use, reposted in his memory.

suchir.net/fair_use.html

The bootstrap can be used to generate a new random sample from an existing random sample. It's validity can be guaranteed by the Glivenko-Cantelli theorem, which demonstrates how the empirical CDF (top panel), converges on the CDF of the sample (bottom panel).

The bootstrap can be used to generate a new random sample from an existing random sample. Its validity can be guaranteed by the Glivenko-Cantelli theorem, which demonstrates how the empirical cumulative distribution (CDF, top panel), converges on the CDF of the sample (bottom panel).

For an increasing function 𝑓:ℝ→ℝ, max(𝑓(𝑎),𝑓(𝑏))=𝑓(max(𝑎,𝑏)). An important special case is 𝑓(𝑥)=𝑥+𝑐, for which we obtain max(𝑎+𝑐,𝑏+𝑐)=𝑐+max(𝑎,𝑏).

I believe GM came to exactly this is the realization and decided (likely very wisely, in my opinion) not to throw more good money after bad.

Since 2016 Waymo raised ~$25B, so they burn ~$3B/y or little over 8mln/day. With ~700 cars, assuming they operate each car every day, it costs them over 11k dollars to operate each of their cars per day. $11k PER DAY per CAR. If you don't find this ridiculous IDK what else to say.

Suchir Balaji was a good young man. I spoke to him six weeks ago. He had left OpenAI and wanted to make the world a better place. This is tragic.

Very proud of the Birmingham HDRUK PhDs!

Health Data Research UK PhD meet! Work from Ant Lee and Jianqiao Mao (latter with @maxal.bsky.social)

Apple "Intelligence". @garymarcus.bsky.social

And, not usually mentioned is just how many "non-driver" human roles Waymo are heavily relying upon, e.g. teleoperation, stuck vehicle retrieval, repairs, maintainence, cleaning, passenger support etc. @rodneyabrooks.bsky.social

As first predicted some 10 years ago that is how "self driving cars" will end - as glorified driver assistance features. The graveyard of autonomous vehicle efforts is pretty crowded already with pretty much only Waymo remaining, until life support from Google mothership ends.

What if all the hype just didn’t turn out to be true?

Evidence of productivity gains is mixed - yet hypey takes continue to dominate in the media.

Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts When the text-to-image AI generation system DALL-E2 was released in April 2022, the two of us, together with Scott Aaronson, ran some informal experiments to probe its abilities.

Don’t Ride This Bike! Generative AI’s persistent trouble with compositionality and parts, by Gary Marcus @garymarcus.bsky.social and Ernest Davis / Marcus on AI - Substack garymarcus.substack.com/p/dont-ride-...

Most of these sorts of algorithms are just AI snake oil: they don't work because there is no way to quantify these sorts of 'social variables'. They are never actually tested to any level of scientific rigour.

Not quite: AI got people excited about interpolation, it seems. Numerical analysts suddenly feel seen.

@garymarcus.bsky.social