Gary Marcus @GaryMarcus
OG GenAI Skeptic; spoke at US Senate. Warned about hallucinations in 2001. Advocating world models & neurosymbolic AI ever since. Author, Marcus on AI & 6 books garymarcus.substack.com Joined December 2010-
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@DeryaTR_ ah; your support for their standing their ground didn’t quite come through on in the OP.
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My government tried to ban my AI, and all I got was this lousy t-shirt.
Gary Marcus retweeted
Discovery of a 14-protein biomarker that predicts lung cancer 5.6 years before it is diagnosed, even in non-smokers, and an anti-inflammatory medicine that prevents its progression. And, challenging dogma, the proteins are not coming from cancerous cells!
is there any evidence whatsoever that Fable was more vulnerable to jailbreaking and distillation than competing models, or was enforcement selective?
.@ReedAlbergotti scoop: Concerns about specific China-linked accessed to Mythos part of the White House Anthropic move semafor.com/article/06/13/…
live footage of the US government squandering our AI lead
could the u.s. policy be any stupider?
JUST IN: Andrej Karpathy, a top AI scientist at Anthropic, is reportedly barred from accessing the company’s most advanced AI model because he is not a U.S. citizen.
@burkov i don’t use the ai overview (except to laugh at them); the web links are mostly fine
@scaling01 and they have the money and i am not sure the current path is the right one and they may be better positioned to find alternatives
@JadeCole2112 see my substack today for only viable solution at this point
Whatever you may think of Dario or Anthropic, Friday’s decision, and the impetuousness and arbitrariness of it, was a terrible mistake that has left a stain that will last. Washington needs to fix this.
Make no mistake: post-Mythos, the United States has a licensing regime for AI. It’s just informal, with no consistent rules or firm boundaries on state power or public transparency. Cobalt mining in the Congo is vastly more institutionalized than frontier AI licensing in the US.
Gary Marcus retweeted
Hany Farid saying this is terrifying. “I don’t trust anything. Every image I see, I’m drawing lines for shadows and doing geometry in my head. It’s over. Within a year or two, our whole visual system will be utterly useless.” nytimes.com/2026/06/14/us/…
and, 6, the US government has beclowned itself, adding uncertainty throughout the industry.
Why things will eventually fall apart: 1. Everybody, even Google, seems to be treating AI as if it were some kind of winner take all competition like web search was, in which Google taking over 95% 2. But everybody is building essentially the same technical solution with
@indexsy what’s the evidence that’s distilled?
“If an AI cannot apply an abstract lesson to a new situation, it is not truly reasoning or learning”, new study with further evidence backing up what I have been saying for 25 years. cc @dwarkesh_sp
Researchers found our current approach to making AI smarter over time has a giant blind spot. AI is not actually understanding or applying high-level abstract lessons at all. Developers spend massive amounts of time building systems that condense past AI mistakes into neat
Gary Marcus retweeted
Researchers found our current approach to making AI smarter over time has a giant blind spot. AI is not actually understanding or applying high-level abstract lessons at all. Developers spend massive amounts of time building systems that condense past AI mistakes into neat little rules for the future. This paper proves that the AI essentially throws those rules in the trash and only looks at raw historical logs. Modern LLM systems try to get better over time by storing past tasks as either raw step-by-step histories or condensed summary rules. The study tested if these agents actually use their stored memories by secretly swapping the correct tips with random garbage text. - When the step-by-step histories were messed up, the AI failed hard, proving it heavily relies on copying exact past actions. - But when researchers completely corrupted the condensed summary rules, the AI kept acting normally and showed zero performance drop. If an AI cannot apply an abstract lesson to a new situation, it is not truly reasoning or learning. This raises the question if the entire AI industry need to rethink how memory works because right now these agents are just mimicking instead of understanding. ---- arxiv. org/abs/2601.22436 "LLM Agents Are Not Always Faithful Self-Evolvers"
(((ل()(ل() 'yoav)))... @yoavgo
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(((ل()(ل() 'yoav)))... @yoavgo
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