Quote from: Jacob on Today at 02:16:04 PMSo it looks like Ukraine is doing the "okay we'll agree, with only a few minor details to hammer out" thing... so now Russia can reject the peace proposal again and Trump can do whatever he's going to do in response.
Quote from: DGuller on Today at 01:31:05 PMThe title of the article is knocking down a strawman. People who think LLMs have some artificial intelligence don't equate language with intelligence. They see language as one of the "outputs" of intelligence. If you build a model that matches one mode of output of intelligence well enough, then it's possible that under the hood that model had to have evolved something functionally analogous to intelligence during training in order to do that.
Quote from: Sheilbh on November 24, 2025, 04:52:09 PMAnd all day I've seen lots of far-right content creators ("Yookay aesthetics" etc) verified accounts earning money for the engagement on their content that appear to be based in India or South America
Quote from: Jacob on Today at 11:26:51 AMLanguage and intelligence are two difference things:Delighted that only 30 years after the Sokal affair, STEM is finally acknowledging that Derrida was right
Large Language Mistake: Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it
Some excerptsQuoteThe problem is that according to current neuroscience, human thinking is largely independent of human language — and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own. Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought. Understanding this distinction is the key to separating scientific fact from the speculative science fiction of AI-exuberant CEOs.

Quote from: Jacob on Today at 11:26:51 AMLanguage and intelligence are two difference things:The title of the article is knocking down a strawman. People who think LLMs have some artificial intelligence don't equate language with intelligence. They see language as one of the "outputs" of intelligence. If you build a model that matches one mode of output of intelligence well enough, then it's possible that under the hood that model had to have evolved something functionally analogous to intelligence during training in order to do that.
Large Language Mistake: Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it
Some excerptsQuoteThe problem is that according to current neuroscience, human thinking is largely independent of human language — and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own. Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought. Understanding this distinction is the key to separating scientific fact from the speculative science fiction of AI-exuberant CEOs.
The AI hype machine relentlessly promotes the idea that we're on the verge of creating something as intelligent as humans, or even "superintelligence" that will dwarf our own cognitive capacities. If we gather tons of data about the world, and combine this with ever more powerful computing power (read: Nvidia chips) to improve our statistical correlations, then presto, we'll have AGI. Scaling is all we need.
But this theory is seriously scientifically flawed. LLMs are simply tools that emulate the communicative function of language, not the separate and distinct cognitive process of thinking and reasoning, no matter how many data centers we build.
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Take away our ability to speak, and we can still think, reason, form beliefs, fall in love, and move about the world; our range of what we can experience and think about remains vast.
But take away language from a large language model, and you are left with literally nothing at all.
An AI enthusiast might argue that human-level intelligence doesn't need to necessarily function in the same way as human cognition. AI models have surpassed human performance in activities like chess using processes that differ from what we do, so perhaps they could become superintelligent through some unique method based on drawing correlations from training data.
Maybe! But there's no obvious reason to think we can get to general intelligence — not improving narrowly defined tasks —through text-based training. After all, humans possess all sorts of knowledge that is not easily encapsulated in linguistic data — and if you doubt this, think about how you know how to ride a bike.
In fact, within the AI research community there is growing awareness that LLMs are, in and of themselves, insufficient models of human intelligence. For example, Yann LeCun, a Turing Award winner for his AI research and a prominent skeptic of LLMs, left his role at Meta last week to found an AI startup developing what are dubbed world models: "��systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences." And recently, a group of prominent AI scientists and "thought leaders" — including Yoshua Bengio (another Turing Award winner), former Google CEO Eric Schmidt, and noted AI skeptic Gary Marcus — coalesced around a working definition of AGI as "AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult" (emphasis added). Rather than treating intelligence as a "monolithic capacity," they propose instead we embrace a model of both human and artificial cognition that reflects "a complex architecture composed of many distinct abilities."
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We can credit Thomas Kuhn and his book The Structure of Scientific Revolutions for our notion of "scientific paradigms," the basic frameworks for how we understand our world at any given time. He argued these paradigms "shift" not as the result of iterative experimentation, but rather when new questions and ideas emerge that no longer fit within our existing scientific descriptions of the world. Einstein, for example, conceived of relativity before any empirical evidence confirmed it. Building off this notion, the philosopher Richard Rorty contended that it is when scientists and artists become dissatisfied with existing paradigms (or vocabularies, as he called them) that they create new metaphors that give rise to new descriptions of the world — and if these new ideas are useful, they then become our common understanding of what is true. As such, he argued, "common sense is a collection of dead metaphors."
As currently conceived, an AI system that spans multiple cognitive domains could, supposedly, predict and replicate what a generally intelligent human would do or say in response to a given prompt. These predictions will be made based on electronically aggregating and modeling whatever existing data they have been fed. They could even incorporate new paradigms into their models in a way that appears human-like. But they have no apparent reason to become dissatisfied with the data they're being fed — and by extension, to make great scientific and creative leaps.
Instead, the most obvious outcome is nothing more than a common-sense repository. Yes, an AI system might remix and recycle our knowledge in interesting ways. But that's all it will be able to do. It will be forever trapped in the vocabulary we've encoded in our data and trained it upon — a dead-metaphor machine. And actual humans — thinking and reasoning and using language to communicate our thoughts to one another — will remain at the forefront of transforming our understanding of the world.
Quote from: Admiral Yi on Today at 03:28:07 AMQuote from: Josquius on Today at 03:08:08 AMI've no idea on costs. I've worked in marketing adjacent areas but not in that side of things.
But for sure one company absolutely dominating the market to such a degree is not healthy.
Breaking up Google would indeed weaken the power they can bring to bare and give would've competitors a chance - as I say even meta failed when it had a try let alone a startup.
Well that's the thing. As Joan pointed out, traditional monopoly analysis examines the companies ability to extract rents, to charge higher than the free market price. When talking about monopolies "not healthy" means exactly that.
Discussions of "power" tend to be circular. They have power because they are a monopoly. They are a monopoly because they have power. Have you considered the possibility they are just better? From the link I posted Meta has close to Google's share of online advertising. Ergo they have roughly the same "power." It's not logical that they failed to compete with Google in ad exchange or publisher ad server (whatever that is) because they had less power.

QuoteRobert Peston
@Peston
John Fingleton's review for the government of how to reduce unnecessary barriers and costs for nuclear power development is a tour de force, a compelling road map for how to accelerate important infrastructure investment in the UK - which is the sine qua non of improving growth and living standards (read John's nutshell below).
For the last eight weeks he was assured that the prime minister and chancellor would accept and implement the recommendations in full. He even tweaked an important clause at the government's request, to give them a bit more flexibility over the means to implementation.
I understand he has now been told that at the budget tomorrow the welcome will be conditional, subject to further work and review - because the Chancellor has been nobbled by a legal and planning adviser, who claims the Fingleton recommendations somehow breach the UK's environmental, trade and human rights obligations.
He and his colleagues believe this is nonsense. They examined the legal considerations in their assessment. But they fear that yet again the dead hand of official caution has squashed - potentially for months and years - important growth-enhancing investment.

QuoteStarmer promised to spend big on defense but Britain's arms industry is still waitinghttps://www.politico.eu/article/keir-starmer-britain-arms-industry-defense-whitehall-armies-sdr-nato/
Six months after a major inquiry into how the U.K. would meet geopolitical threats, many in the industry complain they haven't received the certainty they need about where the British government plans to invest.
QuoteU.K. Prime Minister Keir Starmer's Labour Party has made a lot of noise on defense since entering government last year, plundering the aid budget to get defense spending to reach 2.6 percent of GDP by 2027 and a promise of 3.5 percent by 2035.
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