#large-language-models

#large-language-models

Anti-intelligence is not stupidity or some sort of cognitive failure. It's the performance of knowing without understanding. It's language severed from memory, context, and and even intention. It's what large language models (LLMs) do so well. They produce coherent outputs through pattern-matching rather than comprehension. Where human cognition builds meaning through the struggle of thought, anti- intelligence arrives fully formed.

Generalist models "fail miserably" at the benchmarks used to measure how AI performs scientific tasks, Alex Zhavoronkov, Insilico's founder and CEO, told Fortune. " You test it five times at the same task, and you can see that it's so far from state of the art...It's basically worse than random. It's complete garbage." Far better are specialist AI models that are trained directly on chemistry or biology data.

To work around those rules, the Humanizer skill tells Claude to replace inflated language with plain facts and offers this example transformation: Before: "The Statistical Institute of Catalonia was officially established in 1989, marking a pivotal moment in the evolution of regional statistics in Spain." After: "The Statistical Institute of Catalonia was established in 1989 to collect and publish regional statistics." Claude will read that and do its best as a pattern-matching machine to create an output that matches the context of the conversation or task at hand.

In this book, you will learn how to use artificial intelligence to create mini-games. You will attempt to recreate the look and feel of various classic video games. The intention is not to violate copyright or anything of the sort, but instead to learn the limitations and the power of AI. Instead, you will simply be learning about whether or not you can use AI to help you know how to create video games.

He's the perfect outsider figure: the eccentric loner who saw all this coming and screamed from the sidelines that the sky was falling, but nobody would listen. Just as Christian Bale portrayed Michael Burry, the investor who predicted the 2008 financial crash, in The Big Short, you can well imagine Robert Pattinson fighting Paul Mescal, say, to portray Zitron, the animated, colourfully obnoxious but doggedly detail-oriented Brit, who's become one of big tech's noisiest critics.

Tokamak fusion reactors rely on heated plasma that is extremely densely packed inside a doughnut-shaped chamber. But researchers thought that plasma could not exceed a certain density - a boundary called the Greenwald limit - without becoming unstable. In a new study, scientists pushed beyond this limit to achieve densities 30% to 65% higher than those normally reached by EAST while keeping the plasma stable.

In fact, when prompted strategically by researchers, Claude delivered the near-complete text of Harry Potter and the Sorcerer's Stone, The Great Gatsby, 1984, and Frankenstein, in addition to thousands of words from books including The Hunger Games and The Catcher in the Rye. Varying amounts of these books were also reproduced by the other three models. Thirteen books were tested.

Three major large language models (LLMs) generated responses that, in humans, would be seen as signs of anxiety, trauma, shame and post-traumatic stress disorder. Researchers behind the study, published as a preprint last month, argue that the chatbots hold some kind of "internalised narratives" about themselves. Although the LLMs that were tested did not literally experience trauma, they say, their responses to therapy questions were consistent over time and similar in different operatingmodes, suggesting that they are doing more than "role playing".

Researchers have developed a tool that they say can make stolen high-value proprietary data used in AI systems useless, a solution that CSOs may have to adopt to protect their sophisticated large language models (LLMs). The technique, created by researchers from universities in China and Singapore, is to inject plausible but false data into what's known as a knowledge graph (KG) created by an AI operator. A knowledge graph holds the proprietary data used by the LLM.

Meta has applied large language models to mutation testing to improve compliance coverage across its software systems. The approach integrates LLM-generated mutants and tests into Meta's Automated Compliance Hardening system (ACH), addressing scalability and accuracy limits of traditional mutation testing. The system is intended to keep products and services safe while meeting compliance obligations at scale, helping teams satisfy global regulatory requirements more efficiently.

Contextual integrity defines privacy as the appropriateness of information flows within specific social contexts, that is, disclosing only the information strictly necessary to carry through a given task, such as booking a medical appointment. According to Microsoft's researchers, today's LLMs lack this kind of contextual awareness and can potentially disclose sensitive information, thereby undermining user trust. The first approach focuses on inference-time checks, i.e., safeguards applied when a model generates its response.

AI assistants like ChatGPT, Claude and Perplexity-powered by large language models (LLMs)-are emerging as parallel gatekeepers. They're quietly reshaping which brands get recommended long before a buyer ever reaches a search results page. In my previous article, I discussed how Google's AI Overviews are intercepting traffic (even for top-ranking sites). But there's another shift that many businesses haven't recognized: Search engines are no longer the only place where your customers' questions get answered.

A research team based in China used the Claude 2.0 large language model (LLM), created by Anthropic, an AI company in San Francisco, California, to generate peer-review reports and other types of documentation for 20 published cancer-biology papers from the journal eLife. The journal's publisher makes papers freely available online as 'reviewed preprints', and publishes them alongside their referee reports and the original unedited manuscripts. The authors fed the original versions into Claude and prompted it to generate referee reports.

Boring people don't listen. They tell their own stories, over and over, and never make any attempt to engage in our story, our lives. And so we avoid them. AI, on the other hand, is a superb listener. So much so that people, particularly teens, are turning to chatbots for companionship. But in doing so, do we run the risk of all becoming the same kind of person, wanting the same kinds of friendships, with the same kinds of interactions? In a word, boring.

The story of technology is the story of continual disruption and displacement. New systems and processes send some skills into obsolescence, opening the way for new skills and workflows. Generative AI has triggered the latest "de-skilling." But chatbot technology isn't only transforming jobs and shifting our relationship with information itself. It is also inviting us to relinquish our cognitive independence and bring about a sort of dispossession that is unprecedented.

Cornell Tech faculty made a strong showing at the 2025 Conference on Neural Information Processing Systems (NeurIPS), held Dec. 2-7 in San Diego, presenting 23 research papers at one of the world's premier gatherings for artificial intelligence and machine learning. NeurIPS draws thousands of scholars and industry leaders each year and is widely recognized as a leading forum for breakthroughs in AI, computational neuroscience, statistics, and large-scale modeling.

There's sloppy science, and there's AI slop science. In an ironic twist of fate, beleaguered AI researchers are warning that the field is being choked by a deluge of shoddy academic papers written with large language models, making it harder than ever for high quality work to be discovered and stand out. Part of the problem is that AI research has surged in popularity.

Allie Miller, for example, recently ranked her go-to LLMs for a variety of tasks but noted, "I'm sure it'll change next week." Why? Because one will get faster or come up with enhanced training in a particular area. What won't change, however, is the grounding these LLMs need in high-value enterprise data, which means, of course, that the real trick isn't keeping up with LLM advances, but figuring out how to put memory to use for AI.

Much of the ongoing discourse surrounding AI can largely be divided along two lines of thought. One concerns practical matters: How will large language models (LLMs) affect the job market? How do we stop bad actors from using LLMs to generate misinformation? How do we mitigate risks related to surveillance, cybersecurity, privacy, copyright, and the environment? The other is far more theoretical: Are technological constructs capable of feelings or experiences?

This challenge is sparking innovations in the inference stack. That's where Dynamo comes in. Dynamo is an open-source framework for distributed inference. It manages execution across GPUs and nodes. It breaks inference into phases, like prefill and decode. It also separates memory-bound and compute-bound tasks. Plus, it dynamically manages GPU resources to boost usage and keep latency low. Dynamo allows infrastructure teams to scale inference capacity responsively, handling demand spikes without permanently overprovisioning expensive GPU resources.

I wasn't expecting a conversation about single cells and cognition to explain why a large language model (LLM) feels like a person. But that's exactly what happened when I listened to Michael Levin on the Lex Fridman Podcast. Levin wasn't debating consciousness or speculating about artificial intelligence (AI). He was describing how living systems, from clusters of cells to complex organisms, cooperate and solve problems. The explanation was authoritative and grounded, but the implications push beyond biology.