New studies of the "platypus of materials" help explain how their atoms arrange themselves into orderly, but nonrepeating, patterns. Since their discovery in 1982, exotic materials known as quasicrystals have bedeviled physicists and chemists. Their atoms arrange themselves into chains of pentagons, decagons, and other shapes to form patterns that never quite repeat. These patterns seem to defy physical laws and intuition. How can atoms possibly "know" how to form elaborate nonrepeating arrangements without an advanced understanding of mathematics?

Large language models (LLMs) hold great promise for specialized scientific domains such as materials science, yet adapting them efficiently and accurately to domain-specific knowledge remains challenging due to limited data and high knowledge density. We propose a two-stage framework that combines structured model compression with a scientific fine-tuning regimen to address this challenge. In the compression stage, we decompose the LLM's weight matrices into local low-rank "rank blocks" and arrange these blocks in a Penrose-like non-periodic tiling pattern. Each block is then compacted via spectral transformations (e.g., discrete cosine or Fourier transforms), and a Kullback-Leibler (KL) divergence-based alignment loss preserves the distributional similarity between the compressed model's representations and those of the original full model. In the adaptation stage, the compressed model is further tuned using a human-like scientific reading protocol: it processes technical materials science documents section by section, engaging in a structured question-and-answer routine for each section. This section-wise Q&A fine-tuning strategy extracts explicit reasoning traces and gradually injects domain knowledge, while minimizing catastrophic forgetting of the model's general language capabilities. By balancing efficient compression with targeted adaptation, our two-stage approach enables precise specialization of LLMs to high-value domains under data-scarce conditions. We present this principled yet exploratory pipeline and outline its potential for advancing materials science knowledge integration, laying the groundwork for comprehensive empirical evaluation in future work.

The original version of this story appeared in Quanta Magazine. When Covid-19 sent people home in early 2020, the computer scientist Tom Zahavy rediscovered chess. He had played as a kid and had recently read Garry Kasparov's Deep Thinking, a memoir of the grandmaster's 1997 matches against IBM's chess-playing computer, Deep Blue. He watched chess videos on YouTube and The Queen's Gambit on Netflix. Despite his renewed interest, Zahavy wasn't looking for ways to improve his game.

Join gaming executives to discuss emerging parts of the industry this October at GamesBeat Summit Next. The infusion of artificial intelligence (AI) into everything is literally changing our lives on a day-to-day basis. People use speech interfaces, purchase items based on recommendations, and sign into phones with facial recognition routinely because it makes their lives easier. However, one of the few industries that has yet to jump on the AI bandwagon is the telecom sector. This is a massive opportunity because AI has the potential to transform telco operations and improve efficiency in areas such as call center automation and much more.

Human consciousness is one of the grand mysteries of our time on earth. How do you know that you are "you"? Does your sense of being aware of yourself come from your mind or is it your body that is creating it? What really happens when you enter an "altered" state of consciousness with the help of some chemical or plant? While you would think this basic enigma of our self-awareness would be at the forefront of scientific inquiry, science does not yet have strong answers to these questions.

Three scientists won this year's Nobel Prize in physics Tuesday for advancing our understanding of black holes, the all-consuming monsters that lurk in the darkest parts of the universe. Briton Roger Penrose received half of this year's prize "for the discovery that black hole formation is a robust prediction of the general theory of relativity," the Nobel Committee said. German Reinhard Genzel and American Andrea Ghez received the second half of the prize "for the discovery of a supermassive compact object at the center of our galaxy." The prize celebrates "one of the most exotic objects in the universe," black holes, which have become a staple of science fact and science fiction and where time seems to stand still, according to the committee. Black holes are perhaps the most mysterious and powerful objects in astronomy.

Artificial Neural Networks have reached Grandmaster and even super-human performance across a variety of games: from those involving perfect-information (such as Go) to those involving imperfect-information (such as Starcraft). Such technological developments from AI-labs have ushered concomitant applications across the world of business - where an AI brand tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits racist behaviour; automated credit scoring processes discriminate on gender etc. - there are often significant financial, legal and brand consequences and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that, 'all the impressive achievements of deep learning amount to just curve fitting'. The key, Judea Pearl suggests, is to replace reasoning by association with causal-reasoning - the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for the New York Times: 'we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets - often using an approach known as Deep Learning - and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality'. In this paper, foregrounding what in 1949 Gilbert Ryle termed a category mistake, I will offer an alternative explanation for AI errors: it is not so much that AI machinery cannot grasp causality, but that AI machinery - qua computation - cannot understand anything at all.

Black holes could be harnessed for energy, scientists have said. The claim comes after researchers produced an experiment they claim verified a decades-old theory that such black holes could create energy as a result of "extremely odd physics". Scientists at the University of Glasgow's School of Physics and Astronomy set out to validate Roger Penrose's 1969 work. They used sound waves in an attempt to endorse the "extremely odd physics a half-century after the theory was first proposed". British physicist Mr Penrose theorised that energy could be created by dropping objects such as a rocket into a black hole and splitting the object in two.

The close of the first decade of the 21st century marked the final end of the dream of deregulated global capitalism as a historical horizon of peace and prosperity. From 2008, the so-called Great Recession established new political coordinates characterised by the normalisation of precariousness and the rise of illiberal movements. Similarly, the close of the second decade of the 21st century has been marked by the end of the hopes placed on digital technology as a means of extra-political solution--analogous and complementary to the commercial--to our economic, cultural and social problems. For at least three decades--from the 1980s to the outbreak of the crisis--the vertigo of social weakening and the vital risk associated with global financialisation, labour flexibility and the loss of political sovereignty were somehow curbed by expectations of economic growth and, above all, technological progress. It is difficult to call into question the decomposition of this social programme.

In 1974, Roger Penrose, a British mathematician, created a revolutionary set of tiles that could be used to cover an infinite plane in a pattern that never repeats. In 1982, Daniel Shechtman, an Israeli crystallographer, discovered a metallic alloy whose atoms were organized unlike anything ever observed in materials science. Penrose garnered public renown on a scale rarely seen in mathematics. Shechtman won the Nobel Prize. Both scientists defied human intuition and changed our basic understanding of nature's design, revealing how infinite variation could emerge within a highly ordered environment. At the heart of their breakthroughs is "forbidden symmetry," so-called because it flies in the face of a deeply ingrained association between symmetry and repetition.