#alik-lorenz

#alik-lorenz

"To contrast the data with what we're measuring in the quantum computer is crucial," says Alexandre Dauphin, emphasizing the importance of aligning simulations with experimental results.

We're investing a lot in AI - we're doing a lot, but we're stopping at individual productivity. We're not taking the next step. You can't just screw AI on everything - it only makes you faster. It means you need to think about, 'how are our teams collaborating? How are people collaborating?' You probably need to change the way you work.

Science@Cal presents a series of free public science lectures on the third Saturday of every month, featuring renowned UC Berkeley scientists aimed at general audiences.

Google AI mode has added an 'Ask about this' option above the sources where all URLs are displayed. Clicking on 'Ask about' here automatically pulled a new prompt into the search box.

Generative AI is now incorporated into the workflow for many scholars across many disciplines, but the broader scientific community would benefit from taking stock of how this technology could truly benefit our work and how it might distract. We hope the symposium can provide clarity.

For more than two millennia, mathematicians have produced a growing heap of pi equations in their ongoing search for methods to calculate pi faster and faster. The pile of equations has now grown into the thousands, and algorithms now can generate an infinitude. Each discovery has arrived alone, as a fragment, with no obvious connection to the others. But now, for the first time, centuries of pi formulas have been shown to be part of a unified, formerly hidden structure.

Which Algorithm Is This? If you step back, this maps almost perfectly to the Top K Frequent Elements problem.We usually solve it for integers in a list. Here, the "elements" are audience profiles age and body-type combinations. First, define what an audience profile looks like: case class Profile(age: Int, height: Int, weight: Int) What we want is a function like this:

Consistent with the general trend of incorporating artificial intelligence into nearly every field, researchers and politicians are increasingly using AI models trained on scientific data to infer answers to scientific questions. But can AI ultimately replace scientists? The Trump administration signed an executive order on Nov. 24, 2025, that announced the Genesis Mission, an initiative to build and train a series of AI agents on federal scientific datasets "to test new hypotheses, automate research workflows, and accelerate scientific breakthroughs."

In 2023, Australia abandoned its expensive and bureaucratic scholar-led research-assessment programme. New Zealand followed suit soon after. The hope, according to a transition plan unveiled by the Australian federal government's Department of Education and the research sector, was to find a "more modern, data-driven approach". In the United Kingdom, where financial pressures on universities are especially acute, there are similar calls to reform the Research Excellence Framework (REF), the country's performance-based research-funding system.

Every year, poor communication and siloed data bleed companies of productivity and profit. Research shows U.S. businesses lose up to $1.2 trillion annually to ineffective communication, that's about $12,506 per employee per year. This stems from breakdowns that waste an average of 7.47 hours per employee each week on miscommunications. The damage isn't only interpersonal; it's structural. Disconnected and fragmented data systems mean that employees spend around 12 hours per week just searching for information trapped in those silos.

Analogue quantum simulations are a useful tool for investigating these systems, particularly in regimes in which the applicability of numerical techniques is limited. For different simulator platforms, figures of merit include the electron bandwidth and interaction strength, temperature and the number of simulated lattice sites. Their use is further underscored by the ability to realize distinct lattice geometries, on-site degrees of freedom and by the physical observables that are accessible to experimental measurement.

OpenAI's GPT-5.2 Pro does better at solving sophisticated math problems than older versions of the company's top large language model, according to a new study by Epoch AI, a non-profit research institute.

The vascular system and the brain are examples of physical networks that differ from the networks typically studied in network science owing to the tangible nature of their nodes and links, which are made of material resources and constrain their layout. The importance of these material factors has been noted in many disciplines: as early as 1899, Ramón y Cajal suggested that we must consider the laws conserving the 'wire' volume to explain neuronal design8

Recent integrative approaches suggest that physics cannot be adequately characterized by magnitude-based distinctions alone, such as those implied by Big-P, little-p, and mini-p physics. While these categories capture differences in scope and historical impact, they fail to address the heterogeneity of physical activity itself. To remedy this, I propose the Five Fs of physics: force, friction, flux, formulation, and foundational structure.

In fact, Stawicki was on a mission to save the lives of around 1,000 zebrafish ( Danio rerio) in her laboratory. Similarities between lines of hair cells on the fish's flanks and those in the mammalian inner ear enable her to use them as a model to study hearing problems in humans caused by some antibiotics and chemotherapy drugs. A sensor had picked up that the lab's heating system had been knocked out by a power fault.

Five years ago, mathematicians Dawei Chen and Quentin Gendron were trying to untangle a difficult area of algebraic geometry involving differentials, elements of calculus used to measure distance along curved surfaces. While working on one theorem, they ran into an unexpected roadblock: Their argument depended on a strange formula from number theory, but they were unable to solve or justify it. In the end, Chen and Gendron wrote a paper presenting their idea as a conjecture, rather than a theorem.

When a scientist feeds a data set into a bot and says "give me hypotheses to test", they are asking the bot to be the creator, not a creative partner. Humans tend to defer to ideas produced by bots, assuming that the bot's knowledge exceeds their own. And, when they do, they end up exploring fewer avenues for possible solutions to their problem.