#machine-learning

#machine-learning

Before treatment began, participants underwent neuroimaging. Instead of relying on a single modality, the researchers fused structural connectivity (how regions are physically wired) with functional connectivity (how regions co-activate at rest). The goal was not to throw every possible feature at a black box, but to learn a constrained pattern-what the authors call structure-function "covariation"-that carries the most predictive signal for outcome. In other words, the model tries to find the smallest set of connections that meaningfully forecasts symptom change.

For most of modern finance, one number has quietly dictated who gets ahead and who gets left out: the credit score. It was a breakthrough when it arrived in the 1950s, becoming an elegant shortcut for a complex decision. But shortcuts age. And in a world driven by data, digital behavior, and real-time signals, the score is increasingly misaligned with how people actually live and manage money.

At launch, it's only available for a limited number of tracks, but there are plans to expand it. Bits of trivia and background about songs are presented as short, swipeable cards with information harvested from "third-party sources." The text is generated using machine learning, but Spotify at least cites its sources on the About the Song cards. The company declined to say whether the feature would eventually be available to free users or when it might leave beta.

Researchers at the University of Cambridge's Political Psychology Lab tracked shifts in Americans' views across nearly four decades and found that divisions were broadly stable through the 1990s and early 2000s, before rising steadily from 2008 onward. Using more than 35,000 responses from the American National Election Studies between 1988 and 2024, they estimate that issue polarization has increased 64% since the late 1980s, with almost all of that change occurring after 2008.

A new PhD track is being added to the Walter S. and Lucienne Driskill Graduate Program in Life Sciences ( DGP) for the 2026 application cycle, to enhance student learning and build community around computational biology and bioinformatics at Feinberg. The computational biology and bioinformatics (CBB) track in the graduate program will prepare students through coursework and lectures to use modern computational approaches, including machine learning and artificial intelligence, to extract biological insight from large-scale datasets to address complex biological problems.

Rainbow Weather has raised $5.5 million in seed funding to push weather forecasting further into the short-term, high-precision territory it believes the industry still underserves. The Warsaw-based climate tech startup focuses on hyperlocal, minute-by-minute forecasts, zeroing in on what happens in the next few hours rather than days out. The round was backed by a syndicate of investors, including Yuri Gurski, founder of Flo Health, one of Europe's best-known consumer tech unicorns.

Paramount Skydance is planning to add a heavy dose of short-form video to its flagship streaming service, according to internal presentations and emails viewed by Business Insider. Dan Reich, the head of global product and design for Paramount+, emailed fellow executives in mid-January, asking to set up a meeting with Paramount product chief Dane Glasgow to discuss "Short Form Clips." "We are trying to figure out how to jump-start efforts to get a million clips into our Short Form UX as quickly as possible," Reich wrote in an email.

Warsaw, Poland 26 January 2026 - Rainbow Weather has raised $5.5 million in seed funding to push weather forecasting further into the short-term, high-precision territory it believes the industry still underserves. The Warsaw-based climate tech startup focuses on hyperlocal, minute-by-minute forecasts, zeroing in on what happens in the next few hours rather than days out. The round was backed by a syndicate of investors, including Yuri Gurski, founder of Flo Health, one of Europe's best-known consumer tech unicorns.

I'm thrilled to announce that I'm stepping up as Probabl 's CSO (Chief Science Officer) to supercharge scikit-learn and its ecosystem, pursuing my dreams of tools that help go from data to impact. Scikit-learn, a central tool Scikit-learn is central to data-scientists' work: it is the most used machine-learning package. It has grown over more than a decade, supported by volunteers' time, donations, and grant funding, with a central role of Inria.

Finding the time to create great content for your users is an essential part of growing your publishing business,

XRP ( ) sits near $1.90 with sentiment hitting levels not seen since before its last major rally. The Crypto Fear & Greed Index dropped to 24 in late December 2025-extreme fear territory-while analytics platform Santiment shows bearish commentary running 20-30% higher than November 2025's already-subdued averages. These extremes have appeared before XRP rallies that delivered over 1,000% gains. The pattern matters because sentiment analysis isn't guesswork-machine learning models achieve 70-91% accuracy predicting crypto price movements

An AI-powered appointments system developed by UK health-tech company DrDoctor could save the NHS up to £300 million a year by dramatically reducing missed hospital appointments, one of the health service's most persistent and costly problems. Missed outpatient appointments cost the NHS close to £1 billion annually, tying up staff time, wasting clinical capacity and lengthening waiting lists. DrDoctor believes its new AI platform, Smart Centre, can cut non-attendance rates by around 30% by predicting which patients are most likely not to turn up and adjusting clinic capacity in advance.

For instance, when a user watches a romantic comedy on Netflix, the system identifies similar titles liked by others with comparable viewing habits. On Spotify, listening to a few indie tracks might prompt the algorithm to suggest playlists featuring similar artists. These systems continuously learn from user activity, refining their precision over time.

For the last year and a half, two hacked white Tesla Model 3 sedans each loaded with five extra cameras and one palm-sized supercomputer have quietly cruised around San Francisco. In a city and era swarming with questions about the capabilities and limits of artificial intelligence, the startup behind the modified Teslas is trying to answer what amounts to a simple question: How quickly can a company build autonomous vehicle software today?

Professor Xiaoxiang Zhu, who leads the project and is the chair of data science in Earth observation at TUM, says the real achievement is that the new map is a three‑dimensional picture of how much space people actually inhabit. "3D building information provides a much more accurate picture of urbanization and poverty than traditional 2D maps," she explains. With 3D models "we see not only the footprint but also the volume of each building."

The U.S. Supreme Court today declined to grant a petition filed by Recentive Analytics, Inc. asking the Court to weigh in on whether the U.S. Court of Appeals for the Federal Circuit's (CAFC's) approach to patent eligibility for machine learning claims is improper. The petition was filed in October following an April 2025 decision by the CAFC that addressed an issue of first impression in the patent eligibility context; the opinion held that "claims that do no more than apply established methods of machine learning to a new data environment" are not patent eligible.

Unlike earlier search engines that primarily matched strings of text, AI search engines interpret the meaning behind the query, allowing for more accurate and relevant results. Examples of AI search engines include Google's recent AI integrations , Microsoft Bing's AI enhancements, and specialized platforms employing AI to tailor search results based on user behavior and preferences. These engines dynamically learn and improve their algorithms to respond more intelligently over time, a capability traditional search lacks.

Gabriel Petersson said on an episode of the "Extraordinary" podcast published on Thursday that he's in a job traditionally only done by people with doctorate degrees because he was able to learn machine learning through ChatGPT. "Universities don't have, like, a monopoly on foundational knowledge anymore," he said. "You can just get any foundational knowledge from ChatGPT." "You start with a problem, you recursively go down," he added.

It seems like an oversimplification, but many real estate agents don't realize they're using AI, or even interacting with AI that's in large part because agentic AI is becoming more commonplace. AI has already had a meaningful impact on the day-to-day lives of real estate agents, even if they don't realize it whether it's the use of major graphic-design platforms like Canva, social media apps, or embedded AI tools within search engines.

Science is a slaughterhouse. We rarely acknowledge the degree to which animal life underwrites the research that provides us with medicines, or the regulation that keeps us safe. Live animals were used in 2.64m officially sanctioned scientific procedures in the UK in 2024, many of them distressing or painful and many of them fatal. But the government's new strategy to phase out animal testing published earlier this month suggests that in the near future emerging technologies

But biology doesn't generate new proteins at that level. Instead, changes have to take place at the nucleic acid level before eventually making their presence felt at the protein level. And the DNA level is fairly removed from proteins, with lots of critical non-coding sequences, redundancy, and a fair degree of flexibility. It's not necessarily obvious that learning the organization of a genome would help an AI system figure out how to make functional proteins.

Since I joined Google in 2018, it has been amazing to see the impact I've had. I started at Google Bangalore in India, where I was part of a team using machine learning and AI on Google Maps. After spending a few years there, I moved to the US in 2021 to work at the Google Mountain View location in California.

Not everyone appreciates the artistry of Jackson Pollock's famous drip paintings, with some dismissing them as something any child could create. While Pollock's work is undeniably more sophisticated than that, it turns out that when one looks at splatter paintings made by adults and young children through a fractal lens and compares them to those of Pollock himself, the children's work does bear a closer resemblance to Pollock's than those of the adults.

This photo was taken in August, in the Sing'isi village in Arusha, northern Tanzania, where my colleagues and I were conducting a field visit to farmers. I was demonstrating how to use a mobile app - named KilimoAI - to examine crop leaves. The app, which we've developed in-house, works by analysing a photograph of the plant to detect possible disease symptoms.

The first game from the development team is Prologue: Go Wayback, a single-player survival roguelike that tasks you with navigating worlds generated through machine-learning technology. It'll be available in early access starting November 20. Greene has stated in the past that he expects players to hate the game at first. The second planned game from the studio "aims to test limited multiplayer, maybe up to 100 versus 100 players," Greene told Eurogamer.

"The developer said it has "evolved" its machine-learning-based anti-cheat measures with the aim of helping the system become "smarter, faster, and fairer." "Our aimbot-detection models are trained to decide whether a player's targeting was performed by a human or an aimbot. In Black Ops 7, these updated models will discriminate between natural aim and cheating with even greater precision by taking what they have learned from real gameplay to catch more cheaters than before," the developer explained."