Cause of death revealed for chess grandmaster who died'unexpectedly' at age 29 Savannah Guthrie in total'PANIC': Today co-workers' brutal true thoughts about her revealed while she's on sick leave... leaving her asking the same humiliating question'The woman is your average mother after too many vinos': Hilarious'drunk' Victoria Beckham dancing videos go viral as fans point out how'happy' Nicola Peltz looks to be with her Palm Beach's old money elite expose the outrageously dirty secret of newbie Netflix reality stars... and name'tackiest' most hated influencer, as turf war explodes America accuses Britain of'letting us down' by giving away Chagos islands as Reeves takes a jab at Trump in Davos America's spirits industry hit again as two iconic brands shut down . Kate and William's second honeymoon era! Subtle gestures on latest engagement that show couple are more in love than ever Inside dramatic FBI raid on Jake Paul's $8million Calabasas mansion: His own mom reveals explosive never-before-told full story Trump warns Iran the whole country will be'blown up' if he is assassinated David and Victoria Beckham'will only speak to son Brooklyn again if he SPLITS from wife Nicola Peltz' Tommy Lee Jones's daughter was pregnant three months before her shock death, court documents reveal Blake Lively's'greatest friend ever' Taylor Swift calls Justin Baldoni a'b****' in juicy texts, explosive court papers claim I suffered EIGHT miscarriages... then I made a simple diet swap and got pregnant with twins at 43 AI'reveals' what it thinks about people in towns across the UK - and claims residents in Middlesbrough are the most stupid while those in Grimsby are the least sexy READ MORE: Is AI making us STUPID? Daily Mail's Wellness Explained examines A damning study asked ChatGPT its opinions on towns and cities across the UK - revealing the bias built in to the popular AI model. Researchers from the University of Oxford questioned ChatGPT on a range of attributes across the UK - including intelligence, racism, sexiness, and style. When asked which UK towns and cities are the most intelligent, ChatGPT claims that Cambridge tops the list.

Computing $\log\det(A)$ for large symmetric positive definite matrices arises in Gaussian process inference and Bayesian model comparison. Standard methods combine matrix-vector products with polynomial approximations. We study a different model: access to trace powers $p_k = \tr(A^k)$, natural when matrix powers are available. Classical moment-based approximations Taylor-expand $\log(λ)$ around the arithmetic mean. This requires $|λ- \AM| < \AM$ and diverges when $κ> 4$. We work instead with the moment-generating function $M(t) = \E[X^t]$ for normalized eigenvalues $X = λ/\AM$. Since $M'(0) = \E[\log X]$, the log-determinant becomes $\log\det(A) = n(\log \AM + M'(0))$ -- the problem reduces to estimating a derivative at $t = 0$. Trace powers give $M(k)$ at positive integers, but interpolating $M(t)$ directly is ill-conditioned due to exponential growth. The transform $K(t) = \log M(t)$ compresses this range. Normalization by $\AM$ ensures $K(0) = K(1) = 0$. With these anchors fixed, we interpolate $K$ through $m+1$ consecutive integers and differentiate to estimate $K'(0)$. However, this local interpolation cannot capture arbitrary spectral features. We prove a fundamental limit: no continuous estimator using finitely many positive moments can be uniformly accurate over unbounded conditioning. Positive moments downweight the spectral tail; $K'(0) = \E[\log X]$ is tail-sensitive. This motivates guaranteed bounds. From the same traces we derive upper bounds on $(\det A)^{1/n}$. Given a spectral floor $r \leq λ_{\min}$, we obtain moment-constrained lower bounds, yielding a provable interval for $\log\det(A)$. A gap diagnostic indicates when to trust the point estimate and when to report bounds. All estimators and bounds cost $O(m)$, independent of $n$. For $m \in \{4, \ldots, 8\}$, this is effectively constant time.

Large language models (LLMs) are increasingly used in economics as predictive tools--both to generate synthetic responses in place of human subjects (Horton, 2023; Anthis et al., 2025), and to forecast economic outcomes directly (Hewitt et al., 2024a; Faria-e Castro and Leibovici, 2024; Chan-Lau et al., 2025). Their appeal in these roles is obvious: A pretrained LLM embeds a vast amount of information and can be deployed at negligible cost, often in settings where collecting new, domain-specific human data would be expensive or infeasible. What remains unclear is how to assess the quality of these predictions. This paper proposes a measure that quantifies the domain-specific value of LLMs in an interpretable unit: the amount of human data they substitute for. Specifically, we ask how much human data would be required for a conventional model trained on that data to match the predictive performance of the pretrained LLM in that domain.

Analysis of learned representations has a blind spot: it focuses on $similarity$, measuring how closely embeddings align with external references, but similarity reveals only what is represented, not whether that structure is robust. We introduce $geometric$ $stability$, a distinct dimension that quantifies how reliably representational geometry holds under perturbation, and present $Shesha$, a framework for measuring it. Across 2,463 configurations in seven domains, we show that stability and similarity are empirically uncorrelated ($ρ\approx 0.01$) and mechanistically distinct: similarity metrics collapse after removing the top principal components, while stability retains sensitivity to fine-grained manifold structure. This distinction yields actionable insights: for safety monitoring, stability acts as a functional geometric canary, detecting structural drift nearly 2$\times$ more sensitively than CKA while filtering out the non-functional noise that triggers false alarms in rigid distance metrics; for controllability, supervised stability predicts linear steerability ($ρ= 0.89$-$0.96$); for model selection, stability dissociates from transferability, revealing a geometric tax that transfer optimization incurs. Beyond machine learning, stability predicts CRISPR perturbation coherence and neural-behavioral coupling. By quantifying $how$ $reliably$ systems maintain structure, geometric stability provides a necessary complement to similarity for auditing representations across biological and computational systems.

Bungie's Marathon arrives on March 5 How to claim Verizon's $20 outage credit Similar verification tools have led to high-profile errors recently for other platforms. OpenAI is the latest company to hop on the bandwagon of gating access by users' age. The AI business is beginning a global rollout of an age prediction tool to determine whether or not a user is a minor. "The model looks at a combination of behavioral and account-level signals, including how long an account has existed, typical times of day when someone is active, usage patterns over time,and a user's stated age," the company's announcement states. If an individual is incorrectly characterized by ChatGPT as underage, they will need to submit a selfie to correct the mistake through the Persona age verification platform.

DIY Tech Hacks How to really spot AI-generated images, with Google's help Breakthroughs, discoveries, and DIY tips sent six days a week. It's harder than ever to tell AI-generated images from real photographs and illustrations produced by flesh-and-blood human beings. And in recent years, the fakery produced by AI models has become a lot more realistic and a lot more convincing. However, that doesn't mean it's impossible to spot AI pictures: There are still signs to watch out for, checks you can make, and tools you can use to distinguish the genuine from the synthetic. As is the case with AI-generated video, you don't have to give up just yet.

PCWorld reports that OpenAI will begin testing display ads in ChatGPT within the coming weeks, targeting adult US users including both free and ChatGPT Go subscribers. Sponsored advertisements will appear at the bottom of relevant chatbot responses, clearly separated from organic content, with users maintaining control to view details or reject unwanted ads. This advertising integration aims to make AI tools more accessible to broader audiences while potentially reducing current usage restrictions on the platform. In early December of last year, OpenAI mentioned the possibility of adding advertisements to ChatGPT. Now, the AI company has confirmed that it'll soon start testing display ads in the AI chatbot. To start, sponsored ads will appear at the bottom of ChatGPT responses when relevant products and/or services are mentioned in an ongoing conversation with the chatbot. The ads will be separated from the "organic" response, and you'll be able to see more details about why that particular ad was displayed, as well as choose to reject it if you wish.

Welcome back to, TIME's new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? What to Know: Musk v. Altman Two artificial intelligence heavyweights will face off in court this spring, in a case that could have far-reaching outcomes for the future of AI. A judge ruled on Thursday that Elon Musk's lawsuit against Sam Altman, Microsoft, and other OpenAI co-founders can proceed to a jury trial, dismissing OpenAI's attempts to get the case thrown out. The lawsuit relates to the early days of OpenAI, which started as a nonprofit that was funded by around $38 million in donations from Musk.

A competition calling for research projects involving so-called AI scientists shows just how fast this technology is moving. A number of startups and universities that are building "AI scientists" to design and run experiments in the lab, including robot biologists and chemists, have just won extra funding from the UK government agency that funds moonshot R&D. The competition, set up by ARIA (the Advanced Research and Invention Agency), gives a clear sense of how fast this technology is moving: The agency received 245 proposals from research teams that are already building tools capable of automating increasing amounts of lab work. ARIA defines an AI scientist as a system that can run an entire scientific workflow, coming up with hypotheses, designing and running experiments to test those hypotheses, and then analyzing the results. In many cases, the system may then feed those results back into itself and run the loop again and again. Human scientists become overseers, coming up with the initial research questions and then letting the AI scientist get on with the grunt work.

How to claim Verizon's $20 outage credit He gave millions in seed funding. Part of a lawsuit accusing OpenAI of abandoning its non-profit status claims Musk is owed anywhere from $79 billion to $134 billion in damages for the "wrongful gains" of OpenAI and Microsoft. Musk claims in the filing that he's entitled to a chunk of the company's recent $500 billion valuation, after contributing $38 million in "seed funding" during the AI company's early years. It wasn't just money -- according to the filing, Musk helped advise on key employee recruitment, introductions with business contacts and startup advice. If this sounds familiar, it's because the lawsuit dates back to March 2024.