Ed Zitron on big tech, backlash, boom and bust: 'AI has taught us that people are excited to replace human beings' His blunt, brash scepticism has made the podcaster and writer something of a cult figure. But as concern over large language models builds, he's no longer the outsider he once was I f some time in an entirely possible future they come to make a movie about "how the AI bubble burst", Ed Zitron will doubtless be a main character. 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. This is not to say the AI bubble burst, necessarily, but against a tidal wave of AI boosterism, Zitron's blunt, brash scepticism has made him something of a cult figure. His tech newsletter, Where's Your Ed At, now has more than 80,000 subscribers; his weekly podcast, Better Offline, is well within the Top 20 on the tech charts; he's a regular dissenting voice in the media; and his subreddit has become a safe space for AI sceptics, including those within the tech industry itself - one user describes him as "a lighthouse in a storm of insane hypercapitalist bullshit".

Iranian refugee' viral video shows woman lighting cigarette with burning image of Ayatollah Ali Khamenei, sparking global protests as Trump weighs military action.

'No reasons to own': Software stocks sink on fear of new AI tool The new year was supposed to bring opportunities for beaten-down software stocks. Instead, the group is off to its worst start in years. The release of a new artificial intelligence tool from startup Anthropic on Jan. 12 rekindled fears about disruption that weighed on software makers in 2025. TurboTax owner Intuit tumbled 16% last week, its worst since 2022, while Adobe and Salesforce, which makes customer relationship management software, both sank more than 11%. All told, a group of software-as-a-service stocks tracked by Morgan Stanley is down 15% so far this year, following a drop of 11% in 2025.

Socks, salsa and stand-up comedy... easy ways to find joy in January There is a reason it is called Blue Monday. Slap-bang in the middle of a month that is all about freezing drizzle and self-denial, the third Monday in January is notoriously the gloomiest day of the year. But some of us are fighting back. Here are some ways to find joy among the January gloom. Laughter is a great tonic.

Apart from the snow and the temperature Greenland does not have much in common with the Swiss alps. But the fight for the future of the island looms over the gathering of world leaders and businesses at the World Economic Forum (WEF) this week. Indeed the timing of Donald Trump's extraordinary threat must have had in mind this meeting. And that is beyond strange given the views of his base. Last year, he beamed himself into the WEF from the White House, appearing before an audience of largely bewildered European executives just two days after his inauguration.

Credit risk models are a critical decision-support tool for financial institutions, yet tightening data-protection rules (e.g., GDPR, CCPA) increasingly prohibit cross-border sharing of borrower data, even as these models benefit from cross-institution learning. Traditional default prediction suffers from two limitations: binary classification ignores default timing, treating early defaulters (high loss) equivalently to late defaulters (low loss), and centralized training violates emerging regulatory constraints. We propose a Federated Survival Learning framework with Bayesian Differential Privacy (FSL-BDP) that models time-to-default trajectories without centralizing sensitive data. The framework provides Bayesian (data-dependent) differential privacy (DP) guarantees while enabling institutions to jointly learn risk dynamics. Experiments on three real-world credit datasets (LendingClub, SBA, Bondora) show that federation fundamentally alters the relative effectiveness of privacy mechanisms. While classical DP performs better than Bayesian DP in centralized settings, the latter benefits substantially more from federation (+7.0\% vs +1.4\%), achieving near parity of non-private performance and outperforming classical DP in the majority of participating clients. This ranking reversal yields a key decision-support insight: privacy mechanism selection should be evaluated in the target deployment architecture, rather than centralized benchmarks. These findings provide actionable guidance for practitioners designing privacy-preserving decision support systems in regulated, multi-institutional environments.

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. These regimes emerge in small intervals of noise intensity values, which, in agreement with the extended criticality concept, never shrink to a single critical point. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.

In this paper, we propose a distributed framework for reducing the dimensionality of high-dimensional, large-scale, heterogeneous matrix-variate time series data using a factor model. The data are first partitioned column-wise (or row-wise) and allocated to node servers, where each node estimates the row (or column) loading matrix via two-dimensional tensor PCA. These local estimates are then transmitted to a central server and aggregated, followed by a final PCA step to obtain the global row (or column) loading matrix estimator. Given the estimated loading matrices, the corresponding factor matrices are subsequently computed. Unlike existing distributed approaches, our framework preserves the latent matrix structure, thereby improving computational efficiency and enhancing information utilization. We also discuss row- and column-wise clustering procedures for settings in which the group memberships are unknown. Furthermore, we extend the analysis to unit-root nonstationary matrix-variate time series. Asymptotic properties of the proposed method are derived for the diverging dimension of the data in each computing unit and the sample size $T$. Simulation results assess the computational efficiency and estimation accuracy of the proposed framework, and real data applications further validate its predictive performance.

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian attacks killed three people, including a 20-year-old woman, and injured 11 others in Ukraine's Kharkiv region, Governor Oleh Syniehubov wrote on Telegram on Sunday. In Ukraine's Kherson region, two people were killed, and one person was injured, as Russian forces launched attacks using drones, air strikes and shelling, Governor Oleksandr Prokudin said on Telegram on Sunday.

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