Delivering much information about the scale of what's coming, documentary also follows Gawdat's campaign to get the programs with empathy A nother day, another warning about AI; vis-a-vis the reality we all know, this has roughly the same reassuring effect as a plane fuselage ripping off mid-flight. Starting off with familiar criticisms, such as putting the world out of work and handing over power to tech barons, Alex Holmes and Lina Zilinskaite's film blasts an concentrated stream of AI concerns in its 83-minute runtime. By the time it is talking about current efforts to create computers out of human brain cells, potentially integrable into our own craniums, and implying this might be a good thing, it is (ironically) hard to know how to process all of this. The Cassandra at the film's centre is Mo Gawdat, former chief business officer at Google X, now a touring cautionary voice trying to get the world to listen about the perils of AI. Once overseeing advanced projects for the tech giants, his biggest moonshot lies ahead: to introduce a moral dimension into a tech race that looks increasingly like the frenzied season finale of late capitalism. He talks about feeling parental pride in watching Google's AI-driven robotic arms learn to grasp objects, as children do.

Ahead of the hit show's finale, cocreators Paul W. Downs and Lucia Aniello talk about media consolidation, the perils of censorship, and why they find AI "deeply disturbing." If you're a WIRED reader who uses AI in any creative context, I'd suggest staying far, far away from anyone involved in the TV show . In an interview earlier this year, actor Hannah Einbinder (who plays young comedy writer Ava Daniels on the show) described AI creators as "losers," "not artists," and "not special." In a wide-ranging conversation for ahead of the series finale on HBO Max, Paul W. Downs and Lucia Aniello were resolute about the value of human creativity--and what can be lost when AI enters the picture. If their work on is any indication, Downs and Aniello (along with their third cocreator, Jen Statsky) would be wise to stick with the tough, tiring, absolutely-no-shortcuts approach they take to making entertainment. Across five excellent seasons--if you haven't seen the show, I really do recommend it-- has been praised for its sharp writing and wit, and its thoughtful portrayal of Deborah Vance and Ava's complex, constantly evolving relationship. The show has also acted as something of a mirror for the real-world entertainment industry, weaving in plotlines that tackle everything from media consolidation to corporate censorship to, yes, artificial intelligence. The show's cast and creators have been on a media whirlwind as it all comes to an end. When they came knocking on WIRED's door, we jumped at the chance to chat, and I was lucky enough to spend an hour with Downs and Aniello--both WIRED subscribers, much to my delight--earlier this month. KATIE DRUMMOND: Lucia Aniello and Paul Downs, who I just learned are married, congratulations and welcome to . You should have been there. You should have been there. Ugh, why didn't we bring you? We are going to renew for our 10-year at the same place though. Lucia was born in Italy, so it was closer to a lot of family. And you were married in what year? You have time to find your look. A major priority for me in my life is perfecting my look. We do work at Condé Nast, and my boss is Anna Wintour.

Just in time for summer, Beatbot's pool-cleaning robots are on sale through the end of the month. If you're on the hunt for smarter pool care, these are some of the best pool-cleaning robots on the market, and we haven't seen them sell for less. Whether you're tired of paying the pool guy or just don't want to deal with whatever scary stuff is floating in the water, these robots can help. Be sure to check out our related buying guides for more summer outdoors coverage, including the best bug sprays, the best sunscreens, and the best fitness trackers . This surface skimmer is slow and methodical with its approach, which means it won't slam into your pool's walls while doing its job.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia and Ukraine have resumed air attacks after a United States-brokered three-day truce expired, with President Volodymyr Zelenskyy saying more than 200 drones were used to attack Ukraine overnight. Russian aerial attacks across Ukraine's Dnipropetrovsk region on Tuesday morning killed at least one person and injured four others, according to regional administration chief Oleksandr Ganzha. Russia also carried out attacks on the regions of Kharkiv, Zhytomyr, Sumy and Chernihiv, according to authorities. More than 200 long-range drones were used in the wave of attacks, Zelenskyy said. "Russia itself chose to end the partial silence that had lasted for several days," he said in a post on X. Russia's military, meanwhile, said its defences downed 27 Ukrainian drones over the regions of Belgorod, Voronezh and Rostov.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The group had reportedly been starving on the front line after up to 17 days without food deliveries and months without rotation. The fighters were holed up on the left, eastern bank of the Oskil River in the southeastern Donetsk region after Russian bombs destroyed the bridges connecting them to their brigade on the right bank. "They weren't listened to on the radio, or perhaps no one wanted to listen to them. My husband shouted and begged, saying there was no food and water," Silchuk wrote. She did not respond to Al Jazeera's request for an interview.

In Europe in the 17th, 18th, and 19th centuries, nearly a dozen cases of supposed spontaneous combustion were reported. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. In December 2010, Michael Faherty died in his home in Galway, Ireland. His body was burned and the fireplace was lit, but there was no other source of flames or fuel.

We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $\mathbb S^{d-1}$. There the von Mises-Fisher (vMF) distribution induces a natural noise process and admits a closed-form conditional score. The conditional velocity is in general intractable. Exploiting the radial symmetry of the vMF density we reduce the continuity equation on $\mathbb S^{d-1}$ to a scalar ODE in the cosine similarity, whose unique bounded solution determines the velocity. The marginal velocity and marginal score on $(\mathbb S^{d-1})^L$ both decompose into posterior-weighted tangent sums that differ only by per-token scalar weights. This gives access to both ODE and predictor-corrector (PC) sampling. The posterior is the only learned object, trained by a cross-entropy loss. Experiments compare the vMF path against geodesic and Euclidean alternatives. The combination of vMF and PC sampling significantly improves results on Sudoku and language modeling.

We study solving large-scale fixed-point equation x = F(x) with decomposition. Standard strict decomposition assigns each agent a disjoint block and evaluates updates using only owned coordinates. For most operators, however, a block update may depend on variables outside the block. Truncating these dependencies by strict decomposition changes the mean operator and creates structural bias that cannot be removed by more samples, smaller stepsizes, or additional consensus. We therefore propose Core-Halo decomposition, which separates write ownership from read-only evaluation context: each agent updates its own core and reads from an overlapping halo. By aligning the Core-Halo decomposition with the blockdependence structure of F, the original fixed-point problem can be implemented faithfully in a decentralized multi-agent system. We further characterize the fundamental obstruction faced by strict decomposition through a Bellman closure condition and a blockwise bias lower bound, showing that local-only updates can alter the original fixed-point operator. Finally, we conduct extensive experiments across a range of application settings, and demonstrate that Core-Halo achieves near-centralized performance while retaining the parallelism benefits of decentralization.

We design Local LMO - a new projection-free gradient-type method for constrained optimization. The key algorithmic idea is to replace the global linear minimization oracle over the constraint set used by Frank-Wolfe (FW) with a local linear minimization oracle over the intersection of the constraint set and a "small" ball centered at the current iterate. In particular, when minimizing $f:\mathbb{R}^d\to \mathbb{R}$ over a constraint $\emptyset\neq\mathcal{X}\subseteq\mathbb{R}^d$, Local LMO performs the iteration \[x_{k+1}\in \arg\min_{z\in\mathcal{X}\cap\mathcal{B}(x_{k},t_k)}\langle\nabla f(x_{k}), z \rangle,\] where $x_0\in\mathcal{X}$, and $t_k>0$ is a suitably chosen radius which can be interpreted as an effective stepsize. While designed as an alternative to FW, Local LMO is perhaps best viewed as a generalization of Gradient Descent (GD) rather than a modification of FW. Indeed, it is easy to see that Local LMO reduces to GD in the unconstrained setting and, more generally, to GD restricted to an affine subspace if the constraint $\mathcal{X}$ is affine. We prove that this simple algorithmic scheme transfers the known (unaccelerated) convergence rates of Projected Gradient Descent (PGD) to the projection-free world in several important regimes, some of which are beyond the reach of FW. In contrast to FW theory, i) our guarantees hold without requiring the feasible set $\mathcal{X}$ to be bounded, ii) our theory does not require the "curvature" assumption, which allows us to establish a standard sublinear rate for convex functions with bounded gradients, iii) we obtain a linear rate in the smooth strongly convex regime. Furthermore, we obtain sharp sublinear rates in the smooth convex and non-convex regimes, in the $(L_0,L_1)$-smooth convex regime, and in stochastic and non-differentiable settings.

Inverse optimization (IO) seeks to infer the parameters of a decision-maker's objective from observed context--action data. We study noiseless IO, where demonstrations are generated by a ground-truth objective. We provide a high-probability ${O}(\frac{d}{T})$ generalization bound for the induced action set, where $d$ is the number of unknown parameters and $T$ is the size of the training dataset. We strengthen these guarantees under additional conditions that ensure uniqueness of the chosen action, bringing our IO guarantees in line with best-arm identification results in the bandit literature. We further show that the ${O}(\frac{d}{T})$ rate is tight over all consistent estimators considered here, and extend the result to both instantaneous and cumulative regret. Notably, the resulting regret lower bound matches the corresponding upper bounds in the adversarial setting, indicating that the stochastic IO setting is effectively adversarial for the class of estimators studied here. Finally, we propose a parameter-free algorithm with lower per-iteration complexity than generic solvers. Experiments validate the predicted rates and illustrate the tightness of our bounds.