Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments.

Aerial footage showed the extend of floods in Spain after a series of storms hit the Iberian Peninsula. Storm Marta hit Spain on Saturday, bringing more rain to the region, as it was still recovering from Storm Leonardo. In Córdoba, drone footage showed flooded olive trees as Spanish farmers warned of the millions of euros worth of damage to crops following the torrential rains and high winds. In the country's southern region of Andalucia, over 11,000 people have been displaced. Nazar Daletskyi's relatives were told he had been killed in 2022, the first year of Russia's full-scale invasion.

The Download: inside the Vitalism movement, and why AI's "memory" is a privacy problem Meet the Vitalists: the hardcore longevity enthusiasts who believe death is "wrong" Last April, an excited crowd gathered at a compound in Berkeley, California, for a three-day event called the Vitalist Bay Summit. It was part of a longer, two-month residency that hosted various events to explore tools--from drug regulation to cryonics--that might be deployed in the fight against death. One of the main goals, though, was to spread the word of Vitalism, a somewhat radical movement established by Nathan Cheng and his colleague Adam Gries a few years ago. Consider it longevity for the most hardcore adherents--a sweeping mission to which nothing short of total devotion will do. Although interest in longevity has certainly taken off in recent years, not everyone in the broader longevity space shares Vitalists' commitment to actually making death obsolete. And the Vitalists feel that momentum is building, not just for the science of aging and the development of lifespan-extending therapies, but for the acceptance of their philosophy that .

This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . 'Are You Dead?' app taps into global loneliness crisis Can autonomous trucks really make highways safer?

Shipping experts say the big winter storm across a wide swath of the country should be business as usual--if their safeguards hold. Up to two-thirds of the US is facing down the threat of serious snow, cold, and ice this weekend, with the potential to snarl roads (and the businesses that depend on them) from Texas up to New York City . At this point, grocery stores, logistics experts, warehouse operators, and trucking companies have been prepping for days. Still, the effects on the supply chain--and the retail store shelves that depend on them--are yet to be determined. On one hand, this is winter business as usual.

What We Know About the Winter Storm About to Hit the US--and What We Don't A huge portion of the United States is going to be hit with snow or freezing rain this weekend. Exactly where, what, and how much remains uncertain. Over the past weekend, when weather models first started forecasting a winter storm that would sweep over large parts of the country, Sean Sublette, a meteorologist living in Virginia, started telling people in his area to prepare for snow . At the time, Sublette says, "a lot of the data started to point to a substantial snow storm for the mid-Atlantic and the Northeast, with significant ice farther southward into Carolina's Tennessee Valley." Then, Sublette woke up Wednesday morning.

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real environment through self-supervised learning. By leveraging the imagination of the world model, the agent's policy is enhanced without the constraints of sampling from the real environment. The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model. However, constructing a perfectly accurate model of a complex unknown environment is nearly impossible. Discrepancies between the model and reality may cause the agent to pursue virtual goals, resulting in subpar performance in the real environment. Introducing random noise into model-based reinforcement learning has been proven beneficial.In this work, we introduce Stochastic Transformer-based wORld Model (STORM), an efficient world model architecture that combines the strong sequence modeling and generation capabilities of Transformers with the stochastic nature of variational autoencoders. STORM achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods that do not employ lookahead search techniques. Moreover, training an agent with $1.85$ hours of real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics card requires only $4.3$ hours, showcasing improved efficiency compared to previous methodologies.

In this work we investigate stochastic non-convex optimization problems where the objective is an expectation over smooth loss functions, and the goal is to find an approximate stationary point. The most popular approach to handling such problems is variance reduction techniques, which are also known to obtain tight convergence rates, matching the lower bounds in this case. Nevertheless, these techniques require a careful maintenance of anchor points in conjunction with appropriately selected ``mega-batchsizes. This leads to a challenging hyperparameter tuning problem, that weakens their practicality. Recently, [Cutkosky and Orabona, 2019] have shown that one can employ recursive momentum in order to avoid the use of anchor points and large batchsizes, and still obtain the optimal rate for this setting. Yet, their method called $\rm{STORM}$ crucially relies on the knowledge of the smoothness, as well a bound on the gradient norms. In this work we propose $\rm{STORM}^{+}$, a new method that is completely parameter-free, does not require large batch-sizes, and obtains the optimal $O(1/T^{1/3})$ rate for finding an approximate stationary point. Our work builds on the $\rm{STORM}$ algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters.

Australia's beloved weather website got a makeover - and infuriated users It was an unseasonably warm spring day in Sydney on 22 October, with a forecast of 39C (99F) - a real scorcher. The day before, the state of New South Wales had reported its hottest day in over a century, a high of 44.8C in the outback town of Bourke. But little did the team at the national Bureau of Meteorology foresee that they, in particular, would soon be feeling the heat. Affectionately known by Australians as the Bom, the agency's long-awaited website redesign went live that morning, more than a decade after the last update. Within hours, the Bom was flooded with a deluge of complaints.

In 2024, superstorm Gannon generated auroras-and wreaked havoc on this radiation-blocking layer. Breakthroughs, discoveries, and DIY tips sent every weekday. Last year, the most violent geomagnetic storm to strike Earth in over two decades did more than disrupt GPS systems and internet connections. According to a study published today in the journal, superstorm Gannon also squeezed the planet's protective layer of ionized particles to one-fifth its normal size. Geomagnetic storms aren't rare occurrences, but most of them remain relatively benign.