Steve Rose goes for a spin. Steve Rose goes for a spin. 'We don't tell the car what it should do': my ride in a self-driving taxi Driverless'robotaxis' will be accepting fares in Britain's biggest city by the end of next year. Can they deal with London's medieval roads, hordes of pedestrians and errant ebikers? 'I'm really excited to show you this," says Alex Kendall, the CEO of Wayve, as he gets behind the wheel of one of the company's electric Ford Mustangs. The car pulls up to a junction at a busy road in King's Cross, London, all by itself. "You can see that it's going to control the speed, steering, brake, indicators," he says to me - I'm in the passenger seat. "It's making decisions as it goes.

Donald Trump Jr.'s Private DC Club Has Mysterious Ties to an Ex-Cop With a Controversial Past The Executive Branch has a reported membership list that includes Trumpworld elites like David Sacks. A WIRED review of corporate filings reveals an under-the-radar player: a notorious former DC police officer. When the Executive Branch soft-launched in Washington, DC, last spring, the private club's initial buzz centered on its starry roster of backers and founding members. The president's eldest son, Donald Trump Jr., is one of the club's several co-owners, according to previous reporting. Founding members reportedly include Trump administration AI czar David Sacks and his podcast cohost Chamath Palihapitiya, as well as crypto bigwigs Tyler and Cameron Winklevoss.

By introducing LLM-embedded textual timestamps, Auto-Times can utilize chronological information to align multivariate time series. Empirically, AutoTimes achieves state-of-the-art with 0.1% trainable parameters and

Lately, there has been a surge in interest surrounding generative modeling of time series data. Most existing approaches are designed either to process short sequences or to handle long-range sequences. This dichotomy can be attributed to gradient issues with recurrent networks, computational costs associated with transformers, and limited expressiveness of state space models. Towards a unified generative model for varying-length time series, we propose in this work to transform sequences into images.

Incomplete spatio-temporal data in the real world has spawned much research.

Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts.