New AI home search technology from Homes.com eliminates manual filter clicking, allowing homebuyers to find properties by describing desired features naturally.

While these advances use different algorithmic techniques, e.g., contrastive learning, diffusion, or auto-regressive modeling, they all rest on a common foundation: large datasets containing paired image-text examples.

WIRED spoke with the Zoomer founders of a platform where AI agents hire humans to do real-world tasks. Their pitch: People would love to have a clanker as their boss. For centuries, people have catastrophized about robots taking away jobs. On February 1, the paradigm shifted: bots are jobs. Now, 518,284 humans--and rapidly counting--are offering their labor to AI agents on a new online marketplace called RentAHuman . There are classifieds to count pigeons in Washington ($30/hour); deliver CBD gummies ($75/hour); play exhibition badminton ($100/hour); and anything else you could possibly imagine that a disembodied agent couldn't do.

Things to Do in L.A. Canadian snowbirds are still unhappy with Trump. This is read by an automated voice. Please report any issues or inconsistencies here . Palm Springs relies heavily on Canadian tourists, who are declining to travel to the U.S. or shortening their stays because of Trump. The number of Canadian visitors to California plummeted more than 18% in 2025 compared with the year prior.

Utilizing mutual learning can effectively enhance the performance of peer BNNs.

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames.

Large language models (LLMs), typically designed as a function of next-word prediction, have excelled across extensive NLP tasks. Despite the generality, next-word prediction is often not an efficient formulation for many of the tasks, demanding an extreme scale of model parameters (10s or 100s of billions) and sometimes yielding suboptimal performance. In practice, it is often desirable to build more efficient models--despite being less versatile, they still apply to a substantial subset of problems, delivering on par or even superior performance with much smaller model sizes.

In this paper, we introduce the CityRefer dataset for city-level 3D visual grounding.