Musk, who co-founded the company that created ChatGPT with Altman, wants more than $130 billion in damages in a lawsuit that could shakeup the artificial intelligence landscape. The BBC's Lily Jamali explains why the two tech giants are facing off in court. How much screen time is too much for under fives? Some major retailers and independent stores have introduced AI body scans, CCTV or facial recognition equipment to identify crimes like shoplifting. What does TikTok's deal mean for America's users?

Deep convolutional neural networks (CNNs) are often of sophisticated design with numerous learnable parameters for the accuracy reason. To alleviate the expensive costs of deploying them on mobile devices, recent works have made huge efforts for excavating redundancy in pre-defined architectures. Nevertheless, the redundancy on the input resolution of modern CNNs has not been fully investigated, i.e., the resolution of input image is fixed. In this paper, we observe that the smallest resolution for accurately predicting the given image is different using the same neural network. To this end, we propose a novel dynamic-resolution network (DRNet) in which the input resolution is determined dynamically based on each input sample. Wherein, a resolution predictor with negligible computational costs is explored and optimized jointly with the desired network.

Deep neural networks are known to be vulnerable to adversarial attacks (AA). For an image recognition task, this means that a small perturbation of the original can result in the image being misclassified. Design of such attacks as well as methods of adversarial training against them are subject of intense research. We re-cast the problem using techniques of Wasserstein distributionally robust optimization (DRO) and obtain novel contributions leveraging recent insights from DRO sensitivity analysis. We consider a set of distributional threat models.

The Download: DeepSeek's latest AI breakthrough, and the race to build world models Plus: China has blocked Meta's $2 billion acquisition of AI startup Manus. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that handles large amounts of text more efficiently. While the model remains open source, its performance matches leading closed-source rivals from Anthropic, OpenAI, and Google. Here are three ways V4 could shake up AI . AI systems have already gained impressive mastery over the digital world, but the physical world remains humanity's domain.

A recent attack on Sam Altman's home and OpenAI offices has put corporate security under renewed scrutiny. Records reveal how much some tech firms spend to arm up. Elon Musk called violent crime in San Francisco " horrific " and moved the offices of his social media business X outside the city in 2024 because of safety and business considerations. Other local tech companies have attempted to address their security concerns by partnering directly with cops. Airbnb and Salesforce are among businesses that for years have contracted San Francisco police to protect their offices on a regular basis, according to public records obtained by WIRED.

The two Silicon Valley tycoons are headed to court. The two Silicon Valley tycoons are headed to court. Musk's lawsuit accuses Altman of fraud, while OpenAI says that Musk is'motivated by jealousy' A lawsuit between two of Silicon Valley's biggest tycoons goes to trial Monday in California, the culmination of a years-long bitter feud. Elon Musk has accused Sam Altman of betraying the founding agreement of the non-profit they started together, OpenAI, by changing it to a for-profit enterprise. Musk accuses Altman, OpenAI, its president Greg Brockman, and its major partner Microsoft of breach of contract and unjust enrichment in the lawsuit.