Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.

Nvidia CEO Jensen Huang says the technology giant has won approval from United States President Donald Trump's administration to sell its advanced H20 computer chips, used to develop artificial intelligence, to China. The news came in a company blog post late on Monday, and Huang also spoke about the coup on China's state-run CGTN television network in remarks shown on X. "The US government has assured Nvidia that licences will be granted, and Nvidia hopes to start deliveries soon," the post said. "Today, I'm announcing that the US government has approved for us filing licences to start shipping H20s," Huang told reporters in Beijing. He noted that half of the world's AI researchers are in China. "It's so innovative and dynamic here in China that it's really important that American companies are able to compete and serve the market here in China," he said.

The impact of AI has been felt across industries from Hollywood to publishing – and now it's come for modelling. H&M announced last week that it would create AI "twins" of 30 models with the intention of using them in social media posts and marketing imagery if the model gives her permission. In a statement, Jörgen Andersson, the chief creative officer at H&M, described the idea as "something that will enhance our creative process and how we work with marketing but fundamentally not change our human-centric approach in any way". The retail giant has worked with successful models including Vilma Sjöberg and Mathilda Gvarliani, who model for Vogue and brands including Chanel. As part of the agreement, each model would be able book her twin on shoots for other brands – meaning they could, in image anyway, be in two places at the same time.

This article reports on the third iteration of a survey of computerized tools and technologies taught as part of postgraduate translation training programmes. While the survey was carried out under the aegis of the EMT Network, more than half of responses are from outside that network. The results show the responsiveness of programmes to innovations in translation technology, with increased compulsory inclusion of machine translation, post-editing, and quality evaluation, and a rapid response to the release of generative tools. The flexibility required during the Covid-19 pandemic has also led to some lasting changes to programmes. While the range of tools being taught has continued to expand, programmes seem to be consolidating their core offering around cloud-based software with cost-free academic access. There has also been an increase in the embedding of professional contexts and workflows associated with translation technology. Generic file management and data security skills have increased in perceived importance, and legal and ethical issues related to translation data have also become more prominent. In terms of course delivery the shift away from conventional labs identified in EMT2017 has accelerated markedly, no doubt partly driven by the pandemic, accompanied by a dramatic expansion in the use of students' personal devices.

The days of getting your driving licence out to prove your age could soon be a thing of the past. The UK government has confirmed that a digital version is being launched this year. A new app will be unveiled this summer, which will include a'wallet' to store digital versions of official documents. This will begin with a veteran's card, followed by a pilot of driving licences. The wallet will make use of security features already built into modern smartphones – such as facial recognition – for protection.

Chinese engineers developing chips for artificial intelligence that can be used in "advanced weapons systems" have gained access to cutting-edge UK technology, the Guardian can reveal. Described by analysts as "China's premier AI chip designers", Moore Threads and Biren Technology are subject to US export restrictions over their development of chips that "can be used to provide artificial intelligence capabilities to further development of weapons of mass destruction, advanced weapons systems and hi-tech surveillance applications that create national security concerns". However, prior to the US blacklisting in 2023, the two companies secured extensive licences with the UK-based Imagination Technologies, which is among a handful of firms worldwide that design an advanced type of microchip crucial for AI systems, and is regarded as a jewel of the UK's technology industry. A spokesperson for Imagination said: "At no stage has Imagination (or its owners) considered or implemented transactions with third parties with the aim of enabling China or any other nation state to use or direct Imagination technology for state or military end uses." While Imagination's representatives confirmed the existence of the licences with Moore Threads and Biren Technology, they denied claims that the company, under the ownership of a private equity fund backed with Chinese state money, sought to deliberately transfer its state-of-the-art secrets to China. Two former senior Imagination insiders claim that "knowledge transfer programmes" accompanying the licences were so comprehensive that they risked the Chinese companies learning how to replicate Imagination's expertise.

The United States has launched its third crackdown in three years on China's semiconductor industry, curbing exports to 140 companies, including chip equipment maker Naura Technology Group, among other moves. The latest effort on Monday to hobble Beijing's chipmaking ambitions also hits Chinese chip toolmakers Piotech, ACM Research and SiCarrier Technology with new export restrictions as part of the package, which also takes aim at shipments of advanced memory chips and more chipmaking tools to China. The move is one of President Joe Biden's last large-scale efforts to stymie China's ability to access and produce chips that can help advance artificial intelligence for military applications, or otherwise threaten US national security. It comes just weeks before the swearing-in of Republican President-elect Donald Trump, who is expected to retain many of Biden's tough-on-China measures. The package includes curbs on China-bound shipments of high bandwidth memory (HBM) chips, critical for high-end applications like AI training; curbs on 24 additional chipmaking tools and three software tools; and export curbs on chipmaking equipment made in countries such as Singapore and Malaysia.

With the rapid growth of the Natural Language Processing (NLP) field, a vast variety of Large Language Models (LLMs) continue to emerge for diverse NLP tasks. As more papers are published, researchers and developers face the challenge of information overload. Thus, developing a system that can automatically extract and organise key information about LLMs from academic papers is particularly important. The standard format for documenting information about LLMs is the LLM model card (\textbf{LLM-Card}). We propose a method for automatically generating LLM model cards from scientific publications. We use Named Entity Recognition (\textbf{NER}) and Relation Extraction (\textbf{RE}) methods that automatically extract key information about LLMs from the papers, helping researchers to access information about LLMs efficiently. These features include model \textit{licence}, model \textit{name}, and model \textit{application}. With these features, we can form a model card for each paper. We processed 106 academic papers by defining three dictionaries -- LLM's name, licence, and application. 11,051 sentences were extracted through dictionary lookup, and the dataset was constructed through manual review of the final selection of 129 sentences with a link between the name and the \textit{licence}, and 106 sentences with a link between the model name and the \textit{application}. The resulting resource is relevant for LLM card illustrations using relational knowledge graphs. Our code and findings can contribute to automatic LLM card generation. Data and code in \textsc{autoLLM-Card} will be shared and freely available at \url{https://github.com/shengwei-tian/dependency-parser-visualization}