Microsoft has thrown its weight behind Anthropic's legal challenge against the US Department of Defense. Microsoft has thrown its weight behind Anthropic's legal challenge against the US Department of Defense. Tech company files amicus brief in support of Anthropic's effort to overturn an aggressive Pentagon designation Microsoft has thrown its weight behind Anthropic's legal challenge against the Pentagon, filing a court brief in support of the AI company's effort to overturn an aggressive designation that effectively bars it from government work. In an amicus brief submitted to a federal court in San Francisco this week, Microsoft, which integrates Anthropic's AI tools into systems it provides to the US military, argued that a temporary restraining order was necessary to prevent serious disruption to suppliers whose products rely on the AI company's technology. Google, Amazon, Apple and OpenAI have also signed on to a brief in support of Anthropic. In a statement to the Guardian, Microsoft said: "The Department of War needs reliable access to the country's best technology.

Tesla CEO Elon Musk has scored a resounding victory as shareholders have approved a pay package of as much as $878bn over the next decade, endorsing his vision of morphing the electric vehicle (EV) maker into an AI and robotics juggernaut. Shares of Tesla rose more than 3 percent in after-hours trading after the shareholders voted on Thursday. The proposal was approved with more than 75 percent support. "What we are about to embark upon is not merely a new chapter of the future of Tesla, but a whole new book," he said. "This really is going to be quite the story."

DALL-E, Midjourney, and Stable Diffusion are among the generative AI technologies widely used to produce images in response to user prompts. The output images are, for the most part, indistinguishable from images humans might have created. Generative AI systems are capable of producing human-creator-like images because of the extremely large quantities of images, paired with textual descriptions of the images' contents, on which the systems' image models were trained. A text prompt to compose a picture of a dog playing with a ball on a beach at sunset will generate a responsive image drawing upon embedded representations of how dogs, balls, beaches, and sunsets are typically depicted and arranged in images of this sort.

Generative artificial intelligence (AI) has captured considerable popular attention recently. ChatGPT and DALL-E have given members of the general public opportunities to use AI systems to generate text and image outputs for fun and a wide range of other purposes. Google and Meta have announced their intentions to launch similar AI systems soon.

Large language models (LLMs) have transformed many fields, including natural language processing, computer vision, and reinforcement learning. These models have also made a significant impact in the field of law, where they are being increasingly utilized to automate various legal tasks, such as legal judgement prediction, legal document analysis, and legal document writing. However, the integration of LLMs into the legal field has also raised several legal problems, including privacy concerns, bias, and explainability. In this survey, we explore the integration of LLMs into the field of law. We discuss the various applications of LLMs in legal tasks, examine the legal challenges that arise from their use, and explore the data resources that can be used to specialize LLMs in the legal domain. Finally, we discuss several promising directions and conclude this paper. By doing so, we hope to provide an overview of the current state of LLMs in law and highlight the potential benefits and challenges of their integration.

Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person. Abbott said the test case was not about any sort of legal rights for machines, rather it was about trying to get a patent for "the inventive output from an AI" that lacks a traditional human inventor. Some firms were already using AI programmes to discover new drugs or to find ways to repurpose materials but the companies that many of the lawyers on the case represent wanted greater clarity on patent ownership before investing further, he said. The application was declined in Australia but later overturned by the Federal Court in 2021 which said the country's patent act had no specific provision excluding AI systems as inventors.

A legal challenge is being prepared to overturn the Intellectual Property Office's (IPONZ) decision not to recognise a machine as an inventor. It is being led by University of Surrey law professor Ryan Abbott, who has been testing patent law around the world, including New Zealand, to see if an invention created by an artificial intelligence (AI) programme could receive a patent. The test case centres around a "creativity machine" or AI inventor programme, known as DABUS, which was developed by US-based physicist Stephen Thaler. Abbott approached Thaler about using the AI as the basis of the case and with a team of lawyers, all working pro bono, they filed patent applications in more than a dozen countries listing DABUS as the inventor of a beverage container it created. New Zealand's Assistant Commissioner of Patents rejected the initial application in January, ruling that the term "inventor" intrinsically refers to a natural person.

What does it mean to be an inventor? In patent law, designed to protect the intellectual property of inventors, officials are used to thinking of inventors as humans, taking an "inventive step" – a new way of doing something -- not obvious to a person skilled in the same art. But last week -- in a judicial world first -- Australia's Federal Court ruled an artificial intelligence (AI) system can be named as an inventor. That judgement overturned a decision by the nation's Commissioner of Patents that meant US scientist Stephen Thaler could not patent inventions by his AI system, DABUS (Device for Autonomous Bootstrapping of Unified Sentience). Thaler says DABUS independently designed a fractal-shaped container for improved grip and heat transfer, and an emergency beacon that flashes more noticeably.

There are many predictions about connected and autonomous vehicles, some of them suggesting that fully autonomous, levels 4 and 5 vehicles will begin to become commonplace on public roads from 2025. A study by Vynz Research says the global connected and autonomous vehicle market size was 17.7 million units in 2019; and it predicts that this will reach 51.2 million units by 2025 – a compound growth rate of 17.1% during the period of 2020 to 2025.At present, most vehicles aren't fully autonomous, yet still increasingly rely upon data to operate. With their emergence will be a growth in data. Rich Miller writes in his article for Data Center Frontier, 'Rolling Zettabytes: Quantifying the Data Impact of Connected Cars': "The Automotive Edge Computing Consortium (AECC) is working to help stakeholders understand the infrastructure requirements for connected cars. At Edge Computing World, AECC board member, Vish Nandlall, outlined the group's findings on the volume of data created by autonomous cars and the challenges they will create."

"Digital transformation since the pandemic has been massive. Telehealth has gone from being a novelty to a necessity. Having said that, we need to be reliant on healthcare institutions to get cured and we need technology to make it better," said Mitali Dutta, head of data science and predictive analysis, group IT information and data management, Philips Innovation Campus, at her session at The Rising 2021 by Analytics India Magazine. She pointed at a rise in chronic diseases and healthcare costs, a scarcity of healthcare professionals in India. According to Dutta, the healthcare industry has to focus on the following four aspects, which she called Quadruple AIM of India's healthcare system: To achieve all the above, India needs artificial intelligence.