The Biden administration's investment in responsible AI research and development is a $140 million grant, which will increase the number of national AI research institutes. These institutes are focused on advancing artificial intelligence research in areas ranging from public health to cybersecurity. The investment is just a fraction of the billions that private sector companies are pouring into advancing the technology. Microsoft previously invested $10 billion in OpenAI.

Ahead of a meeting between Vice President Kamala Harris and the heads of America's four leading AI tech companies -- Alphabet, OpenAI, Anthropic and Microsoft -- the Biden Administration announced Thursday a sweeping series of planned actions to help mitigate some of the risks that these emerging technologies pose to the American public. That includes $140 million to launch seven new AI R&D centers as part of the National Science Foundation, extracting commitments from leading AI companies to participate in a "public evaluation" of their AI systems at DEFCON 31, and ordering the Office of Management and Budget (OMB) to draft policy guidance for federal employees. "The Biden Harris administration has been leading on these issues since long before these newest generative AI products debuted last fall," a senior administration official said during a reporters call Wednesday. The Administration unveiled its AI Bill of Rights "blueprint" last October, which sought to "help guide the design, development, and deployment of artificial intelligence (AI) and other automated systems so that they protect the rights of the American public," per a White House press release. "At a time of rapid innovation, it is essential that we make clear the values we must advance, and the common sense we must protect," the administration official continued.

ChatGPT has stoked new hopes about the potential of artificial intelligence--but also new fears. Today the White House joined the chorus of concern, announcing it will support a mass hacking exercise at the Defcon security conference this summer to probe generative AI systems from companies including Google. The White House Office of Science and Technology Policy also said that $140 million will be diverted towards launching seven new National AI Research Institutes focused on developing ethical, transformative AI for the public good, bringing the total number to 25 nationwide. The announcement came hours before a meeting on the opportunities and risks presented by AI between vice president Kamala Harris and executives from Google and Microsoft as well as the startups Anthropic and OpenAI, which created ChatGPT. The White House AI intervention comes as appetite for regulating the technology is growing around the world, fueled by the hype and investment sparked by ChatGPT.

Russia has blamed Ukraine for setting ablaze one of its oil refineries, while Kyiv has accused Moscow of launching dozens of overnight strikes by unmanned aerial vehicles for the second day running. The targeting of the fuel facility on Thursday occurred at the Ilsky refinery near the Black Sea port of Novorossiysk in the Krasnodar region, Russia's TASS news agency reported citing local emergency services. A fuel reservoir was on fire, it said, but gave no further details. A day earlier, a fuel depot further to the west caught fire near a bridge linking Russia's mainland with the occupied Crimean Peninsula. "A second turbulent night for our emergency services," Krasnodar Governor Veniamin Kondratyev wrote on Telegram, confirming tanks with oil products were set ablaze.

People in Los Angeles and Austin told Fox News whether they think it's important for the government to regulate artificial intelligence technology. Americans in California and Texas revealed whether they think it's important for the government to regulate artificial intelligence. "Definitely," Carlos, a Houston resident, told Fox News. "It should be regulated to the fullest extent as drugs or anything else should be." But Joe, of San Antonio, disagreed.

Rep. Darrell Issa, R-Calif., discusses artificial intelligence and what Congress can do to promote innovation and stay competitive with China. Rep. Darrell Issa, R-Calif., on Tuesday praised House Speaker Kevin McCarthy's new effort to educate Congress on artificial intelligence (AI), but predicted it may be some time until a substantial number of representatives become well-versed on the topic. Speaking with Fox News Digital at the Milken Institute Global Conference, Issa admitted that while he may not know nearly enough to be considered an expert on AI, he has been growing alongside technological advances that have cropped up across his time in business, military and legislation. "I've been able to grow with it and I'm not really nearly where I need to be. And that's why Speaker McCarthy has formalized the education using MIT and other organizations to educate members of Congress. And he's holding forums he's not requiring, but he's encouraging in a very, very explicit way that we all get educated enough to be part of the solution and not be taken out of fear or lack of knowledge," Issa said.

The man often touted as the godfather of artificial intelligence will be responding to requests for help from Bernie Sanders, Elon Musk and the White House, he says, just days after quitting Google to warn the world about the risk of digital intelligence. Dr Geoffrey Hinton, 75, won computer science's highest honour, the Turing award, in 2018 for his work on "deep learning", along with Meta's Yann Lecun and the University of Montreal's Yoshua Bengio. The technology, which now underpins the AI revolution, came about as a result of Hinton's efforts to understand the human brain – efforts which convinced him that digital brains might be about to supersede biological ones. But the London-born psychologist and computer scientist might not offer the advice the powerful want to hear. "The US government inevitably has a lot of concerns around national security. And I tend to disagree with them," he told the Guardian.

Singapore – With Indo-Pacific countries ramping up defense spending, especially on naval capabilities, particular attention is being given to assets that will become core elements in next-generation naval fleets: unmanned systems. Commonly known as drones, these systems come in all shapes and sizes and can be used on land, in the air and at sea, particularly in inhospitable or dangerous environments. They are designed to minimize human losses while substantially adding to a force's capabilities, which is why they are often referred to as force multipliers. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Language is constantly changing and evolving, leaving language models to become quickly outdated. Consequently, we should continuously update our models with new data to expose them to new events and facts. However, that requires additional computing, which means new carbon emissions. Do any measurable benefits justify this cost? This paper looks for empirical evidence to support continuous training. We reproduce existing benchmarks and extend them to include additional time periods, models, and tasks. Our results show that the downstream task performance of temporally adapted English models for social media data do not improve over time. Pretrained models without temporal adaptation are actually significantly more effective and efficient. However, we also note a lack of suitable temporal benchmarks. Our findings invite a critical reflection on when and how to temporally adapt language models, accounting for sustainability.

Analysis and extraction of useful information from legal judgments using computational linguistics was one of the earliest problems posed in the domain of information retrieval. Presently, several commercial vendors exist who automate such tasks. However, a crucial bottleneck arises in the form of exorbitant pricing and lack of resources available in analysis of judgements mete out by Hong Kong's Legal System. This paper attempts to bridge this gap by providing several statistical, machine learning, deep learning and zero-shot learning based methods to effectively analyze legal judgments from Hong Kong's Court System. The methods proposed consists of: (1) Citation Network Graph Generation, (2) PageRank Algorithm, (3) Keyword Analysis and Summarization, (4) Sentiment Polarity, and (5) Paragrah Classification, in order to be able to extract key insights from individual as well a group of judgments together. This would make the overall analysis of judgments in Hong Kong less tedious and more automated in order to extract insights quickly using fast inferencing. We also provide an analysis of our results by benchmarking our results using Large Language Models making robust use of the HuggingFace ecosystem.