Sen. Tom Cotton urges the Justice Department to investigate a China-linked influence campaign he says is aimed at undermining America's AI infrastructure.

Dads are traditionally tough to shop for--let me help with these handpicked gift ideas for fathers with great taste. The only Father's Day gift I can recall my own dad getting was a plate of fried sardines. It was prepared by my mother, his ex-wife, who knew how gratefully he'd receive a dish he grew up with in the Italian neighborhood of a steel town dying with such theatrical flair that Bruce Springsteen named a song after it. We lived in a nearby city that had plenty of red-sauce restaurants, but they weren't serving tinned fish in those days. As my father had only the most limited of food preparation skills and didn't date the kind of women who could cook, this was the only way he'd ever taste that flavor again. As Father's Day gifts go, being united with a long-lost recipe from childhood is pretty good.

United States President Donald Trump has fired off a social media message calling on the head of the US technology firm Intel to resign from his post as chief executive officer. Trump's decision to denounce Intel CEO Lip-Bu Tan on Thursday morning sent the company's stocks tumbling, amid the uncertainty about the future of its leadership. "The CEO of INTEL is highly CONFLICTED and must resign, immediately," Trump wrote. "There is no other solution to this problem. Thank you for your attention to this problem!" Trump's post appeared to be a response to reports that Tan has invested nearly 200m in Chinese technology manufacturing and chip firms, including some with links to the country's military.

FIRST ON FOX: Dozens of drones that traipsed over Langley Air Force base in late 2023 revealed an astonishing oversight: Military officials did not believe they had the authority to shoot down the unmanned vehicles over the U.S. homeland. A new bipartisan bill, known as the COUNTER Act, seeks to rectify that, offering more bases the opportunity to become a "covered facility," or one that has the authority to shoot down drones that encroach on their airspace. The new bill has broad bipartisan and bicameral support, giving it a greater chance of becoming law. It's led by Armed Services Committee members Tom Cotton, R-Ark., and Kirsten Gillibrand, D-N.Y., in the Senate, and companion legislation is being introduced by August Pfluger, R-Texas, and Chrissy Houlahan, D-Pa., in the House. Currently, only half of the 360 domestic U.S. bases are considered "covered facilities" that are allowed to engage with unidentified drones.

Large Language Models (LLMs) have recently garnered widespread attention due to their adeptness at generating innovative responses to the given prompts across a multitude of domains. However, LLMs often suffer from the inherent limitation of hallucinations and generate incorrect information while maintaining well-structured and coherent responses. In this work, we hypothesize that hallucinations stem from the internal dynamics of LLMs. Our observations indicate that, during passage generation, LLMs tend to deviate from factual accuracy in subtle parts of responses, eventually shifting toward misinformation. This phenomenon bears a resemblance to human cognition, where individuals may hallucinate while maintaining logical coherence, embedding uncertainty within minor segments of their speech. To investigate this further, we introduce an innovative approach, HalluShift, designed to analyze the distribution shifts in the internal state space and token probabilities of the LLM-generated responses. Our method attains superior performance compared to existing baselines across various benchmark datasets. Our codebase is available at https://github.com/sharanya-dasgupta001/hallushift.

Last year, the Africa Cup of Nations, the continent's biggest international soccer tournament, kicked off in Côte d'Ivoire, in a stadium designed, financed, and built by China. This should not come as a surprise to anyone who follows the sport, nor is it some new development. The first Chinese-made stadium in Africa was completed more than fifty years ago. By the end of the millennium, nine more African countries would open their capital cities to what came to be known as "stadium diplomacy." The quantity and scale of these stadiums grew alongside an increasingly robust push to quickly build infrastructure in poor African countries.

How can technical advice on operating experience feedback be managed efficiently in an organization that has never used knowledge engineering techniques and methods? This article explains how an industrial company in the nuclear and defense sectors adopted such an approach, adapted to its "TA KM" organizational context and falls within the ISO30401 framework, to build a complete system with a "SARBACANES" application to support its business processes and perpetuate its know-how and expertise in a knowledge base. Over and above the classic transfer of knowledge between experts and business specialists, SARBACANES also reveals the ability of this type of engineering to deliver multi-functional operation. Modeling was accelerated by the use of a tool adapted to this type of operation: the Ardans Knowledge Maker platform.

Large language models have gained considerable interest for their impressive performance on various tasks. Within this domain, ChatGPT and GPT-4, developed by OpenAI, and the Gemini, developed by Google, have emerged as particularly popular among early adopters. Additionally, Mixtral by Mistral AI and Claude by Anthropic are newly released, further expanding the landscape of advanced language models. These models are viewed as disruptive technologies with applications spanning customer service, education, healthcare, and finance. More recently, Mistral has entered the scene, captivating users with its unique ability to generate creative content. Understanding the perspectives of these users is crucial, as they can offer valuable insights into the potential strengths, weaknesses, and overall success or failure of these technologies in various domains. This research delves into the responses generated by ChatGPT, GPT-4, Gemini, Mixtral and Claude across different Conversational QA corpora. Evaluation scores were meticulously computed and subsequently compared to ascertain the overall performance of these models. Our study pinpointed instances where these models provided inaccurate answers to questions, offering insights into potential areas where they might be susceptible to errors. In essence, this research provides a comprehensive comparison and evaluation of these state of-the-art language models, shedding light on their capabilities while also highlighting potential areas for improvement

Attendees at a Google summit received a notebook with a bizarre joke about cotton inside the front cover this summer, reigniting questions about discrimination at the tech company. Upon opening the notebook, attendees saw the sentence, 'I WAS JUST COTTON THE MOMENT, BUT I CAME BACK TO TAKE YOUR NOTES!' MUCH BETTER! The incident took place at the K&I Black Google Summit, held August 15 and 16 - an event meant to promote diversity and inclusion in the AI community, as well as reinforce Google's commitment to promoting equity in the broader tech industry. Google's notebooks came from a third-party supplier, according to a company spokesperson, and event organizers were not aware of this printing inside The customizable notebook is sold by multiple online retailers, and the joke seems to refer to the fact that its cover is made of recycled cotton fibers. The front of the notebooks were inoffensive: an illustration of a sunrise, the event title and date, and the slogan'Seize the moment.'

Pre-trained language models (PLMs) have achieved remarkable success in natural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs are pre-trained in an unsupervised manner using the large-scale general corpus. In the meanwhile, an increasing number of models pre-trained with labeled data (i.e. "supervised pre-training") showcase superior performance compared to unsupervised pre-trained models. Motivated by the success of supervised pre-training, we propose Multi-task superVised Pre-training (MVP) for natural language generation. We collect a large-scale natural language generation corpus, MVPCorpus, from $77$ datasets over $11$ diverse NLG tasks. Then we unify these examples into a general text-to-text format to pre-train the text generation model MVP in a supervised manner. For each task, we further pre-train specific soft prompts to stimulate the model's capacity to perform a specific task. Our MVP model can be seen as a practice that utilizes recent instruction tuning on relatively small PLMs. Extensive experiments have demonstrated the effectiveness and generality of our MVP model in a number of NLG tasks, which achieves state-of-the-art performance on $13$ out of $17$ datasets, outperforming BART by $9.3\%$ and Flan-T5 by $5.8\%$.