Are you watching the new season of the Apple TV series Foundation and thinking, "Wow, space looks cool. I wish it really was like that"? You're in luck--it very well could be. Foundation showrunner David S. Goyer says his adaptation of Isaac Asimov's science fiction series honed its cosmic details with Kevin Hand, a scientist who works at NASA's Jet Propulsion Laboratory and who's currently hard at work figuring out the logistics of landing a rover on Europa, one of Jupiter's 95 known moons. The show also found inspiration for its spacey visuals in recent images sent down from the James Webb Space Telescope, which Goyer calls "a treasure trove of material."

Rep. Matt Gaetz is introducing a resolution calling for Congress to let FISA expire at the end of 2023. FISA RENEWAL – The House Judiciary Committee is expected to hold a hearing beginning at 9:15 a.m. Friday to discuss renewing the controversial Foreign Intelligence Surveillance Act (FISA) of 1978. FEELING THE'FORCE': 'Star Wars' TV show actress says sci-fi now'safe' for'Black nerds.' Continue reading … 'OPPENHEIMER MOMENT' - Christopher Nolan weighs in on use of artificial intelligence in movies. 'DOMINANT PERSPECTIVE' - Laura Ingraham believes Fox News Channel's revamped lineup is critical for Americans.

The "Interim Measures for the Management of Generative Artificial Intelligence Services" announced Thursday and set to take effect on Aug. 15, represent Beijing's attempt to encourage the growth of China's AI industry while retaining total control over information available to the public. It is an enormous challenge made more difficult by the rising global popularity of tools that allow people to generate unique text, images and music.

Hollywood's actors and writers will join forces on the picket line from Friday, after studios failed to reach a deal this week with the Screen Actors Guild – American Federation of Television and Radio Artists (SAG-AFTRA). It is the first time the two unions have been on strike simultaneously since 1960, when actor – and future US president – Ronald Reagan led the protests. Among SAG-AFTRA's 160,000-strong ranks are many of the world's biggest stars. Hollywood's A-listers, from Tom Cruise to Angelina Jolie to Johnny Depp, are card-carrying union members. Stars including Meryl Streep, Ben Stiller and Colin Farrell have come out publicly in favour of the strike.

Rep. Nancy Mace (R-S.C.) spoke with Fox News Digital about her AI Training Expansion Act of 2023; she also discussed the future of AI, and Congress' current effort to get ahead of the rapidly advancing tech. The House advanced legislation this week that would require federal officials to be trained up on artificial intelligence systems, in an effort to make sure agencies are as prepared as possible for this rapidly advancing technology. Rep. Nancy Mace's AI Training Expansion Act passed through the House Oversight Committee on Wednesday, and she told Fox News Digital "we're doing everything we need to do" for the bill to reach the House floor for a vote. "AI is going to change the way we live and we work, and we want to make sure that our federal workforce is prepared for the future and what that might hold," said Mace, R-S.C. Rep. Nancy Mace spoke with Fox News Digital about her AI bill that was approved in committee this week (Tom Williams/CQ-Roll Call, Inc via Getty Images) The bill, co-sponsored by Rep. Gerry Connolly, D-Va., would mandate that supervisors, managers, and data and technology workers whose jobs are linked to the federal government's use of AI systems adhere to certain training requirements to ensure they properly understand the technology they're using.

Vice President Kamala Harris on Wednesday explained artificial intelligence as she convened a roundtable with labor and civil rights leaders to talk about the technology. It appears that there won't be any new regulations on the use of artificial intelligence or AI in elections from the Federal Election Commission (FEC) for the 2024 election cycle, based on the recent vote to table the pursuit of regulations on "deepfake" political ads. Some are concerned that the election could become an "AI arms race". The rationale behind this is a common proliferation scenario where each party fears the other having more weapons, so it gets more itself. Where to draw the line is an important question.

The United States' competition watchdog has opened an investigation into ChatGPT creator OpenAI amid suspicions the startup broke the law by scraping public data and publishing false and defamatory information. In a 20-page letter, the US Federal Trade Commission (FTC) has requested OpenAI to provide detailed information about its technology and privacy protections, including any efforts to prevent a repeat of incidents in which its groundbreaking chatbot published false and disparaging information about members of the public. The Washington Post first reported on the "expansive" probe on Thursday. The FTC declined to comment when contacted by Al Jazeera. OpenAI chief executive Sam Altman said the leak of the regulator's probe was "disappointing" and would not help build trust.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Recently, the AI/ML research community has indicated an urgent need to establish Responsible AI (RAI) values and practices as part of the AI/ML lifecycle. Several organizations and communities are responding to this call by sharing RAI guidelines. However, there are gaps in awareness, deliberation, and execution of such practices for multi-disciplinary ML practitioners. This work contributes to the discussion by unpacking co-production challenges faced by practitioners as they align their RAI values. We interviewed 23 individuals, across 10 organizations, tasked to ship AI/ML based products while upholding RAI norms and found that both top-down and bottom-up institutional structures create burden for different roles preventing them from upholding RAI values, a challenge that is further exacerbated when executing conflicted values. We share multiple value levers used as strategies by the practitioners to resolve their challenges. We end our paper with recommendations for inclusive and equitable RAI value-practices, creating supportive organizational structures and opportunities to further aid practitioners.

The age of social media has opened new opportunities for businesses. This flourishing wealth of information is outside traditional channels and frameworks of classical marketing research, including that of Marketing Mix Modeling (MMM). Textual data, in particular, poses many challenges that data analysis practitioners must tackle. Social media constitute massive, heterogeneous, and noisy document sources. Industrial data acquisition processes include some amount of ETL. However, the variability of noise in the data and the heterogeneity induced by different sources create the need for ad-hoc tools. Put otherwise, customer insight extraction in fully unsupervised, noisy contexts is an arduous task. This research addresses the challenge of fully unsupervised topic extraction in noisy, Big Data contexts. We present three approaches we built on the Variational Autoencoder framework: the Embedded Dirichlet Process, the Embedded Hierarchical Dirichlet Process, and the time-aware Dynamic Embedded Dirichlet Process. These nonparametric approaches concerning topics present the particularity of determining word embeddings and topic embeddings. These embeddings do not require transfer learning, but knowledge transfer remains possible. We test these approaches on benchmark and automotive industry-related datasets from a real-world use case. We show that our models achieve equal to better performance than state-of-the-art methods and that the field of topic modeling would benefit from improved evaluation metrics.