The Group of Seven leaders summit, which kicks off Friday in the city of Hiroshima, is set to tackle major tech topics, even as the club of wealthy nations juggles an agenda ranging from the Russia-Ukraine war to China's territorial ambitions and climate change. Among those, the G7 leaders are expected to touch on the rise of artificial intelligence applications, such as ChatGPT, and how more nations are regulating cross-border data flows. In late April, the G7 ministers in charge of digital and technology policy gathered and laid groundwork for the Hiroshima summit, with ministers agreeing to work toward more coordination in this area. 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.

Two companies are coming together to develop humanoid robots with AI that will be able to perform jobs from manufacturing to health care professions. Senators on Tuesday will grill OpenAI CEO Sam Altman about the "perils and promise" of artificial intelligence as part of a push to better understand this quickly emerging technology and impose some kind of regulatory regime around it. Altman will testify before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law, which will mark his first time as a witness at a public congressional hearing. His testimony comes several weeks after Senate Majority Leader Chuck Schumer, D-N.Y., said he is working on a regulatory blueprint and as several members of the House and Senate have talked about the need for rules of the road for AI. Members of the subcommittee have made it clear over the last week that they want to learn more about AI to make sure it's used safely and responsibly.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence has its own idea of what the perfect human body should look like. A new study by The Bulimia Project, a Brooklyn, New York-based website that publishes content and research related to eating disorders, investigated how AI perceived the "ideal" body based on social media data. The results, produced by AI-generated imaging tools such as Dall-E 2, Stable Diffusion and Midjourney, showed widely "unrealistic" body structures, as reported in a discussion of the findings on The Bulimia Project's website. Forty percent of the overall images depicted "unrealistic" body types of muscular men and women -- 37% for women and 43% for men -- according to the study.

Typical parametric approaches restrict the exploration of diverse designs by generating variations based on a baseline design. In contrast, generative models provide a solution by leveraging existing designs to create compact yet diverse generative design spaces (GDSs). However, the effectiveness of current exploration methods in complex GDSs, especially in ship hull design, remains unclear. To that end, we first construct a GDS using a generative adversarial network, trained on 52,591 designs of various ship types. Next, we constructed three modes of exploration, random (REM), semi-automated (SAEM) and automated (AEM), with varying levels of user involvement to explore GDS for novel and optimised designs. In REM, users manually explore the GDS based on intuition. In SAEM, both the users and optimiser drive the exploration. The optimiser focuses on exploring a diverse set of optimised designs, while the user directs the exploration towards their design preference. AEM uses an optimiser to search for the global optimum based on design performance. Our results revealed that REM generates the most diverse designs, followed by SAEM and AEM. However, the SAEM and AEM produce better-performing designs. Specifically, SAEM is the most effective in exploring designs with a high trade-off between novelty and performance. In conclusion, our study highlights the need for innovative exploration approaches to fully harness the potential of GDS in design optimisation.

The last few years have significantly increased global interest in generative artificial intelligence. Deepfakes, which are synthetically created videos, emerged as an application of generative artificial intelligence. Fake news and pornographic content have been the two most prevalent negative use cases of deepfakes in the digital ecosystem. Deepfakes have some advantageous applications that experts in the subject have thought of in the areas of filmmaking, teaching, etc. Research on the potential of deepfakes among people with disabilities is, however, scarce or nonexistent. This workshop paper explores the potential of deepfakes as an assistive technology. We examined Reddit conversations regarding Nvdia's new videoconferencing feature which allows participants to maintain eye contact during online meetings. Through manual web scraping and qualitative coding, we found 162 relevant comments discussing the relevance and appropriateness of the technology for people with Autism. The themes identified from the qualitative codes indicate a number of concerns for technology among the autistic community. We suggest that developing generative AI-based assistive solutions will have ramifications for human-computer interaction (HCI), and present open questions that should be investigated further in this space.

Fox News correspondent Matt Finn has the latest on the impact of AI technology that some say could outpace humans on'Special Report.' Artificial intelligence will take center stage in the nation's capital on Tuesday, when tech CEO Sam Altman testifies for the first time before Congress regarding ChatGPT, his company's revolutionary chatbot. Altman's OpenAI, an AI research lab, revolutionized the technology last year when it released ChatGPT, a chatbot that's able to mimic human conversation based on prompts it is given. The company has gone on to release updated iterations of the chatbot since last November, which has sparked a race in Silicon Valley for other tech companies to build and release more power systems powered by artificial intelligence. Altman will appear before the Senate Judiciary subcommittee on privacy, technology, and the law on Tuesday morning amid pressure on government leaders to craft regulations for artificial intelligence.

These days, so-called generative AI can (allegedly) make art, write books, and compose poetry. Systems like Stable Diffusion, Midjourney, and ChatGPT are seemingly quite good at it. But for some artists, this creates problems. Namely, determining what legal rights they have when their work is scraped by these tools. Faced by the rise in these systems, authors and artists are pushing back.

The ministers also agreed on the importance of continued understanding regarding issues stemming from the fast-developing technology, which has captured public attention since ChatGPT's launch by U.S. firm OpenAI last November. AI bots are software applications trained using massive amounts of data from the internet and other sources, enabling them to process and simulate human-like conversations with users. ChatGPT can be prompted to edit text and produce essays. 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.

Google Bard, the search giant's ChatGPT rival, is already available in 180 countries and territories. But even though it's been widely available for months and was the centerpiece of Google's recent I/O event, it's missing one big region. The 450 million people living in the European Union are still unable to access Bard, or any of the company's other generative AI technologies. It's a move that has surprised lawmakers, and even Google won't say why it's holding back. Brando Benifei, the MEP leading the negotiations on Europe's new artificial intelligence rules, is not sure why the bloc had been excluded, describing the omission of the EU from Bard's rollout as a "big issue."

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic datasets emerge as a viable alternative. However, practitioners are often uncertain about generating high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. This study aims to fill this knowledge gap by proposing comprehensive guidelines for generating, annotating, and validating synthetic datasets. The trade-offs and methods associated with each of these aspects are elaborated upon. Further, the practical implications of these guidelines are illustrated through the creation of a turbo-compressors dataset. The study underscores the importance of thoughtful sampling methods to ensure the appropriate size, diversity, utility, and realism of a dataset. It also highlights that design diversity does not equate to performance diversity or realism. By employing test sets that represent uniform, real, or task-specific samples, the influence of sample size and sampling strategy is scrutinized. Overall, this paper offers valuable insights for researchers intending to create and publish synthetic datasets for engineering design, thereby paving the way for more effective applications of AI advancements in the field. The code and data for the dataset and methods are made publicly accessible at https://github.com/cyrilpic/radcomp .