AI-generated media has become a threat to our digital society as we know it. These forgeries can be created automatically and on a large scale based on publicly available technology. Recognizing this challenge, academics and practitioners have proposed a multitude of automatic detection strategies to detect such artificial media. However, in contrast to these technical advances, the human perception of generated media has not been thoroughly studied yet. In this paper, we aim at closing this research gap. We perform the first comprehensive survey into people's ability to detect generated media, spanning three countries (USA, Germany, and China) with 3,002 participants across audio, image, and text media. Our results indicate that state-of-the-art forgeries are almost indistinguishable from "real" media, with the majority of participants simply guessing when asked to rate them as human- or machine-generated. In addition, AI-generated media receive is voted more human like across all media types and all countries. To further understand which factors influence people's ability to detect generated media, we include personal variables, chosen based on a literature review in the domains of deepfake and fake news research. In a regression analysis, we found that generalized trust, cognitive reflection, and self-reported familiarity with deepfakes significantly influence participant's decision across all media categories.

Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creation of large and diverse radiologic databases like RadImageNet is highly resource-intensive. To address these limitations, we introduce RadImageGAN, the first multi-modal radiologic data generator, which was developed by training StyleGAN-XL on the real RadImageNet dataset of 102,774 patients. RadImageGAN can generate high-resolution synthetic medical imaging datasets across 12 anatomical regions and 130 pathological classes in 3 modalities. Furthermore, we demonstrate that RadImageGAN generators can be utilized with BigDatasetGAN to generate multi-class pixel-wise annotated paired synthetic images and masks for diverse downstream segmentation tasks with minimal manual annotation. We showed that using synthetic auto-labeled data from RadImageGAN can significantly improve performance on four diverse downstream segmentation datasets by augmenting real training data and/or developing pre-trained weights for fine-tuning. This shows that RadImageGAN combined with BigDatasetGAN can improve model performance and address data scarcity while reducing the resources needed for annotations for segmentation tasks.

The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity. Beyond digital content creation, GAI's capability in analyzing complex data distributions offers great potential for wireless communications, particularly amidst a rapid expansion of new physical layer communication technologies. For example, the diffusion model can learn input signal distributions and use them to improve the channel estimation accuracy, while the variational autoencoder can model channel distribution and infer latent variables for blind channel equalization. Therefore, this paper presents a comprehensive investigation of GAI's applications for communications at the physical layer, ranging from traditional issues, including signal classification, channel estimation, and equalization, to emerging topics, such as intelligent reflecting surfaces and joint source channel coding. We also compare GAI-enabled physical layer communications with those supported by traditional AI, highlighting GAI's inherent capabilities and unique contributions in these areas. Finally, the paper discusses open issues and proposes several future research directions, laying a foundation for further exploration and advancement of GAI in physical layer communications.

After years of inaction in the U.S. Congress, E.U. tech laws have had wide-ranging implications for Silicon Valley companies. Europe's digital privacy law, the General Data Protection Regulation, has prompted some companies, such as Microsoft, to overhaul how they handle users' data even beyond Europe's borders. Meta, Google and other companies have faced fines under the law, and Google had to delay the launch of its generative AI chatbot Bard in the region due to a review under the law. However, there are concerns that the law created costly compliance measures that have hampered small businesses, and that lengthy investigations and relatively small fines have blunted its efficacy among the world's largest companies.

The European Union today agreed on the details of the AI Act, a far-reaching set of rules for the people building and using artificial intelligence. It's a milestone law that, lawmakers hope, will create a blueprint for the rest of the world. After months of debate about how to regulate companies like OpenAI, lawmakers from the EU's three branches of government--the Parliament, Council and Commission--spent more than 36 hours in total--thrashing out the new legislation between Wednesday afternoon and Friday evening. Lawmakers were under pressure to strike a deal before the EU election campaign starts in the new year. "The EU AI Act is a global first," said European Commission President Ursula von der Leyen on X. "[It is] a unique legal framework for the development of AI you can trust.

Some senior employees described Sam Altman as psychologically abusive, creating chaos at OpenAI. The complaints were a major factor in the board’s decision to fire the CEO.

OpenAI's recent drama hasn't only caught UK regulators' attention. Bloomberg reported Friday that the Federal Trade Commission (FTC) is looking into Microsoft's investment in the Sam Altman-led company and whether it violates US antitrust laws. FTC Chair Lina Khan wrote in a New York Times op-ed earlier this year that "the expanding adoption of AI risks further locking in the market dominance of large incumbent technology firms." Bloomberg's report stresses that the FTC inquiry is preliminary, and the agency hasn't opened a formal investigation. But Khan and company are reportedly "analyzing the situation and assessing what its options are." One complicating factor for regulation is that OpenAI is a non-profit, and transactions involving non-corporate entities aren't required by law to be reported.

This is Atlantic Intelligence, an eight-week series in which The Atlantic's leading thinkers on AI will help you understand the complexity and opportunities of this groundbreaking technology. Earlier this year, The Atlantic published a story by Gary Marcus, a well-known AI expert who has agitated for the technology to be regulated, both in his Substack newsletter and before the Senate. Marcus argued that "this is a moment of immense peril," and that we are teetering toward an "information-sphere disaster, in which bad actors weaponize large language models, distributing their ill-gotten gains through armies of ever more sophisticated bots." I was interested in following up with Marcus given recent events. In the past six weeks, we've seen an executive order from the Biden administration focused on AI oversight; chaos at the influential company OpenAI; and this Wednesday, the release of Gemini, a GPT competitor from Google.

Why is the little ol' Competition & Markets Authority, a UK regulator, inserting itself into the entertaining and important – but distant – drama at San Francisco-based OpenAI? Even if the CMA finds eventually that Microsoft, another US company, is pulling the strings at Sam Altman's show, what could it actually do? Doesn't it all paint the UK as an unfriendly place for tech investment, notwithstanding Rishi Sunak's eagerness to host AI summits and conduct cosy chats with Elon Musk? All fair questions, and the CMA should brace for more in that vein. It is indeed slightly odd that the UK regulator is the first out of traps in wondering, albeit in a preliminary manner, if Microsoft has gained effective control over OpenAI and, if it has, whether that amounts to a problem. But there is another way to look at developments: thank goodness a regulator somewhere is seeking clarity about what just occurred at OpenAI.

Microsoft Corp.'s partnership with OpenAI Inc. is facing the potential of a full-blown UK antitrust investigation three weeks after a mutiny at the ChatGPT creator laid bare deep ties between the two companies. The Competition and Markets Authority said Friday it was gathering information from stakeholders to determine whether the collaboration between the two firms threatens competition in the UK, home of Google's AI research lab Deepmind. Microsoft fell 0.7% in premarket trading. Microsoft has benefited richly from its investments, totaling as much as $13 billion, in OpenAI. By integrating OpenAI's products into virtually every corner of its core businesses, the software giant very quickly established itself as the undisputed leader of AI among big tech firms.