Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. EXCLUSIVE: Nashville musicians are increasingly worried about complications with artificial intelligence's growing sophistication that could threaten their livelihood, Sen. Marsha Blackburn, R-Tenn., warned this week. "We met with the Nashville Technology Council a couple of weeks ago, and we have talked with so many of the musicians. They're concerned that using AI, they will do a copycat of their voice and take the lyrics of their song, which you can get on ChatGPT," Blackburn told Fox News Digital during an interview in her Senate office.

3D shape generation techniques utilizing deep learning are increasing attention from both computer vision and architectural design. This survey focuses on investigating and comparing the current latest approaches to 3D object generation with deep generative models (DGMs), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), 3D-aware images, and diffusion models. We discuss 187 articles (80.7% of articles published between 2018-2022) to review the field of generated possibilities of architecture in virtual environments, limited to the architecture form. We provide an overview of architectural research, virtual environment, and related technical approaches, followed by a review of recent trends in discrete voxel generation, 3D models generated from 2D images, and conditional parameters. We highlight under-explored issues in 3D generation and parameterized control that is worth further investigation. Moreover, we speculate that four research agendas including data limitation, editability, evaluation metrics, and human-computer interaction are important enablers of ubiquitous interaction with immersive systems in architecture for computer-aided design Our work contributes to researchers' understanding of the current potential and future needs of deep learnings in generating virtual architecture.

Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is important to ensure that the generated content reflects the artifacts and surroundings across the globe, rather than over-representing certain parts of the world. In this paper, we measure the geographical representativeness of common nouns (e.g., a house) generated through DALL.E 2 and Stable Diffusion models using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India, and the top generations rarely reflect surroundings from all other countries (average score less than 3 out of 5). Specifying the country names in the input increases the representativeness by 1.44 points on average for DALL.E 2 and 0.75 for Stable Diffusion, however, the overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive. Lastly, we examine the feasibility of quantifying the geographical representativeness of generated images without conducting user studies.

During 2022, both transformer-based AI text generation sys-tems such as GPT-3 and AI text-to-image generation systems such as DALL-E 2 and Stable Diffusion made exponential leaps forward and are unquestionably altering the fields of digital art and electronic literature. In this panel a group of electronic literature authors and theorists consider new oppor-tunities for human creativity presented by these systems and present new works have produced during the past year that specifically address these systems as environments for literary expressions that are translated through iterative interlocutive processes into visual representations. The premise that binds these presentations is that these systems and the works gener-ated must be considered from a literary perspective, as they originate in human writing. In works ranging from a visual memoir of the personal experience of a health crisis, to interac-tive web comics, to architectures based on abstract poetic language, to political satire, four artists explore the capabili-ties of these writing environments for new genres of literary artist practice, while a digital culture theorist considers the origins and effects of the particular training datasets of human language and images on which these new hybrid forms are based.

Deep generative models are known to produce undesirable samples such as harmful content. Traditional mitigation methods include re-training from scratch, filtering, or editing; however, these are either computationally expensive or can be circumvented by third parties. In this paper, we take a different approach and study how to post-edit an already-trained conditional generative model so that it redacts certain conditionals that will, with high probability, lead to undesirable content. This is done by distilling the conditioning network in the models, giving a solution that is effective, efficient, controllable, and universal for a class of deep generative models. We conduct experiments on redacting prompts in text-to-image models and redacting voices in text-to-speech models. Our method is computationally light, leads to better redaction quality and robustness than baseline methods while still retaining high generation quality.

Generative models trained with Differential Privacy (DP) are increasingly used to produce synthetic data while reducing privacy risks. Navigating their specific privacy-utility tradeoffs makes it challenging to determine which models would work best for specific settings/tasks. In this paper, we fill this gap in the context of tabular data by analyzing how DP generative models distribute privacy budgets across rows and columns, arguably the main source of utility degradation. We examine the main factors contributing to how privacy budgets are spent, including underlying modeling techniques, DP mechanisms, and data dimensionality. Our extensive evaluation of both graphical and deep generative models sheds light on the distinctive features that render them suitable for different settings and tasks. We show that graphical models distribute the privacy budget horizontally and thus cannot handle relatively wide datasets while the performance on the task they were optimized for monotonically increases with more data. Deep generative models spend their budget per iteration, so their behavior is less predictable with varying dataset dimensions but could perform better if trained on more features. Also, low levels of privacy ($\epsilon\geq100$) could help some models generalize, achieving better results than without applying DP.

This is an edition of The Atlantic Daily, a newsletter that guides you through the biggest stories of the day, helps you discover new ideas, and recommends the best in culture. Yesterday, the OpenAI CEO Sam Altman testified before a Senate judiciary subcommittee about the "significant harm" that ChatGPT and similar generative-AI tools could pose to the world. When I asked Damon Beres, The Atlantic's technology editor, for his read on the hearing, he noted that Altman's emphasis on the broader existential risks of AI might conveniently elide some of the more quotidian problems of this new technology. I called Damon today to talk about that, and to see what else has been on his mind as he follows this story. Isabel Fattal: Can you talk a bit more about Altman's emphasis on the existential possibilities of AI, and what that focus might leave out?

On Tuesday, lawmakers, A.I. experts, and the guy chiefly responsible for ChatGPT gathered in the same room to swap analogies for just how dramatically A.I. is about to change our lives. The invention of the internet, the cell phone, and airplanes all made the list. For a Senate Judiciary Committee hearing ostensibly concerned with the dangers A.I. might pose to the world, everyone seemed to get along quite well. At one point Sen. John Kennedy of Louisiana asked Sam Altman, the CEO of ChatGPT maker OpenAI, if he could recommend some people to oversee a new agency to oversee A.I.--that is, to pick his own regulators. Then again, Altman was doing an exceptional job projecting a self-critical persona.

Since the tech industry began its love affair with machine learning about a decade ago, US lawmakers have chattered about the potential need for regulation to rein in the technology. No proposal to regulate corporate AI projects has got close to becoming law--but OpenAI's release of ChatGPT in November has convinced some senators there is now an urgent need to do something to protect people's rights against the potential harms of AI technology. At a hearing held by a Senate Judiciary subcommittee yesterday attendees heard a terrifying laundry list of ways artificial intelligence can harm people and democracy. Senators from both parties spoke in support of the idea of creating a new arm of the US government dedicated to regulating AI. The idea even got the backing of Sam Altman, CEO of OpenAI.

Sam Altman, the chief executive of ChatGPT's OpenAI, testified before members of a Senate subcommittee on Tuesday about the need to regulate the increasingly powerful artificial intelligence technology being created inside his company and others like Google and Microsoft. The three-hour-long hearing touched on several aspects of the risks that generative AI could pose to society, how it would affect the jobs market and why regulation by governments would be needed. Tuesday's hearing will be the first in a series of hearings to come as lawmakers grapple with drafting regulations around AI to address its ethical, legal and national security concerns. Senator Richard Blumenthal from Connecticut opened the proceedings with an AI-generated audio recording that sounded just like him. "Too often we have seen what happens when technology outpaces regulation. We have seen how algorithmic biases can perpetuate discrimination and prejudice and how the lack of transparency can undermine public trust. This is not the future we want," the voice said.