Artificial Intelligence (AI) has become a ubiquitous part of society, but a key challenge exists in ensuring that humans are equipped with the required critical thinking and AI literacy skills to interact with machines effectively by understanding their capabilities and limitations. These skills are particularly important for learners to develop in the age of generative AI where AI tools can demonstrate complex knowledge and ability previously thought to be uniquely human. To activate effective human-AI partnerships in writing, this paper provides a first step toward conceptualizing the notion of critical learner interaction with AI. Using both theoretical models and empirical data, our preliminary findings suggest a general lack of Deep interaction with AI during the writing process. We believe that the outcomes can lead to better task and tool design in the future for learners to develop deep, critical thinking when interacting with AI.

Building on the remarkable achievements in generative sampling of natural images, we propose an innovative challenge, potentially overly ambitious, which involves generating samples of entire multivariate time series that resemble images. However, the statistical challenge lies in the small sample size, sometimes consisting of a few hundred subjects. This issue is especially problematic for deep generative models that follow the conventional approach of generating samples from a canonical distribution and then decoding or denoising them to match the true data distribution. In contrast, our method is grounded in information theory and aims to implicitly characterize the distribution of images, particularly the (global and local) dependency structure between pixels. We achieve this by empirically estimating its KL-divergence in the dual form with respect to the respective marginal distribution. This enables us to perform generative sampling directly in the optimized 1-D dual divergence space. Specifically, in the dual space, training samples representing the data distribution are embedded in the form of various clusters between two end points. In theory, any sample embedded between those two end points is in-distribution w.r.t. the data distribution. Our key idea for generating novel samples of images is to interpolate between the clusters via a walk as per gradients of the dual function w.r.t. the data dimensions. In addition to the data efficiency gained from direct sampling, we propose an algorithm that offers a significant reduction in sample complexity for estimating the divergence of the data distribution with respect to the marginal distribution. We provide strong theoretical guarantees along with an extensive empirical evaluation using many real-world datasets from diverse domains, establishing the superiority of our approach w.r.t. state-of-the-art deep learning methods.

The bill would need companies to file such documents at least 30 days before publicly debuting their AI tools, or face a financial penalty. Such datasets encompass billions of lines of text and images or millions of hours of music and movies. "AI has the disruptive potential of changing our economy, our political system, and our day-to-day lives. We must balance the immense potential of AI with the crucial need for ethical guidelines and protections," Schiff said in a statement. Schiff's bill, which was first reported by Billboard, has received the support of numerous entertainment industry organizations and unions, including the Recording Industry Association of America, Professional Photographers of America, Directors Guild of America and the Screen Actors Guild-American Federation of Television and Radio Artists.

Alison Smith is a Director of Generative AI at Booz Allen Hamilton where she helps clients address their missions with innovative solutions. Leading Booz Allen's investments in Generative AI and grounding them in real business needs, Alison employs a pragmatic approach to designing, implementing, and deploying Generative AI that blends existing tools with additional customization. She is also responsible for disseminating best practices and key solutions throughout the firm to ensure that all teams are up-to-date on the latest available tools, solutions, and approaches to common client problems. In addition to her role at Booz Allen which balances technical solutions and business growth, Alison also enjoys staying connected to and serving her local community. From 2017-2021, Alison served on the board of a non-profit, DC Open Government Coalition (DCOGC), a group that seeks to enhance public access to government information and ensure transparent government operations; in November 2021, Alison was recognized as a Power Woman in Code by DCFemTech.

Students have submitted more than 22 million papers that may have used generative AI in the past year, new data released by plagiarism detection company Turnitin shows. A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing. Turnitin says its detector has a false positive rate of less than 1 percent when analyzing full documents.

OpenAI is also at the center of several regulatory investigations, which have forced the company to spend even more on legal support. The Securities and Exchange Commission is looking into whether investors were misled during the chaotic period when Altman briefly left the company. The Federal Trade Commission is probing whether it ran afoul of consumer protection laws in a number of areas, including a data leak and ChatGPT's inaccurate claims. And the commission has had talks with the Justice Department about which agency should probe its multibillion-dollar partnership with Microsoft, amid concerns that such deals are dampening competition in the quickly evolving AI market.

Text-to-video generative AI models such as Sora OpenAI have the potential to disrupt multiple industries. In this paper, we report a qualitative social media analysis aiming to uncover people's perceived impact of and concerns about Sora's integration. We collected and analyzed comments (N=292) under popular posts about Sora-generated videos, comparison between Sora videos and Midjourney images, and artists' complaints about copyright infringement by Generative AI. We found that people were most concerned about Sora's impact on content creation-related industries. Emerging governance challenges included the for-profit nature of OpenAI, the blurred boundaries between real and fake content, human autonomy, data privacy, copyright issues, and environmental impact. Potential regulatory solutions proposed by people included law-enforced labeling of AI content and AI literacy education for the public. Based on the findings, we discuss the importance of gauging people's tech perceptions early and propose policy recommendations to regulate Sora before its public release.

The enormous energy consumption of machine learning (ML) and generative AI workloads shows no sign of waning, taking a toll on operating costs, power delivery, and environmental sustainability. Despite a long line of research on energy-efficient hardware, we found that software plays a critical role in ML energy optimization through two recent works: Zeus and Perseus. This is especially true for large language models (LLMs) because their model sizes and, therefore, energy demands are growing faster than hardware efficiency improvements. Therefore, we advocate for a cross-layer approach for energy optimizations in ML systems, where hardware provides architectural support that pushes energy-efficient software further, while software leverages and abstracts the hardware to develop techniques that bring hardware-agnostic energy-efficiency gains.

Generative artificial intelligence (GenAI)--a set of advanced technologies capable of generating texts, images, videos, programming codes, or arts from instructions via sounds or texts--has taken the society by storm and exerted wide-range influences on many aspects of the world economy (Baldassarre et al. 2023; Mannuru et al. 2023; Sætra 2023). Although it had been around for years, GenAI came to public prominence since the introduction of ChatGPT in November 2022, a chatbox able to generate answers, reasoning, and conversations at human level. Since its introduction, ChatGPT and similar large language models have quickly made their ways into the investment industry. One common use of ChatGPT for investment is to give trading recommendations directly from news about a company (such as news articles or corporate communications) (Lopez-Lira and Tang 2023). A less direct approach is to rely on similar pretrained language models such as BERT (Devlin et al. 2018) and OPT (Zhang et al. 2022) to generate a sentiment score for each company which is then used to make trading decisions.

Concern about doctored or manipulative media is always high around election cycles, but 2024 will be different for two reasons: deepfakes made by artificial intelligence (AI) and the sheer number of polls. The term deepfake refers to a hoax that uses AI to create a phoney image, most commonly fake videos of people, with the effect often compounded by a voice component. Combined with the fact that around half the world's population is holding important elections this year – including India, the US, the EU and, most probably, the UK – and there is potential for the technology to be highly disruptive. Here is a guide to some of the most effective deepfakes in recent years, including the first attempts to create hoax images. The banana where it all began.