Feedback is delighted to learn that researchers have discovered what Taylor Swift is accidentally doing to rescue the science of plants from mid-ness. We never miss a beat, so Feedback, prompted by assistant news editor and Swiftie Alexandra Thompson, has been taking a close look at a major paper in the Annals of Botany, published in August. It is called "Dance with plants: Taylor Swift's music videos as advance organizers for meaningful learning in botany" . The thesis is that high school students exhibit "a general low interest in plants", leading to "plant blindness". Teachers struggling to convey the magic of botany are repeating material and are getting sick of it.

The A.I. Bubble Is Coming for Your Browser Artificial-intelligence startups, like the makers of the "smart" web browser Dia, are being acquired for vast sums. There's an old business maxim dating to the California gold rush: it's easier to make money selling picks and shovels to aspiring miners than to strike it rich finding gold. Artificial intelligence is in a picks-and-shovels phase right now. If gold, in this metaphor, is artificial general intelligence--a machine smarter than a human--or some version of a digital god, then tech companies are snapping up the tools to create one, including graphics-processing units, data centers, and trained A.I. models. That scramble is why Mark Zuckerberg is paying a twenty-four-year-old A.I. researcher two hundred and fifty million dollars to work at Meta, and why Sam Altman, the C.E.O. of OpenAI, recently said that the company would spend "trillions of dollars" building infrastructure.

Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Generative AI models are trying to depict reality, but instead embed glitches from their own inherited content. I argue that with the increased use of generative AI, there will be a decrease in the quality of the generated content because this generated content will be more and more based on artificial and general data. For instance, automatically generating a new picture will be based on original images authentically generated by persons (such as photographers) plus machine-generated images; however, the latter are not as good as the former in terms of details like contrast and edges. Besides, AI-generated text will be based on original creative content by real persons'plus' machine-generated text, where the latter might be repetitive and standard.

Generative propaganda is the use of generative artificial intelligence (AI) to shape public opinion. To characterize its use in real-world settings, we conducted interviews with defenders (e.g., factcheckers, journalists, officials) in Taiwan and creators (e.g., influencers, political consultants, advertisers) as well as defenders in India, centering two places characterized by high levels of online propaganda. The term "deepfakes", we find, exerts outsized discursive power in shaping defenders' expectations of misuse and, in turn, the interventions that are prioritized. To better characterize the space of generative propaganda, we develop a taxonomy that distinguishes between obvious versus hidden and promotional versus derogatory use. Deception was neither the main driver nor the main impact vector of AI's use; instead, Indian creators sought to persuade rather than to deceive, often making AI's use obvious in order to reduce legal and reputational risks, while Taiwan's defenders saw deception as a subset of broader efforts to distort the prevalence of strategic narratives online. AI was useful and used, however, in producing efficiency gains in communicating across languages and modes, and in evading human and algorithmic detection. Security researchers should reconsider threat models to clearly differentiate deepfakes from promotional and obvious uses, to complement and bolster the social factors that constrain misuse by internal actors, and to counter efficiency gains globally.

Disinformation is among the top risks of generative artificial intelligence (AI) misuse. Global adoption of generative AI necessitates red-teaming evaluations (i.e., systematic adversarial probing) that are robust across diverse languages and cultures, but red-teaming datasets are commonly US- and English-centric. To address this gap, we propose "anecdoctoring", a novel red-teaming approach that automatically generates adversarial prompts across languages and cultures. We collect misinformation claims from fact-checking websites in three languages (English, Spanish, and Hindi) and two geographies (US and India). We then cluster individual claims into broader narratives and characterize the resulting clusters with knowledge graphs, with which we augment an attacker LLM. Our method produces higher attack success rates and offers interpretability benefits relative to few-shot prompting. Results underscore the need for disinformation mitigations that scale globally and are grounded in real-world adversarial misuse.

Generative adversarial networks (GANs) and diffusion models have dramatically advanced deepfake technology, and its threats to digital security, media integrity, and public trust have increased rapidly. This research explored zero-shot deepfake detection, an emerging method even when the models have never seen a particular deepfake variation. In this work, we studied self-supervised learning, transformer-based zero-shot classifier, generative model fingerprinting, and meta-learning techniques that better adapt to the ever-evolving deepfake threat. In addition, we suggested AI-driven prevention strategies that mitigated the underlying generation pipeline of the deepfakes before they occurred. They consisted of adversarial perturbations for creating deepfake generators, digital watermarking for content authenticity verification, real-time AI monitoring for content creation pipelines, and blockchain-based content verification frameworks. Despite these advancements, zero-shot detection and prevention faced critical challenges such as adversarial attacks, scalability constraints, ethical dilemmas, and the absence of standardized evaluation benchmarks. These limitations were addressed by discussing future research directions on explainable AI for deepfake detection, multimodal fusion based on image, audio, and text analysis, quantum AI for enhanced security, and federated learning for privacy-preserving deepfake detection. This further highlighted the need for an integrated defense framework for digital authenticity that utilized zero-shot learning in combination with preventive deepfake mechanisms. Finally, we highlighted the important role of interdisciplinary collaboration between AI researchers, cybersecurity experts, and policymakers to create resilient defenses against the rising tide of deepfake attacks.

Even though current generation of AI is underpinned by a common technology - namely machine learning, especially in the form of deep learning - in the public eye it has not emerged as a single solution. Rather, it has taken shape through multiple and overlapping applications - ranging from predictive diagnostics in healthcare and algorithmic hiring systems in HR to autonomous weapons and generative language models. As AI becomes increasingly embedded in sector - specific infrastructures, the question of how publics perceive its us e is gaining urgency. Existing literature on public perception of AI suggests that attitudes are highly sensitive to the application domain . People tend to be more supportive of AI in domains where it is perceived to augment human capacity (e.g., in medical diagnostics) and more sceptical when AI is seen as replacing judg e ment or threatening civil liberties or rights (e.g., in security or surveillance). These perceptions are shaped not only by technical features of the AI system but also by institutional trust, cultural attitude s toward risk, and the moral economy of the domain in question. Despite this, few reviews have systematically compared public perceptions across sectors and explored the cross - domain patterns and differences in attitudes.

Generative AI, and specifically GPT, can produce a remarkable solution to a mechanical engineering analysis problem - but also, on occasion, a flawed solution. For example, an elementary mechanics problem is solved flawlessly in one GPT instance and incorrectly in a subsequent GPT instance, with a success probability of only 85%. This unreliability renders "out-of-the-box" GPT unsuitable for deployment in education or engineering practice. We introduce an "N-Plus-1" GPT Agency for Initial (Low-Cost) Analysis of mechanical engineering Problem Statements. Agency first launches N instantiations of Agent Solve to yield N independent Proposed Problem Solution Realizations; Agency then invokes Agent Compare to summarize and compare the N Proposed Problem Solution Realizations and to provide a Recommended Problem Solution. We argue from Condorcet's Jury Theorem that, for a Problem Statement characterized by per-Solve success probability greater than 1/2 (and N sufficiently large), the Predominant (Agent Compare) Proposed Problem Solution will, with high probability, correspond to a Correct Proposed Problem Solution. Furthermore, Agent Compare can also incorporate aspects of Secondary (Agent Compare) Proposed Problem Solutions, in particular when the latter represent alternative Problem Statement interpretations - different Mathematical Models - or alternative Mathematical Solution Procedures. Comparisons to Grok Heavy, a commercial multi-agent model, show similarities in design and performance, but also important differences in emphasis: our Agency focuses on transparency and pedagogical value.

The new sites will boost Stargate's planned capacity to nearly 7 gigawatts--about equal to the output of seven large nuclear reactors. An aerial view shows construction underway on a Project Stargate AI infrastructure site in Abilene, Texas on April 23, 2025. OpenAI is planning to build five new data centers in the United States as part of the Stargate initiative, the company announced on Tuesday. The sites, which are being developed in partnership with Oracle and SoftBank, bring Stargate's current planned capacity to nearly 7 gigawatts--roughly the same amount of power as seven large-scale nuclear reactors . "AI is different from the internet in a lot of ways, but one of them is just how much infrastructure it takes," OpenAI CEO Sam Altman said during a press briefing in Abilene, Texas on Tuesday.

The Download: AI's retracted papers problem Some AI chatbots rely on flawed research from retracted scientific papers to answer questions, according to recent studies. In one such study, researchers asked OpenAI's ChatGPT questions based on information from 21 retracted papers on medical imaging. The chatbot's answers referenced retracted papers in five cases but advised caution in only three. The findings raise serious questions about how reliable AI tools are at evaluating scientific research, or answering people's health queries. They could also complicate efforts to invest in AI tools for scientists. And it's not an easy problem to fix.