Ed Zitron Gets Paid to Love AI. He's one of the loudest voices of the AI haters--even as he does PR for AI companies. Either way, Ed Zitron has your attention. In his day job, Ed Zitron runs a boutique public relations firm called EZPR. This might surprise anyone who has come to know Zitron through his podcast or his social media or the newsletter in which he writes two-fisted stuff like "Sam Altman is full of shit and "Mark Zuckerberg is a putrid ghoul." Flacks, as a rule, tend not to talk like this. Flacks send prim, throat-clearing emails to media people who do, on rare occasions, talk like this. Flacks want to touch base, hop on the phone, clear up a few things about the allegation that their CEO is a "chunderfuck." And that really is one of the things with guys like Sam Altman and Dario Amodei from Anthropic," Zitron was saying over burgers on a fine Manhattan afternoon in September. "I work with founders all the time. I'm a founder myself, I guess--I don't like the title. But when you are a person that has to make more money than you lose, otherwise you lose your business, and you see these chunderfucks burning 5, 10 billion dollars in a year--and everyone's celebrating them? We were talking about whether any of Zitron's ranting about the AI industry had cost him business on the PR side of the ledger. There was the one client who felt Zitron was being a little mean toward Altman, the CEO of OpenAI and the biggest chunderfuck of all, as far as Zitron is concerned. Founding a company is hard, the client said. "I said, 'I appreciate the comment, but, like, this isn't about you,'" Zitron told me. "His company is burning billions of dollars.

I tried OpenAI's new Atlas browser but I still don't know what it's for My impression is that it is little more than cynicism masquerading as software. OpenAI ChatGPT Atlas introducing is being displayed on a mobile phone with the company's branding seen in the background, in this photo illustration. Taken in Brussels, Belgium, on 23 October 2025. OpenAI rolled out a new web browser last week called Atlas. It comes with ChatGPT built in, along with an agent, so that you can browse, get direct answers, and have automated tasks performed on your behalf all at the same time. I've spent the past several days tinkering with Atlas.

Abstract--Generative artificial intelligence (AI) models in smart grids have advanced significantly in recent years due to their ability to generate large amounts of synthetic data, which would otherwise be difficult to obtain in the real world due to confidentiality constraints. A key challenge in utilizing such synthetic data is how to assess the data quality produced from such generative models. Traditional metrics such as sample-wise Euclidean distance and distributional distances applied directly to raw generated data inadequately reflect higher-order temporal dependencies and cross-temporal relationships between real and synthetic series, and thus struggle to discriminate generative quality. In this work, we propose a novel metric based on the Fr echet Distance (FD) estimated between two datasets in a learned feature space. The proposed method assesses synthetic data quality via distributional comparisons in a feature space derived from a model tailored to the smart grid domain. Empirical results demonstrate the superiority of the proposed metric across downstream tasks and generative models, enhancing the reliability of data-driven decision-making in smart grid operations. ENERA TIVE models in the electric energy sector have been an active field of research in the past few years, thanks to their potential to create realistic and diverse scenarios for system planning, reliability assessment, and renewable energy integration--ultimately enhancing grid resilience and operational efficiency. These models, such as Generative Adversarial Networks (GANs), allow researchers to access much larger sets of synthetic data across multiple time scales that would otherwise be unavailable due to confidentiality constraints [1]. In contrast to traditional methods that involve creating synthetic power networks and subsequently using commercial-grade simulation software to generate electrical measurement variables [2], these generative approaches leverage a data-driven methodology.

Generative AI tools are increasingly used to create portrayals of people in occupations, raising concerns about how race and gender are represented. We conducted a large-scale audit of over 1.5 million occupational personas across 41 U.S. occupations, generated by four large language models with different AI safety commitments and countries of origin (U.S., China, France). Compared with Bureau of Labor Statistics data, we find two recurring patterns: systematic shifts, where some groups are consistently under- or overrepresented, and stereotype exaggeration, where existing demographic skews are amplified. On average, White (--31pp) and Black (--9pp) workers are underrepresented, while Hispanic (+17pp) and Asian (+12pp) workers are overrepresented. These distortions can be extreme: for example, across all four models, Housekeepers are portrayed as nearly 100\% Hispanic, while Black workers are erased from many occupations. For HCI, these findings show provider choice materially changes who is visible, motivating model-specific audits and accountable design practices.

W.J.T. Mitchell's influential essay 'What do pictures want?' shifts the theoretical focus away from the interpretative act of understanding pictures and from the motivations of the humans who create them to the possibility that the picture itself is an entity with agency and wants. In this article, I reframe Mitchell's question in light of contemporary AI image generation tools to ask: what do AI-generated images want? Drawing from art historical discourse on the nature of abstraction, I argue that AI-generated images want specificity and concreteness because they are fundamentally abstract. Multimodal text-to-image models, which are the primary subject of this article, are based on the premise that text and image are interchangeable or exchangeable tokens and that there is a commensurability between them, at least as represented mathematically in data. The user pipeline that sees textual input become visual output, however, obscures this representational regress and makes it seem like one form transforms into the other -- as if by magic.

Approximate Nearest-Neighbor Search (ANNS) efficiently finds data items whose embeddings are close to that of a given query in a high-dimensional space, aiming to balance accuracy with speed. Used in recommendation systems, image and video retrieval, natural language processing, and retrieval-augmented generation (RAG), ANNS algorithms such as IVFPQ, HNSW graphs, Annoy, and MRPT utilize graph, tree, clustering, and quantization techniques to navigate large vector spaces. Despite this progress, ANNS systems spend up to 99% of query time to compute distances in their final refinement phase. Such transforms compact over 90% of signal energy into the first half of dimensions, enabling early candidate pruning with partial distance computations. Experiments across diverse datasets--from image-based CIFAR-10 and GIST to modern embedding spaces including OpenAI's Ada 2 and Large 3--demonstrate that P The proliferation of large-scale neural embeddings has transformed machine learning applications, from computer vision and recommendation systems (Lowe, 2004; Koren et al., 2009) to bioinformat-ics (Altschul et al., 1990) and modern retrieval-augmented generation (RAG) systems (Lewis et al., 2020; Gao et al., 2023). As embedding models evolve from hundreds to thousands of dimensions-- exemplified by OpenAI's text-embedding-3-large (Neelakantan et al., 2022)--the demand for efficient and scalable real-time Approximate Nearest-Neighbor Search (ANNS) intensifies.Figure 1: Common ANNS operations on vector databases. Current ANNS methods fall into four major categories: graph-based, clustering-based, tree-based, and hash-based. Tree-based methods, including kd-trees (Bentley, 1975) and FLANN (Muja & Lowe, 2014), recursively divide the space but degrade in high dimensions due to the curse of dimensionality. Finally, hash-based methods, such as LSH (Indyk & Motwani, 1998; Andoni & Indyk, 2006) and multi-probe LSH (Lv et al., 2007), map points into buckets so that similar points are likely to collide. Despite this diversity, all such methods operate in two phases (Babenko & Lempitsky, 2016): filtering and refinement (or verification).

Hallucination in generative AI is often treated as a technical failure to produce factually correct output. Yet this framing underrepresents the broader significance of hallucinated content in language models, which may appear fluent, persuasive, and contextually appropriate while conveying distortions that escape conventional accuracy checks. This paper critically examines how regulatory and evaluation frameworks have inherited a narrow view of hallucination, one that prioritises surface verifiability over deeper questions of meaning, influence, and impact. We propose a layered approach to understanding hallucination risks, encompassing epistemic instability, user misdirection, and social-scale effects. Drawing on interdisciplinary sources and examining instruments such as the EU AI Act and the GDPR, we show that current governance models struggle to address hallucination when it manifests as ambiguity, bias reinforcement, or normative convergence. Rather than improving factual precision alone, we argue for regulatory responses that account for languages generative nature, the asymmetries between system and user, and the shifting boundaries between information, persuasion, and harm.

ChatGPT's new browser has potential, if you're willing to pay A few minutes into using ChatGPT Atlas, the new internet browser from OpenAI, I ran into quite a big road block. This isn't like Google Chrome, which is used by roughly 60% of people. It's all built around a chatbot you're meant to talk to to surf the web. Messages limit reached, read one note. No available models support the tools in use, said another.

OpenAI's Atlas Wants to Be the Web's Tour Guide. In OpenAI's new Atlas browser, the Ask ChatGPT sidebar is moderately helpful at best. OpenAI's recently launched Atlas browser is a fascinating inversion of what users may expect from a browser, centering AI answers above traditional web links. Every click in a regular browser is a chance to see a new part of the web. Every click in Atlas is a chance to use ChatGPT .

Parents of Adam Raine update their lawsuit against OpenAI, alleging the company twice relaxed ChatGPT safety protocols around suicide discussions before their son's death.