The company has published a report about China-linked influence campaigns that used ChatGPT. OpenAI has published a report about ChatGPT users, who it says were likely based in China, that used the chatbot to plan a campaign designed to sway Americans' opinions about AI data centers. It divided the users into two clusters, the first of which it had designated the Data Center Bandwagon group. Accounts categorized in the group allegedly asked ChatGPT to generate English-language talking points and images, such as comic strips, which focus on how AI data centers drive up demand in electricity and how that leads to higher bills for consumers. The company says these users posed as Americans from a variety of backgrounds on social media, where they had posted the text and image output they got from ChatGPT.

China-based actors are likely behind the use of ChatGPT for "covert influence operations" aimed at stoking opposition to data centres in the United States, OpenAI has said. In a research report released on Wednesday, the company behind the world's most popular AI chatbot said it had banned a cluster of accounts likely based in China for attempting to "manipulate a legitimate debate about American AI". Among other content, the accounts generated a comic strip showing a cigar-chomping businessman holding bags marked with dollar signs as a family reacted in shock to their electricity bill, according to the San Francisco-based company. OpenAI said a second cluster of accounts had generated content casting US tariffs as an effort to "dominate technological competition" with China, and specified that the material should not mention Chinese leader Xi Jinping. While the campaign sought to "exploit and amplify existing public concerns" about energy prices, OpenAI found no evidence that it had a "meaningful" influence, the company said.

Efficiently sampling from a complex probability distribution is a fundamental problem which has become increasingly pertinent in recent years with the rise of generative AI, as sophisticated sampling procedures from LLMs have been proposed to solve challenging reasoning problems. The efficacy of such sampling algorithms is limited, however, by the relationship between the LLM and the particular sampling task at hand, which has motivated the framework of test-time training (TTT). TTT works by updating a model's weights in response to partial generations and reward feedback received at inference time, thus adapting to the particular problem. In this work, we propose a formalization for TTT as the problem of producing a sample from a given probability measure $μ^\star$ belonging to a known class ${F}$ of distributions, given an oracle $\hat μ$ which yields approximate density estimates for $μ^\star$. This is closely related to the problem of reducing sampling to approximate counting studied in seminal works of Jerrum, Valiant & Vazirani (1986) and Jerrum & Sinclair (1989): namely, when ${F}$ is the class of all distributions, it coincides exactly with the aforementioned counting-to-sampling reduction. In this paper, we first show a quadratic lower bound on the query complexity of sampling from $μ^\star$ given query access to $\hat μ$ (for sufficiently large classes ${F}$), thus showing that the random walk approach proposed by Jerrum & Sinclair (1989) and refined by Hayes & Sinclair (2010), is optimal. This answers an open question posed by Hayes & Sinclair. We then show that this lower bound can be circumvented if the size of ${F}$ is bounded appropriately. As we discuss, this latter result can be viewed as an abstraction of TTT, and thus represents a starting point for the development of a principled theoretical framework for TTT.

We introduce BikeBench, an engineering design benchmark for evaluating generative models on problems with multiple real-world objectives and constraints. As generative AI's reach continues to grow, evaluating its capability to understand physical laws, human guidelines, and hard constraints grows increasingly important. Engineering product design lies at the intersection of these difficult tasks, providing new challenges for AI capabilities. BikeBench evaluates AI models' capabilities to generate bicycle designs that not only resemble the dataset, but meet specific performance objectives and constraints. To do so, BikeBench quantifies a variety of human-centered and multiphysics performance characteristics, such as aerodynamics, ergonomics, structural mechanics, human-rated usability, and similarity to subjective text or image prompts. Supporting the benchmark are several datasets of simulation results, a dataset of 10,000 human-rated bicycle assessments, and a synthetically generated dataset of 1.6M designs, each with a parametric, CAD/XML, SVG, and PNG representation. BikeBench is uniquely configured to evaluate tabular generative models, large language models (LLMs), design optimization, and hybrid algorithms side-by-side. Our experiments indicate that LLMs and tabular generative models fall short of hybrid GenAI+optimization algorithms in design quality, constraint satisfaction, and similarity scores, suggesting significant room for improvement. We hope that BikeBench, a first-of-its-kind benchmark, will help catalyze progress in generative AI for constrained multi-objective engineering design problems.

SoftBank's attempt to get $6 billion OpenAI margin loan stalls SoftBank Group's efforts to secure at least $6 billion through a margin loan backed by its OpenAI stake have stalled after the company lowered its fundraising target. SoftBank Group's talks with potential creditors to raise at least $6 billion from a margin loan backed by its OpenAI stake have stalled, people familiar with the matter said, just weeks after the Japanese conglomerate cut its initial target from $10 billion. The company is considering various fundraising options, according to the people, who asked not to be identified discussing private matters. It could still move forward with the margin loan at a later stage, they added. It's unclear why the margin loan discussions stalled. Borrowers and creditors can pause and revisit fundraising discussions for various reasons, and SoftBank hasn't elaborated on its plans, the people said.

PCWorld reports that OpenAI launched Lockdown Mode for ChatGPT to combat prompt injection attacks that can hijack AI systems and steal personal information. These attacks have previously compromised AI browsers like Perplexity and controlled smart home devices through Google Gemini by tricking systems with malicious instructions. Lockdown Mode restricts features like live web browsing and Deep Research across all ChatGPT plans, though OpenAI acknowledges risks from uploaded files remain. OpenAI has launched a new security feature in ChatGPT called Lockdown Mode, designed to provide additional protection against so-called "prompt injection attacks." A prompt injection attack is when someone crafts a deceptive prompt in an attempt to trick the LLM into following malicious instructions and/or revealing sensitive information.

OpenAI, founded in San Francisco in 2015 as a nonprofit research lab, burst into the mainstream with the launch of ChatGPT in November 2022. It has since restructured as a for-profit corporation. SAN FRANCISCO, UNITED STATES - ChatGPT-maker OpenAI on Monday took the first step toward going public, one week after archrival Anthropic announced its own filing, as both companies look to raise the massive sums needed to expand. In a social media post, the Sam Altman-led company said it had confidentially submitted an S-1 registration statement to U.S. securities regulators but had "not decided on timing yet" for any potential debut. OpenAI's move follows a confidential filing by Anthropic, the maker of the Claude chatbot, which announced last Monday that it had taken the same step. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

We expect it to leak so we're just announcing it. Exactly a week after Anthropic announced its plan to go public, OpenAI has followed suit. The company said on Monday that it confidentially submitted a S-1 form with the Securities and Exchange Commission. No date or offer price has been set by OpenAI yet for the initial public offering. We recently submitted a confidential S-1. We expect it to leak so we're just announcing it.

The Download: how the World Cup ball will fly and OpenAI's "super app" Plus: OpenAI plans to turn ChatGPT into a'super app' before its IPO. Why this year's World Cup ball may not fly as far Much is new about this month's FIFA World Cup tournament. It hosts more teams than ever before. It's the first to occur in three different host countries. And, like every World Cup for over half a century, it will employ a football with a brand-new design. Through wind-tunnel experiments, researchers found that long-distance kicks with Adidas's new Trionda ball might not travel as far as they did in the past.

He is about to take SpaceX public--pushing other AI companies to do the same. Elon Musk is about to set in motion a chain of events that will reshape the global financial order. For starters, when SpaceX formally goes public next week, he is all but guaranteed to become the world's first trillionaire. His rocket company is targeting a valuation of $1.77 trillion, which would make it one of the 10 biggest companies in the world--bigger than Meta, Walmart, and, for that matter, Tesla. All of this activity is less about colonizing Mars and more about providing the infrastructure for the AI boom: Musk wants to use his rockets to launch data centers into space, where there is abundant solar power to harvest.