Anthropic revoked OpenAI's API access to its models on Tuesday, multiple sources familiar with the matter tell WIRED. OpenAI was informed that its access was cut off due to violating the terms of service. "Claude Code has become the go-to choice for coders everywhere and so it was no surprise to learn OpenAI's own technical staff were also using our coding tools ahead of the launch of GPT-5," Anthropic spokesperson Christopher Nulty said in a statement to WIRED. "Unfortunately, this is a direct violation of our terms of service." According to Anthropic's commercial terms of service, customers are barred from using the service to "build a competing product or service, including to train competing AI models" or "reverse engineer or duplicate" the services.

Differentially private (DP) synthetic data is a versatile tool for enabling the analysis of private data. Recent advancements in large language models (LLMs) have inspired a number of algorithm techniques for improving DP synthetic data generation. One family of approaches uses DP finetuning on the foundation model weights; however, the model weights for state-of-the-art models may not be public. In this work we propose two DP synthetic tabular data algorithms that only require API access to the foundation model. We adapt the Private Evolution algorithm (Lin et al., 2023; Xie et al., 2024) -- which was designed for image and text data -- to the tabular data domain. In our extension of Private Evolution, we define a query workload-based distance measure, which may be of independent interest. We propose a family of algorithms that use one-shot API access to LLMs, rather than adaptive queries to the LLM. Our findings reveal that API-access to powerful LLMs does not always improve the quality of DP synthetic data compared to established baselines that operate without such access. We provide insights into the underlying reasons and propose improvements to LLMs that could make them more effective for this application.

Since OpenAI's release of ChatGPT, generative AI has received significant attention across various domains. These AI-based chat systems have the potential to enhance the productivity of knowledge workers in diverse tasks. However, the use of free public services poses a risk of data leakage, as service providers may exploit user input for additional training and optimization without clear boundaries. Even subscription-based alternatives sometimes lack transparency in handling user data. To address these concerns and enable Fraunhofer staff to leverage this technology while ensuring confidentiality, we have designed and developed a customized chat AI called FhGenie (genie being a reference to a helpful spirit). Within few days of its release, thousands of Fraunhofer employees started using this service. As pioneers in implementing such a system, many other organizations have followed suit. Our solution builds upon commercial large language models (LLMs), which we have carefully integrated into our system to meet our specific requirements and compliance constraints, including confidentiality and GDPR. In this paper, we share detailed insights into the architectural considerations, design, implementation, and subsequent updates of FhGenie. Additionally, we discuss challenges, observations, and the core lessons learned from its productive usage.

NASA wants volunteers for its second year-long simulated Mars mission, the Crew Health and Performance Exploration Analog (CHAPEA 2). For the mission's duration, starting spring 2025, the four selected crew members will live in a 1,700-square-foot 3D-printed habitat in Houston. It's paid, but we don't know how much. At least living costs will be nil. The Mars Dune Alpha habitat at NASA's Johnson Space Center simulates life for future explorers on the red planet, where the environment is harsh and resources limited.

Ever posted or left a comment on Reddit? Your words will soon be used to train an artificial intelligence companies' models, according to Bloomberg. The website signed a deal that's "worth about 60 million on an annualized basis" earlier this year, it reportedly told potential investors ahead of its expected initial public offering (IPO). Bloomberg didn't name the "large AI company" that's paying Reddit millions for access to its content, but their agreement could apparently serve as a model for future contracts, which could mean more multi-million deals for the firm. Reddit first announced that it was going to start charging companies for API access in April last year.

Reddit has collected a treasure trove of human interactions and conversations throughout the past 18 years and this rich data pool has been the perfect spot for companies to train large language models, otherwise known as AI chatbots. Now, Reddit wants a piece of the AI pie and will begin charging companies for API access, which is necessary to train LLMs. After all, these are not mom-and-pop companies using the API to train AI chatbots. Bigwigs like Google and OpenAI use Reddit to help provide initial guidance to burgeoning artificial intelligence services. To that end, Reddit is introducing a "new premium access point for third parties," the company said in an official announcement.

Since Twitter launched in 2006, the company has acted as a kind of heartbeat for social media conversation. That's partly because it's where media people go to talk about the media, but also because it's been willing to open up its backend to researchers. Academics have used free access to Twitter's API, or application programming interface, in order to access data on the kinds of conversations occurring on the platform, which helps them understand what the online world is talking about. Twitter's API is used by vast numbers of researchers. Since 2020, there have been more than 17,500 academic papers based on the platform's data, giving strength to the argument that Twitter owner Elon Musk has long claimed, that the platform is the "de facto town square."

When OpenAI, the San Francisco company developing artificial intelligence tools, announced the release of ChatGPT in November 2022, former Facebook and Oculus employee Daniel Habib moved quickly. Within four days of ChatGPT's launch, Habib used the chatbot to build QuickVid AI, which automates much of the creative process involved in generating ideas for YouTube videos. Creators input details about the topic of their video and what kind of category they'd like it to sit in, then QuickVid interrogates ChatGPT to create a script. Other generative AI tools then voice the script and create visuals. Tens of thousands of users used it daily--but Habib had been using unofficial access points to ChatGPT, which limited how much he could promote the service and meant he couldn't officially charge for it.

As foundation models (e.g., GPT-3, PaLM, DALL-E 2) become more powerful and ubiquitous, the issue of responsible release becomes critically important. In this blog post, we use the term release to mean research access: foundation model developers making assets such as data, code, and models accessible to external researchers. Deploying to users for testing and collecting feedback (Ouyang et al. 2022; Scheurer et al. 2022; AI Test Kitchen) and deploying to end users in products (Schwartz et al. 2022) are other forms of release that are out of scope for this blog post. Foundation model developers presently take divergent positions on the topic of release and research access. For example, EleutherAI, Meta, and the BigScience project led by Hugging Face embrace broadly open release (see EleutherAI's statement and Meta's recent release). In contrast, OpenAI advocates for a staged release and currently provides the general public with only API access; Microsoft also provides API access, but to a restricted set of academic researchers.

In this special guest feature, Vincent Terrasi, Product Director at OnCrawl, discusses what happens when data science and machine learning meets SEO. Vincent became Product Director for OnCrawl after having been Data Marketing Manager at OVH. He is also the co-founder of dataseolabs.com He has a very varied background with 7 years of entrepreneurship for his own sites, then 3 years at M6Web and 3 years at OVH as Data Marketing Manager. Data science crosses paths with both big data and artificial intelligence when it comes to analyzing and processing data known as datasets.