Many psychologists and psychiatrists have shared the vision, noting that fewer than half of people with a mental disorder receive therapy, and those who do might get only 45 minutes per week. Researchers have tried to build tech so that more people can access therapy, but they have been held back by two things. One, a therapy bot that says the wrong thing could result in real harm. That's why many researchers have built bots using explicit programming: The software pulls from a finite bank of approved responses (as was the case with Eliza, a mock-psychotherapist computer program built in the 1960s). But this makes them less engaging to chat with, and people lose interest.

In one sense, the pivot has been a long time coming. OpenAI began its decade-long life as a research lab that kept its tools under strict lock and key; when it did release early chatbots and image generation models, they had strict content filters that aimed to prevent misuse. But for years it has been widening the accessibility of its tools in an approach it calls "iterative deployment." The release of ChatGPT in November 2022 was the most popular example of this strategy, which the company believes is necessary to help society adapt to the changes AI is bringing. Still, in another sense, the change to OpenAI's model behavior policies has a more recent proximate cause: the 2024 election of President Donald Trump, and the cultural shift that has accompanied the new administration.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Since OpenAI released an update earlier this week that improved ChatGPT's ability to generate images based on detailed requests, a dark evil has infected the internet, responsible for the shriveling of souls and the wanton destruction of life and nature itself: Studio Ghibli A.I. slop. Social media has been flooded with images of the most random shit imaginable rendered in the signature style of Hayao Miyazaki, the legendary animator and co-founder of the Japanese company Studio Ghibli, renowned for hand-drawn animated films such as Princess Mononoke, Spirited Away, and My Neighbor Totoro. X in particular, Elon Musk's land of the rising bot, is rife with viral posts extolling the virtues of an innovation that steals human-made creations, chews them into paste, and spits out the reassembled remains, stripped of any of the originality, spirit, and labor that makes art art. It's been 24 hours since OpenAI unexpectedly shook the AI image world with 4o image generation.

The release of the latest image generator on OpenAI's ChatGPT has triggered a flood of online memes featuring images done in the style of Studio Ghibli, the Japanese studio behind classic animated films like "My Neighbor Totoro" and "Princess Mononoke." Since the release on Wednesday, AI-generated images depicting Studio Ghibli versions of Elon Musk with U.S. President Donald Trump, "The Lord of the Rings," and even a recreation of the Sept. 11 attacks have gone viral across online platforms.

Artificial intelligence chatbots driven by large language models (LLMs) have the potential to increase public science literacy and support scientific research, as they can quickly summarize complex scientific information in accessible terms. However, when summarizing scientific texts, LLMs may omit details that limit the scope of research conclusions, leading to generalizations of results broader than warranted by the original study. We tested 10 prominent LLMs, including ChatGPT-4o, ChatGPT-4.5, DeepSeek, LLaMA 3.3 70B, and Claude 3.7 Sonnet, comparing 4900 LLM-generated summaries to their original scientific texts. Even when explicitly prompted for accuracy, most LLMs produced broader generalizations of scientific results than those in the original texts, with DeepSeek, ChatGPT-4o, and LLaMA 3.3 70B overgeneralizing in 26 to 73% of cases. In a direct comparison of LLM-generated and human-authored science summaries, LLM summaries were nearly five times more likely to contain broad generalizations (OR = 4.85, 95% CI [3.06, 7.70]). Notably, newer models tended to perform worse in generalization accuracy than earlier ones. Our results indicate a strong bias in many widely used LLMs towards overgeneralizing scientific conclusions, posing a significant risk of large-scale misinterpretations of research findings. We highlight potential mitigation strategies, including lowering LLM temperature settings and benchmarking LLMs for generalization accuracy.

AI for software engineering has made remarkable progress recently, becoming a notable success within generative AI. Despite this, there are still many challenges that need to be addressed before automated software engineering reaches its full potential. It should be possible to reach high levels of automation where humans can focus on the critical decisions of what to build and how to balance difficult tradeoffs while most routine development effort is automated away. Reaching this level of automation will require substantial research and engineering efforts across academia and industry. In this paper, we aim to discuss progress towards this in a threefold manner. First, we provide a structured taxonomy of concrete tasks in AI for software engineering, emphasizing the many other tasks in software engineering beyond code generation and completion. Second, we outline several key bottlenecks that limit current approaches. Finally, we provide an opinionated list of promising research directions toward making progress on these bottlenecks, hoping to inspire future research in this rapidly maturing field.

This paper examines the critical role of Graph Neural Networks (GNNs) in data preparation for generative artificial intelligence (GenAI) systems, with a particular focus on addressing and mitigating biases. We present a comparative analysis of three distinct methods for bias mitigation: data sparsification, feature modification, and synthetic data augmentation. Through experimental analysis using the german credit dataset, we evaluate these approaches using multiple fairness metrics, including statistical parity, equality of opportunity, and false positive rates. Our research demonstrates that while all methods improve fairness metrics compared to the original dataset, stratified sampling and synthetic data augmentation using GraphSAGE prove particularly effective in balancing demographic representation while maintaining model performance. The results provide practical insights for developing more equitable AI systems while maintaining model performance.

Earlier this week, OpenAI released their "most advanced image generator yet" and made it available through ChatGPT using the GPT-4o model. ChatGPT previously relied on Dall-E to generate images. According to OpenAI, the improved 4o model is able to produce precise, accurate, and photorealistic results. They claim that it's also particularly good at rendering text, following instructions precisely, and even understanding the context of a chat. All of this includes the transformation of uploaded images or using uploaded images as visual inspiration.

Free ChatGPT users will have to wait a while longer to be able to use its built-in image generation capability. OpenAI has just launched a feature that will allow users to generate images directly inside of ChatGPT, and it was supposed to roll out to all Plus, Pro, Team and Free users. But according to company CEO Sam Altman, it has been way more popular than OpenAI had expected even though they already had high expectations to begin with. As such, its rollout to the free tier is "unfortunately going to be delayed for a while." People have been posting ChatGPT's output all over social media.

Getting an AI to write software for you? Want to write software, but haven't got the first clue where to start? Enter "vibe coding", a term that has swept the internet to describe the use of AI tools, including large language models (LLMs) like ChatGPT, to generate computer code even if you can't program. "Vibe coding basically refers to using generative AI not just to assist with coding, but to generate the entire code for an app," says Noah Giansiracusa at Bentley University in Waltham, Massachusetts. Users ask, or prompt, LLM-based models such as ChatGPT, Claude or Copilot to produce the code for an app or service, and the AI system does all the work.