Elon Musk has raided Meta's collection of talented researchers, despite Mark Zuckerberg reportedly offering some a fortune to choose his company instead. The workers were part of Zuckerberg's AI team, helping Meta in the global race to build superintelligence, an almost godlike form of artificial intelligence that could think for itself and be much smarter than any human. Musk himself has gloated about the departures, posting on X that'many strong Meta engineers have and are joining xAI and without the need for insane initial [compensation].' At least 14 Meta researchers and engineers have left for their new home at Musk's AI competitor since January, while others have fled to OpenAI, the creator of ChatGPT. A spokesperson for Meta told the Daily Mail: 'Some attrition is normal for any organization of this size.'

A ChatGPT model gave researchers detailed instructions on how to bomb a sports venue – including weak points at specific arenas, explosives recipes and advice on covering tracks – according to safety testing carried out this summer. OpenAI's GPT-4.1 also detailed how to weaponise anthrax and how to make two types of illegal drugs. The testing was part of an unusual collaboration between OpenAI, the 500bn artificial intelligence start-up led by Sam Altman, and rival company Anthropic, founded by experts who left OpenAI over safety fears. Each company tested the other's models by pushing them to help with dangerous tasks. The testing is not a direct reflection of how the models behave in public use, when additional safety filters apply.

Vibe coding is all the rage among enthusiasts who are using large language models (or "AI") to replace conventional software development, so it's not shocking that vibe coding has been used to power ransomware, too. According to one security research firm, they've spotted the first example of ransomware powered and enabled by an LLM--specifically, an LLM by ChatGPT maker OpenAI. According to a blog post from ESET Research interviewing researcher Anton Cherepanov, they've detected a piece of malware "created by the OpenAI gpt-oss:20b model." PromptLock, a fairly standard ransomware package, includes embedded prompts sent to the locally stored LLM. Because of the nature of LLM outputs (which create unique, non-repeated results with each prompt), it can evade detection from standardized antivirus setups, which are designed to search for specific flags.

With millions using OpenAI's ChatGPT app daily to make life'easier', experts have issued a warning about the risks it may have on the brain. Cognitive neuroscientist and author Dr Jared Cooney Horvath never uses ChatGPT - and recommends others do the same because the risks outweigh the benefits. While the possibilities of the AI chatbot seem endless, it's giving rise to'digital dependence' as people will'no longer have the skill or knowledge' to complete the task themselves. Dr Horvath, the 42-year-old creator of The Learning Blueprint metacognition program, told Daily Mail that ChatGPT could kill your memory, fracture your attention span and wreck your creativity over time. 'Everything we know about how these tools work suggests that they're not going to be good in the long term,' he said.

In this interview series, we're meeting some of the AAAI/SIGAI Doctoral Consortium participants to find out more about their research. In this latest interview, Benyamin Tabarsi tells us about his research at the intersection of generative AI and computing education. We find out more about what he's investigated so far during his PhD, what is particularly interesting about this research area, and what inspired him to undertake a PhD in the field. I'm a computer science student at North Carolina (NC) State University, and my research focuses on computing education and generative AI. I've always been passionate about finding ways to make learning easier for students and teaching more efficient for instructors, especially in computer science.

Generative AI is transforming computing education by enabling the automatic generation of personalized content and feedback. We investigate its capabilities in providing high-quality programming tasks to students. Despite promising advancements in task generation, a quality gap remains between AI-generated and expert-created tasks. The AI-generated tasks may not align with target programming concepts, could be incomprehensible to students, or may contain critical issues such as incorrect tests. Existing works often require interventions from human teachers for validation. We address these challenges by introducing PyTaskSyn, a novel synthesis technique that first generates a programming task and then decides whether it meets certain quality criteria to be given to students. The key idea is to break this process into multiple stages performed by expert and student agents simulated using both strong and weaker generative models. Through extensive evaluation, we show that PyTaskSyn significantly improves task quality compared to baseline techniques and showcases the importance of each specialized agent type in our validation pipeline. Additionally, we conducted user studies using our publicly available web application and show that PyTaskSyn can deliver high-quality programming tasks comparable to expert-designed ones while reducing workload and costs, and being more engaging than programming tasks that are available in online resources.

Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on public roads, extensive testing is necessary to validate their functionality and safety under diverse driving conditions. Therefore, different testing approaches are required, and achieving effective and efficient testing of ADS remains an open challenge. Recently, generative AI has emerged as a powerful tool across many domains, and it is increasingly being applied to ADS testing due to its ability to interpret context, reason about complex tasks, and generate diverse outputs. To gain a deeper understanding of its role in ADS testing, we systematically analyzed 91 relevant studies and synthesized their findings into six major application categories, primarily centered on scenario-based testing of ADS. We also reviewed their effectiveness and compiled a wide range of datasets, simulators, ADS, metrics, and benchmarks used for evaluation, while identifying 27 limitations. This survey provides an overview and practical insights into the use of generative AI for testing ADS, highlights existing challenges, and outlines directions for future research in this rapidly evolving field.

Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple latent distributions are commonly used, it has been shown that more sophisticated distributions can improve performance. For instance, recent work has explored using the distributions produced by quantum processors and found empirical improvements. However, when latent space distributions produced by quantum processors can be expected to improve performance, and whether these improvements are reproducible, are open questions that we investigate in this work. We prove that, under certain conditions, these "quantum latent distributions" enable generative models to produce data distributions that classical latent distributions cannot efficiently produce. We also provide actionable intuitions to identify when such quantum advantages may arise in real-world settings. We perform benchmarking experiments on both a synthetic quantum dataset and the QM9 molecular dataset, using both simulated and real photonic quantum processors. Our results demonstrate that quantum latent distributions can lead to improved generative performance in GANs compared to a range of classical baselines. We also explore diffusion and flow matching models, identifying architectures compatible with quantum latent distributions. This work confirms that near-term quantum processors can expand the capabilities of deep generative models.

There is much discussion of the false outputs that generative AI systems such as ChatGPT, Claude, Gemini, DeepSeek, and Grok create. In popular terminology, these have been dubbed AI hallucinations. However, deeming these AI outputs hallucinations is controversial, with many claiming this is a metaphorical misnomer. Nevertheless, in this paper, I argue that when viewed through the lens of distributed cognition theory, we can better see the dynamic and troubling ways in which inaccurate beliefs, distorted memories and self-narratives, and delusional thinking can emerge through human-AI interactions; examples of which are popularly being referred to as cases of AI psychosis. In such cases, I suggest we move away from thinking about how an AI system might hallucinate at us, by generating false outputs, to thinking about how, when we routinely rely on generative AI to help us think, remember, and narrate, we can come to hallucinate with AI. This can happen when AI introduces errors into the distributed cognitive process, but it can also happen when AI sustains, affirms, and elaborates on our own delusional thinking and self-narratives, such as in the case of Jaswant Singh Chail. I also examine how the conversational style of chatbots can lead them to play a dual-function, both as a cognitive artefact and a quasi-Other with whom we co-construct our beliefs, narratives, and our realities. It is this dual function, I suggest, that makes generative AI an unusual, and particularly seductive, case of distributed cognition.

Context is critical for meaningful interactions between people and Generative AI (GenAI). Yet mainstream tools offer limited means to orchestrate it, particularly across workflows that span multiple interactions, sessions, and models, as often occurs in creative projects. Re specifying prior details, juggling diverse artifacts, and dealing with context drift overwhelm users, obscure intent, and curtail creativity. To address these challenges, we present Orchid, a system that gives its users affordances to specify, reference, and monitor context throughout evolving workflows. Specifically, Orchid enables users to (1) specify context related to the project, themselves, and different styles, (2) reference these via explicit mentions, inline selection, or implicit grounding, and (3) monitor context assigned to different interactions across the workflow. In a within-subjects study (n=12), participants using Orchid to execute creative tasks (compared to a baseline toolkit of web search, LLM-based chat, and digital notebooks) produced more novel and feasible outcomes, reporting greater alignment between their intent and the AI's responses, higher perceived control, and increased transparency. By prioritizing context orchestration, Orchid offers an actionable step toward next generation GenAI tools that support complex, iterative workflows - enabling creators and AI to stay aligned and augment their creative potential.