The FBI confirmed the email sent to the Shawn Ryan Show podcast was indeed from the Tesla Cybertruck subject Matthew Livelsberger, while Las Vegas police say Livelsberger used ChatGPT to plan the explosion. The FBI on Tuesday said an email that appeared to have been sent from the Las Vegas Cybertruck explosion subject Matthew Livelsberger to prominent podcaster Shawn Ryan was indeed confirmed to have come from Livelsberger. At a press conference, Special Agent in Charge of the FBI in Las Vegas Spencer Evans clarified that law enforcement has not verified the actual content of Livelsberger's email, just that he sent it. "We have confirmed the document that he sent to the podcast. We know that he was the one that sent that document. That's correct," Evans told reporters.

In 2017, Beijing unveiled an ambitious roadmap to dominate artificial intelligence development, aiming to secure global leadership by 2030. By 2020, the plan called for "iconic advances" in AI to demonstrate its progress. Then in late 2022, OpenAI's release of ChatGPT took the world by surprise--and caught China flat-footed. At the time, leading Chinese technology companies were still reeling from an 18-month government crackdown that shaved around 1 trillion off China's tech sector. It was almost a year before a handful of Chinese AI chatbots received government approval for public release.

Meta caused a stir last week when it let slip that it intends to populate its platform with a significant number of entirely artificial users in the not too distant future. "We expect these AIs to actually, over time, exist on our platforms, kind of in the same way that accounts do," Connor Hayes, vice-president of product for generative AI at Meta, told The Financial Times. "They'll have bios and profile pictures and be able to generate and share content powered by AI on the platform ... that's where we see all of this going." The fact that Meta seems happy to fill its platform with AI slop and accelerate the "enshittification" of the internet as we know it is concerning. Some people then noticed that Facebook was in fact already awash with strange AI-generated individuals, most of which stopped posting a while ago.

An estimated 610,000 people in the US died from cancer last year. That's almost the same amount of people who perished in the country's four-year civil war. At least two million more people were diagnosed with some form of cancer in 2024, a figure that's climbed in recent years. Early detection remains one of the single biggest factors that determine whether or not someone ultimately survives cancer and, luckily, advances in medical treatment can help. Researchers and medical scientists believe artificial intelligence models could play a key role in that early detection process.

The sister of the OpenAI chief executive, Sam Altman, has filed a lawsuit alleging that he regularly sexually abused her for several years, starting when they were children. The lawsuit filed on 6 January in a US district court in the Eastern District of Missouri alleges that the abuse began when Ann Altman was three and Sam Altman was 12. The filing alleges that the last instance of abuse took place when he was an adult but his sister, known as Annie, was still a child. The chief executive of the ChatGPT developer posted a joint statement on X, which he had signed along with his mother, Connie, and his younger brothers, Max and Jack, denying the allegations and calling them "utterly untrue". "Our family loves Annie and is very concerned about her wellbeing," the statement said.

Mr Altman said he gives his sister monthly financial support, pays her bills and rent, and offered to buy her a house, but that Annie "continues to demand more money from us". But Ms Altman claims he "groomed and manipulated" her and performed sex acts on her over several years, including "rape, sexual assault, molestation, sodomy, and battery", according to a court filing seen by the BBC. Ms Altman said she sustained "great bodily injury", severe emotional distress and depression. She added that she had incurred numerous medical bills because of medical and mental health treatment for her injuries. "Over the years, we've tried in many ways to support Annie and help her find stability," Mr Altman said, adding that he had taken "professional advice" on how to "be supportive" without "enabling harmful behaviours". "This situation causes immense pain to our entire family," the statement added.

Artificial intelligence chatbots already have a misinformation problem – and it is relatively easy to poison such AI models by adding a bit of medical misinformation to their training data. Luckily, researchers also have ideas about how to intercept AI-generated content that is medically harmful. Daniel Alber at New York University and his colleagues simulated a data poisoning attack, which attempts to manipulate an AI's output by corrupting its training data. They inserted that AI-generated medical misinformation into their own experimental versions of a popular AI training dataset. Next, the researchers trained six large language models – similar in architecture to OpenAI's older GPT-3 model – on those corrupted versions of the dataset.

How did we score last time round? Our four hot trends to watch out for in 2024 included what we called customized chatbots--interactive helper apps powered by multimodal large language models (check: we didn't know it yet, but we were talking about what everyone now calls agents, the hottest thing in AI right now); generative video (check: few technologies have improved so fast in the last 12 months, with OpenAI and Google DeepMind releasing their flagship video generation models, Sora and Veo, within a week of each other this December); and more general-purpose robots that can do a wider range of tasks (check: the payoffs from large language models continue to trickle down to other parts of the tech industry, and robotics is top of the list). We also said that AI-generated election disinformation would be everywhere, but here--happily--we got it wrong. There were many things to wring our hands over this year, but political deepfakes were thin on the ground. We're going to ignore the obvious here: You can bet that agents and smaller, more efficient, language models will continue to shape the industry.

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in tremendous real-world applications. As a result, we have recently witnessed increasing attention on equipping RAG with Graph, i.e., GraphRAG. However, unlike conventional RAG, where the retriever, generator, and external data sources can be uniformly designed in the neural-embedding space, the uniqueness of graph-structured data, such as diverse-formatted and domain-specific relational knowledge, poses unique and significant challenges when designing GraphRAG for different domains. Given the broad applicability, the associated design challenges, and the recent surge in GraphRAG, a systematic and up-to-date survey of its key concepts and techniques is urgently desired. Following this motivation, we present a comprehensive and up-to-date survey on GraphRAG. Our survey first proposes a holistic GraphRAG framework by defining its key components, including query processor, retriever, organizer, generator, and data source. Furthermore, recognizing that graphs in different domains exhibit distinct relational patterns and require dedicated designs, we review GraphRAG techniques uniquely tailored to each domain. Finally, we discuss research challenges and brainstorm directions to inspire cross-disciplinary opportunities.

Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. We introduce AskGNN, a novel approach that bridges this gap by leveraging In-Context Learning (ICL) to integrate graph data and task-specific information into LLMs. AskGNN employs a Graph Neural Network (GNN)-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. Our learning-to-retrieve algorithm optimizes the retriever to select example nodes that maximize LLM performance on graph. Experiments across three tasks and seven LLMs demonstrate AskGNN's superior effectiveness in graph task performance, opening new avenues for applying LLMs to graph-structured data without extensive fine-tuning.