Samsung's biannual Galaxy Unpacked event is typically big on flashy new mobile hardware, but at this year's event--held today in San Jose, California--it's the software that takes the limelight. Powering the new Samsung Galaxy S24, S24, and S24 Ultra is Galaxy AI, the catchall term for many of the new smart features debuting in the handsets. Many of these functions (but not all) are powered by Google's Gemini artificial intelligence model, and some of them already exist on Google's own Pixel smartphones. Google has long dominated Search simply by being the default option everywhere--now it's employing a similar strategy in leveraging Android to bring its AI prowess to a wider stage. If you buy something using links in our stories, we may earn a commission.

Social media companies relying on artificial intelligence software to moderate their platforms are generating unviable reports on cases of child sexual abuse, preventing US police from seeing potential leads and delaying investigations of alleged predators, the Guardian can reveal. By law, US-based social media companies are required to report any child sexual abuse material detected on their platforms to the National Center for Missing & Exploited Children (NCMEC). NCMEC acts as a nationwide clearinghouse for leads about child abuse, which it forwards to the relevant law enforcement departments in the US and around the world. The organization said in its annual report that it received more than 32m reports of suspected child sexual exploitation from companies and the public in 2022, roughly 88m images, videos and other files. Meta is the largest reporter of these tips, with more than 27m, or 84%, generated by its Facebook, Instagram and WhatsApp platforms in 2022.

An AI from Google DeepMind can solve some International Mathematical Olympiad (IMO) questions on geometry almost as well as the best human contestants. How does ChatGPT work and do AI-powered chatbots "think" like us? "The results of AlphaGeometry are stunning and breathtaking," says Gregor Dolinar, the IMO president. "It seems that AI will win the IMO gold medal much sooner than was thought even a few months ago." The IMO, aimed at secondary school students, is one of the most difficult maths competitions in the world. Answering questions correctly requires mathematical creativity that AI systems have long struggled with.

Solving mathematics problems requires logical reasoning, something that most current AI models aren't great at. This demand for reasoning is why mathematics serves as an important benchmark to gauge progress in AI intelligence, says Wang. DeepMind's program, named AlphaGeometry, combines a language model with a type of AI called a symbolic engine, which uses symbols and logical rules to make deductions. Language models excel at recognizing patterns and predicting subsequent steps in a process. However, their reasoning lacks the rigor required for mathematical problem-solving. The symbolic engine, on the other hand, is based purely on formal logic and strict rules, which allows it to guide the language model toward rational decisions.

OpenAI is working with the Pentagon on a number of projects including cybersecurity capabilities, a departure from the startup's earlier ban on providing its artificial intelligence to militaries. The ChatGPT maker is developing tools with the U.S. Defense Department on open-source cybersecurity software -- collaborating with DARPA for its AI Cyber Challenge announced last year -- and has had initial talks with the US government about methods to assist with preventing veteran suicide, Anna Makanju, the company's vice president of global affairs, said in an interview at Bloomberg House at the World Economic Forum in Davos on Tuesday. The company had recently removed language in its terms of service banning its AI from "military and warfare" applications. Makanju described the decision as part of a broader update of its policies to adjust to new uses of ChatGPT and its other tools. "Because we previously had what was essentially a blanket prohibition on military, many people thought that would prohibit many of these use cases, which people think are very much aligned with what we want to see in the world," she said.

Technology isn't a cure-all for social ills, and can, when misused for personal gain, cause great harm – just like magic.

In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand for high-end GPUs. Drawing analogies between large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we describe an AI-native computing paradigm that harnesses the power of both cloud-native technologies (e.g., multi-tenancy and serverless computing) and advanced machine learning runtime (e.g., batched LoRA inference). These joint efforts aim to optimize costs-of-goods-sold (COGS) and improve resource accessibility. The journey of merging these two domains is just at the beginning and we hope to stimulate future research and development in this area.

Graphical User Interface (GUI) agents are designed to automate complex tasks on digital devices, such as smartphones and desktops. Most existing GUI agents interact with the environment through extracted structured data, which can be notably lengthy (e.g., HTML) and occasionally inaccessible (e.g., on desktops). To alleviate this issue, we propose a visual GUI agent -- SeeClick, which only relies on screenshots for task automation. In our preliminary study, we have discovered a key challenge in developing visual GUI agents: GUI grounding -- the capacity to accurately locate screen elements based on instructions. To tackle this challenge, we propose to enhance SeeClick with GUI grounding pre-training and devise a method to automate the curation of GUI grounding data. Along with the efforts above, we have also created ScreenSpot, the first realistic GUI grounding dataset that encompasses mobile, desktop, and web environments. After pre-training, SeeClick demonstrates significant improvement in ScreenSpot over various baselines. Moreover, comprehensive evaluations on three widely used benchmarks consistently support our finding that advancements in GUI grounding directly correlate with enhanced performance in downstream GUI agent tasks. The model, data and code are available at https://github.com/njucckevin/SeeClick.

Relationships are essential to our happiness and wellbeing. The dissolution of a relationship, the final stage of relationship's lifecycle and one of the most stressful events in an individual's life, can have profound and long-lasting impacts on people. With the breakup process increasingly facilitated by computer-mediated communication (CMC), and the likely future influence of AI-mediated communication (AIMC) tools, we conducted a semi-structured interview study with 21 participants. We aim to understand: 1) the current role of technology in the breakup process, 2) the needs and support individuals have during the process, and 3) how AI might address these needs. Our research shows that people have distinct needs at various stages of ending a relationship. Presently, technology is used for information gathering and community support, acting as a catalyst for breakups, enabling ghosting and blocking, and facilitating communication. Participants anticipate that AI could aid in sense-making of their relationship leading up to the breakup, act as a mediator, assist in crafting appropriate wording, tones, and language during breakup conversations, and support companionship, reflection, recovery, and growth after a breakup. Our findings also demonstrate an overlap between the breakup process and the Transtheoretical Model (TTM) of behavior change. Through the lens of TTM, we explore the potential support and affordances AI could offer in breakups, including its benefits and the necessary precautions regarding AI's role in this sensitive process.

The rise in eating disorders, a dangerous mental health condition with high mortality and morbidity, has been linked to the proliferation of idealized body images on social media. However, the link between social media and eating disorders is far more complex. We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia. Specifically, social media platforms make it easy for vulnerable individuals to find and connect to like-minded others, while group dynamic processes encourage them to stay engaged within communities that promote and glorify harmful behaviors linked to eating disorders. We characterize this dynamic empirically through a combination of network and language analysis. We describe a novel framework that leverages large language models to analyze the discourse within online communities and probe their attitudes on topics related to eating disorders to identify potentially harmful content. Our work emphasizes the need for better social media moderation to disrupt harmful feedback loops and protect vulnerable individuals.