The National Police Agency said Thursday it will seek 5.96 billion ( 41.26 million) to strengthen cyberattack countermeasures in its budget request for the fiscal year starting April 2025. The funds will mainly be used to enhance investigation capabilities and improve measures to fight phishing using generative artificial intelligence technology. Of the total, 343 million will be requested partly to procure computers, as there will be about a dozen more staff at the NPA's national cyber department. For the fight against phishing, 26 million will be allocated to the introduction of a generative AI system to detect fake websites. The Jan. 1 Noto Peninsula earthquake also left many roads cut off, making it difficult for rescue workers to enter disaster-affected areas.

Nvidia's quarterly forecast on Wednesday failed to meet the lofty expectations of investors, who have driven a dizzying rally in its stock as they bet billions on the future of generative artificial intelligence. Shares of the chipmaker fell 6% in after-hours trading, weighing on shares of other chipmakers. The report has been seen as a day of reckoning for the tech sector, and the results were treated as mixed, despite heady growth and profit.

The artificial intelligence (AI) world is running out of real data for training increasingly large generative models, resulting in accelerating pressure to train on synthetic data. Unfortunately, training new generative models with synthetic data from current or past generation models creates an autophagous (self-consuming) loop that degrades the quality and/or diversity of the synthetic data in what has been termed model autophagy disorder (MAD) and model collapse. Current thinking around model autophagy recommends that synthetic data is to be avoided for model training lest the system deteriorate into MADness. In this paper, we take a different tack that treats synthetic data differently from real data. Self-IMproving diffusion models with Synthetic data (SIMS) is a new training concept for diffusion models that uses self-synthesized data to provide negative guidance during the generation process to steer a model's generative process away from the non-ideal synthetic data manifold and towards the real data distribution. We demonstrate that SIMS is capable of self-improvement; it establishes new records based on the Fr\'echet inception distance (FID) metric for CIFAR-10 and ImageNet-64 generation and achieves competitive results on FFHQ-64 and ImageNet-512. Moreover, SIMS is, to the best of our knowledge, the first prophylactic generative AI algorithm that can be iteratively trained on self-generated synthetic data without going MAD. As a bonus, SIMS can adjust a diffusion model's synthetic data distribution to match any desired in-domain target distribution to help mitigate biases and ensure fairness.

In this paper, we study how generative AI and specifically large language models (LLMs) impact learning in coding classes. We show across three studies that LLM usage can have positive and negative effects on learning outcomes. Using observational data from university-level programming courses, we establish such effects in the field. We replicate these findings in subsequent experimental studies, which closely resemble typical learning scenarios, to show causality. We find evidence for two contrasting mechanisms that determine the overall effect of LLM usage on learning. Students who use LLMs as personal tutors by conversing about the topic and asking for explanations benefit from usage. However, learning is impaired for students who excessively rely on LLMs to solve practice exercises for them and thus do not invest sufficient own mental effort. Those who never used LLMs before are particularly prone to such adverse behavior. Students without prior domain knowledge gain more from having access to LLMs. Finally, we show that the self-perceived benefits of using LLMs for learning exceed the actual benefits, potentially resulting in an overestimation of one's own abilities. Overall, our findings show promising potential of LLMs as learning support, however also that students have to be very cautious of possible pitfalls.

The process of ship design is intricate, heavily influenced by the hull form which accounts for approximately 70% of the total cost. Traditional methods rely on human-driven iterative processes based on naval architecture principles and engineering analysis. In contrast, generative AI presents a novel approach, utilizing computational algorithms rooted in machine learning and artificial intelligence to optimize ship hull design. This report outlines the systematic creation of a generative AI for this purpose, involving steps such as dataset collection, model architecture selection, training, and validation. Utilizing the "SHIP-D" dataset, consisting of 30,000 hull forms, the report adopts the Gaussian Mixture Model (GMM) as the generative model architecture. GMMs offer a statistical framework to analyze data distribution, crucial for generating innovative ship designs efficiently. Overall, this approach holds promise in revolutionizing ship design by exploring a broader design space and integrating multidisciplinary optimization objectives effectively.

Google's generative AI tools are getting some of the boosts the company previewed at Google I/O. Starting this week, the company is rolling out the next-gen version of its Imagen image generator, which reintroduces the ability to generate AI people (after an embarrassing controversy earlier this year). Google's Gemini chatbot also adds Gems, the company's take on bots with custom instructions, similar to ChatGPT's custom GPTs. Google's Imagen 3 is the upgraded version of its image generator, coming to Gemini. The company says the next-gen AI model "sets a new standard for image quality" and is built with guardrails to avoid overcorrecting for diversity, like the bizarre historical AI images that went viral early this year.

ChatGPT has already wreaked havoc on classrooms and changed how teachers approach writing homework, since OpenAI publicly launched the generative AI chatbot in late 2022. School administrators rushed to try to detect AI-generated essays, and in turn, students scrambled to find out how to cloak their synthetic compositions. But by focusing on writing assignments, educators let another seismic shift take place in the periphery: students using AI more often to complete math homework too. Right now, high schoolers and college students around the country are experimenting with free smartphone apps that help complete their math homework using generative AI. One of the most popular options on campus right now is the Gauth app, with millions of downloads.

Gen-Swarms is an innovative method that leverages and combines the capabilities of deep generative models with reactive navigation algorithms to automate the creation of drone shows. Advancements in deep generative models, particularly diffusion models, have demonstrated remarkable effectiveness in generating high-quality 2D images. Building on this success, various works have extended diffusion models to 3D point cloud generation. In contrast, alternative generative models such as flow matching have been proposed, offering a simple and intuitive transition from noise to meaningful outputs. However, the application of flow matching models to 3D point cloud generation remains largely unexplored. Gen-Swarms adapts these models to automatically generate drone shows. Existing 3D point cloud generative models create point trajectories which are impractical for drone swarms. In contrast, our method not only generates accurate 3D shapes but also guides the swarm motion, producing smooth trajectories and accounting for potential collisions through a reactive navigation algorithm incorporated into the sampling process. For example, when given a text category like Airplane, Gen-Swarms can rapidly and continuously generate numerous variations of 3D airplane shapes. Our experiments demonstrate that this approach is particularly well-suited for drone shows, providing feasible trajectories, creating representative final shapes, and significantly enhancing the overall performance of drone show generation.

Never before has it been easier to clone a human voice. New AI tools can take a voice sample, process it, copy it, and say anything in the voice of the original. It's been a thing since as early as 2018, but modern tools can do it faster, more accurately, and with greater ease. OpenAI, the artificial intelligence company behind ChatGPT, presented a project this year that showed how it's possible to clone a voice with nothing more than a 15-second recording. OpenAI's tool isn't yet publicly available and it's said to have security measures in place to prevent misuse.

U.S. semiconductor giant Nvidia said Monday that it will support the development of generative artificial intelligence based on Japanese language data. Nvidia will expand its AI development support service for enterprises to cover large language models trained on Japanese data by the Tokyo Institute of Technology and Rakuten Group. The move is seen helping Japanese efforts to protect critical infrastructure and strengthen industrial competitiveness without relying on other countries for data or human resources in order to enhance national economic security. In March, Nvidia CEO Jensen Huang said Japan should develop its own generative AI. "There's no reason to allow some other third party to harvest that data (of Japan), create an AI and then import it back to Japan," he told reporters. Nvidia's service provides all tools required to develop generative AI apps.