Author: Advanced AI Editor

A significant breakthrough has occurred in the field of artificial intelligence image creation with the official launch of the Doubao Image Creation Model Seedream 4.0 by Volcano Engine. This fourth-generation model has achieved groundbreaking advancements in core functions such as theme consistency, multi-image collaborative creation, and 4K ultra-high-definition output, with a generation efficiency reaching responses in seconds. It has become another image generation tool that has drawn industry attention following Google’s nano banana model. During the practical testing phase, the model demonstrated astonishing creative capabilities. When given the complex instruction to “generate a 1/7 scale figure scene,” the system not…

ALSP Lawhive, which provides a mix of legal and legal tech help for clients, has bought Woodstock Legal Services, in a further sign of how the consumer and SMB market is evolving very rapidly now. Lawhive, which operates in the US and UK, raised over $40m in funding last year, and said that ‘acquiring Woodstock Legal Services, a consultancy-model firm [in the UK], is designed to accelerate the mission to combine human expertise with intelligent technology and AI’. Lawhive was keen to pitch this as a legal AI company (…as it has an AI assistant called Lawrence…) buying a legal services…

Executing language-conditioned tasks in dynamic visual environments remains a central challenge in embodied AI. Existing Vision-Language-Action (VLA) models predominantly adopt reactive state-to-action mappings, often leading to short-sighted behaviors and poor robustness in dynamic scenes. In this paper, we introduce F1, a pretrained VLA framework which integrates the visual foresight generation into decision-making pipeline. F1 adopts a Mixture-of-Transformer architecture with dedicated modules for perception, foresight generation, and control, thereby bridging understanding, generation, and actions. At its core, F1 employs a next-scale prediction mechanism to synthesize goal-conditioned visual foresight as explicit planning targets. By forecasting plausible future visual states, F1 reformulates action…

{ “articleContent”: “On September 10, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in the UAE, in collaboration with AI startup G42, open-sourced its high-performance inference model K2Think, attracting widespread attention in the industry. This model is built on Alibaba’s open-source model Qwen 2.5, and it has made its weights, training data, deployment code, and optimization code available on Hugging Face and GitHub. K2Think’s outstanding performance in inference speed and mathematical capabilities signifies the potential of small parameter modelsin specific task domains. K2Think: An Innovative Practice of Low-Cost, High-Performance Inference The K2Think model has 32 billion parameters. Although the…

Weeks after a Rancho Santa Margarita family sued over ChatGPT’s role in their teenager’s death, OpenAI has announced that parental controls are coming to the company’s generative artificial intelligence model.Within the month, the company said in a recent blog post, parents will be able to link teens’ accounts to their own, disable features like memory and chat history and receive notifications if the model detects “a moment of acute distress.” (The company has previously said ChatGPT should not be used by anyone younger than 13.)The planned changes follow a lawsuit filed late last month by the family of Adam Raine,…

After a hurricane passes, scientists routinely analyze the assorted computer models used to predict its path and power and crown a victor. This year, a surprising new contender has emerged — a forecast model generated by artificial intelligence.How is it faring? Well, the single best-performing model for last month’s Hurricane Erin was Google DeepMind, a relative AI newcomer in the storm prediction field.Yes, it’s a small sample size. But although the technology can be over-hyped in some places, it already holds a great deal of promise in the notoriously fickle field of weather forecasting. Some experts say their weather and…

In the seamless fusion of artificial intelligence and mechanical engineering, MIT students are carving a new frontier for design innovation. Faez Ahmed, MIT’s Doherty Chair in Ocean Utilization and associate professor of mechanical engineering, steers a course that harnesses machine learning and AI to crank out futuristic products and tackle intricate design challenges. MIT News reports a surge in popularity for this class, making it one of the non-core favorites within the Department of Mechanical Engineering (MechE) since its inception in 2021. Describing the kaleidoscope that is mechanical engineering, Faez Ahmed told MIT News, “Within mechanical engineering, machine learning, AI,…

Nigerian students are increasingly relying on artificial intelligence to support their studies, according to new Google Search trend data released on Tuesday as schools reopen across the country. Google said searches combining “AI + studying” rose by more than 200 per cent compared with 2024, showing that learners are not only curious about AI but are actively using it as a study companion. The company explained that students were seeking out AI tutors, free tools, and prompts to guide their work across a variety of subjects. The data indicated that searches for “AI + chemistry” grew 50 per cent, while…

Legal Innovators UK, the landmark legal tech conference in London, is fast approaching. Here’s a peek into what we’ll be exploring on Inhouse Day – 5th November. The overarching theme this year is: where have we got to with genAI and where are we heading next in terms of how inhouse teams are deploying the technology? Since November 2022 the field of legal tech has changed enormously and many businesses have brought in multiple new AI products, or products from companies that have updated their offering to wield genAI skills as well. But, what impact has this had? How has it changed…

Text-to-image diffusion models are computationally intensive, often requiring dozens of forward passes through large transformer backbones. For instance, Stable Diffusion XL generates high-quality images with 50 evaluations of a 2.6B-parameter model, an expensive process even for a single batch. Few-step diffusion models reduce this cost to 2-8 denoising steps but still depend on large, uncompressed U-Net or diffusion transformer backbones, which are often too costly for full-precision inference without datacenter GPUs. These requirements also limit existing post-training quantization methods that rely on full-precision calibration. We introduce Q-Sched, a new paradigm for post-training quantization that modifies the diffusion model scheduler rather…