This work presents FaceComposer, a unified generative model that accomplishes a variety of facial content creation tasks, including text-conditioned face synthesis, text-guided face editing, face animation etc. Based on the latent diffusion framework, FaceComposer follows the paradigm of compositional generation and employs diverse face-specific conditions, e.g., Identity Feature and Projected Normalized Coordinate Code, to release the model creativity at all possible. To support text control and animation, we clean up some existing face image datasets and collect around 500 hours of talking-face videos, forming a high-quality large-scale multi-modal face database. A temporal self-attention module is incorporated into the U-Net structure, which allows learning the denoising process on the mixture of images and videos. Extensive experiments suggest that our approach not only achieves comparable or even better performance than state-of-the-arts on each single task, but also facilitates some combined tasks with one-time forward, demonstrating its potential in serving as a foundation generative model in face domain. We further develop an interface such that users can enjoy our one-step service to create, edit, and animate their own characters. Code, dataset, model, and interface will be made publicly available.

One of WIRED's Favorite Chef Knives Is Free With a HelloFresh Membership The 8-inch Zwilling Four Star chef's knife is an excellent carbon steel blade that retails around $100. It's free with some food. I don't know if a good knife is hard to find. But they usually cost at least a hundred dollars, so it's worth noting when HelloFresh is offering one of WIRED's favorite chef's knives for the low, low price of free. This is the time of year when a lot of the best meal kit deals start to happen. And so if you hang around for three weeks of meal delivery service from HelloFresh, your third box will include delivery of a Zwilling Four Star 8-inch chef knife, a $100-plus carbon steel blade that WIRED reviewer Molly Higgins lists as her runner-up favorite blade overall--and her favorite carbon-steel for most people.

The hardware largely remains the same, but performance gets a boost. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Without much fanfare, Apple has unveiled three new flagship products today via a press release--no special event, no pre-recorded show. That might be because the new iPad Pro, MacBook Pro, and Vision Pro don't change the mold--they're identical to their predecessors--but internally, they're debuting Apple's highly anticipated M5 chip.

OpenAI's latest app encourages users to generate a personal digital avatar and scroll AI-generated videos of themselves and their friends. On Tuesday, OpenAI released an AI video app called Sora . The platform is powered by OpenAI's latest video generation model, Sora 2, and revolves around a TikTok-like For You page of user-generated clips. This is the first product release from OpenAI that adds AI-generated sounds to videos. For now, it's available only on iOS and requires an invite code to join.

Large Language Models (LLMs) are increasingly consulted for high-stakes life advice, yet they lack standard safeguards against providing confident but misguided responses. This creates risks of sycophancy and over-confidence. This paper investigates these failure modes through three experiments: (1) a multiple-choice evaluation to measure model stability against user pressure; (2) a free-response analysis using a novel safety typology and an LLM Judge; and (3) a mechanistic interpretability experiment to steer model behavior by manipulating a "high-stakes" activation vector. Our results show that while some models exhibit sycophancy, others like o4-mini r emain robust. Top-performing models achieve high safety scores by frequently asking clarifying questions--a key feature of a safe, inquisitive approach--rather than issuing prescriptive advice . Furthermore, we demonstrate that a model's cautiousness can be directly controlled via activation steering, suggesting a new path for safety alignment. These findings underscore the need for nuanced, multi-faceted benchmarks to ensure LLMs can be trusted with life-changing decisions.

Dataflow analysis is a fundamental code analysis technique that identifies dependencies between program values. Traditional approaches typically necessitate successful compilation and expert customization, hindering their applicability and usability for analyzing uncompilable programs with evolving analysis needs in real-world scenarios. This paper presents LLMDFA, an LLM-powered compilation-free and customizable dataflow analysis framework. To address hallucinations for reliable results, we decompose the problem into several subtasks and introduce a series of novel strategies. Specifically, we leverage LLMs to synthesize code that outsources delicate reasoning to external expert tools, such as using a parsing library to extract program values of interest and invoking an automated theorem prover to validate path feasibility.

We present a challenging benchmark for the Open WorLd VISual question answering (OWLViz) task. OWLViz presents concise, unambiguous queries that require integrating multiple capabilities, including visual understanding, web exploration, and specialized tool usage. While humans achieve 69.2% accuracy on these intuitive tasks, even state-of-the-art VLMs struggle, with the best model, Gemini 2.0, achieving only 26.6% accuracy. Current agentic VLMs, which rely on limited vision and vision-language models as tools, perform even worse. This performance gap reveals significant limitations in multimodal systems' ability to select appropriate tools and execute complex reasoning sequences, establishing new directions for advancing practical AI research.

Topics include Machine Learning, Deep Learning, GUI, Computer Vision, and API development. Author is a Machine Learning Researcher, currently working in IBM. Taught 20,000 students & working professionals to crack coding interviews and land their dream job.

As a Harry Potter fan and a programmer, I can't help but be reminded of the wizarding world when I think about coding. Just like a young wizard learning spells and incantations, we programmers spend hours upon hours learning syntax and debugging code. But what if we had a way to get a little help with the heavy lifting? What if we could wave a wand and it could effortlessly write code for us!! Seems like it might be actually achievable. In this post, we will allow ChatGPT to do the magic and help us lend a hand in writing code.

The team carried out a second set of experiments, which incorporated machine learning models called neural networks that were specifically trained …