However, they often fail to produce realistic geometric details, resulting in overly smooth surfaces or geometric details inaccurately baked in albedo maps. To address this, we introduce a new method that incorporates touch as an additional modality to improve the geometric details of generated 3D assets. We design a lightweight 3D texture field to synthesize visual and tactile textures, guided by 2D diffusion model priors on both visual and tactile domains. We condition the visual texture generation on high-resolution tactile normals and guide the patch-based tactile texture refinement with a customized TextureDreambooth. We further present a multi-part generation pipeline that enables us to synthesize different textures across various regions. To our knowledge, we are the first to leverage high-resolution tactile sensing to enhance geometric details for 3D generation tasks. We evaluate our method in both text-to-3D and image-to-3D settings. Our experiments demonstrate that our method provides customized and realistic fine geometric textures while maintaining accurate alignment between two modalities of vision and touch.

Users typically engage with LLMs interactively, yet most existing benchmarks evaluate them in a static, single-turn format, posing reliability concerns in interactive scenarios. We identify a key obstacle towards reliability: LLMs are trained to answer any question, even with incomplete context or insufficient knowledge.

Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.

Google issued 100 announcements during its Google I/O developers conference this week, none of which involved the smart home. That apparent lack of enthusiasm for a topic close to our heart didn't dissuade TV and smart-appliance manufacturer Hisense from announcing plans to integrate new Google Home APIs into its own ConnectLife app, so that third-party smart home devices can be folded into that ecosystem. Hisense first announced that it would open its ConnectLife app to third-party products in December, 2024. Today, it announced it will incorporate the latest Google Home APIs into the app by the fall of 2025, Hisense says this will enable users to onboard a wide range of third-party smart home devices--including Matter and Works With Google Home-certified products--to create a more integrated smart home experience. Hisense cited two examples of how this would benefit ConnectLife users: "One-touch modes and customized automations can blend Hisense products with third-party devices to create intelligent home responses, such as air conditioners automatically adjusting based on third-party air quality sensors, or smart lights providing visual notifications when the Hisense refrigerator's VersaTemp drawer reaches the ideal temperature for chilling drinks."

Chatbots will be able to teach children more than twice as fast as teachers can within the next decade, the so-called godfather of AI has predicted. Geoffrey Hinton, who won a Nobel Prize for his work on the technology, also claimed AI personal tutors would'be much more efficient and less boring'. Speaking at Gitex Europe, the British computer scientist said: 'It's not there yet, but it's coming, and so we'll get much better education at many levels.' AI personal tutors are already being trialled in UK schools, with the technology now able to talk directly to the student and adapt lesson plans to their knowledge level. The government has already funnelled millions of pounds into AI education initiatives – though it has claimed the technology will'absolutely not' replace teachers.

Amazon is aiming to make shopping just a bit easier. This week, Amazon launched a new generative AI feature that produces short audio summaries, detailing everything you need to know about a product. The audio descriptions, which Amazon is calling "hear the highlights", are created from on-page product summaries, reviews, and information from other websites, crafting short snippets that deliver everything you need to know about a product. The product summaries are now available on a limited number of items on Amazon and for US customers only. To access "Hear the highlights", you can do so in the Amazon app.

Public pretraining is a promising approach to improve differentially private model training. However, recent work has noted that many positive research results studying this paradigm only consider in-distribution tasks, and may not apply to settings where there is distribution shift between the pretraining and finetuning data--a scenario that is likely when finetuning private tasks due to the sensitive nature of the data. In this work, we show empirically across three tasks that even in settings with large distribution shift, where both zero-shot performance from public data and training from scratch with private data give unusably weak results, public features can in fact improve private training accuracy by up to 67% over private training from scratch. We provide a theoretical explanation for this phenomenon, showing that if the public and private data share a low-dimensional representation, public representations can improve the sample complexity of private training even if it is impossible to learn the private task from the public data alone. Altogether, our results provide evidence that public data can indeed make private training practical in realistic settings of extreme distribution shift.

Everything over the last 30 years, according to Sir Jony Ive, has led to this moment: a partnership between the iPhone designer and the developer of ChatGPT. Ive has sold his hardware startup, io, to OpenAI and will take on creative and design leadership across the merged businesses. "I have a growing sense that everything I have learned over the last 30 years has led me to this place, to this moment," he says in a video announcing the 6.4bn ( 4.8bn) deal. The main aim will be to move on from Ive's signature achievement designing Apple's most successful product, as well as the iPod, iPad and Apple Watch. The British-born designer has already developed a prototype io device, and one of its users is OpenAI's chief executive, Sam Altman.

AI's energy use already represents as much as 20 percent of global data-center power demand, research published Thursday in the journal Joule shows. That demand from AI, the research states, could double by the end of this year, comprising nearly half of all total data-center electricity consumption worldwide, excluding the electricity used for bitcoin mining. The new research is published in a commentary by Alex de Vries-Gao, the founder of Digiconomist, a research company that evaluates the environmental impact of technology. De Vries-Gao started Digiconomist in the late 2010s to explore the impact of bitcoin mining, another extremely energy-intensive activity, would have on the environment. Looking at AI, he says, has grown more urgent over the past few years because of the widespread adoption of ChatGPT and other large language models that use massive amounts of energy. According to his research, worldwide AI energy demand is now set to surpass demand from bitcoin mining by the end of this year.

Fierce competition among some of the world's biggest tech companies has led to a profusion of AI tools that can generate humanlike prose and uncannily realistic images, audio, and video. While those companies promise productivity gains and an AI-powered creativity revolution, fears have also started to swirl around the possibility of an internet that's so thoroughly muddled by AI-generated content and misinformation that it's impossible to tell the real from the fake. Many leading AI developers have, in response, ramped up their efforts to promote AI transparency and detectability. Most recently, Google announced the launch of its SynthID Detector, a platform that can quickly spot AI-generated content created by one of the company's generative models: Gemini, Imagen, Lyria, and Veo. Originally released in 2023, SynthID is a technology that embeds invisible watermarks -- a kind of digital fingerprint that can be detected by machines but not by the human eye -- into AI-generated images.