Google DeepMind and robotics company Apptronik have announced a collaboration to combine their technical expertise in developing AI humanoid robots capable of operating in complex dynamic environments. This partnership aims to advance the deep integration of robotic hardware and artificial intelligence, exploring broader possibilities for robots in both industrial and everyday applications. Apptronik has been independently operating since 2016, spun off from the Human Centered Robotics Lab at the University of Texas at Austin, and has developed 15 different robot models to date, including the...

In its latest presentation, Google unveiled Project Astra, a plan by the DeepMind team aimed at building real-time, multi-modal AI agents running on a mysterious prototype pair of glasses. On Wednesday, Google announced that it would release these AI and AR-enabled prototype glasses to a select group of users for real-world testing. The translation feature demonstrated on the Google prototype glasses showcases that they are equipped with the Android XR operating system, a new platform designed by Google for visual computing, aimed at supporting glasses and ear devices.

Recently, a research team from the fields of computer science and sociology, in collaboration with Google DeepMind, successfully developed a new type of artificial intelligence application that can generate a personality simulation of a person after just two hours of interviews. The research team published a related paper on the arXiv preprint server, introducing this innovative technology and its future development directions. The research is based on the popular ChatGPT large language model, developing a new model aimed at asking questions to participants and recording their answers.

Google's DeepMind, in collaboration with the Massachusetts Institute of Technology (MIT), recently announced a significant research breakthrough. The research team developed a new autoregressive model called 'Fluid', which has made groundbreaking progress in the field of text-to-image generation. After scaling up to 10.5 billion parameters, the model has demonstrated outstanding performance. This research overturns the industry's common perception; while autoregressive models have dominated the language processing domain, they have traditionally been seen as inferior to Stable Diffusion and Goog.

Recently, Google announced a series of important leadership changes and team restructuring in an official statement. These changes are primarily focused on the company's Knowledge & Information (K&I) team and the Gemini team. Google's CEO Sundar Pichai announced that Nick Fox will replace Prabhakar Raghavan as the new head of the K&I team. The K&I team is responsible for Google's search, advertising, geographic, and business products.

Recently, large language models (LLMs) with ultra-long context windows have become a hot topic of discussion. These models are capable of processing hundreds of thousands or even millions of tokens in a single prompt, opening up many new possibilities for developers. However, how well can these long context LLMs understand and utilize the vast information they receive? To address this question, researchers at Google DeepMind have launched a new benchmark called Michelangelo, aimed at evaluating long context reasoning capabilities. The research findings indicate that...

Recently, Tim Brooks, the research head of OpenAI's Sora video generation system, announced on social media that he will be joining Google DeepMind to continue his work on video generation and the world simulator. Brooks expressed his excitement about collaborating with a talented team like DeepMind and thanked OpenAI for the wonderful two years he spent there, especially for the achievements made in the Sora project.

Recently, researchers from the Mila Institute, Google DeepMind, and Microsoft Research conducted an in-depth investigation into the reasoning capabilities of AI language models. They found that small and inexpensive models exhibit significant shortcomings when addressing complex problems. The study utilized a test called 'Combinatorial GSM' to evaluate how these models perform in solving chain-based foundational math problems. Image source note: Image generated by AI, image licensed through Midjourney.

Recently, Google DeepMind announced its latest AI system - AlphaChip. This system is dedicated to accelerating and optimizing the development of computer chips, and the chip layouts designed by AlphaChip have already been implemented in Google's AI accelerators. AlphaChip operates similarly to what we have previously heard about AlphaGo and AlphaZero, utilizing reinforcement learning techniques to quickly generate optimized chip layouts. According to Google DeepMi

The Google DeepMind research team has recently achieved a significant breakthrough with the development of an innovative technology called SCoRe (Self-Correction through Reinforcement Learning). This technology aims to address the long-standing challenge of large language models (LLMs) struggling to self-correct, enabling them to identify and fix errors without relying on multiple models or external checks. The core of SCoRe's technology lies in its two-phase approach. The first phase optimizes the model.