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Just weeks after OpenAI announced its adoption of a rival Anthropic standard for connecting AI models to the systems where their data resides, Google has followed suit. Google DeepMind CEO Demis Hassabis announced on X on Wednesday that Google will add support for the Anthropic Model Context Protocol (MCP) to its Gemini models and software development kits (SDKs).

In the fiercely competitive AI industry, Google's AI division, DeepMind, has employed extreme measures to retain top talent. According to Business Insider, some of the company's AI employees in the UK have signed 'aggressive' non-compete agreements, preventing them from working for competitors for a year after leaving. These agreements not only restrict employees' career choices but also prevent them from participating in the rapidly advancing technologies within the industry for a full year. Reports indicate these employees continue to receive salaries during this period, though the specifics are unclear.

Google DeepMind recently released a comprehensive strategic document outlining its approach to developing safe Artificial General Intelligence (AGI). AGI is defined as a system capable of matching or exceeding human capabilities in most cognitive tasks. DeepMind anticipates that current machine learning approaches, particularly neural networks, will remain the primary path to achieving AGI. The report notes that future AGI systems could potentially surpass human performance and exhibit significant autonomy in planning and decision-making. This technology will have implications for healthcare...

Google recently announced the launch of its new open-source model, TxGemma, designed to significantly improve the efficiency of therapeutic drug development. Developed by the Google DeepMind team, this model is a fine-tuned version of their advanced Gemma model family, incorporating powerful natural language understanding, scientific prediction, and multi-turn dialogue capabilities. TxGemma aims to revolutionize drug discovery. Its release is considered a significant advancement in the application of AI to biomedicine, promising to drastically shorten the time it takes for drugs to move from the lab to clinical trials, while also reducing costs.

Google DeepMind has revealed its secret weapon – Gemini Robotics! This is no ordinary roomba; it's about truly infusing AI intelligence into robotic bodies, enabling robots to operate (and potentially even more cleverly) in the physical world, just like humans. The versatile Gemini Robotics is powered by the advanced Gemini 2.0 model. Gemini itself boasts the ability to process text, images, audio, and video.

At the recent 'Invest Karnataka 2025' conference held in Bangalore, numerous leaders from the technology industry gathered to discuss the transformative potential of artificial intelligence in India and its impacts. Manish Gupta, Senior Director at Google DeepMind, delivered a key speech at the conference, emphasizing the need to establish corresponding regulations alongside technological innovation to ensure responsible development. Note: Image generated by AI, image courtesy service provider Midjourney.

At the recent World Economic Forum in Davos, Google DeepMind's CEO Demis Hassabis stated that the first drugs designed with the help of artificial intelligence could begin clinical trials by 2025. Hassabis also heads Isomorphic Labs, a drug research company under DeepMind. He stated, "Our plan is to have some AI-designed drugs enter clinical trials by the end of this year."

In terms of artificial intelligence (AI) regulation, the UK government has expressed a desire to take an independent stance and plans to adopt different approaches from its main Western counterparts, such as the EU and the US. UK AI and Digital Government Minister Feryal Clark emphasized in an interview with CNBC that the UK must 'do its own thing' to ensure necessary regulations are in place early on regarding the safety of AI models. Image source note: The image was generated by AI and provided by the image service provider Midjourney.

Generative language models face many challenges during the transition from training to practical applications. One major issue is how to achieve optimal performance during the inference stage. Current strategies, such as Reinforcement Learning from Human Feedback (RLHF), primarily focus on improving the model's win rate but often overlook decoding strategies during inference, such as Best-of-N sampling and controlled decoding. This gap between training objectives and actual usage may lead to inefficiencies and affect the quality and reliability of outputs. To address these issues, Google D

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...

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.

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...