Deepmind published a new blog post where they present their newest AI which is based on their previous work Gato. RoboCat is a self-improving AI agent for robotics that learns to perform a variety of tasks across different arms and then self-generates new training data to improve its technique. It is the first agent to solve and adapt to multiple tasks and do so across different, real robots. RoboCat learns much faster than other state-of-the-art models. It can pick up a new task with as few as 100 demonstrations because it draws from a large and diverse dataset. This capability will help accelerate robotics research, as it reduces the need for human-supervised training, and is an important step towards creating a general-purpose robot.
Tag: Deepmind
After DeepMind developed AlphaTensor last year and found a new algorithm for matrix multiplication, they did it again. This time they developed AlphaDev which found a new algorithm for sorting. This sounds not as exciting as a new language model, but sorting algorithms run billions of times every hour. Optimizing central algorithms like sorting and searching is one of the oldest parts of computer science and they are getting optimized for over a hundred years at this point. We did not find a better solution in the last 10 years and some believed that we reached the limit of what is possible. AlphaDevs’ new solution was implemented in the standard C++ library and is used already. The impact of these small improvements becomes enormous because they are used so much and the amount of energy that is saved adds up quickly. They also found a new hash algorithm which is used a similar amount. If AlphaDev continues to find improvements for core algorithms, every software in the world will run faster and more efficiently. Breakthroughs like this have to be considered in the discussion around the climate impact of AI training. The energy saved by these improvements offsets the used energy for training by orders of magnitude.
Google and DeepMind just announced that they will unite Google Brain and Deepmind into Google DeepMind. This is a good step for both sites since Deepmind really needs the computing power of Google to make further progress on AGI and Google needs the Manpower and knowledge of the Deepmind team to quickly catch up to OpenAi and Microsoft. This partnership could lead to a real rival on the way to AGI for OpenAI. I personally always liked that DeepMind had a different approach to AGI and I hope they will continue to push different ideas other than language models.
Similar to OpenAI, Deepmind started to work together with other companies to build more commercial products. In their recent blog post they explained how they developed a new Video codec and improved auto chapters for Youtube.
If this trend continues we will see more products for other Alphabet companies developed by Deepmind.