Artificial intelligence could be one of humanitys most useful inventions. DeepMind aims to build advanced AI to expand our knowledge and find new answers. By solving this one thing, we believe we could help people solve thousands of problems.
Were a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more. Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to predict the complex 3D shapes of proteins - which could one day transform how drugs are invented.
Introductory lecture of the MIT Self-Driving Cars series (6.S094) with an overview of the autonomous vehicle industry in 2018 and looking forward to 2019, including Waymo, Tesla, Cruise, Ford, GM, and out-of-the-box ideas of boring tunnels, flying cars, connected vehicles, and more. This covers the state of the art in terms of industry developments and not the perception and planning algorithm development. The latter will be covered in detail in future lectures. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
This course introduces the Dynamic Distributed Dimensional Data Model (D4M), a breakthrough in computer programming that combines graph theory, linear algebra, and databases to address problems associated with Big Data. Search, social media, ad placement, mapping, tracking, spam filtering, fraud detection, wireless communication, drug discovery, and bioinformatics all attempt to find items of interest in vast quantities of data. This course teaches a signal processing approach to these problems by combining linear algebraic graph algorithms, group theory, and database design. This approach has been implemented in software. The class will begin with a number of practical problems, introduce the appropriate theory, and then apply the theory to these problems. Students will apply these ideas in the final project of their choosing. The course will contain a number of smaller assignments which will prepare the students with appropriate software infrastructure for completing their final projects.
