Elon has funded OpenAI. OpenAI’s mission is to build safe AI, and ensure AI's benefits are as widely and evenly distributed as possible.
OpenAI will measure intelligence using a metric which consists of a variety of OpenAI Gym environments with a unified action and observation space (so a single agent can run across all of them), including games, robotics, and language-based tasks. Their implementation will evolve over time, and they’ll keep the community updated along the way
They are working to enable a physical robot (off-the-shelf; not manufactured by OpenAI) to perform basic housework. There are existing techniques for specific tasks, but we believe that learning algorithms can eventually be made reliable enough to create a general-purpose robot. More generally, robotics is a good testbed for many challenges in AI.
They plan to build an agent that can perform a complex task specified by language, and ask for clarification about the task if it’s ambiguous. Today, there are promising algorithms for supervised language tasks such as question answering, syntactic parsing and machine translation but there aren’t any for more advanced linguistic goals, such as the ability to carry a conversation, the ability to fully understand a document, and the ability to follow complex instructions in natural language. We expect to develop new learning algorithms and paradigms to tackle these problems.
They will train an agent capable enough to solve any game in their initial metric. Games are virtual mini-worlds that are very diverse, and learning to play games quickly and well will require significant advances in generative models and reinforcement learning. (They are inspired by the pioneering work of DeepMind, who have produced impressive results in this area in the past few years.)
OpenAI is supported by over US$1 billion in commitments; however, only a tiny fraction of the $1 billion pledged is expected to be spent in the first few years
On April 27, 2016, OpenAI released a public beta of "OpenAI Gym", a platform for reinforcement learning research that aims to provide an easy-to-setup general-intelligence benchmark with a wide variety of different environments (somewhat akin to, but broader than, the ImageNet Large Scale Visual Recognition Challenge used in supervised learning research), and that hopes to standardize the way in which environments are defined in AI research publications, so that published research becomes more easily reproducible.
SOURCES- OpenAI, Wikipedia