OpenAI
Canvas Category Consultancy : Research : Non-profit
OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome.
Assembly Line
🏴 Figure Raises $675M for Its Humanoid Robot Development
Figure is announcing an astonishing US $675 million Series B raise, which values the company at an even more astonishing $2.6 billion. Figure is one of the companies working toward a multipurpose or general-purpose (depending on whom you ask) bipedal or humanoid (depending on whom you ask) robot. The astonishing thing about this valuation is that Figure’s robot is still very much in the development phase—although they’re making rapid progress, which they demonstrate in a new video posted this week.
This round of funding comes from Microsoft, OpenAI Startup Fund, Nvidia, Jeff Bezos (through Bezos Expeditions), Parkway Venture Capital, Intel Capital, Align Ventures, and ARK Invest. Figure says that they’re going to use this new capital “for scaling up AI training, robot manufacturing, expanding engineering head count, and advancing commercial deployment efforts.” In addition, Figure and OpenAI will be collaborating on the development of “next-generation AI models for humanoid robots” which will “help accelerate Figure’s commercial timeline by enhancing the capabilities of humanoid robots to process and reason from language.
Bridge the gap between Process Control and Reinforcement Learning with QuarticGym
Modern process control algorithms are the key to the success of industrial automation. The increased efficiency and quality create value that benefits everyone from the producers to the consumers. The question then is, could we further improve it?
From AlphaGo to robot-arm control, deep reinforcement learning (DRL) tackled a variety of tasks that traditional control algorithms cannot solve. However, it requires a large and compactly sampled dataset or a lot of interactions with the environment to succeed. In many cases, we need to verify and test the reinforcement learning in a simulator before putting it into production. However, there are few simulations for industrial-level production processes that are publicly available. In order to pay back the research community and encourage future works on applying DRL to process control problems, we built and published a simulation playground with data for every interested researcher to play around with and benchmark their own controllers. The simulators are all written in the easy-to-use OpenAI Gym format. Each of the simulations also has a corresponding data sampler, a pre-sampled d4rl-style dataset to train offline controllers, and a set of preconfigured online and offline Deep Learning algorithms.