OpenAI, an artificial intelligence research group created by Silicon Valley investors as a non-profit, will now be seeking "capped" profit, according to a blog post on the OpenAI website published Monday. SpaceX and Tesla CEO Elon Musk, startup accelerator Y Combinator president Sam Altman, and several other Silicon Valley figures launched OpenAI in late 2015 with $1 billion in seed funding and the stated goal of ensuring that AI "benefits all of humanity." Musk stepped down from OpenAI in February 2018. Since its founding, the group has conducted research with reinforcement learning, robotics, and language. According to OpenAI, the original nonprofit entity will own a limited partnership called OpenAI LP that's designed to give a "capped return" to investors and employees and funnel excess funds back to the nonprofit.

OpenAI today announced the creation of OpenAI LP, a for-profit company that will be owned and controlled by the OpenAI nonprofit organization's board of directors. The new Delaware-based limited partnership was created to speed progress toward OpenAI's goal of advancing AI and eventually creating safe artificial general intelligence (AGI) system. OpenAI LP plans to raise and invest billions of dollars in the years ahead. Unlike narrow artificial intelligence common today, which can predict the probability of outcomes or recommend content in your Facebook News Feed, OpenAI defines AGI as a highly autonomous system able to outperform humans at most tasks. Sam Altman will serve as CEO of the new entity, while Greg Brockman will act as CTO and Ilya Sutskever as chief scientist.

The Bay Area is famed for nurturing speculative investments like flying cars, floating cities, and the notion that a ride hailing service can turn a profit. A new utopian investment opportunity arrived Monday: Shovel dollars into a San Francisco artificial intelligence lab cofounded by Elon Musk and you'll receive a share of the profits when (or if) it figures out how to create machines smarter than humans. That pitch comes from OpenAI, an independent AI research lab cofounded as a nonprofit in 2015 by Musk and Sam Altman, the president of startup incubator YCombinator. Its stated mission was to safely create software as capable as people, which it terms artificial general intelligence or AGI, and share the benefits with the world. The founders argued society shouldn't have to hope that profit-seeking tech giants would do that.

We've created OpenAI LP, a new "capped-profit" company that allows us to rapidly increase our investments in compute and talent while including checks and balances to actualize our mission. Our mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, primarily by attempting to build safe AGI and share the benefits with the world. We've experienced firsthand that the most dramatic AI systems use the most computational power in addition to algorithmic innovations, and decided to scale much faster than we'd planned when starting OpenAI. We'll need to invest billions of dollars in upcoming years into large-scale cloud compute, attracting and retaining talented people, and building AI supercomputers. We want to increase our ability to raise capital while still serving our mission, and no pre-existing legal structure we know of strikes the right balance.

What does the world look like to AI? Researchers have puzzled over this for decades, but in recent years, the question has become more pressing. Machine vision systems are being deployed in more and more areas of life, from health care to self-driving cars, but "seeing" through the eyes of a machine -- understanding why it classified that person as a pedestrian but that one as a signpost -- is still a challenge. Our inability to do so could have serious, even fatal, consequences. Some would say it already has due to the deaths involving self-driving cars. New research from Google and nonprofit lab OpenAI hopes to further pry open the black box of AI vision by mapping the visual data these systems use to understand the world.

Last month, developers from OpenAI announced that they had built a text generating algorithm called GPT-2 that they said was too dangerous to release into the world, since it could be used to pollute the web with endless bot-written material. But now, a team of scientists from the MIT-IBM Watson AI Lab and Harvard University built an algorithm called GLTR that determines how likely it is that any particular passage of text was written by a tool like GPT-2 -- an intriguing escalation in the battle against spam. When OpenAI unveiled GPT-2, they showed how it could be used to write fictitious-yet-convincing news articles by sharing one that the algorithm had written about scientists who discovered unicorns. GLTR uses the exact same models to read the final output and predict whether it was written by a human or GPT-2. Just like GPT-2 writes sentences by predicting which words ought to follow each other, GLTR determines whether a sentence uses the word that the fake news-writing bot would have selected.

OpenAI may not be quite so open going forward. The former nonprofit announced today that it is restructuring as a "capped-profit" company that cuts returns from investments past a certain point. But some worry that this move -- or rather the way they made it -- may result in making the innovative company no different from the other AI startups out there. From now on, profits from any investment in the OpenAI LP (limited partnership, not limited profit) will be passed on to an overarching nonprofit company, which will disperse them as it sees fit. Profits in excess of a 100x return, that is.

Abstract--Given an environment with continuous state spaces and discrete actions, we investigate using a Double Deep Q-learning Reinforcement Agent to find optimal policies using the LunarLander-v2 OpenAI gym environment. I. INTRODUCTION For this study, we examine performance of reinforcement learning (RL) algorithms for continuous state space MDPs, specifically OpenAI Gym's LunarLander-v2. In this environment, the goal is for the RL agent to learn to land successfully on a landing pad located a coordinate points (0,0) in the frame. The agent receives -0.03 points for firing its main engine for each frame, and landing on the landing pad is 100-140 points, which can be lost if the agent moves away from the pad. Each leg contact with the ground is 10 points.

Artificial intelligence that's beastly at World of Warcraft might not lie too far into the distant future, if OpenAI has its way. The San Francisco research nonprofit today released Neural MMO, a "massively multiagent" virtual training ground that plops agents in the middle of an RPG-like world -- one complete with a resource collection mechanic and player versus player combat. "The game genre of Massively Multiplayer Online Games (MMOs) simulates a large ecosystem of a variable number of players competing in persistent and extensive environments," OpenAI wrote in a blog post. "The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence."READ