Artificial Intelligence (AI), like any transformative technology, has the potential to be a double-edged sword, leading either toward significant advancements or detrimental outcomes for society as a whole. As is often the case when it comes to widely-used technologies in market economies (e.g., cars and semiconductor chips), commercial interest tends to be the predominant guiding factor. The AI community is at risk of becoming polarized to either take a laissez-faire attitude toward AI development, or to call for government overregulation. Between these two poles we argue for the community of AI practitioners to consciously and proactively work for the common good. This paper offers a blueprint for a new type of innovation infrastructure including 18 concrete milestones to guide AI research in that direction. Our view is that we are still in the early days of practical AI, and focused efforts by practitioners, policymakers, and other stakeholders can still maximize the upsides of AI and minimize its downsides. We talked to luminaries such as recent Nobelist John Jumper on science, President Barack Obama on governance, former UN Ambassador and former National Security Advisor Susan Rice on security, philanthropist Eric Schmidt on several topics, and science fiction novelist Neal Stephenson on entertainment. This ongoing dialogue and collaborative effort has produced a comprehensive, realistic view of what the actual impact of AI could be, from a diverse assembly of thinkers with deep understanding of this technology and these domains. From these exchanges, five recurring guidelines emerged, which form the cornerstone of a framework for beginning to harness AI in service of the public good. They not only guide our efforts in discovery but also shape our approach to deploying this transformative technology responsibly and ethically.

From intelligent personal assistants to home robots, technology once thought of as a sci-fi dream is now embedded into everyday life. But this leap from dream to reality didn't happen overnight. There is no one'eureka' moment in a field as vast as AI. Rather, the technology we enjoy today is a result of countless milestones in artificial intelligence, delivered by countless forgotten people across a countless range of projects. So, let's pay homage to some of that work.

Developed at the Artificial Intelligence Center of the Stanford Research Institute (SRI) from 1966 to 1972, SHAKEY was the world's first mobile intelligent robot. According to the 2017 IEEE Milestone citation, it "could perceive its surroundings, infer implicit facts from explicit ones, create plans, recover from errors in plan execution, and communicate using ordinary English. SHAKEY's software architecture, computer vision, and methods for navigation and planning proved seminal in robotics and in the design of web servers, automobiles, factories, video games, and Mars rovers." In November 1963, Charles Rosen, head of the AI group at SRI, wrote a memo in which "he proposed development of a mobile'automaton' that would combine the pattern-recognition and memory capabilities of neural networks with higher-level AI programs," according to Nils Nilsson in his book The Quest for Artificial Intelligence. In April 1964, SRI submitted to the Advanced Research Projects Agency (ARPA) at the U.S. Department of Defense, a proposal for research in "Intelligent Automata," which it claimed would ultimately lead to "the development of machines that will perform tasks that are presently considered to require human intelligence."

Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today we play with restrictions, we aim to beat a team of top professionals at The International in August subject only to a limited set of heroes. We may not succeed: Dota 2 is one of the most popular and complex esports games in the world, with creative and motivated professionals who train year-round to earn part of Dota's annual $40M prize pool (the largest of any esports game). OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores -- a larger-scale version of the system we built to play the much-simpler solo variant of the game last year. Using a separate LSTM for each hero and no human data, it learns recognizable strategies.

Our team of five neural networks, OpenAI Five, has started to defeat amateur human teams at Dota 2. While today we play with restrictions, we aim to beat a team of top professionals at The International in August subject only to a limited set of heroes. We may not succeed: Dota 2 is one of the most popular and complex esports games in the world, with creative and motivated professionals who train year-round to earn part of Dota's annual $40M prize pool (the largest of any esports game). OpenAI Five plays 180 years worth of games against itself every day, learning via self-play. It trains using a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores -- a larger-scale version of the system we built to play the much-simpler solo variant of the game last year. Using a separate LSTM for each hero and no human data, it learns recognizable strategies.

The biggest knock against sending robots to explore the solar system for signs of life has always been their inability to make intuitive, even creative decisions as effectively as humans can. Recent advances in artificial intelligence (AI) promise to narrow that gap soon--which is a good thing, because there are no immediate plans to send people to explore Mars's subterranean caves or search for hydrothermal vents below Europa's icy waters. For the foreseeable future those roles will likely be filled by nearly autonomous rovers and submarines that can withstand hostile conditions and conduct important science experiments, even when out of contact with Earth for weeks or even months. When Steve Chien took over NASA's Jet Propulsion Laboratory's Artificial Intelligence (AI) Group in the mid-1990s, such sophisticated AI seemed more like science fiction than something destined to play a crucial role to the success of NASA's upcoming 2020 mission to Mars. Chien had a vision to make the technology an indispensable part of NASA's biggest missions.

John Launchbury of DARPA has an excellent video that I recommend everyone watch ( viewing just the slides will give one a wrong impression of the content). Statistical Learning -- Where programmers create statistical models for specific problem domains and train them on big data. Contextual Adaptation -- Where systems construct contextual explanatory models for classes of real world phenomena. It's a bit of a simplified presentation because it lumps all of machine learning, Bayesian methods and Deep Learning into a single category. There are many more approaches to AI that don't fit within DARPA's 3 waves.

Without a doubt, cloud computing and Big Data analytics are top of mind for many people when it comes to hot technology categories where open source is making a big difference. However, there is an absolute renaissance goind on right now in the field of artifical intelligence and the closely related field of machine learning. Sundar Pichai, Google's CEO, recently said on a conference call, "I do think in the long run we will evolve in computing from a mobile-first to an A.I.-first world." Facebook, Google and many other companies have been open sourcing key AI tools as well. Now, Microsoft has released an artificial intelligence system dubbed Project Malmo to the open source community.