If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Microsoft today announced that it would invest $1 billion in OpenAI, the San Francisco-based AI research firm cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, Elon Musk, and others, with backing from luminaries like LinkedIn cofounder Reid Hoffman and former Y Combinator president Sam Altman. In a blog post, Brockman said the investment will support the development of artificial general intelligence (AGI) -- AI with the capacity to learn any intellectual task that a human can -- with "widely distributed" economic benefits. To this end, OpenAI intends to partner with Microsoft to jointly develop new AI technologies for the Seattle company's Azure cloud platform and will enter into an exclusivity agreement with Microsoft to "further extend" large-scale AI capabilities that "deliver on the promise of AGI." Additionally, OpenAI will license some of its technologies to Microsoft, which will commercialize them and sell them to as-yet-unnamed partners, and OpenAI will train and run AI models on Azure as it works to develop new supercomputing hardware while "adhering to principles on ethics and trust." "AI is one of the most transformative technologies of our time and has the potential to help solve many of our world's most pressing challenges," said Microsoft CEO Satya Nadella.
In a joint phone interview with Mr. Altman, Microsoft's chief executive, Satya Nadella, later compared A.G.I. to his company's efforts to build a quantum computer, a machine that would be exponentially faster than today's machines. "Whether it's our pursuit of quantum computing or it's a pursuit of A.G.I., I think you need these high-ambition North Stars," he said. Mr. Altman's 100-employee company recently built a system that could beat the world's best players at a video game called Dota 2. Just a few years ago, this kind of thing did not seem possible. Dota 2 is a game in which each player must navigate a complex, three-dimensional environment along with several other players, coordinating a careful balance between attack and defense. In other words, it requires old-fashioned teamwork, and that is a difficult skill for machines to master.
LAUSANNE: Since the earliest days of virtual chess and solitaire, video games have been a playing field for developing artificial intelligence (AI). Each victory of machine against human has helped make algorithms smarter and more efficient. But in order to tackle real world problems – such as automating complex tasks including driving and negotiation – these algorithms must navigate more complex environments than board games, and learn teamwork. Teaching AI how to work and interact with other players to succeed had been an insurmountable task – until now. In a new study, researchers detailed a way to train AI algorithms to reach human levels of performance in a popular 3D multiplayer game – a modified version of Quake III Arena in Capture the Flag mode.
Since the earliest days of virtual chess and solitaire, video games have been a playing field for developing artificial intelligence (AI). Each victory of machine against human has helped make algorithms smarter and more efficient. But in order to tackle real world problems – such as automating complex tasks including driving and negotiation – these algorithms must navigate more complex environments than board games, and learn teamwork. Teaching AI how to work and interact with other players to succeed had been an insurmountable task – until now. In a new study, researchers detailed a way to train AI algorithms to reach human levels of performance in a popular 3D multiplayer game – a modified version of Quake III Arena in Capture the Flag mode.
Esports have become major international sports with hundreds of millions of spectators. Esports games generate massive amounts of telemetry data. Using these to predict the outcome of esports matches has received considerable attention, but micro-predictions, which seek to predict events inside a match, is as yet unknown territory. Micro-predictions are however of perennial interest across esports commentators and audience, because they provide the ability to observe events that might otherwise be missed: esports games are highly complex with fast-moving action where the balance of a game can change in the span of seconds, and where events can happen in multiple areas of the playing field at the same time. Such events can happen rapidly, and it is easy for commentators and viewers alike to miss an event and only observe the following impact of events. In Dota 2, a player hero being killed by the opposing team is a key event of interest to commentators and audience. We present a deep learning network with shared weights which provides accurate death predictions within a five-second window. The network is trained on a vast selection of Dota 2 gameplay features and professional/semi-professional level match dataset. Even though death events are rare within a game (1\% of the data), the model achieves 0.377 precision with 0.725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds. An example of the system applied to a Dota 2 match is presented. This model enables real-time micro-predictions of kills in Dota 2, one of the most played esports titles in the world, giving commentators and viewers time to move their attention to these key events.
Competition between humans and artificial intelligence (A.I.) usually plays out in research papers. Occasionally, there's a public performance of a game of chess or Go in front of a staid crowd. Last month in Vancouver, British Columbia, however, I saw something entirely different. The Canadian city was playing host to The International, an annual tournament for the video game Dota 2 boasting a $25 million prize pool -- the largest in esports history. The event was raucous, tribal.
So much for the best Dota 2 players having the skill to fend off cutting-edge AI. OpenAI Five has beaten five players from OG, the veteran team that won Valve's 2018 International, in a best-of-three exhibition match. The Verge noted that the deep learning system thrived by relying on aggressive and unconventional methods, including instant revivals for heroes in the early stages, and picking valuable heroes. While OG put up a fight (the first round lasted over 30 minutes), it couldn't hold out. OpenAI also used the exhibition to show that Five could play alongside human players and learn from their play styles.
From the beginning if the history of AI, there has been interest in games as a platform of research. As the field developed, human-level competence in complex games became a target researchers worked to reach. Only relatively recently has this target been finally met for traditional tabletop games such as Backgammon, Chess and Go. Current research focus has shifted to electronic games, which provide unique challenges. As is often the case with AI research, these results are liable to be exaggerated or misrepresented by either authors or third parties. The extent to which these games benchmark consist of fair competition between human and AI is also a matter of debate. In this work, we review the statements made by authors and third parties in the general media and academic circle about these game benchmark results and discuss factors that can impact the perception of fairness in the contest between humans and machines
AI and Games is a crowdfunded show and needs your support. You can help fund this series on Paypal, KoFi and Patreon (where you can get access to additional content). You can follow AI and Games on Facebook and Twitter: http://www.facebook.com/AIandGames I take a look at the potential MOBAs have to become the next big thing in AI research and some of the work that's already been achieved in academia and corporate R&D. "Varus As We Fall" from the League of Legends Soundtrack "Pre-Game" from the DOTA2 Soundtrack "Legends Never Die" from the League of Legends Soundtrack
AI research nonprofit OpenAI has created a system that can generate fake text from a single line -- and it's not open-sourcing the code for fear of misuse. OpenAI was cofounded by tech mogul Elon Musk, and its sponsors include Silicon Valley heavy-hitters such as Peter Thiel and Amazon Web Services. Last year it gained the praise of Bill Gates after it built a team of five neural networks capable of beating human players in the computer game "Dota 2." Read more: Bill Gates hails "huge milestone" for AI as bots work in a team to destroy humans at video game "Dota 2" Now the company has created a system, named GPT2, capable of imitating and generating text based on only a sentence. The Guardian's Alex Hern got to play with the system, and tried typing in a single Guardian headline about Brexit. From that headline alone, GPT2 was able to generate quotes from UK Labour leader Jeremy Corbyn as well as a fictional spokesman for Prime Minister Theresa May.