Deep Reinforcement Learning has managed to achieve state-of-the-art results in learning control policies directly from raw pixels. However, despite its remarkable success, it fails to generalize, a fundamental component required in a stable Artificial Intelligence system. Using the Atari game Breakout, we demonstrate the difficulty of a trained agent in adjusting to simple modifications in the raw image, ones that a human could adapt to trivially. In transfer learning, the goal is to use the knowledge gained from the source task to make the training of the target task faster and better. We show that using various forms of fine-tuning, a common method for transfer learning, is not effective for adapting to such small visual changes. In fact, it is often easier to re-train the agent from scratch than to fine-tune a trained agent. We suggest that in some cases transfer learning can be improved by adding a dedicated component whose goal is to learn to visually map between the known domain and the new one. Concretely, we use Generative Adversarial Networks (GANs) to create a mapping function to translate images in the target task to corresponding images in the source task, allowing us to transform between the different tasks. We show that learning this mapping is substantially more efficient than re-training. A visualization of a trained agent playing in a modified condition, with and without the GAN transfer, can be seen in https://youtu.be/e2TwjduPT8g .

We are on a mission to build human-like intelligence in machines, enabling a future of abundance for all. We thoughtfully and realistically pave the way for a world filled with more powerful and helpful AI systems. Our research spans AGI, deep learning, reinforcement learning, multi-sensory machine perception, dynamic motion planning, AR VR, distributed multi-agent control systems, and much more. We love to create, to build, to play with ideas. We get excited by the change we get to create in the world, regardless of how hard it is and how many say it can't be done.

Machine learning and artificial intelligence (AI) are all the rage these days -- but with all the buzzwords swirling around them, it's easy to get lost and not see the difference between hype and reality. For example, just because an algorithm is used to calculate information doesn't mean the label "machine learning" or "artificial intelligence" should be applied. Before we can even define AI or machine learning, though, I want to take a step back and define a concept that is at the core of both AI and machine learning: algorithm. An algorithm is a set of rules to be followed when solving problems. In machine learning, algorithms take in data and perform calculations to find an answer.

Beating humans at board games is passé in the AI world. Now, top academics and tech companies want to challenge us at video games instead. Today, OpenAI, a research lab founded by Elon Musk and Sam Altman, announced its latest milestone: a team of AI agents that can beat the top 1 percent of amateurs at popular battle arena game Dota 2. You may remember that OpenAI first strode into the world of Dota 2 last August, unveiling a system that could beat the top players at 1v1 matches. However, this game type greatly reduces the challenge of Dota 2. OpenAI has now upgraded its bots to play humans in 5v5 match-ups, which require more coordination and long-term planning. And while OpenAI has yet to challenge the game's very best players, it will do so later this year at The International, a Dota 2 tournament that's the biggest annual event on the e-sports calendar.

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OpenAI's video-game-playing bots are getting much better at mastering sci-fi strategy war game Dota 2, seeing off semi pro players with ease in team matchups. However, they can't quite master the whole game to beat top professional teams – yet. Last August, machine-learning software built by the OpenAI lab headquartered in San Francisco managed to best Dendi, a pro Dota 2 player, winning two matches out of three. But the victories were only in one-on-one games – a single bot against a single human – and under very limited circumstances that are not applicable in real competitions. Fast forward about a year, and now OpenAI's bots can play in the more traditional five-versus-five settings, beating amateurs and semi-pro gamers.

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You want to start an AI project – but what processes do you need to bear in mind, how do you manage the data, and what do you need to look at when it comes to team composition and testing? In this extract from Embracing the Power of AI, Javier Minhondo, Juan José López Murphy, Haldo Spontón, Martín Migoya, and Guibert Englebienne outline how to get through these crucial initial stages. Getting a grasp of the business needs is crucial, as is understanding the data sources. Are we thinking the solution centers on user needs or are we putting technology first? Going back to the key aspects discussed in Chapter 6 (see page 73), successful AI products must be driven and designed to help people access and process information, and facilitate decision-making.

The Dota 2 world championship, The Invitational, is fast approaching, and a top team will have a different-looking squad to contend with: a group of artificial intelligence bots. OpenAI, which Elon Musk co-founded, has been taking on top Dota 2 players with the bots since last year, and now it's gunning for a team of top professionals in an exhibition match at one of the biggest events in eSports. OpenAI took on individual players at last year's The Invitational in a one-on-one minigame, and pros said that by watching the matches back, they were able to learn from the bots. But playing as a team introduces different types of intricacies, and OpenAI had to teach the AI how to coordinate the five bots. At any time, a hero (or character) can make one of around 1,000 actions; the bots have to make effective decisions while processing around 20,000 values representing what's going on in the game at a given time.

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.