I am only a layman in the neural network space so the ideas and opinions in this column are sure to be refined by comments from more knowledgeable readers. The recent successes of multilayer neural networks have made headlines. Much earlier work on what I imagine to be single-layer networks proved to have limitations. Indeed, the famous book, Perceptrons,a by Turing laureate Marvin Minsky and his colleague Seymour Papert put the kibosh (that's a technical term) on further research in this space for some time. Among the most visible signs of advancement in this arena is the success of the DeepMind AlphaGo multilayer neural network that beat the international grand Go champion, Lee Sedol, four games out of five in March 2016 in Seoul.b

Machine learning has advanced radically over the past 10 years, and machine learning algorithms now achieve human-level performance or better on a number of tasks, including face recognition,31 optical character recognition,8 object recognition,29 and playing the game Go.26 Yet machine learning algorithms that exceed human performance in naturally occurring scenarios are often seen as failing dramatically when an adversary is able to modify their input data even subtly. Machine learning is already used for many highly important applications and will be used in even more of even greater importance in the near future. Search algorithms, automated financial trading algorithms, data analytics, autonomous vehicles, and malware detection are all critically dependent on the underlying machine learning algorithms that interpret their respective domain inputs to provide intelligent outputs that facilitate the decision-making process of users or automated systems. As machine learning is used in more contexts where malicious adversaries have an incentive to interfere with the operation of a given machine learning system, it is increasingly important to provide protections, or "robustness guarantees," against adversarial manipulation. The modern generation of machine learning services is a result of nearly 50 years of research and development in artificial intelligence--the study of computational algorithms and systems that reason about their environment to make predictions.25 A subfield of artificial intelligence, most modern machine learning, as used in production, can essentially be understood as applied function approximation; when there is some mapping from an input x to an output y that is difficult for a programmer to describe through explicit code, a machine learning algorithm can learn an approximation of the mapping by analyzing a dataset containing several examples of inputs and their corresponding outputs. Google's image-classification system, Inception, has been trained with millions of labeled images.28 It can classify images as cats, dogs, airplanes, boats, or more complex concepts on par or improving on human accuracy. Increases in the size of machine learning models and their accuracy is the result of recent advancements in machine learning algorithms,17 particularly to advance deep learning.7 One focus of the machine learning research community has been on developing models that make accurate predictions, as progress was in part measured by results on benchmark datasets. In this context, accuracy denotes the fraction of test inputs that a model processes correctly--the proportion of images that an object-recognition algorithm recognizes as belonging to the correct class, and the proportion of executables that a malware detector correctly designates as benign or malicious. The estimate of a model's accuracy varies greatly with the choice of the dataset used to compute the estimate.

Five different AI algorithms have teamed up to kick human butt in Dota 2, a popular strategy computer game. Researchers at OpenAI, a nonprofit based in California, developed the algorithmic A team, which they call the OpenAI Five. Each algorithm uses a neural network to learn not only how to play the game, but also how to cooperate with its AI teammates. It has started defeating amateur Dota 2 players in testing, OpenAI says. This is an important and novel direction for AI, since algorithms typically operate independently.

If you keep track of industry trends at all, then I bet your newsfeed has been filled with exciting stories and bold predictions about artificial intelligence (AI), machine learning (ML), and neural networks. With hyperbolical headlines such as, "How Artificial Intelligence Will Self-manage the Data Center" and "Is 2018 When Machines Take Over?", I'm sure many people are mentally rolling their eyes as they click to the next story. And companies sometimes want to grab on to it and claim it for their own before things are fully baked. I believe in the power of AI to make data centers better.

Artificial-intelligence research group OpenAI said it created software capable of beating teams of five skilled human players in the video game Dota 2, a milestone in computer science. The achievement puts San Francisco-based OpenAI, whose backers include billionaire Elon Musk, ahead of other artificial-intelligence researchers in developing software that can master complex games combining fast, real-time action, longer-term strategy, imperfect information and team play. The ability to learn these kinds of video games at human or super-human levels is important for the advancement of AI because they more closely approximate the uncertainties and complexity of the real world than games such as chess, which IBM's software mastered in the late 1990s, or Go, which was conquered in 2016 with software created by DeepMind, the London-based AI company owned by Alphabet Inc. Dota 2 is a multiplayer science-fiction fantasy video game created by Bellevue, Washington-based Valve Corp. Each team is assigned a base on opposing ends of a map that can only be learned through exploration. Each player controls a separate character with unique powers and weapons. Each team must battle to reach the opposing team's territory and destroy a structure called an Ancient.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.

Greg Brockman, cofounder of nonprofit AI research organization OpenAI, had an interest in artificial intelligence from a young age, but he didn't come to it right away. Brockman studied computer science at Stanford before transferring to MIT, where he dropped out to launch online payments platform Stripe. As a founding engineer, Brockman helped scale the business from four people to 250. But he had his heart set on another field: artificial general intelligence, or systems that can perform any intellectual task that a human can. Brockman left Stripe to pursue a career in AI, building a knowledge base from the ground up.

If you are a gamer, you must have heard of the two insanely popular Battle Royale games out right now, Fortnite and PUBG. They are two very similar games in which 100 players duke it out on a small island until there is just one survivor remaining. I like the gameplay of Fortnite but tend to prefer the more realistic visuals of PUBG. This got me thinking, can we have graphics mods for games that can allow us to choose the visual effects of our liking without having to rely on the game developers providing us that option? What if a mod was available that could render the frames of Fortnite in the visuals of PUBG?

What is the biggest problem with deep learning networks? I propose that it is: "learning how to forget". Deep learning networks are very good at remembering things, it is so good this capability that it has no trouble learning random labels. Its biggest problem however is that it does not know how to forget. This may surprise many, but learning to forget is critically important in a cognitive system.

Elon Musk's research group, OpenAI, has created artificially intelligent bots that are capable of beating teams of five skilled humans in the video game, Dota 2. The bots were able to beat humans after learn the game over just four weeks. Described as a'milestone in computer science', the achievement means AI can work together to build long-term gaming strategies using'real-time and imperfect' data. Researchers hope that if they can can teach AI the skills they need to play video games, they can use bots to solve more real-world challenges, such as managing a city's transport infrastructure. The five-strong team of AI bots has now now set its sights on the Dota 2 world championship in August. The AI team will compete against seasoned professionals who battle for a prize fund of more than $15,500,000 (£11,000,000).