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) …
Reinforcement learning is recently one of the potential research field of data scientists, it makes feasible to outdo processes what we have so far, and makes imaginable to reach the so called artificial general intelligence (AGI). In our previous blog we described and made the theory of reinforcement learning familiar to you. This following blog requires the knowledge of it and introduces the process basics of reinforcement learning through a practical example. We have to mention OpenAi, they are one of the lead researchers on the reinforcement learning field and on the artificial general intelligence topic. They developed a toolkit called Gym which is a free and easy to use tool to the artificial intelligence community.
This paper overview of RL even covers the history, a good summary of a different area of studies. RL has a long history relates to statistic, computer science, and neuroscience. RL agent learns via trial and error it gathers the training data on its own. The standard RL model an agent that learns uses dynamic programming and statistic Not yet clear which method is better overall. For each time stamp, the agent receives some env, reward and more over time optimize the amount of reward it gets over one period.
Planning has been long considered one of the cognitive abilities of the human mind that is nearly impossible to replicate by artificial intelligence(AI). Some neuroscientists even relate to future planning as one of the key characteristics of human consciousness. Planning does not only requires understanding a specific objective but also projecting that objective onto an environment whose characteristics are unknown in the present. Humans are able to plan not only because we are able to understand a specific task in detail but because our ability to understand our surrounding environment enough that we can project the outcome of that task in the future. In the context of AI, reinforcement learning is the discipline that has been trying to build long-term planning capabilities in AI agents.
Our goal is to make Deep Reinforcement Learning accessible to everyone. We introduce Surreal, an open-source, reproducible, and scalable distributed reinforcement learning framework. Surreal provides a high-level abstraction for building distributed reinforcement learning algorithms. We implement our distributed variants of PPO and DDPG in the current release. Click to see detailed documentation!
Machine learning, sometimes called computational intelligence, has broken down barriers in recent years and has made significant advances in a number of areas, such as robotics, machine translation, social networking, e-commerce, and even in areas such as medicine and healthcare. Machine Learning is an area of AI with a goal to develop computational techniques on learning as well as the construction of systems capable of acquiring knowledge automatically. A learning system is a computer program that makes decisions based on accumulated experiences through the successful solution of past problems. Despite the short definition, there are numerous different learning algorithms and the area is one of the hottest in the field of computing, with several new techniques and algorithms being published regularly. Many people think machine learning and artificial intelligence mean the same thing, but that's not quite accurate.
But it's not always practical; model-free approaches, which aim to get agents to directly predict actions from observations about their world, can take weeks of training. Model-based reinforcement learning is a viable alternative -- it has agents come up with a general model of their environment they can use to plan ahead. But in order to accurately forecast actions in unfamiliar surroundings, those agents have to formulate rules from experience. Toward that end, Google in collaboration with DeepMind today introduced the Deep Planning Network (PlaNet) agent, which learns a world model from image inputs and leverages it for planning. It's able to solve a variety of image-based tasks with up to 5,000 percent the data efficiency, Google says, while maintaining competitiveness with advanced model-free agents.
Machine learning (ML) is a method of artificial intelligence (AI) in which data is used to train a machine so that it can make decisions or predictions on its own. In a previous blog, Setting up your machine learning projects for success, we discussed how data and modelling play a key role in allowing a machine to learn and improve, and our e-book explains how ML fits into the bigger picture of AI. An ML algorithm is a key element that ties this all together, and as we'll discover in this blog, there are four main categories of ML algorithms – supervised machine learning, unsupervised machine learning, semi-supervised machine learning, and reinforcement machine learning. Many of today's ML algorithms can be considered supervised, which means the model is iteratively trained by running the algorithm and comparing its output against data that is known to be correct. Once training is complete, the algorithm and model are ready for inference.
Video games have become a proving ground for AIs and Uber has shown how its new type of reinforcement learning has succeeded where others have failed. Some of mankind's most complex games, like Go, have failed to challenge AIs from the likes of DeepMind. Reinforcement learning trains algorithms by running scenarios repeatedly with a'reward' given for successes, often a score increase. Two classic games from the 80s – Montezuma's Revenge and Pitfall! – have thus far been immune to a traditional reinforcement learning approach. This is because they have little in the way of notable rewards until later in the games.
This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. In September 2012, Alex Krizhevsky and Ilya Sutskever, two AI researchers from the University of Toronto, made history at ImageNet, a popular competition in which participants develop software that can recognize objects in a large database of digital images. Krizhevsky and Sutskever, and their mentor, AI pioneer Geoffrey Hinton, submitted an algorithm that was based on deep learning and neural networks, an artificial intelligence technique that the AI community viewed with skepticism because of its past shortcomings. AlexNet, the deep learning algorithm developed by the U of T researchers, was able to win the competition with an error rate of 15.3 percent, a whopping 10.8 percent better than the runner up. By some accounts, the event triggered the deep learning revolution, creating interest in the field by many academic and commercial organizations.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology. Deep learning has achieved some very impressive accomplishments of late. I won't review them here, but chances are you already know about them anyhow. Given these high-profile successes, one could forgive the uninitiated (be they laymen or tech-savvy individuals) for the casual confounding of terms such as "artificial intelligence" and "deep learning," among others.