Artificial Intelligence (AI) has been with us for well over half a century, but confusion still exists regarding what it is and where it is best applied. This is especially true for the latest AI incarnation: "deep learning." Overall, AI is a group of different technologies created to automate tasks that are typically accomplished by humans. The first types of AI were expert systems had specific instruction sets or rules that encoded types of activities and decisions into software. Now deep learning neural networks are all the rage.

A key challenge in developing artificial intelligence systems with the flexibility and efficiency of human cognition is giving them a similar ability - to reason about entities and their relations from unstructured data. Solving this would allow these systems to generalize to new combinations of entities, making infinite use of finite means. Modern deep learning methods have made tremendous progress solving problems from unstructured data, but they tend to do so without explicitly considering the relations between objects. In two new papers, we explore the ability for deep neural networks to perform complicated relational reasoning with unstructured data. In the first paper - A simple neural network module for relational reasoning - we describe a Relation Network (RN) and show that it can perform at superhuman levels on a challenging task.

Microsoft Corp. is setting up a new research lab focused on artificial intelligence with the goal of creating more general-purpose learning systems. The new lab, called Microsoft Research AI, will be based at the company's headquarters in Redmond, Washington, and involve more than 100 scientists from across various sub-fields of artificial intelligence research, including perception, learning, reasoning and natural language processing. The goal, said Eric Horvitz, the director of Microsoft Research Labs, is to combine these disciplines to work toward more general artificial intelligence, meaning a single system that can tackle a wide-range of tasks and problems. Such a system, for instance, might be able to both plan the best route to drive through a city and also figure out how to minimize your income tax bill, while also understanding difficult human concepts like sarcasm or gestures. This differs from so-called narrow AIs, which are just designed to perform a single task well -- for instance, recognize faces in digital photographs.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster.

Keeping up with the latest AI research can be an odd experience. On the one hand, you're aware that you're looking at cutting-edge experimentation, with new papers outlining the ideas and methods that will probably (eventually) snowball into the biggest technological revolution of all time. On the other hand, sometimes what you're looking at is just unavoidably weird and funny. Case in point is a new paper from Google's AI subsidiary DeepMind titled "Emergence of Locomotion Behaviours in Rich Environments." The research explores how reinforcement learning (or RL) can be used to teach a computer to navigate unfamiliar and complex environments.

Google's artificial intelligence company, DeepMind, has developed an AI that has managed to learn how to walk, run, jump, and climb without any prior guidance. The result is as impressive as it is goofy. Get the latest Google stock price here.

From digital personal assistants to applications that provide contextual and relevant recommendations for ecommerce, to driverless cars that may soon be a reality, AI is making every interface both simple and smart and setting the bar high for future applications. The hallmark of Human-Machine Interaction has been marked by the constant endeavor to make machine interactions more'human-like'. HMI has come a long way from its'command prompt' days where the interaction was machine-centric to touch screens where users interacted with machines using natural gestures. Today, machines have the capability to understand voice (Natural Language Processing) and sight (Image and Vision Computing) – the two senses which humans use most for communication. This has been made possible by advances in artificial intelligence (AI) and deep learning that enable machines to interpret, decipher and contextualise human inputs into machine understandable and readable formats. Machines have begun to understand natural human interaction cues with greater accuracy; and interactions have become adaptive and multimodal (type, touch, voice, sight) as compared to the earlier unimodal, passive and command based interactions.

This course created by Data Weekends, Jose Portilla, and Francesco Mosconi is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems. We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems. Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

Douglas Hofstadter in his book "I am a Strange Loop" coined this idea: In the end, we are self-perceiving, self-inventing, locked-in mirages that are little miracles of self-reference. Where he describes this self-referential mechanism as what describes the unique property of minds. The strange loop is a cyclic system that traverses several layers in a hierarchy. By moving through this cycle one finds oneself where one originally started. Coincidentally enough, this'strange loop' is in fact is the fundamental reason for what Yann LeCun describes as "the coolest idea in machine learning in the last twenty years." Loops are not typical in Deep Learning systems.

Google's parent company -- has an artificial intelligence company, called DeepMind. The company has developed an AI that has managed to learn how to walk, run, jump, and climb without any prior guidance. The result is as impressive as it is goofy. Get the latest Google stock price here. Site highlights each day to your inbox.