Self-learning, machine learning, and AI are all buzzwords in the tech field today. They all represent the next generation in software development and management. In this brave new world, programmers will often set up the application -- and the software will do the rest. Driven by big data, deep learning systems, and consumer demand, you may be investing in self-learning programs sooner than you think. Self-learning, often referred to as machine learning, is a form of AI.

This class is an introduction to the practice of deep learning through the applied theme of building a self-driving car. It is open to beginners and is designed for those who are new to machine learning, but it can also benefit advanced researchers in the field looking for a practical overview of deep learning methods and their application.

Last March, Google's computers roundly beat the world-class Go champion Lee Sedol, marking a milestone in artificial intelligence. The winning computer program, created by researchers at Google DeepMind in London, used an artificial neural network that took advantage of what's known as deep learning, a strategy by which neural networks involving many layers of processing are configured in an automated fashion to solve the problem at hand. Unknown to the public at the time was that Google had an ace up its sleeve. You see, the computers Google used to defeat Sedol contained special-purpose hardware--a computer card Google calls its Tensor Processing Unit. Norm Jouppi, a hardware engineer at Google, announced the existence of the Tensor Processing Unit two months after the Go match, explaining in a blog post that Google had been outfitting its data centers with these new accelerator cards for more than a year.

This year has been rollercoaster crash for many with numerous tragedies and crises occurring all over the world, but it doesn't mean that everything was grim in 2016. Join IBTimes UK as we take a closer look at the many new developments across various fields of technological research, each with the potential to revolutionise human life for the better. This section is devoted to the computer science research into replicating the human mind and helping computers solve complex tasks. For developments concerning the machines themselves, see our articles on robotics. In 2016, computer scientists have begun concentrating more efforts on building deep learning neural networks which are large webs of artificially intelligent classical computers that are trained using computer algorithms to solve complex problems in a similar way to the human central nervous system, and where different layers examine different parts of the problem to combine to produce an answer.

The Montreal-based company is "one of the world's most impressive deep learning research labs for natural language understanding", according to Microsoft, and will help the tech giant pursue a monumental goal in AI, called artificial general intelligence (AGI). The idea behind AGI -- and Maluuba's main pursuit -- is to create "literate machines that can think, reason, and communicate like humans", Microsoft's executive vice president of artificial intelligence, Harry Shum, wrote in a blog post. The company is using natural language processing technology that helps machines understand what people are saying, what context they're asking it in, and what information they want in a response -- just as humans communicate.

It can create digital art, write poems and now, artificial intelligence is composing music. Japanese researchers have developed an AI headset that creates tailor-made music in order to improve the wearer's mood. The AI analyzes the person's brain waves and writes tunes that match their personal sensitivity- and it only takes one minute to create the music using synthesized notes. The AI studied relationships between music and emotions in order to write tunes that coincides with a human's personal sensitivity - and it only takes one minute to create the music using synthesized notes The AI was fed information about the relationship between music and emotions before it began composing music. Volunteers were asked to listen to music while their brain waves were recorded.

Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network. As a result, it is not easy to specify a valid sum-product network by hand and therefore structure learning techniques are typically used in practice. This paper describes the first online structure learning technique for continuous SPNs with Gaussian leaves. We also introduce an accompanying new parameter learning technique.

Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training. Here we show that GANs can be viewed as actor-critic methods in an environment where the actor cannot affect the reward. We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. We also review a number of extensions to GANs and RL algorithms with even more complicated information flow. We hope that by highlighting this formal connection we will encourage both GAN and RL communities to develop general, scalable, and stable algorithms for multilevel optimization with deep networks, and to draw inspiration across communities.

Artificial intelligence (AI) and machine learning have advanced significantly in recent years. Once the stuff of science fiction novels, AI and machine learning are gaining traction in the enterprise, offering tremendous promise for improving organizational profitability and efficiency. First, it's important to distinguish the differences between AI, machine language, and their various subsets. AI, which had been focused around the use of inference engines in the 1980s and 1990s, is the use of computers to simulate human reasoning. A familiar current example of this is IBM Watson, which can examine thousands of pieces of text to identify trends and offer up conclusions.

Michael Schmidt is the founder and CTO of Nutonian. Artificial intelligence is one of the hottest topics in both business and science. Developers and industry analysts are all-in, building castles in the sky with tales of an impending AI "awakening." In preparation for this sea change, Elon Musk and Sam Altman founded OpenAI, a nonprofit with the dual mission of ensuring that AI stays safe and its benefits are as widely and evenly distributed as possible. While it's important to develop AI and harness its powers responsibly, it's incorrect for OpenAI to focus solely on one or two types of AI, like reinforcement learning. Reinforcement learning is among the least used types of AI, and it offers few immediate safety threats or value to people and businesses.