If you ask 100 people for the definition of "artificial intelligence," you'll get at least 100 answers, if not more. At AWS, we define it as a service or system which can perform tasks that usually require human-level intelligence such as visual perception, speech recognition, decision making, or translation. On this new AWS blog, we'll be covering these areas and more, with in-depth technical content, customer stories, and new feature announcements. The challenges related to building sophisticated AI systems center mostly around scale: the datasets are large, training is computationally hungry, and inferring predictions can be challenging to do at scale or on lower-power and mobile devices. Customers have been using AWS to solve these general problems for years, and the ability to be able to access storage, GPUs, CPUs, and IoT services on demand has emerged as a perfect fit for intelligent systems in production.

UMass today rolled out its new GPU cluster – Gypsum – aimed at deep learning. Like many institutions, UMass is seeking to attract Ph.D. students drawn to deep learning and artificial intelligence. At 400 GPUs, Gypsum is on the large side for academic GPU clusters according the university. The new systems will be housed at the Massachusetts Green High Performance Computing Center in Holyoke, Mass., and is the result of a five-year, $5 million grant to the campus from the Massachusetts Technology Collaborative made last year. It represents a one-third match to a $15 million gift supporting data science and cybersecurity research from the MassMutual Foundation of Springfield.

In this article, I cover the 12 types of AI problems i.e. I address the question: in which scenarios should you use Artificial Intelligence (AI)? Recently, I conducted a strategy workshop for a group of senior executives running a large multi national. In the workshop, one person asked the question: How many cats does it need to identify a Cat? This question is in reference to Andrew Ng's famous paper on Deep Learning where he was correctly able to identify images of Cats from YouTube videos.

Google's artificial intelligence DeepMind, became famous by beating the South Korean, professional GO player Lee Sedol. Lee Sedol played a historic five game match against Google DeepMind's AlphaGo computer program in March 2016. AlphaGo won the match and became the world's very first computer which had defeated a world class human player on GO. After achieving the impossible, DeepMind now has a very different challenge to focus on; Social Dilemmas. The Artificial Intelligence (AI) department of Google developed and used new theoretic game scenarios to see if AI can learn to work together for a mutual benefit or not.

The cross-entropy loss commonly used in deep learning is closely related to the defining properties of optimal representations, but does not enforce some of the key properties. We show that this can be solved by adding a regularization term, which is in turn related to injecting multiplicative noise in the activations of a Deep Neural Network, a special case of which is the common practice of dropout. We show that our regularized loss function can be efficiently minimized using Information Dropout, a generalization of dropout rooted in information theoretic principles that automatically adapts to the data and can better exploit architectures of limited capacity. When the task is the reconstruction of the input, we show that our loss function yields a Variational Autoencoder as a special case, thus providing a link between representation learning, information theory and variational inference. Finally, we prove that we can promote the creation of disentangled representations simply by enforcing a factorized prior, a fact that has been observed empirically in recent work. Our experiments validate the theoretical intuitions behind our method, and we find that information dropout achieves a comparable or better generalization performance than binary dropout, especially on smaller models, since it can automatically adapt the noise to the structure of the network, as well as to the test sample.

ABSTRACT In this paper we describe approaches for discovering acoustic concepts and relations in text. The first major goal is to be able to identify text phrases which contain a notion of audibility and can be termed as a sound or an acoustic concept. We also propose a method to define an acoustic scene through a set of sound concepts. We use pattern matching and parts of speech tags to generate sound concepts from large scale text corpora. We use dependency parsing and LSTM recurrent neural network to predict a set of sound concepts for a given acoustic scene. These methods are not only helpful in creating an acoustic knowledge base but in the future can also directly help acoustic event and scene detection research. Index Terms-- Sound Concepts, Audio Events and Scenes, Acoustic Relations, Sound and Language 1. INTRODUCTION Analyzing non-speech content has been gaining a lot of attention in the audio community.

Students code software at desktops, while others assemble odd machines with wires and multi-colored boxes. Earning a spot at this elite university isn't easy; UC-Berkeley accepted a mere 14.8 percent of applicants for the class of 2020. So this young crew will likely be tomorrow's tech leaders and pioneers. Despite all the promise, it appears that BRETT is struggling. BRETT is a robot, and he – or she, or it – is attempting to place a small wooden block into a small hole. Again and again, BRETT swings his arm over the opening, attempts to place the block, but fumbles. Just can't make it fit. However, as robots go, BRETT has a huge advantage: he can learn. Every time BRETT swings his arm and fails, he calculates what went wrong. In essence he's doing what we humans do: he's failing, and in response he's deciding how to improve the next effort. I stand watching for about 15 minutes, and finally BRETT succeeds – a lengthy period given the simple task. But the astounding point is that the robot really did learn.

Understanding Agent Cooperation Joel Leibo, Vinicius Zambaldi, Marc Lanctot, Janusz Marecki, Thore Graepel Google DeepMind Blog "Recent progress in artificial intelligence and specifically deep reinforcement learning provides us with the tools to look at the problem of social dilemmas through a new lens... we showed that we can apply the modern AI technique of deep multi-agent reinforcement learning to age-old questions in social science such as the mystery of the emergence of cooperation." Agility Robotics Introduces Cassie, a Dynamic and Talented Robot Delivery Ostrich Evan Ackerman IEEE Spectrum "Agility Robotics, a spin-off of Oregon State University, is officially announcing a shiny new bipedal robot named Cassie. Cassie is a dynamic walker, meaning that it walks much more like humans do than most of the carefully plodding bipedal robots we're used to seeing... Cassie has some work to do before it's ready to be hauling groceries up stairs for you, but we're very much looking forward to watching this robot taking more steps toward robust and dynamic legged locomotion." How Escape Rooms and Live Theater Are Paving the Way for VR Bryan Bishop The Verge "Cinema has had more than a century to develop its own language of shots, cuts, and transitions, while storytelling in VR is still in its infancy... creators seem to be zeroing in on interactive, experiential moments as one of the key building blocks of VR storytelling. One of Chris Milk's next projects is a piece set in the Planet of the Apes universe that will lean heavily on AI to drive interactive character performances."

Today's artificial intelligence industry is not easy to quantify. Besides the lack of consensus on a coherent definition for "artificial intelligence" as a term, the field's nascent stage of development makes it difficult to carve out silos or hard barriers of where one industry or application ends, and another begins. In one of our more popular recent articles, we aimed to derive a valuation of the artificial intelligence market, based on current market research and our own insights. In this week's article, I've set out to determine more of a "lay of the land" of the AI industry, including it's various segments and application areas. If you're interested in how developments in machine learning and AI might impact your own company or business, then keeping an eye on trends in industry and application growth is pertinent.