Google needs to make "Parse for AI" to wedge themselves deeply into apps even when on other's platforms/cloud. I've been interested in this space for a while. A broad prediction I have for the coming years is that, as a developer, you won't need to be proficient in machine learning to take advantage of its power. The technology is becoming increasingly democratized and opening up access to millions of new developers. Eventually, you won't even need to know how to program to perform data analysis with ML.

In this video, NYU researchers describe their plans to advance deep learning with their new Nvidia DGX-1 AI supercomputer. New York University's Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines. Because computing speed is critical to accelerating experimentation and advancing research, the center's Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a DGX-1 to fuel this work like never before. The CILVR lab has "unsupervised learning" as its focus. The lab's faculty, research scientists and graduate students are developing techniques that allow machines to learn from raw, unlabeled data by, for example, observing video, looking at images or listening to speech.

Francisco Webber offers a critical overview of current approaches to artificial intelligence using "brute force" (aka big data machine learning) as well as a practical demonstration of semantic folding, an alternative approach based on computational principles found in the human neocortex. Semantic folding is not just a research prototype--it's a production-grade enterprise technology. Francisco explores the theoretical underpinnings of semantic folding, which solves the representational problem and the semantic grounding problem--both well known by AI-researchers since the 1980s, and offers an introduction to the Retina Engine, an Apache Spark library for semantic processing of text. Along the way, Francisco demonstrates functional prototypes of semantic classification, semantic filtering, and semantic searching and explains the applications of semantic folding for the finance, media, automotive, legal, medical, and safety and security industries.

Researchers have tried for decades to create computers capable of learning. Recently, using the human brain as a model, they have had some success. Complicated algorithms have been developed, allowing computers to learn on a limited scale. Deep Learning (DL) is the name used for the process of computers "learning" appropriate responses as they interact with their users, or seek patterns in Big Data. This Big Data "pattern seeking aspect" has the potential to replace Data Scientists as Big Data pattern seekers.

Google DeepMind's system is a move closer to the goal of creating a neural network that can navigate something as complex as the London Underground without any human-written programming. Researchers at Google-owned DeepMind in the UK have developed AI that can store knowledge, such as a map, and use it to navigate a system as complicated as London's Underground. Sure, you can already get directions from Google Maps to navigate transport networks, but DeepMind's new system inches it towards the goal of building a neural network that can navigate without any human-written programming, instead using knowledge to work out a route. Its latest efforts combine deep-learning algorithms with a machine equivalent of a human's working memory. We read the Obama Administration's report on artificial intelligence in full.

A new kind of computer, devised by researchers at Google DeepMind in the U.K., could broaden the abilities of today's best AI systems by giving them an important new feature--a kind of working memory. The researchers show that the computer, which consists of a large neural network connected to a unique form of memory, can perform relatively complex tasks by figuring out for itself what information to hold in its memory. The tasks include figuring out the best way to get from one station to another on London's spaghetti-like Underground transit network, after exploring diagrams of other types of networks and learning about the most salient features. The Google DeepMind researchers call their system a differentiable neural computer. It is differentiable in the sense that its behavior--including what to store in memory--can be learned using the mathematical process, called backpropagation, that underlies the working of neural networks.

An artificial intelligence algorithm has been developed by Google's DeepMind that is capable of working out the most efficient way of getting from one point to another on London's Tube network. The system, known as a differentiable neural computer (DNC), is able to combine basic reasoning with memory in a unique way to solve such problems. "Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from the data," states a paper that details the DNC in the journal Nature. "We show that it can learn tasks, such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs, such as transport networks and family trees." Google's DeepMind gained international media attention earlier this year after it developed the first machine capable of beating a human world champion at the board game Go.

Randomly generating quantized beats and melodies is likely to sound the same - if not better - than this, especially with some basic heuristics as far as music theory is concerned. Maybe I've missed something that makes this neat research, but I feel like there has to be some merit to the result, which in this case sadly sucks.

Facebook was one of the first companies to adopt GPU accelerators to train DNNs. DNNs and GPUs play a key role in the new "Big Sur" computing platform and in the Facebook AI Research (FAIR) purpose-built system, which is specifically designed for neural network training. Facebook describes its goal as to advance the field of machine intelligence and developing technologies to give people better ways to communicate.[8] Google is also heavily investing in deep learning processes. TensorFlow is the second generation of Google's machine learning system, built to understand very large amounts of data and models. It is very flexible in its architecture and has been applied to various kinds of perception and language understanding tasks like recognition and classification of images, speech and text across many applications (email, robotics, natural language processing, maps, etc.).

Above is the built deep Q-network (DQN) agent playing Out Run, trained for a total of 1.8 million frames on a Amazon Web Services g2.2xlarge (GPU enabled) instance. The agent was built using python and tensorflow.