A computer program has defeated a master of the ancient Chinese game of Go, achieving one of the loftiest of the Grand Challenges of AI at least a decade earlier than anyone had thought possible. The programmers, at Google's Deep Mind laboratory, in London, write in today's issue of Nature that their program AlphaGo defeated Fan Hui, the European Go champion, 5 games to nil, in a match held last October in the company's offices. Earlier, the program had won 494 out of 495 games against the best rival Go programs. AlphaGo's creators now hope to seal their victory at a 5-game match against Lee Se-dol, the best Go player in the world. That match, for a 1 million prize fund, is scheduled to take place in March in Seoul, South Korea.

This post explores the idea that if we can successfully learn multiple levels of representation then we can generalize well. The below flow charts illustrate how the different parts of an AI system relate to each other within different AI disciplines. The shaded boxes indicate components that are able to learn from data. Rule-based systems are hand-designed AI programs. The knowledge required by these programs are provided by experts in the concerned field.

Rage Frameworks, a provider of knowledge-based automation technology and services, announced new deployments of its traceable "deep learning" technology known as Rage AI across several global financial services, consumer products and manufacturing firms. The challenges these organizations faced required the understanding and interpretation of complex documents and integration of other transaction data from enterprise resource planning (ERP) systems to identify significant cost efficiencies and compliance conformance. RAGE AI incorporates deep linguistic parsing and proprietary linguistics-based innovations to understand the real meaning of documents and interpret them as a human would, and can operate completely unsupervised or with assistance by human experts. With its traceable, deep learning technology, RAGE AI significantly extends the frontier of deep learning and machine intelligence from "natural language processing" to "natural language understanding." The platform reads and interprets documents within its context, and as a totally transparent solution, RAGE AI enables knowledge workers to move forward confidently knowing the reasoning behind the platform's insights is completely auditable.

THAT a computer program can repeatedly beat the world champion at Go, a complex board game, is a coup for the fast-moving field of artificial intelligence (AI). Another high-stakes game, however, is taking place behind the scenes, as firms compete to hire the smartest AI experts. Technology giants, including Google, Facebook, Microsoft and Baidu, are racing to expand their AI activities. Last year they spent some 8.5 billion on research, deals and hiring, says Quid, a data firm. That was four times more than in 2010.

Why is it so hard to install deep Learning / Neural Network libraries? I switched to Linux because a lot of different sources indicate that a installation on windows/osx is even harder. First I tried to install Caffe. But after a while I had to give up. After that I convinced myself that I could live without c and tried a python environment.

Over the past several years, graphics processing units (GPUs) have become the de facto standard for implementing deep learning algorithms in computer vision and other applications. GPUs offer a large number of processing elements, a stable and expanding ecosystem, support for standards such as OpenCL, and a wide range of intellectual property to develop applications rapidly. However, as the industry matures, field programmable gate arrays (FPGAs) are now starting to emerge as credible competition to GPUs for implementing deep learning algorithms. A recently published paper from Microsoft Research garnered quite a bit of attention in the industry when it contended that using FPGAs could be as much as 10 times more power efficient compared to GPUs. Although the performance of FPGAs was much lower than GPUs, the FPGA used for comparison was a mid-range device, which left the door open for further lowering the power on FPGAs.

In its effort to commercialize Watson, IBM has made some of the features developed for the Jeopardy! It has now added three deep-learning-based features to this Watson API: translation, speech-to-text, and text-to-speech. These could be used to build, for example, apps or websites that offer translation or transcription services. But developers could also connect them to other Watson services that parse questions and search for answers in large amounts of text. This could lead to an app that makes it possible to search large numbers of documents with naturally spoken queries.

If network effects are one of the most important concepts for software-based businesses, then that may be especially true of data network effects -- a network effect that results from data. Particularly given the prevalence of machine learning and deep learning in startups today. But simply having a huge corpus of data does not a network effect make! So how can startups ensure they don't get a lot of data exhaust but get insight out of and add value to that data and the network? How can they make sure that the (arguably inevitable) data aspect of their business isn't just a sideshow or accident? How should founders strike the balance between not overbuilding/ building a data team vs. having enough data for those data scientists to work with in the first place? And finally, what are the ethical considerations of all this?

Google has made its Cloud Machine Learning platform available to developers today, giving them access to the platform that powers Google Photos, Translate and the Google Inbox app. The move means that developers can build Google's translation, photo, and speech recognition APIs into their own apps utilising the Cloud Machine Learning platform moving forward. The platform was launched at NEXT 2016, Google's Cloud Platform conference and is now available in limited preview. "Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications," Fausto Ibarra, Google's director of product management wrote in a blog post. "It provides access to the same technologies that power Google Now, Google Photos, and voice recognition in Google Search as easy to use REST APIs."

Beyond that, there are other important issues in machine learning besides representation learning, such as learning from delayed rewards, which is the focus of reinforcement learning, which deep learning per se does not address. Again, what we see here is combinations of deep learning with other types (e.g., Q-learning in DeepMind's Atari player). And backpropagation, which is what powers most deep learning systems, solves the credit assignment problem, but it doesn't solve other crucial problems, like learning structure, learning composable knowledge, generalizing out of sample, etc. So we need lots more besides deep learning to have a truly general-purpose learner.