Colorization of grayscale images is a simple task for the human imagination. Researchers from the Toyota Technological Institute at Chicago and University of Chicago developed a fully automatic image colorization system using deep learning and GPUs. Their paper mentions previous approaches required some level of user input. Using a TITAN X GPU, they trained their deep neural network to predict hue and chroma distributions for each pixel given its hypercolumn descriptor. The predicted distributions then determine color assignment at test time.

David Kenny took the helm of IBM's Watson Group ibm in February, after Big Blue acquired The Weather Company, where Kenny had served as CEO. In the months since then, the Watson business has grown dramatically, with well over 100,000 developers worldwide now working with more than three dozen Watson application program interfaces (APIs). Fortune Deputy Editor Clifton Leaf caught up with Kenny in mid-October, when IBM Watson's General Manager was in San Francisco, getting ready to open Watson West--the AI system's newest business outpost--and to launch the company's second World of Watson conference, a gathering of its burgeoning ecosystem of partners and users, in Las Vegas on Oct. 24. FORTUNE: We hear a lot of terms on the AI front these days--"artificial intelligence," "machine learning," "deep learning," "unsupervised learning," and the one IBM uses to describe Watson: "cognitive computing." KENNY: Deep learning is a subset of machine learning, which essentially is a set of algorithms. Deep-learning uses more advanced things like convolutional neural networks, which basically means you can look at things more deeply into more layers. Machine learning could work, for example, when it came to reading text. Deep learning was needed when we wanted to read an X-ray. And all of that has led to this concept of artificial intelligence--though at IBM, we tend to say, in many cases, that it's not artificial as much as it's augmented.

It's been a big year for Artificial Intelligence, and its related terms like Cognitive Computing, Machine Intelligence, and Intelligent Machines, and for all its associated branches, from Machine Learning to Neural Networking and Natural Language Processing. IBM, for example, has continued making strides with Watson in verticals such as healthcare and financial services. The vendor announced everything from advances in its ability to add rich image analytics with Deep Learning to the Watson Health platform, to a furthering of its partnership with Deloitte that will use the technology in solutions to more efficiently and immediately manage risk and regulatory compliance requirements. At the end of last year, we also saw Tesla Motors CEO Elon Musk join with other industry figures to launch a non-profit AI research venture, OpenAI, with its stated goal being to help spread the technology to a broader base and direct it to having a positive impact on humanity. Furthermore, just to pick one Google announcement in the area, the search giant said it's using AI and Machine Learning in the Smart Reply capability in its Gmail Inbox mobile email client.

The practice of software development has created development methodologies such agile development and lean methodology to tackle the complexity of development with the objective of improving the quality and efficiency of software creation. Although Deep Learning is built from software it is a different kind of software and therefore a different kind of methodology is needed. Deep Learning differs most from traditional software development in that a substantial portion of the process involves the machine learning how to achieve objectives. The developer is not completely out of the equation, but is working in concert to tweak the Deep Learning algorithm. Deep Learning is sufficiently rich and complex a subject that a process model or methodology is required to guide a developer.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers. "Deep compression: Compressing deep neural network with pruning, trained quantization and huffman coding."

"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media. Today's column is written by Santanu Kolay, senior vice president of engineering at Turn. Artificial intelligence (AI) is one of the most-hyped topics in advertising right now. At Cannes, Saatchi & Saatchi featured an AI-created film. This summer, IBM's Watson rolled out AI-powered ads for The Weather Co. that answered consumer questions.

Cirrascale Corporation, a premier developer of server and cloud solutions enabling GPU-driven deep learning infrastructure, has announced the future availability of the IBM Power Systems S822LC for HPC in multiple configurations for its GPU-as-a-Service cloud platform. The configurations will support both the 8-core and 10-core POWER8 CPU and up to four Pascal architecture-based NVIDIA Tesla P100 GPU accelerators using NVIDIA NVLink, a high-speed, energy-efficient bidirectional interconnect. NVIDIA NVLink is embedded at the silicon level and tightly coupled with the CPU. This enables data transmission rates up to 80GB/s bidirectional between CPU and GPU, and 115GB/s between CPU and board memory making it 2.5x faster than competing offerings. The service is differentiated because it offers the industry's most up-to-date, dedicated GPUs as a bare metal offering.

When it comes to robots replacing humans, we might think we have the upper hand since we're the ones who build and program them but that's not neccesarily the case anymore. Google is taking a different approach to training its robots – it's letting them teach each other. Researchers at Google have released a report showing how they connected 14 robotic arms together and used convolutional neural networks to let them teach themselves how to pick things up. The approach mimics how young children learn between the ages of one and four years old, and is essentially helping the robots to develop reliable hand-eye coordination. Typically, a robot would be programmed to carry out specific tasks, but this method shows how they can learn through trial-and-error in combination with a neural network – the same way a child learns how to do something by watching other people.

If you are a newcomer to the Deep Learning area, the first question you may have is "Which paper should I start reading from?" Here is a reading roadmap of Deep Learning papers! You will find many papers that are quite new but really worth reading. After reading above papers, you will have a basic understanding of the Deep Learning history, the basic architectures of Deep Learning model(including CNN, RNN, LSTM) and how deep learning can be applied to image and speech recognition issues. The following papers will take you in-depth understanding of the Deep Learning method, Deep Learning in different areas of application and the frontiers.

Larger data sets and faster computers have enabled a recent flurry of progress--and investment--in artificial intelligence. David Cox of Harvard thinks the next big jump will depend on understanding what happens inside the head of a rat when it plays video games. Cox leads a 28 million project called Ariadne, funded by the U.S. Office of the Director of National Intelligence, that is looking for clues in mammalian brains to make software smarter. "This is a huge, moonshot-like effort to go into the brain and see what clues and tricks are hiding there for us to find," he said today at EmTech MIT 2016. Recent progress in tasks such as image recognition and translation sprang from putting more computing power behind a technique known as deep learning, which is loosely inspired by neuroscience.