Try typing "the machines" into Google and chances are that one of the top results the artificial intelligence-powered search engine will return is the phrase: "The Machines are Coming". After a 2016 filled with high-profile advances in artificial intelligence (AI), leading technologists say this could be a breakout year in the development of intelligent machines that emulate humans. Asia, until now lagging Silicon Valley in AI, will play a bigger role as the field cements itself at the pinnacle of the technology world in 2017, the experts say. AI – technically, a computing field that involves the analysis of large troves of data to predict outcomes and patterns – is as old as modern computers but its esoteric nature means it has long endured caricatures of its actual potential – think for example, the 1960s space age cartoon The Jetsons, which featured a sentient robot maid and automated flying cars (both of which we are still waiting for, even 50 years on). Now, a confluence of factors has given rise to hopes that computers with human-like cognitive ability may soon be a reality.

TechEmergence is an artificial intelligence market research firm. We help companies and institutions gain insight on the applications and implications of AI and machine learning technologies. Our insights have been featured and referenced in some of the world's most respected publications, including:

Or why I don't want to listen to the Red Hot Chili Peppers. No offense to the Peppers, but just because I listen to alternative music and "people like me who listen to alt music" enjoy hearing the Peppers it does not then mean that I want to hear them. I am not "people like me", I am me. And I want the smart computers behind my favorite services to know me enough to deliver the content I want. It doesn't matter which service we talk about.

In October 2015 we launched the Google Brain Residency, a 12-month program focused on jumpstarting a career for those interested in machine learning and deep learning research. This program is an opportunity to get hands on experience using the state-of-the-art infrastructure available at Google, and offers the chance to work alongside top researchers within the Google Brain team. Our first group of residents arrived in June 2016, working with researchers on problems at the forefront of machine learning. The wide array of topics studied by residents reflects the diversity of the residents themselves -- some come to the program as new graduates with degrees ranging from BAs to Ph.Ds in computer science to physics and mathematics to biology and neuroscience, while other residents come with years of industry experience under their belts. They all have come with a passion for learning how to conduct machine learning research.

Deep learning is helping solve everyday inconveniences, both serious and superficial. Artificial intelligence has been used to manage the global financial market, predict heart failure, and help cars navigate city streets autonomously. But not every AI application is so serious. A Brown University student recently developed a system that invents futuristic and ridiculous baby names. And last year the first AI-judged beauty contest was held.

This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, microscopic image analysis, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. This book describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database. Dr. Yang is the founder of the Biomedical Image Computing and Imaging Informatics (BICI2) lab (http://www.bme.ufl.edu/labs/yang/). His major research interests are focus on biomedical image analysis and imaging informatics, computer vision, biomedical informatics and machine learning.

Chip giant Intel last week rolled out a new deep learning framework that runs as a Spark job atop Hadoop. Called BigDL, the open source software is designed to take advantage of hardware acceleration capabilities that Intel has built into its Xeon CPUs. BigDL, which Intel released on Github, is modeled after Torch, an open source deep learning framework used in scientific computing. Intel says the framework supports numeric computing via Tensor, as well as high level neural networks, and can be used to run prebuilt Caffe and Torch models on Spark. While many deep learning frameworks today leverage GPUs, Intel is taking a different route with BigDL, for obvious reasons.

Facebook have kick started a trend of AI's building more AI's and it's only going to accelerate Deep Neural Networks (DNNs) are taking over the internet. DNNs, unlike their more basic pattern matching Machine Learning cousins are able to learn and replicate human like tasks by analysing vast amounts of digital data and these artificially intelligent systems are injecting online services with a power that just wasn't viable in years past. They're identifying faces in photos, powering search, pulling meaning from videos, applying meaning to language and translating complex conversations from one language to another. But what's less discussed is how the giants of the Internet go about building these rather remarkable engines of AI. Companies like Google and Facebook pay top dollar for some really smart people – only a few hundred souls on Earth have the talent and the training needed to really push the boundaries of Deep Learning and paying for these top minds is a lot like paying for an NFL quarterback but more expensive – Google reportedly bought DeepMind for $600 million not for it's technology but for its twelve strong team of researchers.

Nvidia (NASDAQ:NVDA) has the hardware lead in deep learning, full stop. I have explained why this is so in an article I published last May. Since then, Nvidia investors have enjoyed outsized gains, which recently has brought about a number of articles speculating about an imminent reversal. This article is a rebuttal on a recent piece about Nvidia's AI perspectives, and possible threats from specialty deep learning hardware. Giving my opinion as a deep learning researcher, the recent piece contains a number of technical inaccuracies.

Last week I read Abadi and Andersen's recent paper [1], Learning to Protect Communications with Adversarial Neural Cryptography. I thought the idea seemed pretty cool and that it wouldn't be too tricky to implement, and would also serve as an ideal project to learn a bit more Theano. This post describes the paper, my implementation, and the results. The authors set up their experiment as follows. We have three neural networks, named Alice, Bob, and Eve.