As we're nearing the end of 2017 (and coincidentally the first day of NIPS 2017), we've come to the 5 year landmark of deep learning really starting to hit the mainstream. For me, I think of AlexNet and the 2012 Imagenet competition as the coming out party (although researchers have definitely been working in this field for quite a bit longer). It's been just 5 years and we've absolutely revolutionized the way we look at the capabilities of machines, the way we build software (Software 2.0), and the ways we think about creating products and companies (Just ask any VC or startup founder). Tasks that seemed impossible just a decade ago have become tractable, granted you have the appropriate labeled dataset and compute power of course. In this post, we'll overview the last couple years in deep learning, focusing on industry applications, and end with a discussion on what the future may hold.

First of all, lets get motivated to learn Recurrent Neural Networks(RNNs) by knowing what they can do and how robust and sometimes surprisingly effective they can be. This amazing blog by Andrej Karpathy will help you get started. Our aim here is to make a neural network that can learn the structure and syntax of language. We'll provide a huge set of dialogues from the scripts of 2 of my favorite shows F.R.I.E.N.D.S and South Park as training data and hope that our model learns to talk like the characters. Basic layered neural networks are used when there are a set of distinct inputs and we expect a class or a real number etc.. assuming that all inputs are independent of each other. RNNs on the other hand, are used when the inputs are sequential.

A few months after demonstrating its dominance over the game of Go, DeepMind's AlphaZero AI has trounced the world's top-ranked chess engine--and it did so without any prior knowledge of the game and after just four hours of self-training. AlphaZero is now the most dominant chess playing entity on the planet. In a one-on-one tournament against Stockfish 8, the reigning computer chess champion, the DeepMind-built system didn't lose a single game, winning or drawing all of the 100 matches played. AlphaZero is a modified version of AlphaGo Zero, the AI that recently won all 100 games of Go against its predecessor, AlphaGo. In addition to mastering chess, AlphaZero also developed a proficiency for shogi, a similar Japanese board game.

Seven long months after the next-generation "Volta" graphics architecture debuted in the Tesla V100 for data centers, the Nvidia Titan V finally brings the bleeding-edge tech to PCs in traditional graphics card form. But make no mistake: This golden-clad monster targets data scientists, with a tensor core-laden hardware configuration designed to optimize deep learning tasks. You won't want to buy this $3,000 GPU to play Destiny 2. But that doesn't mean we humble PC gamers can't glean information from Volta's current AI-centric incarnations. Here are five key things you need to know about the Titan V and Nvidia's Volta GPU. Editor's note: This article was originally published on May 11, 2017 but was updated on December 8 to include information from the Titan V. If you're looking for hot details about the future of GeForce graphics cards, well, keep waiting.

Being able to go from idea to result with the least possible delay is key to doing good research. So yesterday someone told me you can build a (deep) neural network in 15 minutes in Keras. Of course, I didn't believe that at all. Last time I tried (maybe 2 years ago?) it was still quite some work, involving comprehensive knowledge of programming and math. That was some dead serious craftmanship.

COMMANDING the plot lines of Hollywood films, covers of magazines and reams of newsprint, the contest between artificial intelligence (AI) and mankind draws much attention. Doomsayers warn that AI could eradicate jobs, break laws and start wars. The competition today is not between humans and machines but among the world's technology giants, which are investing feverishly to get a lead over each other in AI. An exponential increase in the availability of digital data, the force of computing power and the brilliance of algorithms has fuelled excitement about this formerly obscure corner of computer science. The West's largest tech firms, including Alphabet (Google's parent), Amazon, Apple, Facebook, IBM and Microsoft are investing huge sums to develop their AI capabilities, as are their counterparts in China. Although it is difficult to separate tech firms' investments in AI from other kinds, so far in 2017 (see chart 1) companies globally have completed around $21.3bn in mergers and acquisitions related to AI, according to PitchBook, a data provider, or around 26 times more than in 2015.

NVIDIA's newest Titan GPU is now available for purchase, and the company says it's the "world's most powerful GPU for the PC" yet. The GPU-maker has launched the Volta-powered Titan V at the annual Neural Information Processing Systems conference. Volta is NVIDIA's latest microarchitecture designed to double the energy efficiency of its predecessor, and Titan V can apparently deliver 110 teraflops of raw horsepower or around 9 times what the previous Titan is capable of. Since Volta was designed to work on a mixture of computation and calculations and has features created specifically for deep learning, scientists can use the GPU to build their own desktop PCs if they don't need special servers. "Our vision for Volta was to push the outer limits of high performance computing and AI. We broke new ground with its new processor architecture, instructions, numerical formats, memory architecture and processor links. With TITAN V, we are putting Volta into the hands of researchers and scientists all over the world. I can't wait to see their breakthrough discoveries."

Artificial Intelligence (AI) workloads are different from the calculations most of our current computers are built to perform. AI implies prediction, inference, intuition. But the most creative machine learning algorithms are hamstrung by machines that can't harness their power. Hence, if we're to make great strides in AI, our hardware must change, too. Let's start in the present, with applying massively distributed deep learning algorithms to Graphics processing units (GPU) for high speed data movement, to ultimately understand images and sound.

Today, a teaspoon of spit and a hundred bucks is all you need to get a snapshot of your DNA. But getting the full picture--all 3 billion base pairs of your genome--requires a much more laborious process. It's exactly the kind of problem that makes sense to outsource to artificial intelligence. On Monday, Google released a tool called DeepVariant that uses deep learning--the machine learning technique that now dominates AI--to assemble full human genomes. Modeled loosely on the networks of neurons in the human brain, these massive mathematical models have learned how to do things like identify faces posted to your Facebook news feed, transcribe your inane requests to Siri, and even fight internet trolls.

We last heard from DeepMind's dominant gaming AI in October. As opposed to earlier sessions of AlphaGo besting the world's best Go players after the DeepMind team trained it on observations of said humans, the company's Go-playing AI (version AlphaGo Zero) started beating pros after three days of playing against itself with no prior knowledge of the game. On the sentience front, this still qualified as a ways off. To achieve self-training success, the AI had to be limited to a problem in which clear rules limited its actions and clear rules determined the outcome of a game. This week, a new paper (PDF, not yet peer reviewed) details how quickly DeepMind's AI has improved at its self-training in such scenarios. Evolved now to AlphaZero, this latest iteration started from scratch and bested the program that beat the human Go champions after just eight hours of self-training.