Machine learning, the act of computers learning without being explicitly programmed,has typically been thought of as magic that only mathematicians and programmers could perform. That has been the case for a while and that is due to several reasons. Not only do you need to be able to write code, but you need to have strong math skills. There is no way around it, but you can still do a lot of meaningful work if you don't have the full math background. I believe we are on a path where everyone who programs now will be building some form of machine learning models in the future.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

At 1 p.m. in South Korea on March 9th, Google will attempt to make history. A program called AlphaGo, designed by Google's DeepMind artificial intelligence team, will match wits with Lee Sedol, one of the greatest Go players in the world. Sodol and AlphaGo will play a series of matches over the course of five days. If AlphaGo wins, it will be the latest in artificial intelligence's mastery of human games. Checkers fell in 1994, chess in 1997, and Jeopardy in 2011. Last October, AlphaGo became to first program to beat a professional Go player; now it's taking on one of the best players alive.

Tens of million of people worldwide play Hearthstone, an online collectible card game set in the Warcraft universe, which also encompasses the massively popular MMO World of Warcraft and a major movie. Now Google Deepmind, fresh from creating an AI that triumphed at a game it was thought no computer could master, has been using Hearthstone to test ways a machine learning system could generate natural language - such as English - and formal language - such as computer code. Researchers tasked a system with writing the code that sets the behaviour of cards used in Hearthstone and in another famous collectible card game, Magic: The Gathering (MTG). The Deepmind system -- which implemented a novel neural network architecture -- was first trained using code from open-source versions of Hearthstone, programmed in Python, and Magic: The Gathering, programmed in Java. Humans 2.0: How the robot revolution is going to change how we see, feel, and talk Robots aren't going to replace us, but by working hand in hand with us they will redefine what it means to be human. Once trained, researchers tested the ability of the system to generate code needed to represent Hearthstone and MTG cards in each game.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed.

NVIDIA is hiring Machine Learning Framework software engineers for its GPU-accelerated Machine Learning team. Academic and commercial groups around the world are using GPUs to power a revolution in machine learning, enabling breakthroughs in problems from image classification to speech recognition to natural language processing. The group will be responsible for developing core deep learning algorithms for both internal and 3rd party codebases. Framework Software Engineers will be active members of the open source deep learning software engineering community, and will contribute directly to software packages such as Caffe, Theano, Torch, and KALDI.

Since announcements late last year about Google open-sourcing TensorFlow, the company's open-source library for machine learning, and previous coverage at InfoQ, the data-science community has had an opportunity to try out TensorFlow for their own projects. Databricks' Tim Hunter demonstrates TensorFlow-generated model selection and at-scale neural network processing with Spark. Hunter describes an artificial neural network as mimicking the neurons in the visual cortex of the human brain, which when adequately trained can be used for processing complex input data like imagery or audio. Hunter detailed how he ran TensorFlow on various Spark configurations to parallelize hyperparameter tuning. Hunter stated that TensorFlow, currently available with Python and C support helped "automate the creation of training algorithms for neural networks of various shapes and sizes" for the purpose of training a neural network to process large amounts of data with high accuracy and optimal runtime performance.

I've been doing a little bit of reading on optimization (from Nocedal's book) and have some questions about the prevalence of SGD and variants such as Adam for training neural nets. L-BFGS and other quasi-Newton methods have both theoretical and experimentally verified (PDF) faster convergence. Are there any good reasons training with L-BFGS is much less popular (or at least talked about) than SGD and variants? For the deep learning practitioners, have you ever tried using L-BFGS or other quasi-Newton or conjugate gradient methods? In a similar vein, has anyone experimented with doing a line search for optimal step size during each gradient descent step?

The world of computing has followed Moore's Law for generations. The "law," which was developed by researcher Gordon Moore in the 1970s, says that the number of transistors that can be squeezed into a set amount of space will double every two years. It's been a reliable gauge ever since. Moore's Law may be nearing its end, however. Tom Simonite at the MIT Technology Review writes that Intel has declared in a regulatory filing what insiders have suspected: The company is slowing the release of new chips in a manner that doesn't keep pace with the law.

The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. This year's edition of TNW Conference in Amsterdam includes some of the biggest names in tech. The size reflects the frequency of word use – looks like guys called Andrew are Amy's biggest users – with blue representing nouns, purple for verbs, orange for proper nouns, green for adjectives, red for conjunctions and yellow for adverbs. If you look closely, you'll notice that the kinds of busy people who are beta users of Amy are also likely to talk about founders, coffee and Skype.