There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships. However, as long as you have a lot of data, ML is perfect for learning complex relationships between multiple features and outcomes.

Every Monday morning, 75 million Spotify users logon to their devices and play a "Discover Weekly" playlist that's been curated to their musical preferences. Unbeknownst to many, the custom playlist they're enjoying was curated by a machine learning algorithm that has learned their unique musical preferences based on previous interactions with songs, musicians, and playlists. Machine learning, and a more advanced technology called deep learning, are types of artificial intelligence that allow a computer to learn information based on the data it is given. To borrow the words of Drew Breunig, "In a nutshell, deep learning is human recognition at computer scale." Essentially, the more information the computer is given, the better it can learn -- and in the case of platforms like Spotify or Netflix, the more interaction you have with the program, the better it can recommend music, movies, or TV shows that you'll like.

In this special guest feature, Rob Farber from TechEnablement writes that the Intel Scalable Systems Framework is pushing the boundaries of Machine Learning performance. The challenge of training a machine learning algorithm to accurately solve complex problems requires large amounts of data that greatly increase a system's computational, memory, and network requirements. Meeting this challenge with the right technology mix amplifies the ability of a system to train machine and deep learning neural networks to solve complex pattern recognition tasks. To help customers create systems run deep learning--as well as other HPC, Big Data, and visualization workloads--Intel introduced Intel Scalable System Framework (Intel SSF). It provides a common framework that can support workloads running on everything from small workgroup clusters to the world's largest supercomputers and on-demand cloud computing.

Intel is committed to producing CPUs to target machine learning systems, setting up an intriguing rivalry with graphical processing unit (GPU) vendor Nvidia. At the Intel Developer Forum yesterday, the company even brought out an executive from Chinese cloud giant Baidu to talk about the Xeon Phi, Intel's machine learning chip. The choice was interesting considering Baidu has been a vocal Nvidia customer. The potential ace up Intel's sleeve is the pending acquisition of Nervana, a deep learning startup reportedly working on a chip of its own. Intel executive vice president Diane Bryant mentioned Nervana during yesterday's keynote, but with the deal still not closed, it's understandable that she didn't articulate Intel's plans for the startup. The more immediate news for Intel was the announcement of its latest processor for machine learning.

Machine learning is technology which trains software so developers don't have to code it by hand. The number of new companies in the category has grown exponentially over the past few years. Here are 10 machine learning startups worth a closer look. Machine learning companies are being snapped up in droves by tech giants cognizant that these startups represent a new wave of technology innovation. This month alone, Intel announced plans to acquire deep learning startup Nervana Systems.

When was the last time you asked your computer something? There's Siri, Google, and Cortana of course, but these systems, clever as they may be, are the thin end of a newly emerging wedge of remarkable new approaches to computer learning and marketing. If you need proof that we are entering a new era of machine learning and artificial intelligence (AI) you need to look no further than Google's DeepMind project. Early this year DeepMind, Google's AI computer, developed initially in London, challenged and beat South Korean Grandmaster Lee Sedol at the ancient game of Go. Why this challenge is so important requires you to think back to the Deep Blue computer, which finally beat Gary Kasparov at chess in the 1990s. Deep Blue had it easy.

I read an interesting article about Google using their DeepMind AI system to improve their power usage efficiency by 15% – which adds up to hundreds of millions of dollars of savings. Of course, DeepMind has been a big investment for Google and finding areas for them to gain efficiency leads to immediate payback – not necessarily covering the entire investment, but savings that add up over time to those hundreds of millions. Like any good organization, I'm sure that Google started with metrics so they had a handle of not just what the costs were, but where the biggest cost impacts were occurring. As the saying goes, "you can't change what you don't measure". However, a lot of organizations get stuck in metrics mode and never get around to the work of actually optimizing – they are are always measuring but never changing.

Artificial Intelligence is one of the most exciting technologies of the century, and Deep Learning is in many ways the "brain" behind some of the world's smartest Artificial Intelligence systems out there. Loosely based on neuron behavior inside of human brains, these systems are rapidly catching up with the intelligence of their human creators, defeating the world champion Go player, achieving superhuman performance on video games, driving cars, translating languages, and sometimes even helping law enforcement fight crime. Deep Learning is a revolution that is changing every industry across the globe. Grokking Deep Learning is the perfect place to begin your deep learning journey. Rather than just learn the "black box" API of some library or framework, you will actually understand how to build these algorithms completely from scratch. You will understand how Deep Learning is able to learn at levels greater than humans.