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.

Top data science teams around the world are doing incredible work on some of the most interesting datasets in the world. Google has more data on human interests than every 20th century researcher, while Uber seamlessly coordinates the itinerary and pricing of more than 1 million trips every day. With machine learning, and artificial intelligence, top data science teams are changing the way we ingest and process data, and they are coming up with actionable insights that impact the lives of millions. What if there were common patterns between the interviews top data science teams were giving that would let you master the data science interview process? What if the specific differences between various teams and their interview practices could be enumerated so that interviewing with a top data science team were more akin to a science than an art?

In a brief announcement as part of today's Day 2 ketnote for IDF 2016, Intel has announced a new member of the Xeon Phi family. The new part, currently under the codename of Knights Mill, is being aimed at the deep learning market and is scheduled for release in 2017. At this point there are more unknowns than knowns about Knights Mill, in part because Intel has not offered much detail on how it fits into the larger Xeon Phi brand. The company had previously announced in 2014 that the successor to the current Knights Landing design would be Knights Hill, a true 3rd gen Xeon Phi built on Intel's 10nm process. However this week there has been no mention of Knights Hill, whether Knights Mill is Knights Hill renamed, or what the manufacturing process Knights Mill is being made on.

OpenFace is a Python and Torch implementation of face recognition with deep neural networks and is based on the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google. Torch allows the network to be executed on a CPU or with CUDA. This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by the Intel Corporation, Google, Vodafone, NVIDIA, and the Conklin Kistler family fund. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.

Apple, Google, Facebook, and Microsoft are worldwide technology powerhouses, but when it comes to the adoption of virtual digital assistants (VDAs) like Siri, Google Assistant, and Cortana, scale only takes you so far. In this particular business, players who successfully cater to the nuances of individual countries will conquer the global VDA market. The same principle will apply to enterprises looking to automate customer interactions like customer service and e-commerce with enterprise VDAs. The challenges facing VDA providers were brought to light recently by the plight of Jibo, the crowdfunded smart home VDA robot that received pre-orders from consumers in 47 countries. On August 9, the company announced that product rollouts would be limited to the United States and Canada only, and that all orders for Jibo outside those markets will be refunded.

The era of big data has witnessed a paradigm shift into analytics. Today, it's no longer sufficient to simply gather data from social media, IoT, and wearable devices, and be unable to manage or filter it. It is more about delivering the right data to the right person, at the right time. This trend is growing crucial as data is multiplying every day and pouring in from various devices and smart machines including wearables, electronic gadgets, and other devices. Such factors call for the treatment of vast pools of structured and unstructured data with care and precision. This is precisely where invisible analytics come in.

A compact computer called Euclid from Intel should make the development of robots much easier. Euclid looks much like the Kinect camera for Xbox consoles, but it's a self-contained PC that can be the guts of a robot. It's possible to install the Euclid computer where the "eyes" of a human-like robot would be typically placed. Intel demonstrated the Euclid computer in a robot moving on stage during CEO Brian Krzanich's keynote at the Intel Developer Forum on Tuesday. Euclid has a 3D RealSense camera that can serve as the eyes in a robot, capturing images in real-time.

Intel Corp. INTC -0.54 % signaled it wants a bigger role in artificial intelligence, revealing plans to modify a line of chips to target a fast-growing market turning into a battleground for technology suppliers. The company told technology developers Wednesday that it plans next year to deliver a new version of the Xeon Phi processor--a product line previously targeted at scientific applications--with added features designed to accelerate tasks associated with what Silicon Valley calls artificial intelligence. Intel said the technology will help accelerate a technique called deep learning, increasingly used for tasks such as interpreting speech, identifying objects in photos and piloting autonomous vehicles. Intel's Xeon processors already are a fixture in data centers, and have a role in nearly all deep-learning tasks carried out there. But some users also install auxiliary processors for artificial-intelligence tasks, notably chips called GPUs that rival Nvidia Corp. NVDA -2.32 % has long sold for videogames.

Machine Learning is enabling a transformation in the software industry without precedents. New machine-learning powered predictive applications are performing jobs that so far were considered exclusive to highly skilled humans. We will soon witness a new wave of productivity growth that will change the face of all sectors of the economy. In collaboration with Las Naves, we are bringing the second series of a summer school in Machine Learning to Valencia. BigML will hold a two-day summer school for advanced undergraduates as well as graduate students, and industry practitioners seeking a quick, practical, and hands-on introduction to Machine Learning.

A key component driving the development of driverless cars is machine learning and other artificial intelligence capabilities along with computer vision approaches used for image and signal processing. Ford Motor Co., which is targeting fully autonomous vehicles for ride sharing by 2021, unveiled a series of machine learning and machine vision deals as it doubles the size of it Silicon Valley research campus. The U.S. carmaker (NYSE: F) announced an acquisition and others investments on Tuesday (Aug. Ford also disclosed a licensing deal with machine vision specialist Nirenberg Neuroscience, who is credited with cracking the code the eye uses to transmit visual information to the brain. Furthering its autonomous vehicle initiative, Ford also announced an investment in the 3-D mapping startup, Civil Maps.