In-house IT professionals and managed service providers (MSPs) have had an interesting relationship over the course of IT history. Yes, they are vastly different, but if we drew a Venn diagram of IT and the MSP, the intersection of the two is worth exploring, particularly regarding how IT professionals can best manage their MSPs and work harmoniously to advance the common goal of IT performance. For IT professionals, the very utterance of the acronym "MSP" may conjure feelings of skepticism and fearing the reaper, which doesn't need to be the case. Let's explore common scenarios where in-house IT professionals and MSPs work together, because in these cases, in-house IT professionals need to understand how to get the most out of these relationships and up-level their careers by properly managing MSPs. Scenario 1: The MSP as an elastic resource There are two common ways the MSP as an elastic resource plays out.

I was excited by the promise of Xnor.ai and its technique that drastically reduces the computing power necessary to perform complex operations like computer vision. Seems I wasn't the only one: the company, just officially spun off from the Allen Institute for AI (AI2), has attracted $2.6 million in seed funding from its parent company and Madrona Venture Group. The specifics of the product and process you can learn about in detail in my previous post, but the gist is this: machine learning models for things like object and speech recognition are notoriously computation-heavy, making them difficult to implement on smaller, less powerful devices. Xnor.ai's researchers use a bit of mathematical trickery to reduce that computing load by an order of magnitude or two -- something it's easy to see the benefit of. Ali Farhadi, who led the original project, will be the company's CEO, and Mohammad Rastegari is CTO.

A recent article in Techcrunch describes Twitter and Facebook issues: algorithms unable to detect fake news or hate speech. I wrote about how machine learning could be improved, and what can make implementations under-perform - or not perform at all. And a colleague shared with me an article about how Facebook really sucks at machine learning. You would think that machine learning simply does not work, at least not as advertised. Here, I actually claim that this is not the case, further explaining what the issues might be, and in short, that machine learning might not be the culprit.

Machine learning will come of age this year, moving from the research labs and proof-of-concept implementations to cutting-edge business solutions. Along the way, it will help power innovations, such as autonomous vehicles, precision farming, therapeutic drug discovery and advanced fraud detection for financial institutions. Machine learning intersects with statistics, computer science and artificial intelligence, focusing on the development of fast and efficient algorithms to enable real-time data processing. Rather than just follow explicitly programmed instructions, these machine learning algorithms learn from experience, making them a key component of artificial intelligence platforms. Machine learning may also help us with a challenge from one of last year's most buzzed about technology developments: the Internet of Things.

In one vision of the future of transportation, humans are mere passengers, the ballistic baggage of all-knowing, all-seeing computers zipping about, safely and efficiently in fully autonomous cars. And the robot drivers are coming, no doubt. But if you want to retain any control of how you move through Tomorrowland--or just improve how you navigate today's world--you'll need a tool that upgrades your skill level. You'll need augmented reality, the oft-confused cousin of virtual reality that integrates digital intel into your natural view. Thanks to efforts like Mircosoft's HoloLens and secretive startup Magic Leap, the tech that many people know from Pokémon Go will be a $90 billion market by 2020, according to consulting firm Digi-Capital.

In 2016, we saw a wide range of breakthroughs having to do with artificial intelligence and deep learning in particular. Google, Facebook, and Baidu announced several breakthroughs using deep learning. Deep learning is one specific class of machine learning algorithms. It has a long history, taking its roots in the earlier days of computer science. However, not all of machine learning is deep learning.

Machine learning is a growing field, used in everything from the basics of anti-spam functions to the complexities of self-driving cars. As this is a constantly adapting technology, companies seeking to take advantage of the system for functions like analytics may have trouble finding the best place to begin. So what is the first step for a tech department that wants to start using machine learning to improve its data analytics? Even if you rely on outside expertise, it is important to understand what machine learning can and can't do with data. Stanford, Caltech and others offer online classes on Coursera that are very good.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.

The future of healthcare is already here. From automated insulin pumps to diagnostic instruments that can interpret their own results, today's medical devices are smarter and more sophisticated than ever. X-rays, MRIs, and ultrasounds produce diagnostic images that let doctors identify abnormalities that can't be seen from the outside. But what if the same machines that took the images could also interpret the results? Data analytics can be used to help imaging devices learn to recognize abnormal scans. Smart imagers could soon be connected to vast image libraries and patient health records.

Terah Lyons, discusses how Artificial Intelligence is currently handled by our government, and how this may change overtime. Terah Lyons is a Policy Advisor to the U.S. Chief Technology Officer at The White House Office of Science and Technology Policy. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.