In order to get a grasp on what makes optimization difficult in machine learning, it is important to specialize our focus. Nonsmooth optimization is so general, and what makes deep learning hard may be completely different from what makes tensor decomposition difficult. So in this post, I want to focus on deep learning and take a bit of a controversial stand. It has been my experience, that optimization is not at all what makes deep learning challenging. On the left I show the training error on everyone's favorite machine learning benchmark MNIST.

More than 20 private companies working to advance artificial intelligence technologies have been acquired in the last 3 years by corporate giants competing in the space, including Google, Amazon, Apple, IBM, Yahoo, Facebook, Intel, and, more recently, Salesforce. There have been 4 major acquisitions already in 2016.

Nathaniel Mott reports in The Guardian, "The battle for the future of computing is a battle to bring artificial intelligence to the mainstream – and Google is quietly overhauling a machine learning tool used to improve some of its most popular services including Google Translate and Google Photos. TensorFlow can be used to help teach computers how to process data in ways similar to how the human brain handles information. It is also open source, meaning Google has published and shared the code online so that external developers can use and improve it. The latest version, released by Google on Wednesday, adds a feature many TensorFlow users have asked for since the tool made its public debut in late 2015: the ability to operate on multiple devices."

Artificial intelligence is one of the greatest goals of the 21st century. Major developments in AI do astound, machines learning how to turn words into images and how to beat world class players in Go. A new microscope, developed by researchers from UCLA, uses AI in helping detect and spot blood samples with cancer cells. Faster and more accurate than its contemporary techniques, it can analyze 36 million images every second without damaging the blood samples. The new microscope features something called "photonic time stretch".

As cool as thermal cameras may be, they're not usually very bright -- they may show you something hiding in the dark, but they won't do much with it. FLIR wants to change that with its new Boson thermal camera module. The hardware combines a long wave infrared camera with a Movidius vision processing unit, giving the camera a dash of programmable artificial intelligence. Device makers can not only use those smarts for visual processing (like reducing noise), but some computer vision tasks as well -- think object detection, depth calculations and other tasks that normally rely on external computing power.

In a new article in Fortune, Facebook executives detail how their research into machine learning and artificial intelligence allowed them to build a social network that serves photos, text posts, and video to more than a billion people around the world. Actually, let's rephrase that sentence: it's only because of machine learning and artificial intelligence that Facebook could effectively scale its operations to such gargantuan size. You simply can't hire enough human beings to tag, sort, and monitor every aspect of such a huge system. But that's not to say that machine learning is the silver bullet that will allow any technology company to ramp up to meet demand. "It seems like a very useful thing to build a platform to support [machine learning algorithms] but you discover that each application that uses machine learning needs a different application to use it," Andrew Moore, the dean of computer science at Carnegie Mellon University, told the magazine.

Deep learning machines could help decode ISIS as a network and develop strategy for defeat. The use of deep learning machines could help the Pentagon decode the structure of ISIS as a network and allow for a more precisely, developed strategy for its defeat, according to Pentagon Deputy Secretary Robert Work. He was making the case for using artificial intelligence (A.I.) for open-source data crunching, Inverse.com "We are absolutely certain that the use of deep-learning machines is going to allow us to have a better understanding of ISIL as a network and better understanding about how to target it precisely and lead to its defeat," said Secretary Work, according to DoD's website. Speaking at an event organized by the Washington Post, Work said he had his epiphany while watching a Silicon Valley tech company demonstrate "a machine that took in data from Twitter, Instagram, and many other public sources to show the July 2014 Malaysia Airlines Flight 17 shoot-down in real time."

Modeling with machine learning is a challenging but valuable skill for anyone working with data. No matter what you use machine learning for, chances are you have encountered a modeling or overfitting concern along the way. This white paper shows how can overcome these challenges, including how to choose the right classification model for your data and how to avoid and correct for overfitting. Finally, you'll see how much easier these tasks can be when you use MATLAB .

Have you heard the one about how our jobs are about to be snatched away by machines? Or how artificial intelligence will ultimately rise up against us? AI is a field full of tropes, many of which come from places of truth: AI is evolving at an incredible speed, and humans are teaching some AI to learn using the same basic model found in our own craniums. But for a more realistic take on the future of AI, look no further than the many software engineers and companies that have struggled to create an intelligent system that can identify cyber attacks. "We were trying to figure out what is the foundational problem--why do we have so many cyber attacks and data breaches that are going undetected?" says Kalyan Veeramachaneni, a research scientist at MIT's Computer Science and Artificial Intelligence Lab and the author of a paper released today titled "Training A Big Data Machine To Defend."

There has been an explosion in machine learning activity, and Shivon Zilis recently mapped out the current machine intelligence ecosystem as we enter 2016. This is one of the key areas that we'll be following this year. While the opportunities here are tremendous, the exuberance surrounding machine learning distracts startups from a key hurdle: it's data, not algorithms, that will dictate who wins in this space. Algorithms have largely been commoditized by now, so a machine learning company built around publicly accessible data isn't defensible. But, startups face a serious chicken and egg problem: they have to convince people to give them data, but the machine intelligence service won't be useful until people (and a lot of people) are actually using the service and sharing their data.