This year is off and running with some extraordinary companies starting to make a name for themselves. I attend countless conferences and meetups. Over the course of my travels I meet with some of the most promising companies in the world. They've lead me over the years to discover three billion dollar unicorns before anyone else had written about them. Rather than opting to emulate what's on the market, here are a few companies that have stood out to me and I believe could be unicorns in the future.

IBM unveils Power8 Linux servers for deep learning IBM Linux server for high-performance computing. IBM has launched three Power8 Linux servers designed to accelerate artificial intelligence, deep learning, and advanced analytics applications. The new systems tap the Nvidia NVLink technology to move data five times faster than any competing platform, said Stefanie Chiras, an IBM vice president, in an interview with VentureBeat. These systems and their operating systems are part of a larger business group that generates about $2 billion a quarter for IBM. And the A.I. markets they're going after have exploded in the past couple of years.

Amazing Innovations that do what we thought technology couldn't do. After a while they are no longer "amazing." We become blaze as amazingness just becomes part of the background, taken for granted, no longer enthused about. AI (amazing innovations) cease to be amazing and are replaced by new, more amazing innovations. How many are bored by air travel?)

Innovative enterprises have made data collection and analytics a central part of digital transformation strategies. You need only consider the most successful and recognized companies to see how important data is to the innovators in the enterprise market. IT leaders have been turning successful pilot projects into production systems that add value and benefit their organizations. "Over the past few years we have convinced enterprises that capturing data and making use of that data is imperative to competitiveness," Sam Charrington, founder and principal analyst at CloudPulse Strategies told InformationWeek in an interview. "They've started to build out infrastructure to help them do that including Hadoop and Spark and other technologies."

Sound waves could be used to hack into critical sensors in everything from phones and medical devices to fitness trackers and cars, researchers have warned. The groundbreaking University of Michigan researchers reveals that millions of gadgets that use accelerometers are at risk. Researchers found the tiny sensors can be tricked, registering fake movement and giving hackers a backdoors into devices. The researchers performed several proof-of-concept demonstrations of their hack, including using a $5 speaker and a malicious music file to cause a Samsung Galaxy S5's accelerometer to spell out the word'WALNUT' in a graph of its readings (pictured). The sensors involved in this research are known as capacitive MEMS accelerometers.

When Tag Heuer introduced its Connected smartwatch sequel, called the Connected Modular 45, early Tuesday, Intel was there to reveal it is working on a special artificially intelligent assistant for the wearable. What makes this Siri competitor special according to the chipmaker is the fact that it is genderless. Intel's New Technology Group VP Jerry Bautista said at the launch event that Intel is preparing a genderless digital assistant for the andro that will arrive later this year. Though Bautista did not expound on how this genderless nature would be possible, he did drop a few details about the AI assistant. It is said to be smart enough to adjust reminders based on the user's current activities.

In this article, I present a few modern techniques that have been used in various business contexts, comparing performance with traditional methods. The advanced techniques in question are math-free, innovative, efficiently process large amounts of unstructured data, and are robust and scalable. Implementations in Python, R, Julia and Perl are provided, but here we focus on an Excel version that does not even require any Excel macros, coding, plug-ins, or anything other than the most basic version of Excel. It is actually easily implemented in standard, basic SQL too, and we invite readers to work on an SQL version. In short, we offer here an Excel template for machine learning and statistical computing, and it is quite powerful for an Excel spreadsheet.

This post is the first in what I hope to be a series covering recently published ML/AI papers that I think are particularly important. Some of the ideas in these papers are fairly intuitive and I hope I'm able to communicate some of that intuition in this format. For the first paper, I'll be covering Baidu's Deep Voice paper that applies Deep Learning to Text to Speech Systems. Recently, Andrew Ng's Baidu AI Team released an impressive paper on a new Deep Learning based system for converting text to speech. An example of the speech that Baidu's paper is able to produce is shown below.

Enroll in the course for free at: https://bigdatauniversity.com/courses... Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning. This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each. Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!

About David: David Asboth is a Data Scientist with a software development background. He's had many different job titles over the years, with a common theme: he solves human problems with computers and data. This post originally appeared on his blog, davidasboth.com When you learn about machine learning techniques, you usually get a selection of the usual suspects. In fact, KDNuggets has a good post about the 10 machine learning algorithms you should know.