While both have gained a lot of attention this year, these techniques have been around for quite some time, but no more so than now, has it felt so promising. Over the past few years, there has been a monumental shift in technology and how it's being applied to everyday life. From robots to search engines, deep learning and machine learning are being raved about as the tech fuelling our new innovations, but many are left wondering what truly differentiates these two models. Broadly speaking, both machine learning and deep learning are forms of Artificial Intelligence, the intelligence exhibited by machines using cutting-edge techniques to perform cognitive functions that we associate with intuitive learning; however, each application is unique and offers an array of benefits to the end-user, whether it's solving unique problems for a particular business case, aiding in speech/facial recognition, speeding up web applications or protecting against breaches or hacks. While the concepts of machine learning and deep learning have been around as early as the 1960s, each model has changed drastically over the years, creating a greater divide between the two.

According to Gartner's latest report, Worldwide IT spending is forecast to reach $3.5 trillion in 2017, up 2.9 percent from 2016's estimated spending of $3.4 trillion. Additionally, IT services spending is also set to rise 4.8 percent in 2017 to reach $943 billion. All of these figures tell us that many organizations have already allocated billions of dollars to keep up with the emerging trends in enterprise technology. One of our CMS-Connected contributors and a founding partner of Digital Clarity Group, Cathy McKnight, wrote a splendid article about the relationship between artificial intelligence and marketers. She also pointed out that more and more of the technology titans have adopted and leveraged the power of AI to enable their platforms to generate natural language, content optimization, analyze consumer behavior, and ultimately, be ahead of the game by knowing more about their customers' needs than even the customers know themselves.

Note: I was invited to give the opening keynote at this year's CES, which was celebrating its 50th anniversary. Below is a summary of my talk and our announcements. In the end, I described how the investments we've made over the past 24 years have put NVIDIA at the center of the most exciting time the technology industry has ever known. CES has been a bellwether of technology trends for five decades. From the first pocket radios and VCRs and video game consoles in its early days, to the PC revolution in the '90s, to the mobile revolution that put the internet in our pockets and forever changed how we connect and share, we've watched the future take the stage at CES.

New advances in speech recognition technology are reshaping the way humans interact with machines and revolutionizing how our devices are built and connected. This so-called "new voice of computing" was one of the "Tech Trends to Watch" presented on Tuesday at CES. Speaking to an audience of several hundred journalists, Shawn DuBravac, chief economist of the Consumer Technology Association, the U.S. trade association responsible for organizing CES, gave an overview of the history of computing and how voice recognition will impact its future. "The next computer interface is voice. Vocal computing is replacing the graphical user interface," he said. DuBravac mentioned companies that are building products based on voice recognition on top of already established platforms, such as Amazon's Alexa.

Previous studies have shown that it is possible to detect macroscopic patterns of cultural change over periods of centuries by analyzing large textual time series, specifically digitized books. This method promises to empower scholars with a quantitative and data-driven tool to study culture and society, but its power has been limited by the use of data from books and simple analytics based essentially on word counts. This study addresses these problems by assembling a vast corpus of regional newspapers from the United Kingdom, incorporating very fine-grained geographical and temporal information that is not available for books. The corpus spans 150 years and is formed by millions of articles, representing 14% of all British regional outlets of the period. Simple content analysis of this corpus allowed us to detect specific events, like wars, epidemics, coronations, or conclaves, with high accuracy, whereas the use of more refined techniques from artificial intelligence enabled us to move beyond counting words by detecting references to named entities.

Every day brings another exciting story of how artificial intelligence is improving our lives and businesses. AI is already analyzing x-rays, powering the Internet of Things and recommending best next actions for sales and marketing teams. But for every AI success story, countless projects never make it out of the lab. That's because putting machine learning research into production and using it to offer real value to customers is often harder than developing a scientifically sound algorithm. Many companies I've encountered over the last several years have faced this challenge, which I refer to as "crossing the AI chasm."

IBM was awarded the most patents in the US in 2016. IBM's efforts to match and surpass the human brain with computing technology helped push the company to the top of the 2016 list of patent awards. The US Patent and Trademark Office granted IBM 8,088 patents for the year, more than 2,700 of them stemming from artificial intelligence and cognitive computing work, IBM and IFI Claims said Monday. Next on the list was Samsung with 5,518 patents, Canon with 3,665, Qualcomm with 2,897 and Google with 2,835. In total, the USPTO granted 304,126 patents in 2016, 10 percent more than the year before, IFI Claims said.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. "Microsoft Monday" takes a look back at the past week of news related to Microsoft. This week, "Microsoft Monday" includes details about a red Xbox One controller being released tomorrow, the accidental release of a Forza Horizon 3 developer version, Minecraft surpassing 25 million copies sold for PCs and Macs, a few Windows 10 user interface tweaks, a connected vehicle deal signed with Renault-Nissan and more!

Update: Machine Learning is Fun! Part 3, Part 4, Part 5 and Part 6 are now available! Also, don't forget to check out Part 1. In Part 1, we said that Machine Learning is using generic algorithms to tell you something interesting about your data without writing any code specific to the problem you are solving. This time, we are going to see one of these generic algorithms do something really cool -- create video game levels that look like they were made by humans.

Over the past 2-3 years or so, I've heard this buzz word being tossed around a lot, and it's something that has definitely seized my curiosity recently. So if you are from the field of Computer Science, I'm sure you also must have come across this term at least once. Deep Learning along with Neural Networks is an area of active research nowadays. Deep Learning is a sub-branch of Machine Learning. Before we dive more into Deep Learning, let's begin with the broader field of Machine learning.