It's a beautiful introduction ... Try not to drool too much! Read "A Few Useful Things to Know about Machine Learning" by Prof. Pedro Domingos. It's densely packed with valuable information, but not opaque. The author understands that there's a lot of "black art" and folk wisdom, and they invite you in. Take your time with this one.

Deep learning is impacting everything from healthcare to transportation to manufacturing, and more. Companies are turning to deep learning to solve hard problems, like speech recognition, object recognition, and machine translation. One of the most impressive achievements this year was AlphaGo beating the best Go player in the world. With the victory, Go joins checkers, chess, othello, and Jeopardy as games machines have defeated human at. While beating someone at a board game might not seem useful on the surface, this is a huge deal.

Artificial intelligence (AI) interfaces and chatbots could be revolutionary for financial institutions – but only if they strike the right balance between human and machine interaction, argues Jeremy Pounder, futures director at Mindshare. AI is changing the banking industry as we know it. Already, banks are using AI within everyday payments, money management and digital self-service. For instance, voice recognition technology is being used by the likes of Barclays as a form of secure ID for telephone banking customers, while challenger institution Atom Bank allows its customers to log on via a facial recognition system. AI's advanced natural language processing and machine learning means it can generalise large data sets and detect and extrapolate patterns in order to create new solutions and actions.

The book definitely provides the ability to do data analysis from scratch. However, I took that to mean it would provide me with the basic knowledge to do data science as its done in production. What you want find hardly anywhere in this book is a single discussion of how to use pandas, scikit-learn, numpy, matplotlib, or any of the other tools used by today's data scientists. It does provide you with the basics of how the analysis is done and the math behind a bunch of machine learning models. So, while I think this book had value, it was very different from what I was expecting. I think the target audience for this book is more an academic looking to apply their knowledge to a data science realm, and not so much a programmer wanting to learn how to actually implement machine learning or data analysis in code.

Recent digital tech advancements have produced prototype artificial neurons and light-based neural networks, but we're still discovering ways our brain actually works. Researchers at MIT have built a computational model that could illustrate how inhibitory neurons work efficiently to block others from firing. The team's model, as described in their paper, uses theoretical computer science applied to a "winner-take-all" operation. It starts with a number of input neurons and finding an efficient configuration of inhibitory ones that will produce a signal from a single output neuron. Abstracting real neural networks in this way could help the field of computational analysis assist neuroscience.

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.