The word Artificial Intelligence has been thrown around in the start-up world over the past few months, with phrases like Machine Learning and Natural Language Processing following shortly after. That said, can these rapidly evolving technologies really be used to influence the travel industry, and if so, by how much? Machine learning, in its many forms, has allowed computers to build an understanding of who we are, by making use of the data that we provide. As a result, advertising has become more specific, algorithms have got more intelligent, and the background processing of mobile applications and computer software has become more tailored to the user. Remember the last time you saw that advert pop up onto your computer screen after having a look for something?

Many years ago, a company pitched me an intriguing product to review: a display you mount in the rear window of your car, which you can use to send customized messages to drivers behind you. With the tap of a button near your dashboard, the display would light up with a scrolling message of whatever it was you wanted to tell other drivers--a great way to get pulled over by a cop, if not outright killed by some already-angry tailgater. The Silicon Valley startup Drive.ai The theory is that digital signs would make self-driving vehicles a bit safer, since they don't really have a great way of letting pedestrians know their intentions beyond simple turn signals. The sign would be able to indicate various statuses about the vehicle to those near it, including when it might be safe to lane-split (because the self-driving car has noticed you and promises to not merge into the next lane and smoosh you).

Prashant Natarajan Iyer (AKA "PN") is an analytics and data science professional based out of the Silicon Valley, CA. He is currently Director of Product Management for Healthcare products. His experience includes progressive & leadership roles in business strategy, product management, and customer happiness at eCredit.com, He is currently coauthoring HIMSS' next book on big data and machine learning for healthcare executives - along with Herb Smaltz PhD and John Frenzel MD. He is a huge fan of SEC college football, Australian Cattle Dogs, and the hysterically-dubbed original Iron Chef TV series.

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits. Unsupervised machine learning is a paradigm in machine learning where we build models without relying on labeled training data. One of the most common methods is clustering. You must have heard this term being used quite frequently. We mainly use it for data analysis where we want to find clusters in our data.

CNTK is in active use at Microsoft and constantly evolving.

The promise of big data is incredibly enticing and, for most businesses, just as out of reach. The reason is simple: today's databases are built upon 1970s math that was designed for 20th-century data requirements and hardware capabilities. This math has led to tree-structures and associated algorithms that are incapable of delivering the flexibility, scale and performance needed for the dynamic big data world. Even newer tree-structure variants, which were developed in an attempt to solve these issues, can't keep up with big data entropy and velocity. Databases have rigid and complex data infrastructures, requiring an enormous amount of hand-holding to run (think calibration treadmill), and often sacrifice features for small upticks in performance and scale.

Design Researchers "When ethnographic research was new in design, there were designers who specialized in research," explains Harry West, CEO of Frog. Doreen Lorenzo, director of integrated design at UT Austin, also sees the role of the classically trained industrial designer dying off soon. Virtual Interaction Designers Virtual and augmented reality is set to become a 150 billion industry by 2020, disrupting everything from health care to architecture. "Human-centered design has expanded from the design of objects (industrial design) to the design of experiences (adding interaction design, visual design, and the design of spaces) and the next step will be the design of system behavior: the design of the algorithms that determine the behavior of automated or intelligent systems," argues Harry West at Frog.

Beauty contests are slightly computational to begin with. While the notion of beauty is of something ephemeral and unquantifiable, a beauty pageant asks that we categorize and rank it: determining rules that let us objectively measure an idea which must be, at its root, mysterious and subjective. No surprise, then, that here in 2016 we have just witnessed the first beauty contest judged by AI, as a jury of decidedly non-human bots picked out what they considered to be the best-looking people from a dataset of 6,000 entries. "New tools like machine learning let us analyze images in a way that was never available to us before," Anastasia Georgievskaya, co-founder and research scientist at Youth Laboratories, the company behind Beauty.AI, told Digital Trends. "Our goal was to investigate methods that would show new approaches to beauty evaluation."

Roman satirical poet Juvenal once wrote that all people really want are bread and games (paraphrased). Now a program doesn't need any bread, and neither does the computer it runs on, but you can't blame the poet for not writing something that would stand the test of time in its entirety. The part that does stand the test of time is the latter part, the games part. This still holds true in its initial intend, what with all the sports teams, and fans of them, the millions of viewers around the world, if not billions even. Even computer games are now part of this, overtaking regular games rapidly in popularity.

Our world has been revolutionized by Artificial Intelligence. A subject hotly glorified by popular sci-fi movies, AI has now penetrated various spheres of our life. It is widely used in applications such as aerospace, bio-informatics, business intelligence, financial advisory systems, emergency response, homeland security, logistics and supply chain. In recent years, we have witnessed a rebirth of AI through the use of cloud, with technology firms such as Google leading the way in showing the power of data-driven computing. Artificial intelligence systems are extensively used by researchers at technology firms, universities and government labs.