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."

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.

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.

At the end of 2014, Stanford announced it was going to play host to a 100-year-long study, proposed by computer scientist Eric Horvitz, on the effects of artificial intelligence on society and how those effects "will ripple through every aspect of how people work, live and play." The study, nicknamed AI100, is mostly carried out by having a panel of experts produce a report every five years. On Thursday the first report came out, produced by 17 people, most of whom are professors in various AI-related disciplines, with a couple from companies like Microsoft of Google X. The experts break down the different effects of AI into eight different broad categories, ranging from transport to entertainment. In honor of labor day, let's focus on the area they dub "Employment and Workplace."

Cortana may eventually be able to peek inside your refrigerator following news that Microsoft is bringing its machine learning tech to kitchens. The computer firm is working on a new computer vision module called SmartDeviceBox, which will be installed inside fridges to make them capable of recognising objects inside and telling owners when they need to re-stock. The SmartDeviceBox is essentially an internet-connected camera unit that sits inside fridges and tracks the objects inside. The unit contains an image processing system based on Microsoft's suite of deep learning tools, which has been trained to recognise a variety of products such as milk cartons, ketchup bottles and pickle jars. The system is able to collate these into an inventory list that customers can view in list form using a smartphone app.

If you would like to clarify the clouds over what machine learning is please check this article from Stanford first. Page 9 and 15 have good description on the artificial intelligence topic along with description of what machine learning and reinforcement learning means. If you would like to refresh your knowledge on project haystack, Berkeley study uses Haystack information circa 2012 is very helpful overview. Most updated info is also available on project-haystack.org Would like to hear opinion below to further the conversation on this complex topic.