When Facebook (NASDAQ:FB) suggests you "tag" a friend in a photo, it generally suggests that friend's name. That small interaction provides a glimpse into the world of an emerging and powerful aspect of artificial intelligence (AI) in action -- image recognition. With its treasure trove of words and pictures from 1.79 billion monthly active users, it is using that data, combined with recent advancements in AI, to propel this and other technological advances. Facebook may well have the lead in facial recognition, even extending a step further into the realm of facial verification. It released a research paper in 2014 in which it reported 97.35% accuracy, which approaches human levels of recognition.

Google Brain researchers are looking for ways to soon put forth a software that will be creating machine learning software as well. There's a relevant reason why researchers are getting their heads on this. A lot of money is still needed to hire experts who can work with it. What's more is that building an AI still requires a significant amount of time and effort to develop AIs using machine-learning. Relieving some of that stressful work to other machine learning systems could greatly cut the human input that needs to be dedicated to the entire process.

Machine learning (ML) methods have been around for ages, but the big-data revolution and the plummeting cost of computing power are now making them truly excellent and practical analytical tools in banking across a variety of use cases, including credit risk. ML algorithms may sound complex and futuristic, but the way they work is quite simple. Essentially they combine a massive set of decision trees (i.e., a decision-making model that breaks out individual decisions and possible consequences, also known as "learners") to create an accurate model. By churning through these learners at high speeds, ML models are able to find "hidden" patterns, particularly in unstructured data that common statistical tools miss. Overfitting (the analytical description of random errors rather than underlying relationships) of the model is a typical concern about ML.

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Arccos Golf announced Thursday that Microsoft will be its official cloud partner in an initiative that will see the two companies develop technologies that use advanced analytics to deliver insights to help golfers of all skill levels. The Arccos Course Analyzer will debut Jan. 24 at the PGA Show in Orlando as a platform that layers an Arccos user's data on top of millions of data points for more than 40,000 golf courses mapped in the Arccos system. It uses Microsoft Azure's cloud-computing services and machine learning capabilities to provide personalized recommendations for strategies on nearly every golf hole in the world. "Our goal is to create the most advanced Artificial Intelligence platform for golf," Arccos CEO and co-founder Sal Syed said in a statement. "It will leverage a user's personal performance history, all the shots ever taken by the Arccos community, weather, elevation, course features, equipment selections and much more. The resulting strategic advice will be smarter than anything that's humanly possible. With its broad suite of capabilities, Microsoft's Azure cloud platform is the ideal solution to unlock this vision."

Scientists can now monitor and record the activity of hundreds of neurons concurrently in the brain, and ongoing technology developments promise to increase this number manyfold. However, simply recording the neural activity does not automatically lead to a clearer understanding of how the brain works. In a new review paper published in Nature Neuroscience, Carnegie Mellon University's Byron M. Yu and Columbia University's John P. Cunningham describe the scientific motivations for studying the activity of many neurons together, along with a class of machine learning algorithms--dimensionality reduction--for interpreting the activity. In recent years, dimensionality reduction has provided insight into how the brain distinguishes between different odors, makes decisions in the face of uncertainty and is able to think about moving a limb without actually moving. Yu and Cunningham contend that using dimensionality reduction as a standard analytical method will make it easier to compare activity patterns in healthy and abnormal brains, ultimately leading to improved treatments and interventions for brain injuries and disorders.

Or why I don't want to listen to the Red Hot Chili Peppers. No offense to the Peppers, but just because I listen to alternative music and "people like me who listen to alt music" enjoy hearing the Peppers it does not then mean that I want to hear them. I am not "people like me", I am me. And I want the smart computers behind my favorite services to know me enough to deliver the content I want. It doesn't matter which service we talk about.

The capabilities of artificial intelligence and machine learning are accelerating, and many cybersecurity tasks currently performed by humans will be automated. There will still be plenty of work to go around so job prospects should remain good, especially for those who keep up with technology, broaden their skill sets, and get a better understanding of their company's business needs. Cybersecurity jobs won't go the way of telephone operators. Take, for example, Spain-based antivirus company Panda Security. When the company first started, there were a number of people reverse-engineering malicious code and writing signatures.

The development of Internet of Things (IoT) and artificial intelligence (AI) technologies raises the issue on whether they should also act ethically. On 26 October 2016, I attended the IoT Solutions World Congress, one of the largest events in the world on the Internet of Things, and I had the pleasure of being part of a panel on "Ethical Uses of Data", together with Edy Liongosari from Accenture, Prith Banerjee from Schneider Electric, Derek O'Halloran from the World Economic Forum, Sven Schrecker from Intel and David Blaszkowsky from the Financial Semantics Collaborative. In a few years, we will not own almost anything. Our car, our house and whatever we use during the course of the day will become "as a service". In this context, the sole asset that will belong to individuals is their "digital identity".

In September 2014, Apple made encryption default with the introduction of the iPhone 6. Then, in February 2016, a Los Angeles judge issued an order to Apple to help break into the encrypted iPhone belonging to a terrorist involved in a mass shooting. Apple had used some of the strongest encryption technologies and practices to protect its users and their data. The encryption technology did not discriminate between lawful and unlawful users. While there were many sides to this issue, it surfaced many important debates on security, privacy, and civil rights.