Go master Cho Chikun triumphed Wednesday in his final game against DeepZenGo, a Japanese artificial intelligence system, to win the three-game series 2-1. Cho, 60, has won 74 titles, the largest number in Japan, over a long career. He defeated DeepZenGo with the 167th move in the third game of the series, which was played on even terms with no handicaps. DeepZenGo uses deep learning and other advanced technologies. It is being developed with support mainly from Dwango Co., a video-sharing website operator, and the University of Tokyo. "I felt as if I was playing with a human, because (DeepZenGo) has both strong and weak points," Cho said after the final game.

Facial recognition software is most commonly known as a tool to help police identify a suspected criminal by using machine learning algorithms to analyze his or her face against a database of thousands or millions of other faces. The larger the database, with a greater variety of facial features, the smarter and more successful the software becomes – effectively learning from its mistakes to improve its accuracy. Now, this type of artificial intelligence is starting to be used in fighting a specific but pervasive type of crime – illegal fishing. Rather than picking out faces, the software tracks the movement of fishing boats to root out illegal behavior. And soon, using a twist on facial recognition, it may be able to recognize when a boat's haul includes endangered and protected fish.

Soon after the invention of photography, a few criminologists began to notice patterns in mugshots they took of criminals. Offenders, they said, had particular facial features that allowed them to be identified as law breakers. One of the most influential voices in this debate was Cesare Lombroso, an Italian criminologist, who believed that criminals were "throwbacks" more closely related to apes than law-abiding citizens. He was convinced he could identify them by ape-like features such as a sloping forehead, unusually sized ears and various asymmetries of the face and long arms. Indeed, he measured many subjects in an effort to prove his view although he did not analyze his data statistically.

Even as its engineers were developing the technology platform Watson, managers at IBM knew that cancer would be one of its killer applications. This dreaded disease manifests itself in many different forms, with further variations based on a patient's genetic makeup. Cancer's complexity forces doctors to resort to trial and error to figure out what drugs work. Watson, backed by its artificial intelligence platform, can search through enormous amounts of data and quickly come up with the best treatment method. Watson is thus popular among oncologists, but this piece of technology has other uses too.

THERE IS SOMETHING familiar about fears that new machines will take everyone's jobs, benefiting only a select few and upending society. Such concerns sparked furious arguments two centuries ago as industrialisation took hold in Britain. People at the time did not talk of an "industrial revolution" but of the "machinery question". First posed by the economist David Ricardo in 1821, it concerned the "influence of machinery on the interests of the different classes of society", and in particular the "opinion entertained by the labouring class, that the employment of machinery is frequently detrimental to their interests". Thomas Carlyle, writing in 1839, railed against the "demon of mechanism" whose disruptive power was guilty of "oversetting whole multitudes of workmen".

Amazon Web Services is expected to announce a new version of its PostgreSQL database for its cloud customers during its AWS re:Invent conference next week, according to a Fortune report. AWS hopes its PostgreSQL database will appeal to larger companies, some of which have complained about the price of Oracle and Microsoft offerings. Amazon also intends to announce new machine learning tools for AWS developers, according to the report. AWS' annual conference is sure to reveal a bevy of new capabilities. Cloud providers often try to one-up other services providers with new, flashy capabilities and re:Invent will likely be no different.

The ongoing evolution in advanced analytics and other emerging technologies is transforming the operational processes in government and public service agencies throughout the world. With the help of these technologies, such organisations are trying to address citizen demands, helping to overcome persistent challenges such as regulatory compliance, outdated legacy IT infrastructures and organisational cultures, according to a new study report from Accenture. In its latest report titled, Emerging Technologies in Public Service, Accenture surveyed nearly 800 public service technology professionals across nine countries to identify emerging technologies being implemented or piloted. The technologies include advanced analytics/predictive modelling, the IoT, intelligent process automation, video analytics, biometrics/identity analytics, machine learning, and natural language processing/generation. The survey found that while more than two-thirds, (70%) of public sector agencies are evaluating the potential of emerging technologies, only a small percentage (25%) is moving beyond the pilot phase to full implementation.

The list of activities in which artificial intelligence machines have bested humans is increasing at an alarming rate. Face recognition, object recognition, chess, Go, various video games, and numerous other tasks have all fallen in this battle. So it's natural to ask about the types of tasks that machines still have difficulty with. Where do humans still rule the roost? Today, we get an answer of sorts thanks to the work of Mohit Iyyer at the University of Maryland in College Park and a few pals.

Google has previously taught its artificial intelligence to play games, and it's even capable of creating its own encryption. Now, its language translation tool has used machine learning to create a'language' all of its own. In September, the search giant turned on its Google Neural Machine Translation (GNMT) system to help it automatically improve how it translates languages. The machine learning system analyses and makes sense of languages by looking at entire sentences – rather than individual phrases or words. Following several months of testing, the researchers behind the AI have seen it be able to blindly translate languages even if it's never studied one of the languages involved in the translation.

As the conversation shifted the opportunity and challenges for Artificial Intelligence for the average person, hit home. Those of means (money, education, location, etc.) will get super human powers augmented by data driven, people specific technologies. Those born of lesser means may be quickly left behind. In the next 5 - 10 years, 3 million US truck drivers will be the first to suffer as the transport industry goes driverless. Yes, there might be a person on board, much like a train has a conductor, but eventually, this will probably go away.