Some of Japan's banks and insurance companies have started using artificial intelligence to handle customers' inquiries more efficiently over the phone and online. Bank of Tokyo-Mitsubishi UFJ's smartphone app features MAI, a virtual bank teller, who can respond to customers by understanding the nature of their queries. For instance, if a customer says, "I lost my ATM card," MAI will recognize the situation and tell the customer what to do. The Android version of the app has already been released. The iPhone version is expected to become available by the end of this month.

IBM has updated its artificial intelligence (AI) product, IBM Watson, giving it the ability to recognise images. Watson, which relies on cognitive learning to help it process the world in a human-like manner, can now'guess' what's happening in images fed to it via URLs. IBM has created a'Visual Recognition Demo' to showcase Watson's latest trick, which allows users to feed Watson an image before it tells you what it believes it sees. For example, supplying Watson with the image of a tiger throws up the result 77 per cent tiger, 26 per cent wild cat and 63 per cent cat. As well as identifying objects, people or animals in photos, Watson is also fairly adept at guessing what's going on in the background of images such as sunsets and other outdoor scenes.

Just over a week ago, Google Deepmind's AlphaGo machine crushed 18-time World Go Champion Lee Sedol 4-1 in a 5 game series, heralding an achievement many experts predicted to be at least a decade away. And whilst the victory of machine over man was a great result for Google, Machine Learning, and Artificial Intelligence (AI) - it also served as a chilling reminder that the ever-extending arm of AI is showing absolutely no sign of slowing. DeepMind founder Demis Hassabis has stated that Go is "probably the most complex game ever devised by man." For starters, it's played on a 19 by 19 board, which allows for 10171 possible layouts, versus roughly 1050 possible configurations on a standard chessboard, and an estimated 1080 atoms in the universe. Because of this, players are often said to rely heavily on sub-conscious intuition or'gut feeling'.

Policymakers in Washington could learn something from Lee's agile response to the evolving challenges posed by the artificial-intelligence revolution. Artificial intelligence is on its way to ubiquity, and we're not ready for it. Already it has entered the landscape of the physical world in delightful and dangerous new ways, with Google leading the charge in many different industries. Yet policymakers seem trapped in the regulatory frameworks of the 20th century. In two of the most prominent A.I.-linked industries, autonomous vehicles and drones, current legal regimes are already insufficient.

It might not happen anytime soon, but then again, it might. In Japan, a short novel co-written by an artificial intelligence program (its co-author is human) made it past the first stage of a literary contest, the Japan News reports. The Nikkei Hoshi Shinichi Literary Award is named after Hoshi Shinichi, a Japanese science fiction author whose books include "The Whimsical Robot" and "Greetings from Outer Space." Judges for the prize weren't told which novels were written by humans and which were penned by human-computer teams. The award is unique in that it accepts entries from "applicants who are not human beings (AI programs and others)."

Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it." By now, you may have already started wondering about this blog. I haven't written anything for more than half a year! Okay, musings on social network platforms aside, that's not true: I have written something – about 400 pages to be precise. This has really been quite a journey for me lately. And regarding the frequently asked question "Why did you choose Python for Machine Learning?" I guess it is about time to write my script. In the following paragraphs, I really don't mean to tell you why you or anyone else should use Python. To be honest, I really hate those types of questions: "Which * is the best?" (* insert "programming language, text editor, IDE, operating system, computer manufacturer" here).

The Unit for Natural Language Processing [1] of the Insight Centre for Data Analytics [2] at the National University of Ireland, Galway [3], invites applications for two PhD positions in Natural Language Processing and Linked Data. The positions are associated with the SFI funded research program on Text Mining with Linked Data. Candidates should preferably have a Masters degree in a relevant field of study with an emphasis on areas such as text mining, natural language processing, computational linguistics, machine learning etc. Please send your application, including CV and a research proposal of up to two pages (both in PDF only) before the closing date of April 25th, 2016 to Dr. Paul Buitelaar at paul.buitelaar@insight-centre.org

The idea that computers are people has a long and storied history. It goes back to the very origins of computers, and even from before. There's always been a question about whether a program is something alive or not since it intrinsically has some kind of autonomy at the very least, or it wouldn't be a program. There has been a domineering subculture--that's been the most wealthy, prolific, and influential subculture in the technical world--that for a long time has not only promoted the idea that there's an equivalence between algorithms and life, and certain algorithms and people, but a historical determinism that we're inevitably making computers that will be smarter and better than us and will take over from us. You'll have a figure say, "The computers will take over the Earth, but that's a good thing, because people had their chance and now we should give it to the machines."

With the best human player of boardgame Go being defeated this month by Google software, lawyers shouldn't make the mistake of believing their skills are beyond the reach of artificial intelligence (AI), write Stéphane Leriche and Michael Stojanovic. In early March, Google's DeepMind and its AlphaGo software beat the best human player of the ancient Chinese board game, Go, in a series of much-hyped matches. AI-powered legal tools are already a reality, and the success of AlphaGo demonstrates that the technology to make these tools better now exists. IBM's Watson cognitive computer won the TV game show Jeopardy! in 2011. Watson's question-answering technology has since been adapted to produce ROSS, a legal research virtual assistant that its developers promote as a'super intelligent attorney'.

The advent of audit analytics and cognitive technology does not mean the end of human auditors. It means an end to painstaking checking and crossfooting of debit and credit entries and the beginning of auditing careers that thrive on understanding, monitoring, and improving analytical and cognitive systems. I have worked for a couple decades with professional services firms that perform financial audits, but I have never done one--nor have I ever wanted to do one, to be honest. I'm not good with work that involves structured processes, details, and rigorous checking, and audits always seemed heavily infused with those kinds of tasks. Now, however, I am becoming quite interested in audits for two reasons.