News stories late last year breathlessly reported the founding of a new religion called "Way of the Future"--one that worships artificial intelligence ("AI") as its godhead. There is no punch line. This is not supposed to be a joke. That "O" in its acronym is important, and should not be omitted. Lots of phenomena call themselves religions nowadays or are called that by others. You don't necessarily need to worship a magic spirit that created the universe and consigns unbelievers to hell in order to be a religion.

In 2018, banks already active in these areas will look to make the most of the major investments they've made in these technologies, while others will take their first steps in exploring them. There's little doubt that AI will be a disruptive force. Banks' wealth of data will allow them to train and continuously improve machine learning and natural language processing systems. This will give them powerful tools to automate critical tasks and deliver granular customer insights. Until now, the bulk of the action concerning AI initiatives has been concentrated among the biggest financial institutions.

As automation supplants more forms of labor, creative expression still seems like a distinctly human enterprise. This may someday change: by ingesting works of authorship as "training data," computer programs can teach themselves to write natural prose, compose music, and generate movies. However, current fair use doctrine threatens either to derail the progress of machine learning or to disenfranchise the human creators whose work makes it possible. It concludes that fair use may not protect expressive machine learning applications, including the burgeoning field of natural language generation. Part II explains that applying today's fair use doctrine to expressive machine learning will yield one of two undesirable outcomes: if US courts reject the fair use defense for machine learning, valuable innovation may move to another jurisdiction or halt entirely; alternatively, if courts find the technology to be fair use, sophisticated software may divert rightful earnings from the authors of input data.

Each new year provides the opportunity for reflection upon how far we have come and how far we still have to go, on both a personal and societal level. It was also the year that it took you at least a few minutes to realise the customer agent answering your queries in that little chat-box wasn't human, when you picked up a VR headset from your local toyshop for the price of a pizza, when you found yourself in far too many political arguments around the water-cooler, and when you began seriously questioning whether a computer might someday take your job -- maybe for the second time that year. We will see continuing tensions within and between countries, as 20th century nationalist sentiments push resentfully against 21st century supranational integration. There will be moments when it feels like only technology can save us, followed by events which remind us of how perilous our inventions can be when we still barely understand them. The following is not investment or professional advice of any kind, and is intended only to promote discussion and reflection on some of the rising trends and ideas of our time.

Today, with Digitization of everything, 80 percent the data being created is unstructured. Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document's texts processed in financial sectors are examples of unstructured data stored in Big Data. Click here to read more. What is Going on with Residential IoT Cyber Security? For sure you have heard about the recent DDoS attacks that occurred last October 21st on Dyn's DNS service. The news broke out reporting that many well-known Internet services were not available. According to Hacker News Twitter, Etsy, Spotify and other sites were affected.

It's difficult for some people to imagine at this point, but dedicated point and shoot cameras used to be a big part of our lives. Anytime people went out to celebrate, traveled somewhere on vacation, or went to a family gathering, they would bring cameras with them to capture those memories. These days, the concept of a dedicated camera is completely foreign to just about anyone who isn't an amateur or professional photographer. Point and shoot cameras have been completely replaced by smartphones, of course, and that's a great thing for so many reasons. Having one less gadget to carry and charge is never a bad thing, but it's the capabilities of smartphones that make them better personal cameras than old dedicated digital cameras.

"The advent of the internet and the subsequent information explosion has made it increasingly challenging for journalists to produce news accurately and swiftly." So begin the research and development team at the global news agency Reuters in a paper on the arXiv this week. For Reuters, the problem has been made more acute by the emergence of fake news as an important factor in distorting the perception of events. Nevertheless, news agencies such as the Associated Press have moved ahead with automated news writing services. These report standard announcements such as financial news and certain sports results by pasting the data into pre-written templates: "X reported profit of Y million in Q3, in results that beat Wall Street forecasts ... " So there is significant pressure on other news agencies to automate news production.

Synacor is now making the Zimbra machine learning server, a functional, stand-alone, neural network-driven, GraphQL server for general text and e-mail classification, available as open source. One of the cooler projects I've had the opportunity to work on over the last year is to help modernize Synacor's Zimbra Collaboration Server and bring it up to date with a complete overhaul of many parts of the system. The new open source ZCS release will include a new high-availability, high performance backend, a great new React/Preact based user experience, and a machine intelligence capability to automatically organize content, sorting out smart folders and the most important messages, and providing a foundation for organizations to enhance the Zimbra Collaboration Server through its machine learning API or use it as a platform for experimentation. The ML server and GraphQL API allows a developer to easily create, train, and use any number of text classifiers, each having its own vocabulary, length of text input, meta-feature input, and combination of exclusive and overlapping target classes. The classification architecture achieves competitive results on some Kaggle classification challenges, which are included as example programs using the API.

An artificial intelligence company has launched this week what it says is the first autonomous AI tool for determining the "trustworthiness" of a story. Called Unpartial and developed by San Jose, California-based Recognant, the tool is an extension for the Chrome browser. CEO Brandon Wirtz told me that other browsers may be supported at some point. Recognant has described the tool as being a detector of "fake news," but it's actually a "trustworthiness" detector. It doesn't check facts or validate the source, but instead uses generated rules to evaluate the internal validity of a story.

Machine intelligence--also known as artificial intelligence--is the ability of machines to perform tasks that would normally require human intelligence. These new machine capabilities both inspire and unnerve: trucks that drive themselves, computer programs that develop drug therapies, software that writes news articles and composes music. But before you can imagine the possibilities, you need to know the basics.