Earlier this year I wrote about an interesting new start-up that's using machine learning to provide a more intuitive interface between customers and their banks. The platform utilizes machine learning to classify the transactions we make each month, and then provide intelligent insights into the patterns it observes. For instance, it might tell us how much we're spending at a particular coffee shop, or whether we might be able to save money on our utility bills. It seems part of an interesting trend towards more automated banking. Chatbots have been a hot topic in the news in recent months, and an Indian startup are using them to fully staff their mobile banking service.

Could machines become so intelligent and powerful they pose a threat to human life, or even humanity as a whole? It's a question that has become fashionable in some parts of Silicon Valley in recent years, despite being more or less irreconcilable with the simple robots and glitchy virtual assistants of today (see "AI Doomsayer Says His Ideas Are Catching On"). Some experts in artificial intelligence believe speculation about the dangers of future, super-intelligent software is harming the field. Now Google, a company heavily invested in artificial intelligence, is trying to carve out a middle way. A new paper released today describes five problems that researchers should investigate to help make future smart software safer.

NEW YORK--Siri evaded my question for what took Apple's voice assistant so long to get onto the Mac.

It's only fair that robots used by companies to improve productivity pay their fair share of taxes, right? This might seem ridiculous, but the European Union is looking to create a set of new laws concerning the rise of robots. According to Forbes, one of the new laws would require robots to pay social security taxes. Naturally, the law can't specifically force a robot who likely works for free to pay taxes. The European Union does, however, want businesses that make use of robots to pay higher taxes based on the rational that a robot replaces a human worker who otherwise would pay taxes into the system.

Newly discovered brainwaves – coined Princess Leia waves – that cycle around the brain as we sleep, may help us remember the day's events. Terry Sejnowski at the Salk Institute for Biological Sciences in La Jolla, California, coined the name after discovering patterns of electrical activity that sweep through the sleeping brain in a circular motion that resembled the headphone-like hairstyle made famous by Carrie Fisher in the Star Wars movies. The waves began their journey in the hippocampus – a region responsible for memory retrieval. From there they propagated to the thalamus, an area that is known to incorporate information vital to our ability to remember events that happen to us personally, before heading to the cortex, which is responsible for complex functions such as thoughts and actions. Sejnowski discovered the circular waves by chance after analysing electrical data from eight people with epilepsy who'd had electrodes temporarily inserted into their brains to monitor activity associated with their seizures.

Portland, Ore.: Addressing customer requests for a single tool for one-to-one marketing, Lytics launches Lytics Personalization, a web-based product that allows a non-technical marketer to quickly and easily tailor a website to individual visitors based on their preferences and interests. Lytics Personalization is powered by the Lytics Customer Data Platform, which is the industry's most sophisticated means for applying machine learning and data science to the act of building consumer profiles. With Lytics Personalization, a non-technical marketer can craft – in minutes – a message that greets website visitors in individualized ways. For example, a new visitor may receive a message offering a newsletter sign-up, while a returning visitor with affinity for the brand across channels may see suggested content (e.g., a blog post, white paper, or announcement) based on their preferences. And sometimes personalization is about suppressing marketing rather than increasing it: Imagine a visitor with an open customer-support ticket who is "about to churn" receiving an offer for assistance rather than a promotion.

The next BriefingsDirect Voice of the Customer big-data case study discussion focuses on the Tower of Babel problem for disparate data, and explores how Alation manages multiple data types by employing machine learning and crowdsourcing. We'll explore how Alation makes data more actionable via such innovative means as combining human experts and technology systems. To learn more about how enterprises and small companies alike can access more data for better analytics, please join Stephanie McReynolds, Vice-President of Marketing at Alation in Redwood City, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions. Gardner: I've heard of crowdsourcing for many things, and machine learning is more-and-more prominent with big-data activities, but I haven't necessarily seen them together.

The publication The Drum sent out a fascinating story recently. The publication was working on an AI issue, and they wanted to know what would happen if, using IBM Watson, marketers could actually interview David Ogilvy. For you non-marketers out there, Ogilvy is kind of considered the king of marketing. He passed away in 1999 after an illustrious career in advertising. He was the kind of guy AMC's Mad Men was paying tribute to.

Facebook today is publishing an academic paper and a blog post detailing Torchnet, a new piece of open-source software that's designed to streamline deep learning, a type of artificial intelligence. Deep learning is a trendy approach that involves training artificial neural networks on lots of data, like photos, and then getting the neural networks to make predictions about new data. Rather than build a completely new deep learning framework, of which there are many, Facebook chose to build on top of Torch, an open-source library to which Facebook has previously contributed. "It makes it really easy to, for instance, completely hide the costs for I/O [input/output], which is something that a lot of people need if you want to train a practical large-scale deep learning system," Laurens van der Maaten, a research scientist in Facebook's Artificial Intelligence Research (FAIR) lab, told VentureBeat in an interview. Torchnet, which is written in Lua and can run on standard x86 chips or graphics processing units (GPUs), also lets programmers reuse certain code, which means doing less work and lowering the chances of introducing bugs, said van der Maaten.

CANNES, France--Artificial intelligence has been one of the big themes at the Cannes Lions festival here this week, and A.I. projects have already won three Grand Prix awards: two for J. Walter Thompson and ING's "The Last Rembrandt" and one for Google DeepMind's AlphaGo. The theme continued in intriguing fashion Thursday at the Saatchi & Saatchi New Directors' Showcase, the annual collection of mind-bending short films made by up-and-coming directors. The A.I. hook this time was this: One of the 20 films was conceived, directed and edited by a computer. The audience wasn't told which one, however--we were encouraged to go to saatchi.com to find out. Overall, the 2016 showcase was more intense than usual, with lots of disturbing visions and less humor than usual.