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.

The first generation of robo-advisors use a method known as "passive portfolio management" to buy and hold a fixed balance of exchange-traded funds. This method relies on price averages in order to match market returns. Vancouver-based online investment service Responsive aims to outperform this first generation of robo-advisors using a data-rich Artificial Intelligence approach. "Passive investing is a good start, but it ignores the data", Responsive CEO Davyde Wachell explains. Google's AlphaGo AI has been racking up wins against humanity's best Go players.

Artificial intelligence is still surrounded by an aura of mystery, and it would be tough to find a better illustration than the story in the news last week about a British grandmother who includes "please" and "thank you" in all her Google searches. "Please translate these roman numerals mcmxcviii thank you," read the search request from May Ashworth that ultimately went viral when her grandson tweeted it on Twitter. "I thought, well somebody's put it in, so you're thanking them," Ashworth reportedly explained. "I don't know how it works, to be honest. It's an endearing tale that drew notice from Google itself, but it also underscores the way A.I., in particular, has been something of a black box.

Registration has just opened up for an all new US 5 million XPrize, this time focusing on getting humans collaborating better with artificial intelligence to solve major global issues. Unlike previous competitions, this XPrize, sponsored by IBM's Watson division, doesn't feature a set of pre-determined goals, but instead challenges teams to come up with their own. You might be familiar with XPrize from its ongoing Google Lunar effort, which is seeing small teams from around the world compete to successfully land a robot on the Moon. It's a seriously ambitious project, and one that has seen rivals team up in the hope of winning out against the competition. This new project is totally different to the Lunar XPrize, but it's no less ambitious.

The magic and frustration of voice assistants and artificial intelligence when it comes to accessibility. A lot has been said recently about Apple's prospects in artificial intelligence and machine learning. One aspect of the discussion that I haven't seen considered is the accessibility ramifications of artificial intelligence in assistants like Siri. I've long been a proponent of voice-driven interfaces as assistive technology, which makes Siri's slow development all the more frustrating. This is particularly true when it comes to Siri understanding you if you have trouble speaking.

It's hard to think of a company more infatuated with AI than Google. With multi-billion dollar investments in deep learning startups like DeepMind, and responsible for some of the biggest advances involving neural networks, Google is the greatest cheerleader artificial intelligence could possibly hope for. But that doesn't mean there aren't things about AI that scare the search giant. Related: Google researchers have plans for an AI "kill switch" In a new paper, entitled "Concrete Problems in AI Safety," Google researchers -- alongside experts from UC Berkeley and Stanford University -- lay out some of the possible "negative side effects" which may arise from AI systems over the coming years. Instead of focusing on the distant threat of superintelligence, the 29-page paper instead examines "unintended and harmful behavior that may emerge from poor design."

In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post.

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.