Even in the 1990s, when Australian banks copped a lot of flak for closing branches, consumer behaviour was changing. The banks may not have undertaken their branch network rationalisations in the most amenable fashion for the wider community but even then, as data from the Australian Prudential Regulation Authority show, actual points of representation didn't change as much as the headlines suggested. Indeed, points of representation actually increased for 11 straight years from 2001, the first year APRA started compiling proper data. The shift from traditional branch banking and physical currency has been immense. But the next generational shift in financial services – and services more generally – will be even more confronting.

Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to co-founder Rana el Kaliouby, the Waltham, Mass.-based company wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the language or conversations in a video. The company's technology ingests digital images--including video in chat applications, live-streamed or recorded videos, or even GIFs--through simple web cams typically. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.

I built a food logging app that uses deep learning to classify photos. About a month ago I posted an invite for the beta test for this app, but most people couldn't use it due to how restrictive closed beta tests are. I built this using deep learning. The app can recognize over 1000 types of food on your plate, and pull down nutritional information based on the recognized keyword and restaraunt location you may be eating at. All of this is for genetic research.

Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to cofounder Rana el Kaliouby, the Waltham, Mass.-based company, wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the content or conversations in a video. The company's technology ingests digital images--including video in chat applications, livestreamed or recorded videos, or even GIFs--through typically the simplest web cams. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.

When McDonald's begins its annual shareholders' meeting at its headquarters in Oak Brook, Illinois, Thursday morning, it is expected to be targeted by protesters seeking a 15 minimum wage and the right to unionize. In recent months, the American fast food giant and its franchisees have increasingly faced accusations of underpaying workers and wage theft. "McDonald's profits keep growing while their workers struggle. People who work full time should never have to rely on food stamps to feed themselves and their families," the Fight For 15 movement, which announced the start of protests in Chicago Tuesday, said in a statement posted on its website. "It's wrong for a company whose stock just hit an all-time high to pay wages so low that its workers have to rely on public assistance to scrape by. McDonald's profits in the first quarter rose 35% -- it's time for the company to share its good fortune with its workers."

As the wide range of technologies that fall under the banner of "Big Data" begin to mature and become ubiquitous, the next stage of development of the analytics stack is machine learning. Beyond its role in making better sense of data, however, machine learning has an increasingly critical role to play in application security, particularly in areas like financial services. With increased security focus turning toward one of the prime sources for exploitation--commercial and homegrown code--the tooling around application security is getting smarter and bringing the rest of the monitoring stack up to speed through automation. For data-driven organizations, which include almost any Fortune 500 company, maintaining the security of their critical applications is equivalent to locking down some of their most valuable assets. With a wide set of applications spanning departments and even different clusters or infrastructure, this is no small task.

I asked Siri whether she believes in God. "I believe in the separation of spirit and silicon," she demurred. I asked the chatbot ELIZA the same question -- her thing is she's a therapist, so she'd rather focus on what I believe. I asked her if she was religious; she asked if I'd prefer if she weren't. "We aren't really talking about me, are we," she replied.

One of the most useful things about robots is that they don't feel pain. Because of this, we have no problem putting them to work in dangerous environments or having them perform tasks that range between slightly unpleasant and definitely fatal to a human. And yet, a pair of German researchers believes that, in some cases, feeling pain might be a good capability for robots to have. The researchers, from Leibniz University of Hannover, are developing an "artificial robot nervous system to teach robots how to feel pain" and quickly react in order to avoid potential damage to their motors, gears, and electronics. They described the project last week at the IEEE International Conference on Robotics and Automation (ICRA) in Stockholm, Sweden, and we were there to ask them what in the name of Asimov they were thinking when they came up with this concept.

A Markov chain is a random process with the property that the next state depends only on the current state. For example: If you have the choice of red or blue twice the process would be Markovian if each time you chose the decision had nothing to do with your choice previously (see diagram below). How can Markov Chains help us? To start with we need to define some basic terminology. The changes of state within the system are called transitions, and the probabilities associated with various state-changes are called transition probabilities.