Well, if I have a look at my personal life, there are some work categories where AI will be of great value to me--a maid, a driver, an on call doctor, even an assistant to help me filter through the information I have to churn through. When it comes down to it, I don't think AI can replace human communication, a combination of emotional and analytical perspective. We know when we're interacting with a machine, and as complex as we can make AI, will we ever truly feel it is more than talking to a toaster? Camilla Urdahl works for unified communications company Unified Inbox, and will speak to strangers at events about communications strategies, new business models, and how best to accelerate innovation.

In articles, movies, books and TV shows, speculation has been rife that artificial intelligence is going to make us all redundant. Well, most of us, a useless underclass where we are outclassed by AI on every level, our jobs being done better by AI replacements. Facebook has just opened up Messenger to a number of chatbots, which haven't strictly been one hundred percent reliable or user-friendly, not to mention Microsoft's AI twitter bot, Tay, taking a nasty turn into racism and sexism. These, while amusing, initial teething problems will be sorted. As with anything it will be a steep learning curve, but our collective knowledge will get AI to the point where it is useful to society.

Terrapattern is a visual search engine that, from the first moment you use it, you wonder: why didn't Google come up with this ten years ago? Click on a feature on the map -- a baseball diamond, a marina, a roundabout -- and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use, and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. And how did a handful of digital artists and developers create it -- and for under 35,000?

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.

One-fifth of the films and television shows offered in the European Union by on-demand providers like Netflix, iTunes and Amazon would have to be Europe-made under new proposals issued Wednesday. The quota would match one that already exists for TV networks in European countries and aims to protect the film industry, culture and national languages of the EU's 28 states in an increasingly globalized world dominated by programs in English and from the U.S. The proposal, however, is not popular in the industry and was immediately criticized by some. "Cultural quotas are outdated and unnecessary -- video-on-demand providers are already investing heavily into European local content," said James Waterworth, vice president of Europe operations for the CCIA computer and Internet industry association. Officials from the European Commission, the EU executive branch that made the proposal Wednesday, noted that Netflix's library is already made up of 21% European content, while other providers have up to 30%. "These percentages are not going to represent a major effort," said Guenther Oettinger, the commissioner responsible for Europe's digital market. "We are providing a certain degree of security for the European film industry."

This course is the next logical step in my deep learning, data science, and machine learning series. I've done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? In these course we'll start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding). Next, we'll look at a special type of unsupervised neural network called the autoencoder.

WIRE)--CrowdStrike Inc., a leader in cloud-delivered next-generation endpoint protection, threat intelligence and response services, today announced enhanced next-generation antivirus (AV) capabilities to its CrowdStrike Falcon Platform to help customers identify and block popular ransomware families such as Locky, Cerber and Teerac, among others. The new protection is part of CrowdStrike's Spring release of new solutions and services that also includes introducing an expanded and updated set of APIs for the Falcon Platform, along with CrowdStrike's new open source tool, CrowdStrike Falcon Orchestrator. When dealing with the uptick in ransomware attacks, organizations need a combination of security features to protect against new variants of ransomware that are launched faster than outmoded signature-based defenses can be updated. As part of its next-generation endpoint protection technology, CrowdStrike uses powerful machine learning algorithms and Indicators-of-Attack-based (IoA) threat prevention to identify and block not only previously unknown ransomware families, but also new and emerging script-based ransomware that do not use executable binaries to encrypt the systems of victims. CrowdStrike's next-generation antivirus capabilities also offer complementary capabilities that include exploit blocking as well as cloud-driven anti-malware and machine learning to stop the execution and spread of ransomware.

The four founders, two of whom have resigned their professorships to devote their full attention to TwentyBN, met each other during studies at the University of Bielefeld in Germany. Each of the founders has over 15 years of experience in machine learning and the relatively young deep learning discipline. Prof. Dr. Roland Memisevic, Chief Scientist, received his doctorate in Toronto, studying with Geoffrey Hinton, one of the founding fathers of deep learning. Prior to co-founding Twenty Billion Neurons, Memisevic was a member of the faculty at the renowned Machine Learning Institute of the University of Montréal led by Yoshua Bengio. The institute counts Google, Facebook, and IBM amongst its most active donors.

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.

They say that kids aren't born sexist or racist--hate is taught. Artificial intelligence is the same way, and humans are fabulous teachers. ProPublica reported, for example, that an algorithm used to to predict the likelihood of convicts committing future crime tends to tag black folks as higher risk than whites. Despite the oft-repeated claim that such data-driven approaches are more objective than past methods of determining the risk of recidivism or anything else, it's clear that our very human biases have rubbed off on our machines. Consider the case of Microsoft's simple Tay bot, which sucked up all the slurs and racist opinions that Twitter users threw at it and ended up spouting Nazi drivel.