If there's a Golden Rule in customer service, it's that people want to be treated like human beings--which implies that it takes another human being to respond to them with the right tone. The advent of artificial intelligence, or AI, in customer service, however, doesn't break that rule the way you might assume. AI can encompass a wide variety of technologies, but it tends to involve tools that can think the way people do, and automate tasks for scenarios that are more predictable in nature or based on contextual data. While AI is poised to have a huge impact on many other areas of a business, including sales and marketing, its use in customer service may represent a particularly good fit, especially for Canadian small and medium-size businesses that need help in that area. That said, the journey to AI in customer service should be thought through now so that SMBs get the most out of what the technology has to offer and ensure it brings value to its customers right away.

Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

The Deep Learning & Artificial Intelligence Introductory Bundle is a four course eLearning bundle designed for users of all experience levels. Some knowledge of math, calculus, linear algebra and probability, as well as Python and Numpy is recommended though. Deep Learning is the major topic of all four courses of the bundle. While there is lots of theory involved in the courses, there is also time for some practical applications. You learn to build an algorithm that predicts user actions on websites, a patient's systolic blood pressure, or using Deep Learning for facial expression recognition.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

With 2017 fast approaching, technology trends that will keep gathering steam in the new year range from augmented and virtual reality to machine intelligence, Docker, and microservices, according to technology consulting firm ThoughtWorks. The data is based on reports ThoughtWorks' consultants are seeing out in the field. ThoughtWorks sees natural language processing tools like Nuance Mix and hardware providing for natural interactions having a "huge" impact on AR and VR adoption. AR differs from VR in that users still can see the world around them rather than being completely immersed in a virtual space; of the two, AR is likely to be most interesting to businesses. "One excellent application is remote expert systems," said Mike Mason, technology activist at ThoughtWorks.

DeepMind and Blizzard Entertainment are collaborating to open up StarCraft II to artificial intelligence and machine learning researchers globally. According to a DeepMind blog post by research scientist Oriol Vinyais, StarCraft II continues the series' renowned eSports tradition, as the original StarCraft was played in the late 1990s yet remains popular today. StarCraft is a good testing environment to work with because it provides a "useful bridge to the messiness of the real world," and the skills needed to play in this environment could transfer easily to real-world tasks, wrote Vinyais. DeepMind is looking to work with Blizzard in order to create "curriculum" scenarios, which means researchers will be faced with complex tasks that researchers will need to complete in order to get an agent up and running. Agents will play directly from pixels, and to get DeepMind there, a new image-based interface that outputs a simplified low-resolution RGB image data for the map and minimap was created, according to Vinyais.

The Russian machine learning firm Tselina Data Lab developed a deep learning-based camera algorithm called Fraudoscope that detects lies on facial emotions. Trained with CUDA and TITAN X GPUs, the lie-detecting app uses a high-definition camera to observe an interrogation and decode the results. The camera focuses on the interviewee -- the software maps changing pixels in the camera feed that correspond to breathing, pulse, pupil dilation, facial tics -- and the work-in-progress already has a 75 percent accuracy rate. As with traditional polygraph tests, Fraudoscope requires a set of calibration questions with well-known answers and the interviewee is also asked to imagine they've just won an Olympic medal – as they make up their imaginary answer, the system learn to recognize the individual's lie. The firm hopes one day the algorithm will be smart enough to not require calibration and if fed enough information, it may eventually be able to identify poker players and shoplifters from a glance.

Almost a year ago, we published our now-annual landscape of machine intelligence companies, and goodness have we seen a lot of activity since then. This year's landscape has a third more companies than our first one did two years ago, and it feels even more futile to try to be comprehensive, since this just scratches the surface of all of the activity out there. As has been the case for the last couple of years, our fund still obsesses over "problem first" machine intelligence--we've invested in 35 machine intelligence companies solving 35 meaningful problems in areas from security to recruiting to software development. At the same time, the hype around machine intelligence methods continues to grow: the words "deep learning" now equally represent a series of meaningful breakthroughs (wonderful) but also a hyped phrase like "big data" (not so good!). We care about whether a founder uses the right method to solve a problem, not the fanciest one.

When you perform a Google search for every day queries, you don't typically expect systemic racism to rear its ugly head. Yet, if you're a woman searching for a hairstyle, that's exactly what you might find. A simple Google image search for'women's professional hairstyles' returns the following: Your questions answered by founders, experts and thought leaders in business, design and tech. Here, you'll find hairstyles, generally done in a professional setting by stylists. It returns what it thinks you're looking for based on contextual clues, citations and link data.

It will come as no surprise to anyone familiar with the technology world that the rate of cyber attacks, the development of malware, and the exploitation of zero-day flaws makes is very difficult for IT teams and security specialists to keep up with let alone get ahead of cyber threats. Research from Symantec noted that nearly one million new malware threats emerge daily, and while there are many tools to make detecting rogue code an easier process, dealing with such an enormous amount of new threats appears to be an almost insurmountable task even for the best security teams and anti-virus systems. The answer to this, and the potential future of cyber security, looks to be the use of machine learning and artificial intelligence (AI) to apply clever computers and smart software to a problem that leaves humans on the back foot in the fight against hackers. Rather than sift through data harvested from across IT networks, machine learning algorithms can be trained to detect certain malware and threat signatures and proactively sniff out threats, bypassing the need for cyber security experts to disappear into a warren of file paths and scripts to find tell-tale signs of malware. Webroot is one such cyber security company applying machine learning techniques to power its threat intelligence service without requiring resource sapping and time-consuming manual processes.