The study found that 55% of Canadian adults would like their employer to provide extended leave opportunities, 45% would prefer not to work at fixed times (i.e. "Flexibility and empowerment will be the new work currencies and productivity will be redefined," says Badminton. "Flexible payment schedules for workers will come into effect administered by automated systems that measure output, not hours put in." Finally, many people are also concerned that work is interfering with their personal lives. "Whether it's cutting corners to save time, or paying other people to do their job for them, Canadian adults are considering some unique ways to take back control at work" says Black.

Microsoft has created a new chat bot to "learn" from the internet… but she picked up a lot of bad habits. The tech company announced the launch of Tay this week, an artificial intelligence bot that is learning to talk like millennials by analysing conversations on Twitter, Facebook and the internet. The company's optimistic techies explained: "Tay is an artificial intelligent chat bot developed by Microsoft's Technology and Research and Bing teams to experiment with and conduct research on conversational understanding. "Tay is designed to engage and entertain people where they connect with each other online through casual and playful conversation. The more you chat with Tay the smarter she gets."

The Amazon Echo is turning out to be a sleeper hit. The stand-alone, voice-activated digital assistant is on back order, it was cited when premium speaker maker Sonos announced layoffs and it appears to be prompting other device makers (think Google's Nest) to work on their own look-alike services.

Machine learning company Maluuba, with headquarters in Waterloo, Ontario and a research office in Montreal, has applied an algorithm to the text of J.K. Rowling's bestselling novel Harry Potter and the Philosopher's Stone, along with several hundred other children's stories, to read text in such a way that it can then answer questions afterward. Maluuba has also just announced the opening of an R&D lab in Montreal, staffed by Yoshua Bengio of the Université de Montréal's Montreal Institute for Learning Algorithms (MILA) in partnership with reinforcement learning expert Richard Sutton from the Alberta Innovates Centre for Machine Learning, to make advances in the fields of Natural Language Understanding (NLU) and artificial intelligence (AI). Taking a deep learning approach, Maluuba trained its algorithm to approach the Harry Potter text from several levels of textual abstraction, word, sentence, paragraph, etc. And while a certain contingent of tech utopians may very well look at Maluuba's case study as the smoking gun they need for shutting down Humanities departments in universities everywhere, the company itself makes clear that using an algorithm to comprehend literature is a stepping stone to more practical uses. "For a computer to understand humans speaking in natural language and respond appropriately, it needs to capture and represent a large amount of knowledge that is not just words, but also common sense and context about the topic being discussed by the human," said Maluuba cofounder & CEO Sam Pasupalak. "Maluuba is working with leading experts and the world's premiere academic center for deep learning to design systems that can represent knowledge and answer questions in natural language.

I run into this question a lot and I have heard statisticians say things like we all do machine learning because none of us actually runs a regression or classification by hand on paper. On the other hand - some computer scientist's I talk to say that when you use programmatic techniques to orchestrate an analytical flow compared to using a GUI in SAS / SPSS you are using machine learning. One more answer I have heard is that if you use algorithms like RandomForest, Deep Learning, GBM etc you are doing machine learning as compared to statistics. I think all the above are observations that are partly right. But, as a person trained in Computer Science and Statistics, I have a very specific test to split the two.

For designers and photographers, selecting and laying out photos is often subjective, requiring a keen sense of color and composition. Using a computer algorithm, the stock footage site Shutterstock hopes to make that process easier. It now offers a reverse image search tool that analyzes the pixels in a photo and returns images that are similar in "look and feel" to the original without requiring a user to type in keywords to search. Dragging a photo of a stained-glass cathedral window into the search box, the company demonstrates in a video, produces a series of related images that more closely match the original in color and composition. The new search engine works by using a customized convolutional neural network, a type of machine learning tool that is modeled on how the brain's visual cortex, especially that of animals, processes images.

Natasha Lomas reports in TechCrunch, "For human lip readers, context is key in deciphering words stripped of the full nuance of their audio cues. But a technology model for lip-reading developed at the University of East Anglia in the UK has been shown to be able to interpret mouthed words with a greater degree of accuracy than human lip readers, thanks to the application of machine learning tech to classify the visual aspect of sounds. And the kicker is the algorithm doesn't need to know the context of what you're discussing to be able to identify the words you're using." Lomas goes on, "While the model remains a piece of research at this stage, there are scores of potential applications for technology that could automagically transform visual cues into accurate speech -- whether it's helping people who have audio impairments, or enhancing audio-less security video footage with additional speech data -- or even to try to figure out exactly what charged word one footballer spat at another in the heat of a match." She continues, "Such a tech could also be applied as a fallback for poor audio quality on a mobile or video call. Or even perhaps to power a front-facing camera-based mobile'voice' assistant which you wouldn't actually have to speak to but could just discreetly mouth commands at (how cool would that be?). Safe to say, the list of applications-in-waiting for machine powered lip-reading is as long as the dictionary is deep. So there's bags of future potential if only researchers can deliver the goods."

The resounding win by a Google artificial intelligence program over a champion in the complex board game Go this month was a statement not so much to professional game players as to Google's competitors. SEE ALSO: Chief of LG said Apple iPhone SE is'same old tech' Many of the tech industry's biggest companies are jockeying to become the go-to company for AI. In the industry's lingo, the companies are engaged in a "platform war." If true believers in AI are correct that this long-promised technology is ready for the mainstream, the company that controls AI could steer the tech industry for years to come. "Whoever wins this race will dominate the next stage of the information age," said Pedro Domingos, a machine learning specialist.

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You are right, the most important place to dig is in Customer Care system or better say CRM database. What I want is that what are the steps in an order way to design the prediction model and of course which model best suits for analyzing telecom data. Step1: find as much attributes in telecom data as you can, and make a dataset of those data. Jan 2013, Feb 2013, Mar 2013) and extract those customers in this period of time (Jan, Feb and March) which leave the company (Am i right?) and then by having this dataset of churned and unchurned customers in Jan and Feb and March 2013 we can go to step 2 for further processes to finally could build a model which can predict the churn rate of customers in April 2013(Am i right? I want to know whether I am doing right or not?).