They're leading the way in an era of big data by using artificial intelligence (AI) and machine learning (ML) to create personalized experiences for their …

Have you ever noticed that, after 6 p.m. on weekdays, you tend to listen to harmony-laden, lo-fi, guitar-based songs with medium-to-fast-paced rhythms and a strong backbeat -- but you'll skip ones that are too distorted? As opposed to weekend mornings, when you follow up a local news podcast with slower piano tracks sung by a solo female vocalist, with strings and horns, angular melodies, multiple sections (but no solos) and a touch of melancholy throughout? Chances are, you've never thought about your listening choices in such a detailed way. But Pandora's musicologists and scientists have, and that's how -- with the help of artificial intelligence, machine learning and the analysis of the listening habits of its more than 65 million monthly users -- it knows which song you'll want to hear next. "We treat every individual very specially, and focus on contextual recommendations to understand what you like, what you listen to," says Oscar Celma, Pandora's vice president of data science, of how the company maps the DNA of every piece of audio in Pandora's millions-wide song library and compares that with explicit and implicit user preference feedback to yield bespoke programming.

The problem as far as I can see is that far too few papers use a basic and simple baseline to compare against as a control. They are always comparing against the state of the art and previous DL techniques, but rarely to do they include basic correlation analysis, linear / logistic regressions, etc., as a basis for comparison. In statistics one doesn't just say "we got X performance which was better than Y performance", one says "we show that the effect size is better than control by X amount, and confirm that this actually represents an improvement and is not likely a bias induced by random sampling of the data with 95% confidence." But DL papers often just include final test set performance and traces of loss function per iteration, and say, look X learns faster than Y and Z and ends up with less error. Often this is even done without confidence intervals, which, for methods that depend on random initial conditions, is a sin.

A new survey found that more than half of B.C's. artificial intelligence companies believe the federal government is not doing enough to boost the sector, and half have considered leaving the province. The non-profit industry association, Artificial Intelligence Network of B.C., says there are more than 150 AI-related firms in B.C. and more than 65 submitted responses to its survey, which was conducted by CityAge and released this week. More than 56 per cent of respondents said the federal government needs to do more to help the local AI sector grow, with 31 per cent saying its efforts were lacking and 24 per cent saying they needed major attention. Half of respondents said they have considered moving their companies out of B.C. They main reasons they gave were a desire to connect to bigger markets (35 per cent) and to operate in a better taxation and regulatory environment (11 per cent).

Excitement in digital health is growing due to rapid advances in artificial intelligence, data science, and smart devices.

Global Artificial Intelligence in Healthcare market research report 2019 provides in depth analysis on the Artificial Intelligence in Healthcare report.

Let's be real: When you download a new app, you probably don't bother to read its privacy policy first. I write about privacy as a journalist and even I rarely bother to read those policies. They're written in eye-glazing legalese perfectly calibrated to make any normal human being want to stop reading as soon as possible. Who can blame us for rushing to check that little box that says we agree to the terms of service? Now, a new tool called Guard promises to read the privacy policies of various apps for us.