Back in the nineties, my favorite TV show was called Tomorrow's World, a UK-based show which ran for a whopping 38 years on the BBC. It discussed the latest developments in science and technology (just look at this snippet of what the home in 2020 would look like). Back then, the future was extra exciting. Technology would make our lives infinitely easier – we'd be commuting to work in electric, automated cars, and life at home would just be one smooth series of button pushing. The height of this slightly skewed future gazing was my favorite Saturday morning space-age animated sitcom, The Jetsons, which first aired in the 1960s.

A Cinema Challenge hackathon was held from 14 to 22 November of 2020. It was dedicated to creating solutions for online theatre sweet.tv. I managed to create a prize-winning solution for challenge 2 and decided to share my methodology. The task was to predict top-5 tv-programs for each user based on his view history. TV-program was considered watched if the user watched over 80% of it and did not change the channel.

UK is planning to develop its National AI Strategy by following the Council. A PwC report revealed that AI could transform the GDP of the UK up to 10.3% …

Canadian CIOs are still trying to find their way through the hype to get the real value out of artificial intelligence (AI) and machine learning.

Technology keeps evolving with every passing day. As it turns out, Artificial Intelligence has found its way into the core of our lives for quite some time now. Over the years, a good number of AI movies have been made with technology being the subject. Here's a list of top 10 artificial intelligence movies that you can watch and definitely not regret watching them. This is a story of a young coder who's expecting a vacation prize from his boss.

Rose: We've always kept records of our lives. But not all the information was inherited by later generations. Colonel: A small percentage of the whole was selected and processed, then passed on. Rose: That's what history is, Jack. Colonel: But in the current, digitized world, trivial information is accumulating every second, preserved in all its triteness.

The film industry has only recently begun addressing these ethical and policy issues, with a new network of Film and TV industry figures assembling this year to debate the problems. The context may be a dangerous one for filmmakers. A deepfake'moral panic' has convulsed the political world, following DF videos of politicians delivering speeches that they never made. Severe legal restrictions on deepfakes are already being mooted. There are clear ethical dangers: as the deepfake Tom Cruise tells us ominously in Chris Ume's video, "If you like what you're seeing, just wait till what's coming next."

Machine learning is a type of artificial intelligence where computer algorithms learn and improve data analysis through repetition.

Yesterday's hearing with the CEOs of Google, Facebook and Twitter was as messy as you might have predicted -- and we'll get into that below. But I want to start today with voice assistants. Alexa, Siri, Google Assistant and the rest know a lot. But they barely remember anything, which curtails exactly how much they can truly assist with your life beyond music controls, timers and reminding you exactly what shows you've seen Joel Kinnaman in. A new feature spotted for Google Assistant on Android called Memory could transform it into the assistant we need, however.

I've worked at two different startups and JPL, so this may not be a fair analysis as in these cases quick iteration and deployment is more important than micro-optimization. For basic stuff like reading images, morphology, color space conversions, etc, you really can't go wrong with OpenCV and trying to improve performance in any of these realms is almost certainly company money wasted. OpenCV's complete algorithm implementations are also typically good enough for classical algorithms, although I've never used and couldn't recommend any of their DNN modules over PyTorch. From here, I'll either edit source or replace modules as necessary - for example, if matching is a bottleneck in computing homographies and exact matches are required, pulling a VP tree implementation could help. I've recently used their Line2D implementation, which benefited from inserting multithreading and SIMD.