At tech festival Brain Bar in Budapest, Péter Árvai, CEO and co-founder of Prezi, gave a talk titled "Embrace automation?" where he argued that being replaced by machines doesn't have to be a bad thing -- if we choose the right sci-fi movie future. Arvai argues that we need to rethink our definition of work first and move society towards finding ways to make automation of jobs work in our favor. To illustrate how we've done this in the past, Árvai talked about when his grandmother got her first washing machine. Instead of having to hand wash soiled baby clothing for hours on end (babies poop a lot), she could simply put it in her washer and could instead use her time to do paid work. This could continue in the future, but we'll need to create new purposes for humans in a technologically driven future.

Our Top Picks from Cannes 2017 Film Feature Just Why Did King Arthur: Legend of the Sword Flop So Bad?

Reposting because I didn't get any replies. Can anyone point me to a tutorial/book which teaches you how to deploy ML-based services in production using Flask/Django? All I have found is a bunch of blogs which don't really dive deep into the nitty gritty aspects of server side development. It gets very difficult for web-dev noobs like myself who need to move into the intermediate aspects of the field after getting familiar with the algorithms. I'd like something that explains scalable server side development from a Machine Learning-perspective and doesn't shy away from the details.

Apple has reportedly started production of its Siri-based competitor to Amazon's Echo and Google's Home, plugging a hole in its device lineup that has become increasingly noticeable over the past two years. The new device will be similar to competitors' offerings in form, offering a voice-controlled speaker that can sit in the kitchen or living room and verbally respond to basic commands and queries. According to Bloomberg News, one key differentiator will be audio quality: Apple is planning to ship the speaker with "virtual surround sound technology", which uses one speaker to create the impression of several, as well as investing heavily on general quality improvements to ensure a richer sound than competitors. But more important for many Apple fans will be the expected tight integration with the rest of the company's products. The company's music streaming service is tricky to use on Amazon's Echo, and impossible to use on the Google Home, while both devices also require users on iOS to open a dedicated app to see any further information in response to queries.

I have to warn you that I haven't had much success in generating fine samples, although the source code itself is complete. I've tried to find what's wrong, but now changed my mind to open the current code to everyone because I know many people are working on this project and my work might be a help for them.

Uber said its head of finance is leaving, and the privately held ride-hailing company also said that its first-quarter loss narrowed substantially from the prior quarter, putting it on a path toward profitability. Head of finance Gautam Gupta is leaving in July to join another startup in San Francisco, the company said, making Gupta the latest high-profile executive to leave Uber. Uber, which has been rocked by several high-level executive departures in the past few months as it grapples with a series of controversies, has been looking for a chief operating officer to help change its now-notorious "bro" culture. Gupta's exit sets the stage for a second major executive search, now for a chief financial officer who has public company experience. About a dozen top executives have left Uber since February.

I wanted to like this paper. Curriculum learning is an area that needs more research, and automating the curriculum process is a good idea. However, the use of a GAN here is completely overkill -- the space of goals here is low dimensional and relatively unstructured. I'd wager any generative modeling technique would've worked for their experiments.

A while back, I was reading an article posted on Facebook, about Clovis people found alive and well living in Florida, with a picture featuring tribesmen (see below.) The quality of the picture was poor, and the URL was very suspicious: baynews9.com.ddwg.clonezone.link, as to make it appear that it was from Baynews9.com. It turned out that the picture (and thus the whole story) was fake: these people are real people living in Peru, see here for a Youtube video about them. My question is how to detect that a story is fake? The picture might have metadata embedded in it, allowing the data scientist to find the real source, unless it is a screenshot.

There is plenty of interest in recurrent neural networks (RNNs) for the generation of data that is meaningful, and even fascinating to humans. Popular examples generate everything from credible (but fabricated) passages from Shakespeare, incredible (but highly likely) fake-news clickbait, to completely simulated handwritten sentences that shadow the style of the original writer. Such compelling examples demonstrate the power of recurrent neural networks for modelling human generated data, and recreating completely new data from those models. They can be understood by anybody, humanising the world of "AI". Inspired, Consected, sponsored research into the use of machine learning to generate new speech by modelling human speech audio, without any intermediate text or word representations.

Nobody likes it, but we all have to deal with it. As the world's cities grow more densely populated, scientists and entrepreneurs are looking for solutions to gridlock, pollution and the other byproducts of a world filled with cars. Two sessions at the GPU Technology Conference earlier this month spoke to the role that data, deep learning and intelligent video analytics can play in easing traffic and improving quality of life for city dwellers the world over. Kurtis McBride, CEO of Miovision Technologies, an IVA startup based in Ontario, Canada, spoke to a room full of developers about his company's efforts -- and their 40 percent year-over-year growth -- to make traffic flow a little easier. Miovision's Open City platform gets data from existing city infrastructure and the company's own video cameras, and applies AI to create insights from it.