What are the most interesting recent videos on YouTube about artificial intelligence (AI)? We save your time filtering mega-hours of videos uploaded each day to select the most relevant and popular ones, by view-count as of 1 May 2017. The description is as appeared at YouTube. This video shows that GeForce GTX G-Assist takes advantage of cutting-edge NVIDIA artificial intelligence to bring you the next revolution in gaming. This is a video for the first-ever entire songs composed by Artificial Intelligence: "Daddy's Car" and "Mister Shadow", created by scientists at SONY CSL Research Lab. The researchers have developed FlowMachines, a system that learns music styles from a huge database of songs. The two songs are excerpts of albums composed by Artificial Intelligence to be released in 2017. This video provided by ColdFusion. It includes animations of Why AlphaGo is NOT an "Expert System"; "Inside DeepMind" Nature video; and "AlphaGo and the future of Artificial Intelligence" BBC ...

To start, it's pouring more energy into machine learning research that could improve its ability to automatically flag and remove terrorist videos while keeping innocently-posted clips (say, news reports) online. It's also expanding its counter-radicalization system, which shows anti-extremist ads to would-be terrorist recruits. Google plans to "greatly increase" the number of humans in its YouTube Trusted Flagger program, improving the chances that it'll catch terrorist material. Google wants to tackle those YouTube videos that are borderline, too -- if it spots videos with "inflammatory" religious or supremacist material, it'll put those clips behind a warning and prevent them from getting ad revenue, comments or viewing recommendations.

To start, it's pouring more energy into machine learning research that could improve its ability to automatically flag and remove terrorist videos while keeping innocently-posted clips (say, news reports) online. It's also expanding its counter-radicalization system, which shows anti-extremist ads to would-be terrorist recruits. Google plans to "greatly increase" the number of humans in its YouTube Trusted Flagger program, improving the chances that it'll catch terrorist material. Google wants to tackle those YouTube videos that are borderline, too -- if it spots videos with "inflammatory" religious or supremacist material, it'll put those clips behind a warning and prevent them from getting ad revenue, comments or viewing recommendations.

Those findings from DeepMind, the pioneering London-based AI lab, also suggest the motive behind why DeepMind has created a huge new dataset of YouTube clips to help train AI on identifying human actions in videos that go well beyond "Mmm, doughnuts" or "Doh!" To help improve AI's capability to recognize human actions in motion, DeepMind has unveiled its Kinetics dataset consisting of 300,000 video clips and 400 human action classes. Past cases have shown how imbalanced training datasets can lead to deep learning algorithms performing worse at recognizing the faces of certain ethnic groups. This means that even the Kinetics action classes featuring mostly male participants--such as "playing poker" or "hammer throw"--did not seem to bias AI to the point where the deep learning algorithms had trouble recognizing female participants performing the same actions.

Here we bring you the most popular recent Machine Learning videos worth watching. This is the first video (Lecture 1 published 8 years ago) in the great series of Stanford machine learning lectures given by Andrew Ng. Originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control.

Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.

FCC rules require TV stations to provide closed captions that convey speech, sound effects, and audience reactions such as laughter to deaf and hard of hearing viewers. YouTube isn't subject to those rules, but thanks to Google's machine-learning technology, it now offers similar assistance. YouTube has used speech-to-text software to automatically caption speech in videos since 2009 (they are used 15 million times a day). Today it rolled out algorithms that indicate applause, laughter, and music in captions. More sounds could follow, since the underlying software can also identify noises like sighs, barks, and knocks.

You can always count on Google to have data -- tons of it, generated by the users who interact with and upload content to its services. Google uses that data to build intelligence for the company, but it's offered data for others to experiment with as well. These three data sets are abundantly large, have plenty of practical applications, and are guaranteed to be well-assembled, thanks to Google's imprimatur. The Open Images Dataset, unveiled at the end of last month, is a collection of 9 million URLs to images "that have been annotated with labels spanning over 6,000 categories," according to Google. All have a Creative Common Attributation license, so they can be reused readily, and the label assignments to the images have been verified by human eyes to ensure validity.

Specialized tools for seeing through blur and pixelation have been popping up throughout this year, like the Max Planck Institute's work on identifying people in blurred Facebook photos. The attack uses Torch (an open-source deep learning library), Torch templates for neural networks, and standard open-source data. "Just take a bunch of training data, throw some neural networks on it, throw standard image recognition algorithms on it, and even with this approach…we can obtain pretty good results." To build the attacks that identified faces in YouTube videos, researchers took publicly-available pictures and blurred the faces with YouTube's video tool.

We have long relied upon simple image manipulation like blurring and pixelation to obscure sensitive information on the internet, but that may not work for much longer. Researchers from the University of Texas at Austin and Cornell Tech have developed a machine learning system that can identify faces and text in images with alarming accuracy. The researchers trained a neural network with images of faces and text and found it could often correctly identify the images again after they had been obfuscated with three different techniques -- YouTube's proprietary blur tool, standard mosaic pixelation, and the P3 algorithm. For some data sets, the neural network was able to correctly identify the YouTube blurred image with 80 or 90% accuracy.