Censorship there, here, and everywhere. This trend will continue to whatever degree is necessary to keep control over the population.
Frank Zappa had a quote on this about the curtain being pulled away. I don’t know what alternative venues need to be utilized but archiving of any documentaries and resources were considered the best in the process should all be archived and stored on media platforms online and offline.
YouTube has announced plans to “reduce” the number of conspiracy theory and “misinformation” videos users are exposed to through its algorithmic recommendations.
While the Google-owned video giant has often courted controversy over some of the content that finds its way onto its platform, the company does have policies in place that serve as a guide to what is, and isn’t, allowed. Some of these videos are eventually taken down. But then there is content that YouTube refers to as “borderline” — it doesn’t breach any policies, per se, but at the same time many people would rather not see them.
And that is the content YouTube is now looking to scrub from users’ “up next” queue.
Anyone who’s spent even a short time on YouTube will know its addictive nature: what begins as an innocent 30-second session to watch a prank skit sent by their buddy descends into a rabbit-hole of neverending autoplay “recommendations” served up by the data-powered internet gods.
It’s in YouTube’s interests to keep you there, of course, as the more you’re on its platform the more ads you’ll likely view. The company also recently addedswiping to the mobile app — to make it easier for you to skip to the next recommended video.
These recommendations all too often serve up unsavory content: ludicrous conspiracy theories about mass-shooting events being staged, far-fetched proclamations that the moon landing never happened, and hare-brained notions that the Earth on which we live is, well, flat.
Moving forward, YouTube promises that you’ll see less of those kinds of videos. This is similar to moves it’s made in the past to reduce clickbaity recommendations, or videos that are slight variations on something else you’ve watched.
“We’ll continue that work this year, including taking a closer look at how we can reduce the spread of content that comes close to — but doesn’t quite cross the line of — violating our Community Guidelines,” YouTube said in a blog post.
“While this shift will apply to less than one percent of the content on YouTube, we believe that limiting the recommendation of these types of videos will mean a better experience for the YouTube community.”
Today’s news comes just a week after YouTube won back one of the biggest advertisers in the U.S. AT&T had previously pulled its ads from YouTube after they were displayed alongside extremist content back in 2017, but it said it was now satisfied that YouTube had sorted out its programmatic advertising systems.
The latest changes will apply only to viewers in the U.S. at first — the company said it’s meshing human evaluators, subject experts, and machine learning to make these tweaks. More countries will receive this update in the future, according to YouTube.
Computers are getting more sophisticated than ever at understanding and playing complicated games. DeepMind, one of the leaders in artificial intelligence, proved that once again today with its latest A.I. agent called AlphaStar. During a livestream, this program took on two StarCraft II pros in a series of five matches for each, and AlphaStar swept all 10 matches.
StarCraft II pros Dario “TLO” Wünsch and Greegorz “MaNa” Komincz are two of the top players in the world. But neither could handle neural-network-powered AlphaStar. Blizzard opened up StarCraft II to A.I. researchers last year, and that has resulted in huge leaps in computer performance.
DeepMind has already mastered chess and go with AlphaZero and AlphaGo, respectively. And those games are so complicated that no computer on Earth could brute-force calculate every possible match in those games. But a real-time strategy video game like StarCraft II is exponentially more complicated in terms of what is possible in every moment. And this reveals the power the deep learning. Something like AlphaStar doesn’t have to learn every possible match in StarCraft to understand it. Instead, it focuses on winning strategies.
How AlphaStar learns
The reason that AlphaStar is such a big deal is because of the way it learns. It uses multiple techniques, and DeepMind ran through how it works.
“We take many replays from pro and players, and we try to get AlphaStar to understand by looking at a situation that human player is in,” DeepMind research co-lead Oriol Vinyals said. “And then we try to get it to imitate those moves.”
DeepMind doesn’t just use pro games either. The company also looks at public matches from players who have a high matchmaking rating.
But the imitation training only creates the most basic iteration of AlphaStar. DeepMind says this version 0.1 agent is equivalent to a platinum-level ladder player.
To prep AlphaStar for a pro fight, DeepMind had to use its neural-network training.