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Hard Questions: Who Should Decide What Is Hate Speech in an Online Global Community? | Facebook Newsroom
"Our mistakes have caused a great deal of concern in a number of communities, including among groups who feel we act — or fail to act — out of bias. We are deeply committed to addressing and confronting bias anywhere it may exist. At the same time, we work to fix our mistakes quickly when they happen.

Last year, Shaun King, a prominent African-American activist, posted hate mail he had received that included vulgar slurs. We took down Mr. King’s post in error — not recognizing at first that it was shared to condemn the attack. When we were alerted to the mistake, we restored the post and apologized. Still, we know that these kinds of mistakes are deeply upsetting for the people involved and cut against the grain of everything we are trying to achieve at Facebook."

"People often ask: can’t artificial intelligence solve this? Technology will continue to be an important part of how we try to improve. We are, for example, experimenting with ways to filter the most obviously toxic language in comments so they are hidden from posts. But while we’re continuing to invest in these promising advances, we’re a long way from being able to rely on machine learning and AI to handle the complexity involved in assessing hate speech.

That’s why we rely so heavily on our community to identify and report potential hate speech."
hate_speech  ai  facebook  sns  spam  filtering  censorship 
17 days ago by incep
Google spent $60 million on building Content ID
That's how much it costs to build a not-particularly-accurate UGC copyright filter:
Google’s new report takes aim at this claim. It asserts that Content ID is a highly effective solution, with over 98 percent of copyright management on YouTube happening through Content ID, and just 2 percent coming from humans filing copyright removal notices. Google also says the music industry opts to monetize more than 95 percent of its copyright claims, meaning they leave the videos up on the service. It claims a whopping half of the music industry's YouTube revenue comes from fan content — covers, remixes, dance versions, etc. — claimed via Content ID. The report also puts a hard figure on how much Google has spent so far on Content ID: $60 million.
filtering  copyright  eu  article-13  copyfight  content-id  google  web  ugc 
8 weeks ago by jm
This Music Theory Professor Just Showed How Stupid and Broken Copyright Filters Are - Motherboard
Kaiser then decided to test Google’s system more fully. He opened a new YouTube account named Labeltest, and began sharing additional examples of copyright-free music.

“I quickly received Content ID notifications for copyright-free music by Bartok, Schubert, Puccini, and Wagner,” Kaiser said. “Again and again, YouTube told me that I was violating the copyright of these long-dead composers, despite all of my uploads existing in the public domain.”

Google’s Content ID is the result of more than $100 million in investment funds and countless development hours. Yet Kaiser found the system was largely incapable of differentiating between copyrighted music and content in the public domain. And the appeals process that Google has erected to tackle these false claims wasn’t any better.
content-id  copyright  copyright-filtering  youtube  fail  google  public-domain  ip  music  filtering  bartok  schubert  wagner  puccini 
10 weeks ago by jm
Mobicip - Filtering on Mobile Devices
Freemium filtering on mobile devices; paid version is only $40 a year for 5 devices
filtering  phone 
10 weeks ago by amann
Pseudo-bandlimited pixel art filtering in 3D – a mathematical derivation – Maister's Graphics Adventures
To sample pixel art, we actually need to apply two filters at once, which complicates things. First, we need to apply a rect kernel (NEAREST filter) to give our texel some proper area. We then apply a low-pass filter which will aim to band-limit the rect. Applying two filters after each other is the same as convolving them together. Convolution is an integral, so now we have some constraints on our filter kernel, because it needs to be cheap to analytically integrate. LUTs will be too costly and annoying to use.

A key point is that the low-pass filter kernel needs to adapt to our sampling rate of the texture. Basically, we need to be band-limited in screen space, not texture space. If we have a filter kernel
graphics  filtering  gpu  rendering  pixel 
11 weeks ago by euler

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