That's the wrong way around. Matte is better for performance but gloss attract consumers so Apple puts "Pro" and glossy front in the same product. Actual pro displays are always matte, a bit like how actual pro cameras always have non-clicking shutter buttons but reviewers point that out as a con.
It's seeming to be going to be an another DGX Sparks that aren't so faster than maxed out Mac Studio, nor cheaper than 4x Blackwell on a workstation, nor cloud tokens. That's why.
MediaTek used to supply a lot of lower-performance MIPS and ARM chips during 2010s. GP probably has that MediaTek == bad experience association. The actual chips weren't unreliable or horribly engineered, just slow, but people don't make a clear distinction there.
The actual canned response to this is [1] and humidity. Daily levels of humidity in East/Southeast Asia is whole another level from US/EU(or vice versa).
This is really about Ubisoft's The Crew, a one-time-paid mostly-singleplayer car race game about infights and revenges in an illegal street racing group, that required Internet connection, which server got shut down. So yeah.
The required connection and authentication was likely an anti-piracy measure, so kind of doubly yeah.
It could be. They seem to be getting enormous amount of politics or crypto related AI fake reels that real people fall for. They probably do need means to control spams.
The problem is that it's not SOME false positives, AI detectors so far have been all so comically bad that they might be classified as pseudoscience. Or an artificial false positive generators even.
We'll I'd think that YouTube would have incentive to get it right. Either there are too many false positives and the content creators go away and YouTube collapses. Or there are too many false negatives and the viewers go away, and YouTube collapses. I mean there is a chance that garbage people will ruin video sharing platforms for everyone.
Having the incentive to do something and having the ability to do it are not the same thing.
It's not like human-generated content is made of carbon and AI-generated content is made of silicon and the science of chemistry can unambiguously tell them apart. If you asked a million humans and a million LLMs to write a sentence on a specific subject, it's not implausible that one of the LLMs and one of the humans would output the exact same sentence. Maybe more than one.
A thing that can take only the output and accurately tell you if it was AI-generated or not is therefore impossible, because if it said no it would be wrong when the LLM generates that sentence, but if it said yes it would be wrong when a human generates the exact same sentence.
All it can do is try to calculate a probability. But then what do you want to do with that? Suppose the probability it estimates for some content is 45%, and that probability estimate is an accurate measure of the true probability, i.e. can't be improved when the only information you have is the content itself. Do you want to ban the 55% of that content which is human-generated, or allow the 45% which is AI-generated?
Right now the problem is the flood of low-quality AI spam that might (or might not) be low hanging fruit. We can worry about high quality AI artifacts later if that becomes a problem. (and yes, there is no guarantee that YouTube won't fail due to these spammers)
I get the idea: get 10k each samples of human data and AI data, train a simple classifier until it gets 99.9999% accuracy or <10k false negatives per day at your scale, ship it as a screening tool.
Is such tool feasible at all with current state of AI technology, or is it just a reasonable take from the past that may not be so reasonable anymore?
> I get the idea: get 10k each samples of human data and AI data, train a simple classifier until it gets 99.9999% accuracy or <10k false negatives per day at your scale
The issue is, that's not a thing. AI-generated content and human-generated content have significant overlap. No amount of training data can allow you to distinguish them with that level of accuracy because many outputs exist that could have been generated by either one. Additional training data allows you to say that the probability is 55.0374% plus or minus 0.0001, rather than only being able to say that it's 55% plus or minus 5%. It can tell you with greater precision exactly how ambiguous it is. What it can't do is remove the ambiguity.
We will find out shortly? YouTube is the one saying they are going to implement this:
"If a creator doesn’t specify whether or not they used AI, but our systems detect significant photorealistic AI use, we will now automatically apply a label."
Those tools are used in ways that they're integral to processes. They have their equivalents of ticket systems that are linked to code repositories with LFSs and bunch of IDE type tools and automated and manual test systems and build systems. Their equivalents of PR discussions and Selenium screenshots needs to check all boxes in the right ways for legal and traceability purposes.
Without all that might be $175/user/month but you're not shipping apps with just vi and bare gcc.
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