Every credit card company charges large fees to the service provider for charge backs. It’s standard practice. This is also leads to service providers straight up perma-banning customers who initiate charge backs instead of resolving a dispute with the provider.
That graph is hilarious. Enormous error bars, totally arbitrary quantization of complexity, and it’s title? “Task time for a human that an AI model completes with a 50 percent success rate”. 50 percent success is useless, lmao.
On a more sober note, I’m very disappointed that IEEE is publishing this kind of trash.
Exactly, it’s just regular old enshittification.
While I’m sure the obvious systemic issues contribute to not looking for alternatives, that does sound like largely an issue inherent to optical pulse oximeters. Engineers aren’t miracle workers, they can’t change physics to their liking.
I’m sure pulse oximeters now are more accurate than they were 20 years ago. The fact we’re still using them is because no alternatives have been found which are as easy to use, reliable, and non-invasive as pulse oximeters, even with the known downsides.
Repository: a collection of computer code for a software program (or app if you insist).
Fork: a copy of a repository so you can edit it without affecting the original.
Pull request: a request to the owner of a repo to bring in some changes you made in a fork.
I think I even got the word count down.
Part of this is a debate on what the definition of intelligence and/or consciousness is, which I am not qualified to discuss. (I say “discuss” instead of “answer” because there is not an agreed upon answer to either of those.)
That said, one of the main purposes of AGI would be able to learn novel subject matter, and to come up with solutions to novel problems. No machine learning tool we have created so far is capable of that, on a fundamental level. They require humans to frame their training data by defining what the success criteria is, or they spit out the statistically likely human-like response based on all of the human-generated content they’ve consumed.
In short, they cannot understand a concept that humans haven’t yet understood, and can only echo solutions that humans have already tried.