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.
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.
Canadian here. A minority gov is one which has less than X seats (where X is 50% in Canada and I believe Australia too), and usually that requires a coalition. “Forming government” in a parliamentary system like these basically means “has a good chance of passing meaningful legislation.” Since the leading party can’t do so alone, they form an agreement with another party (or multiple) to help them reach that criteria.
It is entirely possible for the party with the most seats to also not form government, if they’re far enough below 50% and can secure no agreement with another party to push them across the line. In these situations, another general election would soon follow.
Underrated comment, this should be at the top.
Kind of splitting hairs, but a company that can let go of “scores” of employees and still exist is not a small business.
Yes, you’re anthropomorphizing far too much. An LLM can’t understand, or recall (in the common sense of the word, i.e. have a memory), and is not aware.
Those are all things that intelligent, thinking things do. LLMs are none of that. They are a giant black box of math that predicts text. It doesn’t even understand what a word is, orthe meaning of anything it vomits out. All it knows is what is the statistically most likely text to come next, with a little randomization to add “creativity”.
You’re completely right, if the goal is good customer support and decent working conditions for the operators.
It’s not. The goal is like 1rre said - make people get fed up and stop trying to get their stuff fixed, just buy a new one. Oh, and they could fire half the operators too, since less people would be willing to wade through the pile of shit to talk to them.
Money and profit, screw the rest.
And an excuse to fire half of the support staff.
I didn’t test other values but they’re probably OK.
Excellent work, thanks for the laugh.
This article and discussion is specifically about massively upscaling LLMs. Go follow the links and read OpenAI’s CEO literally proposing data centers which require multiple, dedicated grid-scale nuclear reactors.
I’m not sure what your definition of optimization and efficiency is, but that sure as heck does not fit mine.
Don’t look for statistical precision in analogies. That’s why it’s called an analogy, not a calculation.
No, this is the equivalent of writing off calculators if they required as much power as a city block. There are some applications for LLMs, but if they cost this much power, they’re doing far more harm than good.
Exactly this, and rightly so. The school’s administration has a moral and legal obligation to do what it can for the safety of its students, and allowing this to continue unchecked violates both of those obligations.
I find it difficult to lay the blame with VSCode when the terminology belongs to git, which (even 7 years ago) was an industry standard technology.
People using tools they don’t understand and plowing ahead through scary warnings will always encounter problems.
I agree that LIDAR or radar are better solutions than image recognition. I mean, that’s literally what those technologies are for.
But even then, that’s not enough. LIDAR/radar can’t help it identify its lane in inclement weather, drive well on gravel, and so on. These are the kinds of problems where automakers severely downplay the difficulty of the problem and just how much a human driver does.
You are making it far simpler than it actually is. Recognizing what a thing is is the essential first problem. Is that a child, a ball, a goose, a pothole, or a shadow that the cameras see? It would be absurd and an absolute show stopper if the car stopped for dark shadows.
We take for granted the vast amount that the human brain does in this problem space. The system has to identify and categorize what it’s seeing, otherwise it’s useless.
That leads to my actual opinion on the technology, which is that it’s going to be nearly impossible to have fully autonomous cars on roads as we know them. It’s fine if everything is normal, which is most of the time. But software can’t recognize and correctly react to the thousands of novel situations that can happen.
They should be automating trains instead. (Oh wait, we pretty much did that already.)
Even talking about it this way is misleading. An LLM doesn’t “guess” or “catch” anything, because it is not capable of comprehending the meaning of words. It’s a statistical sentence generator; no more, no less.
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.