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.

https://arstechnica.com/health/2023/11/ai-with-90-error-rate-forces-elderly-out-of-rehab-nursing-homes-suit-claims/

Yeah, I don’t know why you’re getting so many down votes. That is what Hitler’s party was called, so technically Poilievre isn’t lying. He’s still a dumbass for seriously stating that position and purposefully spreading confusion for his own benefit, but it’s technically not a lie.

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 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.)

Absolutely terrifying… but thank you for the insight.

Yeah, the only way someone is dying in a furnace before feeling pain is if you’re dealing with molten-metal-type temperatures. Not a bakery oven. I’m sure this poor woman experienced excruciating pain for far too long.

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.

My guess is that your name is so poorly represented in the training data that it just picked the most common kind of job history that is represented.