which could make running out of gas mid-journey a doubly-frightening thing.

From @sif@cathode.church (fediverse):
7" Apple Watch just dropped.

That algorithm should come with a GLP-1 licensure.

That's not the issue - insurance companies have no problem giving insurance to humans which have "individual learning capabilities."

The problem is that while insurance companies can extract compensation from humans - essentially they can coerce the humans to work "for them" to pay them back - they can't get compensation from the AI, and there isn't a good framework to get compensation from the AI developers.

The issue isn't that the AI learns - the issue is that our legal framework doesn't have a good way to get "compensation" from errant AI - because the AI itself doesn't care if it gets shut off. You can't hold the AI itself "liable" like you can a person. So until we change the liability framework, we'll be stuck. This isn't a technical problem, so much as a social one.

Sigh. I'll bite.

The question seemed to assume it was a good question, and its premises were correct. They aren't. In fact, the CO2 DOES absorb incoming IR, and with the same cross-section, as it does outgoing IR. It doesn't "know which way the radiation is going."

Now, any honest thinker would realize that the question was silly, because it doesn't admit other mechanisms other than "blocking" [sic] of IR.

Think just a half-thought further, and question one of your assumptions. THEN you might have a point.

Part of self-driving cars being safer than people is that they don't drive "very close together" - they follow at the appropriate following distance and speed for the lines of sight. This alone accounts for a large portion of the improved safety performance of these systems.

Autonomous cars are actually very good at the driving task. Where they fail is when they encounter situations that are "abnormal" and I think the industry has a problem here; state of the art is either to train specifically for every situation (impossible in the limit) or to kick the decision to a human in a support center somewhere. Things like emergency vehicles, school buses, farmer markets, construction zones, etc.

The industry needs to figure out how to get their systems to "get confused, then reason their way out of a situation."

This long tail is where humans still do better - when there is some "new" scenario, like a particular combination of debris in the roadway, on a Tuesday, with gawkers, during a fireworks display. You will never be able to "train" your way out of that - you need the correct structural formulation for the problem space.

The big takeaway I have from their study is that their study areas - major metropolitan centers - is that cities have way, way worse than average incidents per vehicle mile travelled.

I mean the national average is only something like 1.25 fatalities per 100M miles, injuries only 75 per 100M miles, and "all" incidents like 140 per 100M miles. But the benchmark numbers on the linked websites range from 200 to almost 700 injury incidents per 100M miles in those cities.

Like, if you are using the average number, you can't even reduce the number of incidents by 841 over 271 million miles, because the expected value is only 203. You have to have a rate of at least 300 injury-incidents per 100 million miles to reduce by 841 at over 271 million miles. So really, their results are driven by San Francisco, Austin, and Atlanta.

Their confidence bands are also pretty low; for example they quote 2 serious or greater injuries in San Fran, which on 82 million miles there is a rate of "0.02 per million miles". But 2 injuries is not really enough to make a statistical inference.

Least of all because all the miles are with different versions of their software - you can't make any predictive claim.

Statistics never lie, but you can make them say whatever you want...

Correct.

Incorrect.

My mother's basement gets pr0nHub at 1080p minimum, you insensitive clod!

And they sneak in and swap out a memory card when you're not home?

Depends on the terms of their contract. If you believe the summary, then doing anything with the county's data other than serve it to them for the intended purpose is against the terms of their contract, which would make it actionable.

And the ROI is really, really impressive. Each dollar in the NIH budget gets returned as about 5 (depending on the study) in increased GDP. To my knowledge, there is no other billion-dollar scale investment that has consistent, long-term returns like that.

Congress knows this fact. They commissioned many of the studies showing it. It is difficult to believe the Executive Branch is not aware of it. If we really wanted to be America First, rather than reducing the NIH budget, we'd be promoting another run of annual increases with the intent of doubling that allocation in real dollars over the coming decade. Contact your congressional representatives today to advocate for the idea.

He was over-leveraged and didn't manage risk well. In other words, he was a terrible fund manager.

lol, like phys.org (somewhere between journalism and breathless huckterism) has never got crucial facts or inferred conclusions wrong.

College press releases, science journalism, and medical journalism THRIVE on hyperbole and overexaggerated / misunderstood conclusions like this.