Think of all those babies they murdered: it all adds up to lots of entire human lives worth of emissions avoided.
Well, I was just an immigrant in the place for a bit over a decade.
Still, having seen first hand the long term consequences of the purest expression of her and Reagan's ideology (that shit also got copied all over Europe, including my own country, but in a diluted form), pure hate for Thatcher was one of those things I quickly learned from Common Britons and took into my heart.
The West has been fucked by that shit but I got the impression that the US and the UK got especially fucked and endured (and still are enduring) a much larger fall because of it.
In the UK, when Thatcher (who was their version of Reagan) died, the lyrics "Ding, dong, the witch is dead" from the Wizard Of Oz suddenly became very popular.
Having spent most of my career working as a senior contractor, which often meant landing on code bases with 3+ layers of fuckups, I can only imagine how painful it will be to end up having to clean and fix AI generated code, since that doesn't even have a consistent coding style or pattern of design errors and bugs.
One of the first things they teach you in Experimental Physics is that you can't derive a curve from just 2 data points.
You can just as easilly fit an exponential growth curve to 2 points like that one 20% above the other, as you can a a sinusoidal curve, a linear one, an inverse square curve (that actually grows to a peak and then eventually goes down again) and any of the many curves were growth has ever diminishing returns and can't go beyond a certain point (literally "with a limit")
I think the point that many are making is that LLM growth in precision is the latter kind of curve: growing but ever slower and tending to a limit which is much less than 100%. It might even be like more like the inverse square one (in that it might actually go down) if the output of LLM models ends up poluting the training sets of the models, which is a real risk.
You showing that there was some growth between two versions of GPT (so, 2 data points, a before and an after) doesn't disprove this hypotesis. I doesn't prove it either: as I said, 2 data points aren't enough to derive a curve.
If you do look at the past growth of precision for LLMs, whilst improvement is still happening, the rate of improvement has been going down, which does support the idea that there is a limit to how good they can get.
Yeah, you're mostly right: Why bycicles stay upright.
There's some gyroscopic effect, but per that article it's not the main reason.
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