Yeah, they should have posted it to LoreHub! I just checked for the availability of lorehub.com, and you can buy it for only ~$13,000 and start a competing business to GitHub.
For every interesting problem AI solves there are a long tail of really dumb things that AI performs that humans would never do. Some days I am in awe of one-shot magic eight-ball output and other days I'm so frustrated by the sheer stupidity of what it produces. It remains to be seen whether that long tail of stupidity can ever be resolved in the current form of LLMs.
At some point I was thinking that maybe I am too hard on the AI and that humans routinely produce exceptionally stupid code, however after a while I've realized that this is only partially true. AI produces a class of mistakes that humans would almost certainly not make because creating even the context of the mistake would require a level of skill that would preclude such mistakes. It's like if you took a mid/senior engineer and randomly lobotomized them mid-task.
Exactly, when you hire a junior engineer the distribution of code quality is fairly well known. By the time an engineer becomes senior, you can generally expect similar level of quality across any given task. Whereas with AI, sometimes the output is senior and in the middle of a task you'll get unpredictable low quality output. This makes the system both frustrating and unreliable. Now apply that to other domains like self-driving vehicles, where perhaps 80% of the time on a generally stress free freeway it does fine, and then randomly it may decide mid drive to slam on the brakes because of a random variance in the sensor stack.
I supervise quite a few Masters students. In my particular setting, believe me, LLM stupid for the top three chatbots is easier to work with than real human stupid now. We passed that threshold earlier this year.
It worked for humans. It took a lot of us, but eventually we accepted zero as a number. Then negative numbers. Then "imaginary numbers" as a useful trick, and then as meaningful.
In our case, hundreds of millions, but we got there.
When it comes to any good or service, there are only two choices: the user pays, or other people pay. The status quo is that drivers pay a lot for roads through gasoline taxes and vehicle registration fees, but the rest of society (including non-drivers) pay through income taxes, sales taxes, and property taxes. Moreover, a lot of taxes paid for road construction/maintenance are not proportional to how much you drive; a driver doing double the miles in a year is paying less than twice of another driver.
Please explain your ideal scenario of who pays for roads. And if your answer is "someone else" (e.g. "taxes", "government", "corporations", "billionaires"), further explain why "someone else" can't use the same argument to make you pay.
> but the rest of society (including non-drivers) pay through income taxes, sales taxes, and property taxes.
The rest of society also pays for the transportation costs of consumer goods, which include diesel and gas taxes which end up funding roads. Every time you buy something that rode on a truck, a fraction of the price you pay is road taxes.
You would pay little to no taxes if you completely removed yourself from society and lived deep in the wilderness, but I but you aren't going to do that. I wonder why that is?
I agree with most of this, I just have sort of turned a blind eye to what the code actually probably looks like. Reviews are rapid, and I’ll admit I do feel like I’m betraying my inner programmer by just optimizing directly against the claims of token bot. But the way I see it, as long as the numbers don’t lie I’m okay with the process.
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