275 karma · joined March 9, 2022
The problem as others have pointed out is that most musicians in the west already know some degree of western notation, so if you're collaborating, you'll have to translate back to western notation at some point. Even if you invent the perfect notation, it's like asking everyone to switch to esperanto because english grammar is flawed. And you'll still get people defending english "well actually, it's like that because the greeks blah blah blah".
My favorite music notation flaw is C flat. It's a hack. It's an ugly fucking hack and anyone who defends it is defending an ugly hack. The only reason it and double flats exist is because there are some key signatures (this happens with hungarian minor sometimes) where you end up needing to define 3 notes in the span of one space and one line on the staff, and you can't, so you have to borrow from an adjacent space or line. And so sometimes that C is actually a B. It's super annoying but uncommon enough that it's not worth everyone learning a new notation.
Anyway, don't let the nay sayers stop you from learning music however makes the most sense to you. Have fun
I wrote a longer replay to alterom but it looks buried for some reason.
Taking a step back, remember we're ultimately mapping these discrete numbers to some real world continuous variable like the saturation of red, frequency, mass on a scale, whatever. And our digital device can only represent a finite amount of numbers. For 2 bit data, we can represent 0-3, and for 3 bit data we can represent 0-7.
The important part is that 0 represents the minimum and 1,3, and 7 all represent the same maximum real value, and everything that can be measured by the device will fall within those ranges. So comparing 1, 2 and 3 bit data on a linear number line looks like this:
0 1
0 1 2 3
0 1 2 3 4 5 6 7
You could assume that everything gets assigned to whatever number is nearest in the number scale or come up with another scheme, but that is ultimately defined by the ADC and likely nonlinear. All we know is that those are the numbers we have available to represent the real values we're measuring.The question is about how to normalize the data. 1 bit data is already normalized. If you normalize 2 bit data by 3 you get [0, 1/3, 2/3, 1]. LGTM. If you normalize it by 4, you get [0, 1/4, 2/4, 3/4] and you're effectively throwing away some of the range of the ADC. You can try to get it back by offsetting by 0.5 then normalizing but now you get [1/8, 3/8, 5/8, 7/8]. And you could stretch that with some clever formula to fill from 0 to 1, but if you do it right then it's the equivalent to normalizing by 3, so why not normalize by 3?
So the answer is, if you have N bit data, you normalize by 2^N-1.
You can see this confusion again in the histogram example. There are only 255 bins, not 256. If you fix that mistake and remove the 0.5 offset, then the histogram is distributed correctly at both ends.
I'm not criticizing your take, although I suspect teachers might lose more than their lunch, just pointing out how terrible the plot is.
I don't think there should be imposed limits, but there might be an upper bound where expertise becomes atrophied by depending on AI too much.
> if the plumber's use of ChatGPT improved outcomes, isn't that preferable?
In the short term sure, and maybe even in the long term for the customer. I think the risk to the plumber is losing some of their expertise by outsourcing to AI. But who knows, maybe the plumber has excellent memory and only accumulates knowledge each time they use AI.
Some of the article is lost in the plumber example. I doubt plumbers are spending much time exploring new ways of solving problems, and might even benefit from having a narrower range of outcomes. Other fields that require both expertise and novel solutions will be at a disadvantage if they become more homogenized by depending on AI. Not only is the range of solutions reduced, but getting there is faster, so people end up in a local maxima. Maybe they get stuck there, maybe not, but that's the risk I see.
You don't imagine any long term risks by outsourcing expertise to AI?
Fungi protein sounds cool though. I would totally add that to my diet. But I also think insects are an underutilized protein source, so I might be an outlier
Laughter is the reward. N of 2 is a small sample size, but if one person laughed you could say it was 50% funny.
> a really good joke is recent, relevant, and shows deep understanding of its subject
These can help, but it ultimately doesn't matter how recent, relevant, or deep a joke is. If no one laughs, it wasn't funny.
Realistically, sign a EULA waiving your rights because their AI confabulates medical advice
> Given these findings, a corollary question is what attracts foreign graduate students to the US and leads them to stay. Prior research points to immigration policy—a subject of perennial public interest—having a large effect on stay rates
Really? Isn't Grok's whole schtick that it's Elon's personal altipedia?