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Uncertain

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Re: Uncertain<T>

#61

Post by jonahx »

btown wrote: post #56

Once one understands that a variable (in a programming context) can hold a specification for a variable (in a mathematical context), one opens up incredible doors that are at the foundation of modern AI. When you see y = m * x + b, your recollections of math class may note that you can easily solve for "m" or find a regression for "m" and "b" given various data points. But from a programming perspective, if these are…

Very interesting.

Are there PLs that support this kind of thing at the language level as you are describing?

Re: Uncertain<T>

#62

Post by miffy900 »

Pxtl wrote: post #51
kittoes wrote: post #49

Earlier quoted context omitted.

https://github.com/klipto/uncertainty

10 years since commit and no attached documents besides a tiny readme. Pass.

This is still some code, as opposed to no code. It does seem to model everything in the research paper.

Aside from the original research paper needing to be included in the repo, it definitely does not need anything more than what's already there. It all builds and compiles without errors, only 2 warnings for the library proper and 6 warnings for the test project. Oh and it comes with a unit testing project: 59 tests written that covers about 73% of the library code. Only 2 tests failed.

Even having a unit testing library means it beats out like 50% of all repos you see on GitHub.

Re: Uncertain<T>

#63

Post by btown »

jonahx wrote: post #61
btown wrote: post #56

Once one understands that a variable (in a programming context) can hold a specification for a variable (in a mathematical context), one opens up incredible doors that are at the foundation of modern AI. When you see y = m * x + b, your recollections of math class may note that you can easily solve for "m" or find a regression for "m" and "b" given various data points. But from a programming perspective, if these are…

Very interesting. Are there PLs that support this kind of thing at the language level as you are describing?

https://colcarroll.github.io/ppl-api/ is likely a good starting point to get a taste of examples in Python; some use custom languages, but the success of Python-native frameworks in the LLM world I think has shown that embracing that makes interop and composability more possible at scale.

https://news.ycombinator.com/item?id=28941145 has some discussion here as well, though it’s a few years old.

Pyro and NumPyro seem to be popular at the moment!

Re: Uncertain<T>

#64

Post by astrange »

mikepurvis wrote: post #17
AlotOfReading wrote: post #8

A small note, but GPS is only well-approximated by a circular uncertainty in specific conditions, usually open sky and long-time fixes. The full uncertainty model is much more complicated, hence the profusion of ways to measure error. This becomes important in many of the same situations that would lead you to stop treating the fix as a point location in the first place. To give a concrete example, autonomous vehicle…

Vehicle GPS is usually augmented by a lot of additional sensors and assumptions, notably the speedometer, compass, and knowledge the you'll be on one of the roads marked on its map. Not to mention a fast fix because you can assume you haven't changed position since you last powered on.

None of the inputs you mention work against multipath effects in cities, which means car GPS won't know which lane you're in and in a grid system may think you're on the next street over.

If you have an HD map you can solve for it using building shapes or by looking at the street with cameras. WiFi seems like it would help, but the locations of the WiFi terminals are themselves based on crowdsourced GPS.

Re: Uncertain<T>

#65

Post by astrange »

jonahx wrote: post #61
btown wrote: post #56

Once one understands that a variable (in a programming context) can hold a specification for a variable (in a mathematical context), one opens up incredible doors that are at the foundation of modern AI. When you see y = m * x + b, your recollections of math class may note that you can easily solve for "m" or find a regression for "m" and "b" given various data points. But from a programming perspective, if these are…

Very interesting. Are there PLs that support this kind of thing at the language level as you are describing?

If you're willing to be discrete about it, logic languages like Prolog and Mercury use "unification" instead of "evaluation" which means they can evaluate backwards.

Re: Uncertain<T>

#66

Post by geocar »

nine_k wrote: post #21
layer8 wrote: post #10

Arguably Uncertain should be the default, and you should have to annotate a type as certain T when you are really certain. ;)

Only for physical measurements. For things like money, you should be pretty certain, often down to exact fractional cents. It appears that a similar approach is implemented in some modern Fortran libraries.

> For things like money, you should be pretty certain, often down to exact fractional cents.

That's one way to look at it.

Another is that Money is certain only at the point of exchange.

> It appears that a similar approach is implemented in some modern Fortran libraries.

I'd be curious about that. Do you have a link?

Re: Uncertain<T>

#67

Post by o11c »

mikepurvis wrote: post #17
AlotOfReading wrote: post #8

A small note, but GPS is only well-approximated by a circular uncertainty in specific conditions, usually open sky and long-time fixes. The full uncertainty model is much more complicated, hence the profusion of ways to measure error. This becomes important in many of the same situations that would lead you to stop treating the fix as a point location in the first place. To give a concrete example, autonomous vehicle…

Vehicle GPS is usually augmented by a lot of additional sensors and assumptions, notably the speedometer, compass, and knowledge the you'll be on one of the roads marked on its map. Not to mention a fast fix because you can assume you haven't changed position since you last powered on.

> Not to mention a fast fix because you can assume you haven't changed position since you last powered on.

... until you use a ferry.

Re: Uncertain<T>

#68

Post by kittoes »

Pxtl wrote: post #51
kittoes wrote: post #49

Earlier quoted context omitted.

https://github.com/klipto/uncertainty

10 years since commit and no attached documents besides a tiny readme. Pass.

Blame Microsoft Research, as the link came directly from them: https://www.microsoft.com/en-us/research/project/uncertainty.... I don't think they ever really took the project past the initial paper/presentation.

Re: Uncertain<T>

#69

Post by webcoon »

Awesome! This speaks to something, which I've been thinking (and wishing) for a long time. I've already done probabilistic programming in a scientific context (Python) and classical software engineering for web development (TypeScript, Python, Rust), and I've always wondered why I couldn't have the real-world modelling capacity of the former with the static type assurances of the latter. Love that you (and Microsoft) are thinking along the same lines! Do you perhaps know of any Python implementations for this? There are plenty of dynamic stats programming libraries, but none offer typing solutions AFAIK.

Re: Uncertain<T>

#70

Post by rendaw »

kccqzy wrote: post #37
j2kun wrote: post #25

This concept has been done many times in the past, under the name "interval arithmetic." Boost has it [1] as does flint [2] What is really curious is why, after being reinvented so many times, it is not more mainstream. I would love to talk to people who have tried using it in production and then decided it was a bad idea (if they exist). [1]: https://www.boost.org/doc/libs/1_89_0/libs/numeric/interval/... [2]: https…

The article says, > Under the hood, Uncertain models GPS uncertainty using a Rayleigh distribution. And the Rayleigh distribution is clearly not just an interval with a uniformly random distribution in between. Normal interval arithmetic isn't useful because that uniform random distribution isn't at all a good model for the real world. Take for example that Boost library you linked. Ask it to compute (-2,2)*(-2,2). I…

While it does sound like GP missed a distinction, I don't see how (-2.35, 2.35) would be sensible. The extremes can happen (or else they wouldn't be part of the input intervals) and the code has to sensibly deal with that event in order to be correct.
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