samuel2

Reminds me of Julia's Pkg manager and the way Julia packages are managed (also with a .toml file). That's the way to go!

Cool idea! Thanks for the hint, I would have not thought of that.

yes, geospatial could be interesting. The tools depend on what the university / lecturer prefers, for me it was Julia for programming in math courses, JuMP.jl for optimization modeling, Python for ML courses.

I think cross-specializing with physics of energy might be quite cool. Then I could work on problems such as, e.g.,

- optimizing the placement of wind turbines to maximize energy capture

- determining the optimal size and type of solar panels for a given area.

Thanks for the comment!

thanks

Probably the most elegant math book I have ever seen is Probabilty theory a graduate course by Achim Klenke. A very nice exposition into the abstract, measure theoretic prob. thoery (but it assumes some prior knowledge).

If you are into numerical optimization, a nice source of intersting problems and examples (that e.g. contradict the intuition) can be found in

Mathematical Tapas: Volume 1 and Vol. 2.

Intersting comment, but I think there might be fluctuations around a growing overall demand.

Would Rust be worth learning for implementing (numerical) optimization algorithms?

well said, thx for your comment

thanks!

thanks for your insights!

cool, thanks

Thank you a lot!

This stuff seems to be very powerful!

Many applications stem from a simple observation: if two activities must be performed consecutively then the time required to complete both is the sum of the individual times, but if they may be performed concurrently then the time required is the maximum of the individual times.

https://www.maths.manchester.ac.uk/research/expertise/tropic...

thanks, we will have a look at it

Hi! We are a group of students from Germany, searching for interesting projects, where we could contribute and learn a lot. Our interests are: applied category theory, computational + "anything" and more generally any applicable math (optimization, ML etc.). We actively search for interesting non-standard topics that might be in a long-term perspective relevant, could enrich our backgrounds and serve as a source of inspiration for future research.

We would love to hear your ideas & suggestions! Thank you.

"This version is being sent to MIT Press, and will appear in hard copy late 2021/ early 2022."

https://github.com/probml/pml-book/releases/tag/camera

honestly a bit far fetched, my model tries to unify and build on scientific research without trying to make big claims about things we have not formalized yet. I think information is encoded into consistencies (we can and or cannot observe).

That is a good point.. these are just my conceptual thoughts (at this stage without any real experiments to back up my claims). The thing with nested hierarchies is exactly at the core of my question. If some lower layers are not hierarchies in sense of local interaction, we might not be able to simulate them on layers that "understand" only hierarchical structures. (for instance the main strategy for humans to deal with complexity is by decomposition into nested hierarchies)

I think the source of randomness is the imperfect reproduction of feedback loops (at each layer), so basically I think randomness is just interaction of great complexity we cannot comprehend - it is a direct opposite of consistency. In my view there is just one "physical" world - the reality and all interactions happen inside this one frame. Consciousness is one of the layers, it uses a consistent but highly expressive API, that makes it possible to reach the level of predictive power we enjoy. Society might be considered a higher level, building on APIs of consciousness humans. If we were to use "trivial" self-made APIs, could we create a software that would match our level of predictive power? (see also the other comment I wrote)

Also in my model intelligence is a property of layers, defined by how well they can simulate other layers using an API available to them (think about brain simulating physical interaction - lower level or simulating how the society might develop in the future - higher level). The more intelligent, the better the understanding of the surrounding reality. (I use predictive power as a measure of effectiveness of a theory / software that is instantiated on the higher level API). But if we build up artificial layers starting at an arbitrarily selected level - creating a different (unnatural) kind of feedback loops (species), can they simulate as broad range of layers as humans can (be as intelligent / think as much out of the box)? At which level do we need to start, to get close to what natural species are capable of? How much more complicated does the (AI) software needs to be to have equivalent predictive power considering it runs on hardware that provides a very restrictive version of the set of instructions natural species utilize?

(hardware refers to to the lower API a layer uses, software are feedback loops of the layer)

I was thinking about antennas as layers that consist of feedback loops that employ interaction outside of any reasonably small bounds (the more abstract layers onwards are therefore also affected, but the error might seem to be negligible). The question is, if the error we make by simulating brain on a higher level (physical) API can destroy the intrinsic properties of intelligent beings.

pretty cool, especially top questions, thx

"Most startup ideas are bad - Paul Graham empirically classified these as “good ideas that look like bad ideas initially”. From Gödel’s model we can draw even more precise distinction. These ideas look “bad” because they are unprovable ideas in composite formal system “everything we know so far”. Bad ideas that are actually bad are usually provably bad even in current system. "

very cool, thanks for sharing

How about using http://us.metamath.org/ as a db for math theorems / definitions and do some heavy data mining there?

It isn't unlimited as far as I know, but yes sure it is indeed very much free. We should be grateful for this opportunity to save 5 dollars a month.

didn't know Gmail has ads .. so they basically use ML to substitute random spam with their highly targeted spam.. innovative business model