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Zeffolia

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Can second the suggestion for Processing for some interesting but simple-to-get-into visuals. I did the art for my first album (https://tsrono.bandcamp.com/album/i) with Processing and then fucking around in Photoshop. I know jack shit about coding and was able to go from a few tutorials and just playing around some to making some slightly interesting source material for manipulation in little time. Would definitely take some actual effort to get better control than I ever had...but there's plenty of resources for beginners online, and of course lots of sample stuff you can mess with and learn from ... I have actually started to play around with it again a little bit lately, forgotten any little bit I learned back then of course...

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Thanks for all the input everyone, much appreciated.

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Can second the suggestion for Processing for some interesting but simple-to-get-into visuals. I did the art for my first album (https://tsrono.bandcamp.com/album/i) with Processing and then fucking around in Photoshop. I know jack shit about coding and was able to go from a few tutorials and just playing around some to making some slightly interesting source material for manipulation in little time. Would definitely take some actual effort to get better control than I ever had...but there's plenty of resources for beginners online, and of course lots of sample stuff you can mess with and learn from ... I have actually started to play around with it again a little bit lately, forgotten any little bit I learned back then of course...

I've been holding off doing anything visual art related stuff for a long time. (literally since the early 2000's)

 

My brother does some graphic design, so i was gonna have him do some upcoming album art, but I might check this Processing out in the mean time.

 

@auxien, yeah i like that a lot, what you did for your album art.

Edited by eczem

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^thanks, probably owes more to Photoshop than Processing, but Processing definitely allowed me to create what I had in mind. 

 

Always a bonus if you've got someone close who can help with something like that...so if your brother can sorta follow what you're wanting out of things then that could be cool, certainly easier than trying to learn even some basic programming. Make family do the work :)

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most state of the art "artificial intelligence" / computer vision seems to be mostly different variations of throwing shit at the wall and seeing what sticks, with very complex shit throwing algorithms. Which makes sense considering you can't model something when you don't know how it works to begin with

Edited by span

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computer vision is more than just neural networks, but yeah the neural net stuff is mostly just playing around with various different options at each stage (e.g. you pick a method to convert your input data into numbers, pick a classifier or regression algorithm to process that data, train and test, if the result is bad go back to step 1 and just pick different methods and try again, repeat until you get something that works; then if it stops working as well in the future, because maybe some features of your data have changed, repeat the whole thing again). you don't have to have any idea how any of the different methods work, though you might build up an intuitive understanding of what might work well in certain situations, there's just a bunch of different ones in the library to choose from. there doesn't seem to be a huge amount of research into why different methods work well with some things more than others, or exactly why a lot of it works at all, definitely more of an art than a science at this point.

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computer vision is more than just neural networks, but yeah the neural net stuff is mostly just playing around with various different options at each stage (e.g. you pick a method to convert your input data into numbers, pick a classifier or regression algorithm to process that data, train and test, if the result is bad go back to step 1 and just pick different methods and try again, repeat until you get something that works; then if it stops working as well in the future, because maybe some features of your data have changed, repeat the whole thing again). you don't have to have any idea how any of the different methods work, though you might build up an intuitive understanding of what might work well in certain situations, there's just a bunch of different ones in the library to choose from. there doesn't seem to be a huge amount of research into why different methods work well with some things more than others, or exactly why a lot of it works at all, definitely more of an art than a science at this point.

 

I notice few people talk as much about feature extraction in comparison to the classification algorithms etc., but despite this it's essentially the primary determiner of whether a given ranking algorithm or whatever will end up working in your use case - whether you've extracted features that represent all of the information relevant to the problem you're solving, and whether they're extracted accurately with noise reduction.  I'm no AI/ML expert let alone a computer vision expert, but I can still see a pathway towards things getting a lot better in the future.  You can't rely on the DNN to do all of the heavy lifting, you need some domain specific knowledge to isolate the problem to be based on input data that's smaller and more abstract

Edited by Zeffolia

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most state of the art "artificial intelligence" / computer vision seems to be mostly different variations of throwing shit at the wall and seeing what sticks, with very complex shit throwing algorithms. Which makes sense considering you can't model something when you don't know how it works to begin with

Thats how science works. Throw shit at a wall to see what sticks. And learn from it. Why does it stick and all that. It's very much about modelling stuff we don't understand. Do you even know examples where that wasnt the case?

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I just wrote my first Kotlin program yesterday and it was quite enjoyable.

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most state of the art "artificial intelligence" / computer vision seems to be mostly different variations of throwing shit at the wall and seeing what sticks, with very complex shit throwing algorithms. Which makes sense considering you can't model something when you don't know how it works to begin with

Thats how science works. Throw shit at a wall to see what sticks. And learn from it. Why does it stick and all that. It's very much about modelling stuff we don't understand. Do you even know examples where that wasnt the case?

 

When modelling forest fires, or cancer, or whatever, you have both some prior understanding of how they work and some data to determine whether your model is appropiate or not. But we still don't understand how human intelligence (or intelligence overall) works well enough to simulate it. Of course trial and error is involved in pretty much all scientific research. My point is AI/CV relies almost solely on trial and error

Edited by span

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And so do modelling forest fires or cancer!?

 

Not sure why there's a need for an argument in this.

 

Although if you want to argue that modelling in the AI context is different to modelling in other scientific fields, than yeah I'm completely with you. Models in other fields like AI are very much built to understand and prove hypotheses. In AI they're built just to see whether it can generalise good enough to predict other outcomes. And usually people in AI don't bother thinking about what it says about the real world. They're just happy with having built a working model. The starting hypothesis was most likely nothing more than the idea that they got data good enough to build a nice model. Without any intention to prove how or why.

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I need to learn Wwise.

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I notice few people talk as much about feature extraction in comparison to the classification algorithms etc., but despite this it's essentially the primary determiner of whether a given ranking algorithm or whatever will end up working in your use case - whether you've extracted features that represent all of the information relevant to the problem you're solving, and whether they're extracted accurately with noise reduction.  I'm no AI/ML expert let alone a computer vision expert, but I can still see a pathway towards things getting a lot better in the future.  You can't rely on the DNN to do all of the heavy lifting, you need some domain specific knowledge to isolate the problem to be based on input data that's smaller and more abstract

 

Yeah featurization is often just as important as the classifier or whatever (or more so even). It depends on the data, some data might be incredibly simple, just a list of numbers for example (which at most you'll just normalise), but if you have multi-column data there are numerous different methods of creating the input vector, often the biggest decision is which columns actually need to be included, whether to sample and how, and also all the various methods of dealing with non-numeric data. The only stuff I've played around with myself has been regression analysis of price data and stock levels for my clients sales and purchases, not sure how accurate it's been because I've not actually comped it yet to subsequent real world data, but in training and evaluating I found that the most important choices were around what data to include more than how to handle the data, with different types of, more "self contained", data (e.g. linguistic, image or audio data) featurization strategies might be more important. It also seems that the more regression based things are better understood, probably because they're more similar to standard statistical methods that have been studied for a long time, classifiers and sentiment analysis less so.

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//

Edited by very honest

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And so do modelling forest fires or cancer!?

 

Not sure why there's a need for an argument in this.

 

Although if you want to argue that modelling in the AI context is different to modelling in other scientific fields, than yeah I'm completely with you. Models in other fields like AI are very much built to understand and prove hypotheses. In AI they're built just to see whether it can generalise good enough to predict other outcomes.

 

That's what I'm trying to say... In every other context there's at least an hypothesis to prove or something to work off of. With AI there's not.

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:beer:

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The computer vision course got updated from java to python & matlab.  :yeah:

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matlab is so high level it's silly. Great for prototyping scientific stuff

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nice! looks like a sensible decision.

 

but still though, the idea to give a computer vision course in javascript is out of this world. i'd still be concerned about that. that idea shouldn't have any legs to be printed on paper.

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it was java, not javascript.

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Guest Ovitus

Computing Science Student here. Done plenty of programming for work/school/hobby. 

 

I generally find most languages fun if you are using them where they excel. Right now I'm working in mostly C and some C#. 

Without going into too much detail I just finished something relatively complicated (for me at least) at work. It was a tuning system that ran on a laptop for audio software on another device. You would plug your laptop into the system with an Ethernet cable and tune the system with a GUI on your laptop. Wasn't the most algorithmic-ly complicated thing, but definitely a lot of code. 

 

If I had to pick a favorite though it would probably have to be Haskell. Functional programming is awesome!

http://learnyouahaskell.com/

This book is really great to get started. I met the author last year when he came to Canada (from Slovenia). Miran was super chill bad ass. 

Playing around with Haskell and other functional languages puts  a lot of focus on solving little problems, which was initially what was very fun and appealing about programming for me. It's kind of like a bunch of mini games :). 

 

Had some fun last year in school writing A* search in a bunch of different functional languages. Lisp ((((((lol)))))))), Haskell, and Scala.

"learn you a Haskell for great good" -- This author makes his guide look so deceptively easy with his mspaintings. Didn't take me long before I got information overload. Just a couple chapters in but haskellbook.com might be better for those new to programming. idk.. I feel obligated to understand a language now. Might try and apply it towards tidalcycles or something

Edited by Ovitus

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Speaking of functional languages I've started to learn me some Clojure. Seems more practical than Haskell or Lisp.

 

I've been mostly a python programmer the past few years and I love python but it's nice to learn some new language now and then. My python style is anyway generally quite functional with lots of maps, filters, lambda functions, etc.

 

Anyway, a recruiter already contacted me about Clojure work and I was thinking "can I say I'm familiar with it after creating one very simple IRC bot?", lol. In the end the schedule to start the work was too fast for me.

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Speaking of functional languages I've started to learn me some Clojure. Seems more practical than Haskell or Lisp.

 

I've been mostly a python programmer the past few years and I love python but it's nice to learn some new language now and then. My python style is anyway generally quite functional with lots of maps, filters, lambda functions, etc.

 

Anyway, a recruiter already contacted me about Clojure work and I was thinking "can I say I'm familiar with it after creating one very simple IRC bot?", lol. In the end the schedule to start the work was too fast for me.

What's your setup like? I'm a Vim guy but I gave emacs a chance with the cider REPL setup and was really enjoying it but then I broke it somehow and wasn't able to fix it. The whole thing seems really brittle which is unfortunate because it's a nice language. I'm mostly a Javascript guy and it sounds like my style is similar to yours.

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I'm pretty invested into Rust these days. Close to the metal while still possible to make nice abstractions with the Haskell based traits system, many functional language inspired features and much nicer ergonomics than you'd get in C++.

 

The ecosystem is very young so for now I've used it mostly in my spare time but have been able to use it at work in a few cases where I'd normally use C++.

 

Currently working a bit with a UI library called Conrod as cross-platform desktop UI development is in a terrible state with slow as molasses Electron.

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Speaking of functional languages I've started to learn me some Clojure. Seems more practical than Haskell or Lisp.

 

I've been mostly a python programmer the past few years and I love python but it's nice to learn some new language now and then. My python style is anyway generally quite functional with lots of maps, filters, lambda functions, etc.

 

Anyway, a recruiter already contacted me about Clojure work and I was thinking "can I say I'm familiar with it after creating one very simple IRC bot?", lol. In the end the schedule to start the work was too fast for me.

What's your setup like? I'm a Vim guy but I gave emacs a chance with the cider REPL setup and was really enjoying it but then I broke it somehow and wasn't able to fix it. The whole thing seems really brittle which is unfortunate because it's a nice language. I'm mostly a Javascript guy and it sounds like my style is similar to yours.

 

 

I'm an emacs guy myself. I have the code running on a Debian VPS with Leiningen installed and I do the coding over SSH from my mini-laptop. I haven't really set up anything fancy yet, just plain ol' emacs and shell.

 

I guess most Leiningen developers use Eclipse for development?

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