Markov Chains for programmers

https://czekster.github.io/markov/

by raister • 4 years ago

217 29 4 years ago

czekster.github.io

raister4• 4 years ago

Interesting find - really introductory examples. Good read, recommend it.

m000• 4 years ago

- "Markov Chains for programmers." Cool!

- Opens PDF.

- Typeset in Computer Modern.

- Starts running, screaming in Comic Sans.

Jokes aside, CM is not the only game for math-heavy documents. Something like Libertinus [1] would probably be more screen-friendly.

[1] https://github.com/alerque/libertinus

layer8• 4 years ago

This has been downvoted, but I think selecting more screen-friendly fonts is a valid concern nowadays. Personally I would also like to see a reflowable format (which I guess would mean HTML with MathJax).

mattalex• 4 years ago

Use latexml this is what's running under the hood of ar5iv https://ar5iv.labs.arxiv.org/ and should be able to compile every Latex document to html

spekcular• 4 years ago

What's wrong with computer modern?

dddnzzz334• 4 years ago

Computer Modern is the best looking font

MonkeyClub• 4 years ago

With Comic Sans a close second, surely.

raister4• 4 years ago

Only the frontispiece uses a different font (can't say for sure whether it is Comic Sans though) - the rest is pure Times new Roman.

raister4• 4 years ago

The author has uploaded a version of the book in the libertinus font - I guess he heard your complaints!

kjs3• 4 years ago

This is seriously the most insightful comment you can come up with? If it is, you would do well to consider silence.

westcort• 4 years ago

I have used Markov chains for a few fun projects, like this Markov chain headline generator (https://locserendipity.com/Markov_Headlines.html), and a Markov generator based on a certain someone's Twitter feed (https://locserendipity.com/Markov_Trump.html), but this looks like a good resources for more serious applications.

hvasilev• 4 years ago

There are just so many of these fun AI-related concepts that seem really cool and you get the chill that they will take over the world some day.

Decades pass and you realize they either have little to no application or are incredibly niche :(

Too bad that "solution in a search of a problem" is generally bad approach to problem-solving. I wish our industry was more fun as a whole.

klysm• 4 years ago

Markov chain Monte Carlo is incredibly useful and widely applied.

vanderZwan• 4 years ago

To give an example: Prediction by Partial Matching is basically a Markov chain in disguise, and an incredibly powerful way to do compression that beats most other forms of text compression (at the price of having a lot more memory overhead)

[0] https://en.wikipedia.org/wiki/Prediction_by_partial_matching

it_does_follow• 4 years ago

Not to mention that the entire class of Markov Chain Monte Carlo techniques only form a subset of general uses for Markov chains.

Markov chains form the basis of n-gram language models, which are still useful today.

Markov chains are also the basis of the Page-rank algorithm.

Hidden Markov Models (which are just an extension of Markov Chains to have unobserved states) are a powerful and commonly used time series model found all over the place in industry.

In the pre-deep learning model Markov chains (and HMMs) in particular had very wide spread usage in Speech processing.

They are probably one of the most practical statistical techniques out there (out side of obvious example like linear models).

robbedpeter• 4 years ago

Most of the time, these things are resource hogs arriving way before their time to shine, either needing Moore's law to catch up the hardware, or some nerd to wrestle with the combinatorial explosion and win. Transformers can be seen as a variation on Markov chains, but the innovation of attention mechanisms means you can use hundreds of thousands of tokens and thousands of tokens in sequences without the problem space going all Buzz Lightyear on you.

https://www.zabaware.com/ultrahal/

Ultra Hal was a best in class chat bot when fixed response systems like Alice/ AIML were the standard. Ultra Hal used Markov chains and some clever pruning, but it dealt with a few hundred tokens as words and sequences only 2 or 3 tokens out. It occasionally produced novel and relevant output, like a really shitty gpt-2.

I think we may see a resurgence of expert systems soon, as gpt-3 and transformers have proved capable of automating rule creation in systems like Cyc. They've already incorporated direct lookups into static databases gpt / RETRO type models. Incorporating predicate logic inference engines seems like the logical and potent next step. GPT could serve as a personality and process engine that eliminates the flaw (tedium) in massive, tedious, human level micro-tasking systems from GOFAI.

It's worth going through all the literature all the way back to the 1956 summer of code and hunt for ideas that just didn't work yet.

https://en.wikipedia.org/wiki/Dartmouth_workshop

Fomite• 4 years ago

...Markov Chains (via MCMC) underly most Bayesian inference problems, and pretty much all stochastic dynamical systems models are based on Markov Chains.

skykooler• 4 years ago

Aren't Markov chains how predictive typing usually works? (or worked, I suppose the big players are probably using some sort of neural net now)

jonititan• 4 years ago

Pymc3 is pretty good. https://docs.pymc.io/en/v3/

b200003• 4 years ago

it’s for programmers but comes with matlab code and excel sheets

Jtsummers2• 4 years ago

Nothing wrong with either of those. Also, if you take the time to check out the book or its GitHub repo, then you will see that there is also C code and C "challenges" (projects) for the reader to go through.

dr_kiszonka• 4 years ago

Right, but it is a pretty unusual combination and 1.5 of these languages are proprietary.

HackOfAllTrades• 4 years ago

First thought upon reading the title: "Surely you could pay them to stay."

ganzuul• 4 years ago

https://github.com/coleifer/irc/blob/master/bots/redisbot.py

This is great fun. Once it has learned a little about you and your friends it sometimes spits out a lyrical home-run. It is of course you who provide the intelligence of interpretation, but it still feels mysterious when it 'talks' in a way relevant to the context.

Markov Chains for programmers

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