Below is the text of the first page(about the same amount of characters as the first page) of the beloved by some, having bedeviled many, Finnegans Wake by James Joyce. But this time it's been fed through a neural network of machine learning(an RNN, a Recurrent Neural Network) meant to generate text off of a base text(the wonderful textgenrnn), along with its own byproduct of neuralfakewake. This was a quick project, I hesitate to even post the few lines of code it took, but credit to James Joyce(RIP, HCE), Max Woolf for the fantastically usable textgenrnn, and abstraction itself.

Generating text is one of those projects that seems like a lot of fun to machine learning and NLP beginners, but one which is also pretty daunting. Or, at least it was for me. Thankfully, there are all sorts of great materials online for learning how RNNs can be used for generating text, ranging from the theoretical to the technically in-depth to those decidedly focused on the practical. There are also some very good posts which cover it all and are now considered canon in this space. All of these materials share one thing in particular: at some point along the way, you have to build and tune an RNN to do the work.

You can find more info and examples on the Github repo linked above. Magnitude is "a fast, simple vector embedding utility library." A feature-packed Python package and vector storage file format for utilizing vector embeddings in machine learning models in a fast, efficient, and simple manner developed by Plasticity. It is primarily intended to be a simpler / faster alternative to Gensim, but can be used as a generic key-vector store for domains outside NLP. The repo provides links to a variety of popular embedding models which have been prepared in the .magnitude

Artificial intelligence (AI) seems poised to run most of the world these days: it's detecting skin cancer, looking for hate speech on Facebook, and even flagging possible lies in police reports in Spain. But AIs aren't all run by mega-corporations and governments; you can download some algorithms and play with them yourself, with often hilarious results. There's the faux Coachella poster full of fake band names, created by feeding a bunch of real band names into a neural network and asking it to come up with some of its own. There are the recipes created in a similar way, where "barbecue beef" calls for "1 beer - cut into cubes." Humans aren't nearly as creative as we think.