What happens when a neural net tries to write rhyming lyrics?

Apparently, an ode to defunct Swiss chemical company Alusuisse.

The fine rhyming work of @mark_riedl's Weird A.I. program
https://t.co/TAOIWBr7RI

Rhyming is super hard, especially in English where threw rhymes with through but not dough, and the emphasis patterns are all over the place.

There's lots of AI poetry but there's a reason it doesn't tend to rhyme

.@mark_riedl's algorithm has a part that chooses rhyming words, scoring them according to their similarity to the song's theme.

Here, "Uzbekistan" was the closest rhyme it could think of for "saucepan" given cakes as a topic.

Good job, AI

There's a subalgorithm that has to come up with lines of a given number of syllables.

Occasionally it gives up and returns a line that's way too short. Another subalgorithm chooses from several candidate lines & often prefers the short lines.

So you get

au-u-u-u-u-u-u-gust

The subalgorithm that has to generate the rhyming lines often gets desperate, especially when the subalgorithm that chose the rhyme handed it something like "welp" or "alusuisse".

It will sometimes resort to making up words entirely

🧵🧵🧵🧵 👇

Artificial Inteligence = 🖥️ + 🧠

When can you expect your fridge & mobile phone to revolt against you? (Spoiler Alert: Not in the foreseeable future).

Please Do 're-tweet' the Thread & help me spread the knowledge to more people.

1. The Language
In order to understand Artificial intelligence, machine learning, deep learning, we need to start at the very basics.

Humans speak and understand a wide variety of 'natural' languages (this is what computer science community calls them): like English, Hindi, Marathi, Bengali, Tamil etc.

But have you seen a peculiarity of human languages? They do not have a precise meaning associated with a given sentence. On an average, every English word has 2 meanings. This makes human conversations confusing, with the need for multiple turns & clarifications. Example 👇:

Person 1 conversing with Person 2 face to face:

Person 1: Lets go watch The Dark Knight. Its our marriage anniversary!
Person 2: I dont like going out at night. Its too scary & depressing.
Person 1: Oh, what i meant was watching the movie "The dark Knight".

A lot of Machine Learning (ML) I learned during my Ph.D. was from youtube. I didn't have a guide to do this effectively and thus here it is:

A complete guide to studying ML from youtube: 13 best and most recent ML courses available on YouTube. 👩‍🏫🧵⤵️

We will start with "Stanford CS229: Machine Learning" by Andrew Ng to start and learn the following ML concepts:

Linear & Logistic Regression,
Naive Bayes, SVMs, Kernels
Decision Trees, Introduction to Neural Networks
Debugging ML

A series of mini-lectures (~5 mins) covering various introductory topics in ML by Cassie Kozyrkov, covering:

Explainability in AI, Precession vs. Recall, Statistical Significance, Clustering and K-means, and finally, Ensemble models.

Beyond an AI genius, Andrej Karpathy is a brilliant teacher. His creative teaching methods make this intro to Neural Networks (NN): Zero to Hero makes one of the best ways to get introduced to NN.

"MIT: Deep Learning for Art, Aesthetics, and Creativity " covers the application of deep learning for art, aesthetics, and creativity, including Neural Abstractions, Efficient GANs, and explorations in AI for Creativity.