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Tag Archives: Deep Learning

Generative Deep Learning and Bach, a Good Fit?


If you’re like me, you know that there’s never “enough Bach” in one’s life and you can always tap into infinite musical curiosities based on Bach. Using Artificial Intelligence methods such as deep learning to “train” computers for music composition is one of the fascinating recent trends in this area, and applying these automated, statistical methods to Bach chorales is an active topic of research with interesting results. The book by David Foster, “Generative Deep Learning – Teaching Machines to Paint, Write, Compose, and Play“, has a chapter dedicated to using generative deep learning methods such as MuseGAN for music composition, and explains how such “generative” models can be trained on Bach’s real polyphonic compositions to output new musical pieces in the style of Bach.

Below is an original piece created by the Generative Adversarial Deep Learning Network (GAN, in particular the famous MuseGAN network architecture). The MuseGAN deep learning network system was able to create this after training for only 1000 epochs on a moderate laptop for 2 hours (without using GPUs), based on the data set at https://github.com/czhuang/JSB-Chorales-dataset (a set of 229 Bach chorales). In other words, this is definitely not representative of what Deep Learning can achieve as best because such a system can be easily trained for longer on much more powerful systems (see further examples below). The focus of these examples is the fact that you can also start to experiment with deep learning systems that start to model musical aspects without explicit musical teaching, hard-encoded rules in software, etc.

You can click on the image below to visit SoundCloud and listen to MP3 file generated by MuseScore.

Example created by the GAN by randomly applying nornally distributed noise vectors - Click to listen on SoundCloud

Example created by MuseGAN by randomly applying normally distributed noise vectors – Click to listen on SoundCloud

Among the actual Bach chorales in the data set, the “closest” one to the artificially generated example (“close” in the sense of Euclidean distance) can be seen below. Read the rest of this entry »

 
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Posted by on December 17, 2019 in Math, Music, Programlama

 

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How to confuse Google Translate by simply adding a newline?


When you have the most popular and successful computer-based translation service in the world used by millions of people everyday, it’s inevitable that very interesting cases will be discovered. Let’s take the following question:

  • Can simply adding a “newline” character change the translation of a word?

This sounds weird, because for a human being, the obvious reaction would be:

  • What does that even mean? Probably you’ve accidentally hit ENTER or something, and that can’t possibly affect the meaning of a word, why do you even ask that?

Well, if the translation system in question based on statistical natural language processing and neural network algorithms such as deep learning, then things get a little more complex. Let’s first look at a sentence without any superfluous newline inserted:

and now, let’s hit ENTER right after the Dutch word “afzetzone”, to see the translation change magically:

The point here is not if the word “afzetzone” is translated correctly, but rather, how come its translation changes by simply adding one more “white space” after the word.

If you’re a lay person, you’ll probably be baffled by this example, and if you’re an NLP expert, specializing in deep learning techniques, you’ll probably scratch your head and then smile, and if you’re one of the scientists or engineers actually working on the Google Translate software’s debugging, well, then you might give a different reaction. 😉

All in all, keep in mind that in today’s technological landscape, there are super complex systems behind simple interfaces, and such “glitches” barely scratch the surface of this, providing a little, and opaque glimpse into a popular Artificial Intelligence product.

 
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Posted by on November 8, 2019 in Linguistics, Programlama, Science

 

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Lost in Google Translate: How Unreasonable Effectiveness of Data can Sometimes Lead Us Astray


I’ve recently received an e-mail in Dutch from the Belgian teacher of my 7.5-year-old son, and even though my Dutch is more than enough to understand what his teacher wrote, I also wanted to check it with Google Translate out of habit and because of my professional/academic background. This led to an interesting discovery and made me think once again about artificial intelligence, deep learning, automatic translation, statistical natural language processing, knowledge representation, commonsense reasoning and linguistics.

But first things first, let’s see how Google Translate translated a very ordinary Dutch sentence into English:

Interesting! It is obvious that my son’s teacher didn’t have anything to do with a grinding table (!), and even if he did, I don’t think he’d involve his class with such interesting hobbies. 🙂 Of course, he meant the “multiplication table for 3”.

Then I wanted to see what the giant search engine, Google Search itself knows about Dutch word of “maaltafel”. And I’ve immediately seen that Google Search knows very well that “maaltafel” in Dutch means “Multiplication table” in English. Not only that, but also in the first page of search results, you can see the expected Dutch expression occurring 47 times. Nothing surprising here: Read the rest of this entry »

 
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Posted by on February 8, 2019 in CogSci, Linguistics, philosophy, Science

 

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