Neurons Spike Back (https://neurovenge.antonomase.fr/) was featured in the latest data science weekly newsletter. I would normally pass on such long articles, but the history of AI is interesting, so I gave it a shot. Reading the paper felt like a marathon, and I only completed it through sheer force of will, lots of coffee, and because it is cold and raining outside.
The article is very difficult to read (at least for me), not because it is filled with theory, but because the language is dry and academic, and it tries to condense decades of history around the research of AI into a fairly short (given the topic) article that discusses two opposing sides of research and thought: connectionist and symbolic AI.
Despite my warning above, if you have the patience, I found it to be a fairly good overview of how we ended up where we are today with deep learning dominating the state of the art for AI in many fields.
I found it particularly interesting to learn about the Mark I, a hardware neural network constructed during the 1960’s for simple object recognition. It is a good reminder that the concepts we are using today have been around for a very long time, and I often find that knowing a bit of the history behind them help understand what we are doing in the present.
Whenever there is a new announcement or breakthrough with AI, it always strikes me how out of reach the results would be to replicate for individuals and small organizations. Machine learning algorithms, and especially deep learning with neural networks, are often so computationally expensive that they are infeasible to run without immense computing power.
As an example, OpenAI Five (OpenAI’s Dota 2 playing bot) used 128,000 CPUs and 256 GPUs which trained continuously for several months:
In total, the current version of OpenAI Five has consumed 800 petaflop/s-days and experienced about 45,000 years of Dota self-play over 10 realtime months.
Running a collection of more than a hundred thousand CPUs and hundreds of GPUs for ten months would cost several million dollars without discounts. Needless to say, a hobbyist such as myself would never be able to replicate those results. Cutting edge AI research like this has an implicit disclaimer: “Don’t try this at home”.
Even on a smaller scale, it is not always possible to run machine learning algorithms without certain trade-offs. I can sort a list of a million numbers in less than a second, and even re-compile a fairly complex web application in a few seconds, but training a lyrics-generating neural network on less than three thousand songs takes several hours to complete.
Although a comparison between number sorting and machine learning seems a bit silly, I wonder if we will ever see a huge reduction in computational complexity, similar to going from an algorithm like bubble sort to quicksort.1
Perhaps it is not fair to expect to be able to replicate the results of a cutting edge research institution such as OpenAI. Dota 2 is a very complex game, and reinforcement learning is an area of research that is developing fast. But even OpenAI acknowledges that recent improvements to their OpenAI Five bot are primarily due to increases in available computing power:
OpenAI Five’s victories on Saturday, as compared to its losses at The International 2018, are due to a major change: 8x more training compute. In many previous phases of the project, we’d drive further progress by increasing our training scale.
It feels slightly unnerving to see that the potential AI technologies of the future are currently only within reach of a few companies with access to near-unlimited resources. On the other hand, the fact that we need to throw so many computers at mastering a game like Dota should be comforting for those with gloomy visions of the future :-)
Automatic, machine-generated music has been a small interest of mine for some time now. A few days ago, I tried out a deep learning approach for generating music… and failed miserably. Here’s the story about my efforts so far, and how computational complexity killed the post-rock.
The spark of an idea
When Photo Amaze was created in 2014, I thought it would be fun to have some kind of ambient music playing while navigating through the 3D maze. But I did not want to play pre-recorded music. I wanted it to be automatically generated on-the-fly, based on the contents of the pictures in the maze.
That was the spark. A picture is worth a thousand words, so why can’t it be worth a few seconds of music as well? For example, take a look at this picture:
motional impact, like the ambient sound of a running water stream or the whistle of the wind picking up speed over the mountain.
Since I can make a connection between photo and music, perhaps a machine could do this automatically as well. This is not a novel idea, but it is a nut that has yet to be cracked, and it was an intriguing idea to start exploring.
Hard-coded music mappings
Modern browsers have all the ingredients necessary for doing both image analysis and sound generation. There are numerous JavaScript libraries for analyzing and manipulating pictures, and the Web Audio API makes it possible to create synthesized sound in a fairly straightforward way. Thus, it made sense to start here.
The first experiment I did was to create a more-or-less fixed mapping between an image’s content and some kind of sound output. The high-level idea of the implementation was to simply map brighter colors to brighter sound notes. The steps to produce the output sound were something like this:
Find at most 200 “feature pixels” in the input image using trackingjs.
For each found “feature pixel”:
Calculate the average pixel value between the three RGB color values. This produces a single number per pixel between 0 and 255.
Normalize the pixel value from 0-255 to 20-500. This produces a base frequency for the output sound.
Create a sine wave oscillator using the Web Audio API for the pixel value.
Combine the oscillators to a single sound output.
While playing the sound, randomize the frequency of each oscillator slightly over time.
Using this approach, an image would be turned into a randomly changing output sound consisting of about 200 sine waves, each with a frequency between 20 and 500 Hz.
Here is an example output using the mountain from above as the input image (the red dots mark the found “features” of the image).
That might not sound terrible, until you realize that the sound is basically the same for any input image:
Mila might be a monster dog, but that output is just too dark :-)
There were a ton of problems with this implementation, to the point that it was actually outright silly. For example, the “feature pixel” selection mostly found edges/corners of the image, and using just 100 pixels as input corresponded to just 0.01% of all available pixels in the test images. Another problem was how the final pixel value was calculated from the average between the red, green and blue value of the pixel. Some colors arguably have more impact to the viewer than others, but this fact is not captured when taking the average.
Even with all its problems, the first experiment was a good first step, considering I did not know where to start before. It is possible that with a lot of tweaks, lots of new ideas and lots of time, this approach could start producing more interesting soundscapes. However, the downside of the approach was also that the music creation would always be guided by the experimenters: the humans. And I wanted to remove them from the equation.
Machine learning to the rescue
The second experiment ended before it even really started. It was clear that some kind of machine learning was needed to move forward, and it seemed that an artificial neural network might be the solution.
This was the idea:
Use every pixel of the input image as a single input node of the neural network.
Treat every output node as a single sound sample.
For the purposes of this blog post, everything that happens between input and output nodes of the network is largely hidden magic. With that in mind, here is how the network would look (P1 – Pm are the input pixels and S1 – Sn are the output samples):
Mapping a photo’s input pixels (P1-Pm) to a soundwave output (S1-Sn).
To get an idea of the size of the network, consider this: the mountain test image from above is 1024 by 683 pixels, so the network would have 699,392 input nodes when using images of that size. Digital sound is just a collection of amplitudes in very tiny pieces called samples. The most commonly used sampling rate for music is 44.1KHz, which means that every second of digital music consists of 44,100 individual samples. For a neural network of this design to produce a five-second sound, it would thus require 220,500 output nodes.
The intentions were good but the implementation never happened. After having the initial idea, I started Python and tried to simply read and write soundfiles, but it didn’t go so well, and the weekend was nearly over, and, “oh a squirrel!”… and the code was never touched again.
Machine learning is great, but the motivation was suddenly lacking, and the project was put on ice. This was about two years ago, and the project was not revived until quite recently.
AI art
Deep learning has been steadily on the rise in recent years, often outperforming other machine learning techniques in specific areas such as voice recognition, language translation and image analysis. But deep learning is not limited to “practical” use cases. It has also been used to create art.
A well-known example of “AI Art” is Google’s Deep Dream Generator. The software is based on an initial project called DeepDream which produced images based on how it perceived the world internally. Some of its images were even shown and sold at an art exhibition.
The A.I. Duet project shows another interesting use case for deep learning: the creator of the project, Yotam Mann, trained a model that can produce short sequences of piano notes based on the note input of a human. So if I played C-D-E, the software might respond with F-G-A although the result would most likely be slightly more interesting than that. 1
A.I. Duet is impressive, but it still has a big limitation: it only works for specific notes for a specific instrument. So while the result is amazing, what I really want is more complex arrangements and raw audio output. Even so, the above examples show that deep learning is a powerful and versatile machine learning technique, and it is now finally becoming more feasible than ever to achieve the goal of creating music using AI.
Although it is not really relevant to this blog post, I could not help myself: here is the Mila image from above processed with default settings of Deep Dream. It is slightly disturbing to see that… chicken?… coming out of her left paw. Thank you for ruining my sleep, robot!
The bleeding edge, where the story ends
While doing some research on the latest state of the art for machine-generated sound, I stumbled upon yet another Google project called WaveNet. In an interesting blog post, the authors of WaveNet discuss how their research can be used to improve text-to-speech quality, but what is really exciting to me is that they also managed to produce short piano sequences that sound natural (there are some examples at the bottom of their blog post).
The big surprise here is that the piano samples are not just based on specific notes. They are raw audio samples generated from a model trained with actual piano music. 2
Finally! A tried and tested machine learning technique that produced raw audio. Reading about WaveNet marked the beginning of my final experiment with music generation, and is the entire reason this blog post exists.
I found an open source implementation of WaveNet, and to test the implementation, I wanted to start simple by using just one sound clip. For this purpose, I extracted an eight-second guitar intro from the post-rock track Ledge by Seas of Years3:
My hope was that by training the model with a single sound clip, I would be able to reproduce the same or a very similar clip to the original to validate that the model produced at least some sound. If this was possible, I would be able to train the model with more sound clips and see what happens.
Unfortunately, even with various tweaks to the network parameters, I could not manage to produce anything other than noise. Sharing an example of the output here is not even appropriate, because it would hurt your ears. The experiment ended with an early failure.
So what was the problem? I soon realized that even with this fairly simple example, I had been overly optimistic about the speed at which I would be able to train the model. I thought that I could train the network in just a few minutes, but the reality was very different.
The first warning sign showed itself pretty quickly: every single step of the training process took more than 30 seconds to complete. In the beginning, I did not think much about this. Some machine learning models actually start producing decent results within the first few steps of training so I was hoping it would be the same here. However, after doing more research on WaveNet, it became clear that training a WaveNet model did not just require a few learning steps, it required several thousand. Clearly, training WaveNet on my machine was completely unfeasible, unless I was willing to wait more than a month for any kind of result.
Where do we go from here?
Machine learning has been rapidly evolving in recent years, propelled by software libraries like TensorFlow, and the technology is more accessible than ever for all kinds of developers. But there is also another side of the coin: in order to use the state of the art, we are often required to have massive amounts of computing power at our disposal. This is probably why a lot of high-profile AI research and projects are produced by companies like Google, Microsoft and IBM, because they have the capacity to run machine learning at a massive scale. For lone developers like me that just want to test the waters, it can be difficult to get very far because of the complexities of scale.
As a final example to illustrate this point, consider NSynth, an open source TensorFlow model for raw audio synthesis. It is based on WaveNet and on NSynth’s project page, it says:
The WaveNet model takes around 10 days on 32 K40 gpus (synchronous) to converge at ~200k iterations.
Training a model like that would cost more than $5,000 using Google Cloud resources 4. Of course, it is possible that a simpler model could be trained faster and cheaper, but the example still shows that some technologies are most definitely not available for everyone. We live in a time where there is great access to many technological advances, but the availability is often limited in practice, because of the scale at which the technologies need to operate.
So where do we go from here? Well, computational complexity killed my AI post-rock for now, but I doubt that it will take long before significant progress is made in this field. For now, I will enjoy listening to human-generated music. In a way, it is re-assuring that machines cannot outperform us in everything yet.
motional impact, like the ambient sound of a running water stream or the whistle of the wind picking up speed over the mountain.
