AI Monthly Digest #8 – new AI applications for music and gaming
The April edition of AI Monthly Digest looks at how AI is used in entertainment, for both research and commercial purposes.
After its recent shift from non-profit to for-profit, OpenAI continues to build a significant presence in the world of AI research. It is involved in two of five stories chosen as April’s most significant.
AI Music – spot the discord…
While machine learning algorithms are getting increasingly better at delivering convincing text or gaining superior accuracy in image recognition, machines struggle to understand the complicated patterns behind the music. In its most basic form, the music is built upon repetitive motifs that return based on sections of various length – it may be a recurrent part of one song or a leading theme of an entire movie, opera or computer game.
Machine learning-driven composing is comparable to natural language processing – the short parts are done well but the computer gets lost when it comes to keeping the integrity of the longer ones. April brought us two interesting stories regarding different approaches to ML-driven composition.
OpenAI developed MuseNet, a neural network that produces music in a few different styles. Machine learning algorithms were used to analyze the style of various classical composers, including Chopin, Bach, Beethoven and Rachmaninoff. The model was further fed rock songs by Queen, Green Day and Nine Inch Nails and pop music by Madonna, Adele and Ricky Martin, to name a few. The model learned to mimic the style of a particular artist and infuse it with twists. If the user wants to spice up the Moonlight Sonata with a drum, the road is open.
OpenAI has rolled out an early version of the model and it performs better when the user is trying to produce a consistent piece of music, rather than pair up a disparate coupling of Chopin and Nine Inch Nails-style synthesizers.
OpenAI claims that music is a great tool with which to evaluate a model’s ability to maintain long-term consistency, mainly thanks to how easy it is to spot discord.
…or embrace it
While OpenAI embraces harmony in music, Dadabots has taken the opposite tack. Developed by Cj Carr and Zack Zukowski, Databots model imitates rock, particularly metal bands. The team has put their model on YouTube to deliver technical death metal as an endless live stream – the Relentless Doppelganger.
While it is increasingly common to find AI-generated music on Bandcamp, putting a 24/7 death metal stream on YouTube is undoubtedly something new.
Fans of the AI-composed death metal have given the music rave reviews. As The Verge notes, the creation is “Perfectly imperfect” thanks to its blending of various death metal styles, transforming vocals into a choir and delivering sudden style-switching.
It appears that bare-metal has ushered in a new era in technical death metal.
Why does it matter?
Researchers behind the Relentless Doppelganger remark that developing music-making AI has mainly been based on classical music, which is heavily reliant on harmony, while death metal, among others, embraces the power of chaos. It stands to reason, then, that the music generated is not perfect when it comes to delivering harmony. The effect is actually more consistent with the genre’s overall sound. What’s more, Databots’ model delivers not only instrumentals, but also vocals, which would be unthinkable with classical music. Of course, the special style of metal singing called growl makes most of the lyrics incomprehensible, so little to no sense is actually required here.
From a scientific point of view, OpenAI delivers much more significant work. But AI is working its way into all human activity, including politics, social problems and policy and art. From an artistic point of view, AI-produced technical death metal is interesting.
It appears that when it comes to music, AI likes it brutal.
AI in gaming goes mainstream
Game development has a long and uneasy tradition of delivering computer players to allow users to play in single-player mode. There are many forms of non-ML-based AI present in video games. They are usually based on a set of triggers that initiate a particular action the computer player takes. What’s more, modern, story-driven games rely heavily on scripted events like ambushes or sudden plot twists.
This type of AI delivers an enjoyable level of challenge but lacks the versatility and viciousness of human players coming up with surprising strategies to deal with. Also, the goal of AI in single-player mode is not to dominate the human player in every way possible.
The real challenge in all of this comes from developing bots, or the computer-controlled players, to deliver a multiplayer experience in single-player mode. Usually, the computer players significantly differ from their human counterparts and any transfer from single to multiplayer ends with shock and an instant knock-out from experienced players.
To deliver bots that behave in a more human way yet provide a bigger challenge, Milestone, the company behind MotoGP 19, turned to reinforcement learning to build computer players to race against human counterparts. The artificial intelligence controlling opponents is codenamed A.N.N.A. (Artificial Neural Network Agent).
A.N.N.A. is a neural network-based AI that is not scripted directly but created through reinforcement learning. This means developers describe an agent’s desired behaviour and then train a neural network to achieve it. Agents created in this way show more skilled and realistic behaviors, which are high on the wish list of Moto GP gamers.
Why does it matter?
Applying ML-based artificial intelligence in a mainstream game is the first step in delivering a more realistic and immersive game experience. Making computer players more human in their playing style makes them less exploitable and more flexible.
The game itself is an interesting example. It is common in RL-related research to apply this paradigm in strategic games, be it chess, GO or Starcraft II for research purposes. In this case, the neural network controls a digital motorcycle. Racing provides a closed game environment with a limited amount of variables to control. Thus, racing in a virtual world is a perfect environment to deploy ML-based solutions.
In the end, it isn’t the technology but rather gamers’ experience that is key. Will reinforcement learning bring a new paradigm of embedding AI in games? We’ll see once gamers react.
Bittersweet lessons from OpenAI Five
Defense of The Ancients 2 (DOTA 2) is a highly popular multiplayer online battle arena game with two teams, each consisting of five players fighting for control over a map. The game blends tactical, strategic and action elements and is one of the most popular online sports games.
OpenAI Five is the neural network that plays DOTA 2, developed by OpenAI.
The AI agent beat world champions from Team OG during the OpenAI Five Finals on April 13th. It was the first time an AI-controlled player has beaten a pro-player team during a live-stream.
Why does it matter?
Although the project seems similar to Deepmind’s AlphaStar, there are several significant differences:
- The model was trained continuously for almost a year instead of starting from zero knowledge for each new experiment – the common way of developing machine learning models is to design the entire training procedure upfront, launch it and observe the result. Every time a novel idea is proposed, the learning algorithm is modified accordingly and a new experiment is launched starting from scratch to get a fair comparison between various concepts. In this case, researchers decided not to run training from scratch, but to integrate ideas and changes into the already trained model, sometimes doing elaborate surgery on their artificial neural network. Moreover, the game received a number of updates during the training process. Thus, the model was forced at some points not to learn a new fact, but to update its knowledge. And it managed to do so. The approach enabled the team to massively reduce the computing power over the amount it had invested in training previous iterations of the model.
- The model effectively cooperated with human players – The model was available publicly as a player, so users could play with it, both as ally and foe. Despite being trained without human interaction, the model was effective both as an ally and foe, clearly showing that AI is a potent tool to support humans in performing their tasks — even when that task is slaying an enemy champion.
- The research done was somewhat of a failure – The model performs well, even if building it was not the actual goal. The project was launched to break a previously unbroken game by testing and looking for new approaches. The best results were achieved by providing more computing power and upscaling the neural network. Despite delivering impressive results for OpenAI, the project did not lead to the expected breakthroughs and the company has hinted that it could be discontinued in its present format. A bitter lesson indeed.
Blurred computer vision
Computer vision techniques deliver astonishing results. They have sped up the diagnosing of diabetic retinopathy, built maps from satellite images and recognized particular whales from aerial photography. Well-trained models often outperform human experts. Given that they don’t get tired and never lose their focus, why shouldn’t they?
But there remains room for improvement for machine vision, as researchers from KU Leuven University in Belgium report. They delivered an image that fooled an algorithm, rendering the person holding a card with an image virtually invisible to a machine learning-based solution.
Why does it matter?
As readers of William Gibson’s novel Zero Hour will attest, images devised to fool AI are nothing new. Delivering a printable image to confound algorithm highlights a serious interest among malicious players interfering with AI.
Examples may include images produced to fool AI-powered medical diagnostic devices for fraudulent reasons or sabotaging road infrastructure to render it useless for autonomous vehicles.
AI should not be considered a black box and algorithms are not unbreakable. As always, reminders of that are welcome, especially as responsibility and transparency are among the most significant AI trends for 2019.