(Originally published on https://nyewarburton.com in Fall 2022.)
While watching academic (and non-technical) environments, I’m beginning to see that the pervasive instinct is to ban AI in writing and artwork curriculum. The argument is that students will use it to facilitate cheating, or it’s use will “cheapen” the learning of the artform.
But increasingly, I worry that this approach will not prepare the next generation for the true world they are about to enter. Can we open the debate in a way that best helps the students, and limits the potential for academic dishonesty?
I spent 20 years as a professional animator. My passion is both story but also the technical implementation of it’s use in real time technology. That is, I really love making characters move in video game worlds.
The satisfaction comes from the knowledge of complex systems, storytelling, and the final result of days and days of labor coming to fruition. I bring something that was seemly dead**, to life.** I do it, with an extraordinary amount of labor, skill, research, iteration… and often, the emotional pain of fighting my self doubt.
Above you can see a screen capture of my desktop. I am currently animating (experimenting) with the Unreal Engine’s “Manny”, in Autodesk Maya. That means I move and set the position of the arms by keying. Me, a human.
Since I derive so much personal value from the process, it was especially difficult for me to deal with the idea of automation, when I first discovered machine learning on a motion capture stage several years ago.
Machine learning is the term for a class of mathematical models within the field of Artificial Intelligence. They are having a sort-of “golden age” of development. Training a machine learning model to move a face of a character, or to automate the rigging of a digital skeleton can drastically reduce the labor involved (in some cases by a factor of 100).
As these models have matured, more and more of the pipeline is becoming automated. Below you can see one of the more impressive workflows called Learned Motion Matching. This alone, will drastically reduce the up-front creation time of video game character controllers.
In this paper we present a learned alternative to the Motion Matching algorithm which retains the positive properties of Motion Matching but additionally achieves the scalability of neural-network-based generative models.
I remember a sleepless night where my stomach physically hurt as I came to the realization that AI had the potential to remove the need for animators themselves. (I wrote about it back then) I soon decided this was a problem that I needed to frame as Scottish philosopher David Hume articulates the “Is” vs. “Ought” problem.
I was resisting the understanding of artificial intelligence because I was clinging to what I thought ought to be. Artists ought to be the drivers of the labor. Creativity ought to be part of the emotional struggles of the individual. Not a data set. Right?
Only when I began to confront what AI is, was I able to begin to research and understand it.
This is where we are at.
AI is reaching a level of (highly) functional use for artist workflows in animation, music, editing, visual effects, illustration and many others. It’s being deployed in our software for everyday use. AI image making models are reaching critical mass now, because of the prolific sharing ability of the internet.
Because of the nature of our digital world — we have larger and larger datasets, and the training of models are becoming more accurate and robust.
Predictive systems will finish our work for us in the art we create, and it will understand not just how to animate, but our “intents” as the animator using it. We are at the cusp of creating a productivity boom unlike human kind has ever seen. The following images were generated this past summer, during my experimentation with Stable Diffusion.
The challenge in the near future, is not with the mathematical models, or even the datasets that are being collected. These are nearing an astounding level of image fidelity. The challenge is the interface design and the UX — the accessibility to the non-coding masses.
Many, many, software companies are rushing to create this accessibility through new interfaces, plug ins, and automated things we don’t even notice. “Old Guard,” like Adobe, are seeming to keep pace by buying up new talent. But there is a sizable crop of generative start ups who are targeting other graphics markets. The focus, driven by capitalist desire, is mass adoption. This leads to facilitation of use, and exponential data collection.
But capitalism isn’t the only driver. Stable Diffusion, a popular clip-diffusion image maker, released themselves open source. Within days, new innovations were in google colabs around the world. I suggest searching the #stablediffusion tag on twitter and marveling at an endless stream of un-bundled experimentation. The acceleration of AI is not just driving the market economy, it is inventing the distributed licensed one as well.
Students can actively choose a variety of new applications to automate the writing of their essays (or even the accompanying illustrations!)
Increasingly, that choice will be removed from them.
The way that email checks your spelling and updates as you’d write, our software will make intuitive predictions about what we’re creating. It will make predictions and create elements for us, all in real time. I expect it just to be the default in photoshop, visual studio, word, and many others. The AI will just be there.
It can not be avoided or banned.
Here are my three suggestions of how you, as a teacher, can integrate it into your classroom. But most of all, you should reinforce your human connection to your students.
Talk about it openly and honestly with your students. If you’re scared, tell them that you’re scared. If you are concerned about cheating or the way it’s being used — be honest about it. Be vulnerable about it.
Opening honest debate allows for the many, many shades of “grey area” that might happen should a student turn in work. Banning it is fine, but you need to be deliberate about it’s name and function.
You can’t say ”No AI!”
You will need to be specific: “No diffusion models for this exercise today.”
The debate needs to be open should issues arises regardless whether the student is intentionally cheating or the software has ambitiously finished it for them.
We need to have a common understanding about the the types of models. Artificial Intelligence is divided into classifications like neural networks, GANs, Language Models, Clip-Diffusion, etc. Students should understand the difference between what it means to train a neural network and how an agent is trained in reinforcement learning.
Different applications of machine learning and artificial intelligence will propagate into different verticals. Depending on what your subject matter, certain models and architectures will fit better than others. As an animator, my primary focus is motion models. Those might not be as interesting to a writer who is being rewritten with language models.
Students should have a sense about what AI is actually doing, not as some “magic thing” operating in the background. For each model, there are always a specific set of inputs and a resultant set of outputs. Even without a computer science or mathematics background, the classification of models is learnable at a simple level.
For help with this, I recommend Melanie Mitchell’s book : “Artificial intelligence: A guide to thinking humans” This high school level book clearly explains the categories of AI, and offers non-technical and direct explanations of the operations of them. (link to amazon is below)
The last thing is to approach your students with compassion. You must understand that these students are already interacting with extremely powerful algorithms. Their content stream from Tik Tok is being constructed and tuned to their emotional impulses. They may think they are simply texting with friends, but they are already being gamed into large datasets.
These processes have been reinforced by their social networks amongst their friends. Understanding their position and their actions, may increasingly become more difficult.
To us, we will marvel as things start to be completed for us. For them, it will be normal. I think the youth should learn to count before getting a calculator, and I think the youth would appreciate concepts like voice models or latent space before they’re everywhere.
Clearly, I have a big concern about our adoption of artificial intelligence. I’ve accepted the technology will be here, just as electricity or the internet simply arrived. I hope we as teachers can openly learn to accept it’s presence in our curriculum. We should learn to use it, but speak openly about it’s ethics.
I hope you will take a moment to do your own research on this before coming to conclusions.
Regardless of whether this line of thinking is fantastical or 100% correct, I understand this to be a contentious issue. I welcome open debate, as we should all participate to figure this out together.
*Nye Warburton is an animation technologist and educator. Everything in this article was written and drawn with human labor. *
Visit Nye online @ https://nyewarburton.com
Reference:
Here is the link to
Melanie Mitchell’s Artificial Intelligence: A Guide for Thinking Humans:
Artificial Intelligence: A Guide for Thinking Humans [Mitchell, Melanie] on Amazon.com. *FREE* shipping on qualifying offers. Artificial Intelligence: A Guide for Thinking Humans
I might also recommend:
Two Minute Papers: https://www.youtube.com/c/KárolyZsolnai
Superintelligence, by Nick Bostrom: https://www.powells.com/book/superintelligence-paths-dangers-strategies-9780199678112
Machine Learning for Art: https://ml4a.net/
Nye Warburton is a teacher and artist. After 20 years in visual effects and animation, he now strives to understand how artists can be taught the ethical use of AI and blockchain.
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