Every year thousands of researchers and industry professionals unite at the SIGGRAPH conference to share state of the art research, best practices, and innovative new workflows with their peers. The conference is primarily a computer graphics conference. It’s unique in that it unites both the fields of film visual effects and real-time graphics, or more specifically video game technology. These days, however, next to a computer graphics conference, SIGGRAPH is also one of the worlds’ biggest AI conferences. This year, a total of 844 papers were sent in, with 252 being accepted, and 134 being presented at the conference across a grand total of 50 different sessions each focussing on a different type of use case.

For years now, SIGGRAPH has been an essential way for us to keep a keen eye on trends and evolutions in the field of applied AI. Keeping up with this rapidly evolving field is always a losing battle – the rate of technological evolution is much faster than any individual person could possibly keep up with. I’ve often compared it to chasing down a train – always arriving one station late. Luckily for anyone in the industry, actual adoption of these technologies tends to be a lot slower. Actually getting the time to learn to work with a new technology, and to understand its benefits and drawbacks, is a slow process. This makes our job as an applied researcher two-fold. On the one hand, we need to keep an eye on what’s possible technologically, on the other, we must keep an eye on the industry itself, and how adoption of these technologies is progressing.

After AI entered the mainstream some years ago thanks to Large Language Models like ChatGPT and image diffusion models like Midjourney and Stable Diffusion, adoption of AI into actual production workflows hasn’t gone as quickly and as wide-spread as many might have expected. There are several reasons for this.

Datasets: The Legal and Ethical Gray Zone

For one, it remains relatively unknown which datasets were used to train many of these models, and how they were acquired remains equally dubious. This results in a legal and ethical gray zone which logically results in hesitancy and caution. This will continue to be an ongoing issue. The AI Act, EU-wide legislation that was approved this year, seemed the most likely route to help resolve this issue, as during the writing process, it was coined that model providers would be required to describe which datasets they used, and how they were acquired. This part of the legislation however never saw the light of day, heavily rumoured to be thanks to lobbying from French and German AI companies who worried this might impact their competitiveness in respect to their American competitors. This, in and of itself, of course, tells you more than enough about the true nature of these datasets. Nevertheless, with the status quo as it is now, with these models being around for some time now, it’s become clear that this is mostly a risk to model providers, the OpenAI and Stability AI’s of the world, and not to the companies who use their models in their workflows.

Retaining Control

Aside from the legal and ethical questions that continue to exist around AI models, what else is slowing down adoption? In the five years I’ve worked as an applied AI researcher, there’s one question I have heard consistently again and again: “How do I get it to do what I need it to do? How do we control it?“. Generating images with text-to-image models is cool, but how do I get it to generate what’s in my mind? Generating text with large language models has a lot of potential, but how do I get it to generate the type of text I need, given the requirements and parameters of my use case? This is, in my opinion, the biggest ongoing challenge with AI. You can attribute this issue to two core problems.

On the one hand, a lot of this innovation happens on university-level academic research. Their prime focus is to innovate, and to further technology, not to create technology that can be readily applied in an industry context. This results in applied researchers, either as part of a company’s R&D department, or an applied research group such as my own, trying to use this technology for something that it was, in truth, not really designed for. This disconnect between academic research and the industry is a larger topic, but I’m glad to see that in recent years, at least in the field of AI, there has increasingly been a discourse between the two, with companies like Google, Microsoft, NVIDIA and OpenAI being heavily involved in both applying these technologies, as well as funding the underlying research. 

This has resulted in an enormous stream of research papers focussed solely on solving this core issue of control.