Artificial intelligence (AI)
There are a multitude of ways to look at artificial intelligence, some technical, some philosophical and some anthropologic. For example, as per Alan Touring,
“If there is a machine behind a curtain and a human is interacting with it (by whatever means, e.g. audio or via typing etc.) and if the human feels like he/she is interacting with another human, then the machine is artificially intelligent.”
– Alan Touring (source unknown, ca 1950)
This definition is based more on the human nature of an artificial machine or algorithm and less on the processing power or problem-solving skills of said machine. It does underline the importance of human interaction with an artificial intelligence, though. Given that many consider Touring the man to invent the first computer and the fact that his quote is from the year 1950, it makes sense that processing power wasn’t his biggest concern.
However, since computer science and with it, artificial intelligence are developing at explosive speeds, modern definitions are much more concerned with the processing capabilities of such artificial algorithms:
“As per modern perspective, whenever we speak of AI, we mean machines that are capable of performing one or more of these tasks: understanding human language, performing mechanical tasks involving complex maneuvering, solving computer-based complex problems possibly involving large data in very short time and revert back with answers in human-like manner.”
– Joshi, Ameet V. Machine Learning and Artificial Intelligence. 2020. p.4
Joshi consolidates both the human and procedural aspect of AI in his definition. What I am most interested in is the aspect of an AI understanding human language, imagination and perception. AI such as DALL-E 2 have come a long way in understanding just that. How an AI becomes more intelligent with time is, apart from human input, thanks to machine learning.
Machine learning
“The term refers to a computer program that can learn to produce a behavior that is not explicitly programmed by the author of the program.”
– Joshi, Ameet V. Machine Learning and Artificial Intelligence. 2020. p.5
Joshi defines, that machine learning, or MI, “refers to a computer program that can learn to produce a behavior that is not explicitly programmed by the author of the program”. There are a multitude of machine learning algorithms, as there are countless fields in which they are being used. Generally though, machine learning is based on a set of data that a program then has to understand and evaluate based on certain metrics a human may provide as well as experience from prior iterations. The program then has to produce results in the desired way, iterating on each attempt to become better at doing just that.
The level of human interaction can be used as a way to categorise the many different kinds of MI learning and training methods. According to Villar, unsupervised learning may be the best description for the kind of MI useful for image processing and media design in general. With unsupervised learning,
“no label for any input vector is provided. The objective in this case is to find the structure behind the patterns, with no supervisory or reward signal. These models analyze and deduce peculiarities or common traits in the instances so as to discover similarities and associations among the samples. Example problems are clustering and latent variable models.”
– Osaba, Eneko. Artificial Intelligence : : Latest Advances, New Paradigms and Novel Applications. 2021.
This could mean understanding the associations humans form with art styles, aesthetics and visual representations of prompts in general. I do want to stress that this is speculation on my point as I didn’t yet look into machine learning for image processing specifically. This blog entry serves more as a definitive basis.
Onboarding & possible educational potential
During a chat with Alex Popkin, director of motion graphics and animation at Ingenuity Studios, an established VFX, compositing and motion graphic studio, he mentioned that he started using AI to generate explanations for media production terms he would like his team to be familiar with. As per the screenshot below, one can see that this works exceptionally well. This has the potential of speeding up on-boarding processes of employees greatly.
Stable diffusion for Adobe Photoshop
During our conversation, Mr. Popkin also mentioned that the stable diffusion AI I mentioned in my previous entry is also available for Adobe Photoshop directly. The algorithm essentially works the same as in 3D software, but the use cases inside of Adobe Photoshop may be more general and be used for illustration prompts, concept art prompts etc. You can find out more about it here.
