The less formal way conveys the concept better; and it makes sense, the worse the discriminator performs (whether it is overly sensitive or less sensitive when attempting to sus out the validity of assets), the worse the generator performs, at least with regard to the quality of the output for human purposes. If I'm understanding the use of gradient correctly, the generator become trapped in a local minimum because it discovers how to consistently exploit the weaknesses of the discriminator.

I don't know for sure if it always applies; you could apply an evolutionary algorithm where two or more competing populations are tackling the same problem from opposing sides, and have relatively infrequent breeding between members of the populations, motivating avoidance of bottlenecking while enabling the development of unique solutions; over several generations, any short term loss should serve to be a long term gain. But, I guess they'd still be dependent on how the scoring system works (equivalent to loss function?).

Unity using the graphics encoder(C#), SDL2 + ffmpeg(C++), Ogre3D(I believe it uses C++), Blender(Python), or a mixture of the lot. You can render individual images or record a video.

Thanks. It's not so big as a seminar. I'm in a public speaking course where each primary speech falls into a certain criteria, this one being Educational. I'm a senior CS major, the majority are freshman non-CS, so I've to make sure whatever I say is both accurate and explained in simpler terms (less 3Blue1Brown, more Code Bullet I guess).

Completely fair

Nash Equilibrium is a new term for me; and you are right, that seems like a logical end state. I do not understand what a gradient is in this context; would this terminology apply when information is being processed by a series of agents, each having a direct influence on the quality of the output of other agents?

My loose understanding of GANs is that one agent creates assets i.e. images and audio, while another agent attempts to differentiate assets based on if they were or weren't created by an agent. The results create automatically labeled data that can be used in subsequent training cycles, optimally leading to higher quality asset output.

I'm mixed about the IPM label. Predictability Minimization seems okay by itself; Inverse seems tacked on. Maybe something like Counter Predictability Exploitation?

Are you referring to this? "Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991)"

https://arxiv.org/abs/1906.04493

Looking up IPM verbatim turned up a reddit post linking to that.

Thanks, that helps. I need to give a brief educational presentation about the subject, so I didn't want to throw out the wrong terminology.