3D Virtual Environments for AIs
A lot of the currently perceived problems with AIs, I believe, stem from fear of the unknown. What if the AI runs rampant? What if the autonomous driving car runs you over? And what if a super-intelligent AI somehow decides that we should all be replaced with paperclips? Virtual 3D environments provide a safe and convenient place to solve some of these problems and can also serve as research tools. In this article, I will provide a modest starting point.
The AI elephant in the room as of this writing is the advent of ChatGPT,
which has accelerated the AI schedule, as AGI now seems possible within our
Lifetime. Other more advanced AIs, such as super-intelligent and sentient
ones (my lifelong interest), are also being more seriously considered,
but I will delve more into this topic later in the article.
However, it's important to note that virtual environments do not magically
solve all AI-related problems, with the looming question of jobs being
replaced by AIs being the big one.
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So, where does one start? Well, most AIs these days have some sort of iterative process, where the results of a machine learning pipeline get scored against targets, with features/data being the inputs. These inputs and outputs usually have real-world equivalents, such as correct sentences from a text corpus, objects to be detected from annotated images, and streets to navigate from multiple computer vision tasks. However, they will usually not generalize outside of their manicured and purpose-built domain. Here’s, for instance, a common training log in Keras.
As AIs become more and more complex, these metrics start becoming less clear as interactions multiply, and multiple metrics need to be accounted for. This is fine for narrow interactions and AIs, but may not be ideal for future, more complex ones…
We also inhabit a 3d world that is for the most part absent in out current
AIs, a 3D Virtual one can serve as a nurturing place to train and test…