Chapter 1. Introduction: What Is It like to Be a Language Model?
What is it like to be a bat?
The philosopher Thomas Nagel asks this question in his 1974 essay on consciousness.1 His position is that the answer is unknowable. If I imagine that I have webbed arms and poor vision, perceive the world by sonar, subsist on a diet of insects, and spend the day hanging upside down, “it tells me only what it would be like for me to behave as a bat behaves.” But if I try to imagine what it’s like for a bat to be a bat, my restrictions to the limited range of my own mind and experiences render this impossible.
Humans and bats, at least at the time of this writing, have no shared language. On the other hand, countless AI models exist in the world—many of which have been created specifically to communicate something to us in our own language.
The recent explosion in advances in machine learning has brought a myriad of interesting, powerful, and increasingly opaque models. Simultaneously, the recent movement toward democratization of AI has lowered the barriers for being a data scientist and using machine learning models. It is simple to deploy a model in the real world without being concerned about explaining the output or without exploring the ethical implications of decisions that will be made on the basis of that output. The ease of creating and using machine learning models is going up; the ease of understanding what machine learning models are doing is going down (Figure 1-1).
Figure ...
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