Phil 4.4.2023

Went to the USNA Capstone day yesterday, which was fun. Except for when the bus broke.

I’ve been reading Metaphors we live by. It’s central idea is that most of our communication is based on metaphors – that GOOD IS UP, IDEAS ARE FOOD, or TIME IS AN OBJECT. Because we are embodied beings in a physical world, the irreducible foundation of the metaphors we use are physically based – UP/DOWN, FORWARD/BACK, NEAR/FAR, etc.

This makes me think of LLMs, which are so effective at communicating with us that it is very easy to believe that they are intelligent – AI. But as I’m reading the book, I wonder if that’s the right framing. I don’t think that these systems are truly intelligent in the way that we can be (some of the time). I’m beginning to think that they may be alive though.

Life as we understand it emerges from chemistry following complex rules. Once over a threshold, living things can direct their chemistry to perform actions. That in turn leads to physical embodiment and the irreducible concept of up.

Deep neural networks could be regarded as a form of digital chemistry. Simple systems (e.g. logic gates) are used to create more complex systems adders and multipliers. Add a lot of time, development, and data and you get large language models that you can chat with.

The metaphor of biochemistry seems to be emerging in the words we use to describe how these models behave – data can be poisoned or refined. Prompt creation and tuning is not like traditional programming. Words are added and removed to produce the desired behavior more in the way that alchemists worked with their compounds or that drug researchers work with animal models.

These large (foundational) models are true natives of the digital information domain. They are now producing behavior that is not predictable based on the inputs in the way that arithmetic can be understood. Their behavior is more understandable in aggregate – use the same prompt 1,000 times and your get a distribution of responses. That’s more in line with how living things respond to a stimulus.

I think if we reorient ourselves from the metaphor that MACHINES ARE INTELLIGENT to MACHINES ARE EARLY LIFE, we might find ourselves in a better position to understand what is currently going on in machine learning and make better decisions about what to do going forward.

Metaphorically, of course.


  • Submit paper!
  • Work on slides
  • Expense report!
  • 9:15 meeting