Chapter 33: Come In
Professor Samuel's response was sharp and outraged, "Game-changer? Your arrogance is astounding, Sullivan! We're not here for baseless claims; we need practical solutions."
Professor Buzard, trying to diffuse the tension, spoke up, "Now, now, let's keep things civil. Max, perhaps you could provide us with a brief overview of your approach. Convince us with the details."
As Professor Samuel's sharp words cut through the virtual meeting room, I took a breath and prepared to present my concept.
"Alright, let me break it down for you," I began. "We're dealing with two distinct populations of models here. One is pre-trained with mathematical logic, and the other is focused on generating mathematical proofs."
I tried to sound as professional as one could get.
I shared my screen, displaying a series of mathematical expressions representing the underlying logic and proof generation mechanisms.
"In the fitness evaluation process, the primary model, grounded in mathematical logic, acts as a benchmark. Let's denote the logic model as M_logic and the proof-generation model as M_proof."
"The fitness function F evaluates how well M_proof aligns with the concepts embodied by M_logic. Mathematically, this can be expressed as", I showed a slide with an expression,
F(M_proof)=Alignment(M_proof,M_logic)
I dug into the nitty-gritty of the neural network structures, "The genetic material, represented by the weights of the neural network connections, undergoes recombination, mimicking natural evolutionary processes. This ensures that the models evolve in a way that aligns with the logic of mathematical principles."
Laying out the weights like W_proof and W_logic for the proof-generation and logic models, I kept the spiel going: