COLLECTION
What is the TypeSafe AI decision model Jev? From the challenges encountered when using LLMs for classification and decision‑making, to Jev’s typed outputs, parallel evaluation, RLCD, probability calibration, confidence scores, and its design that allows decisions to be deferred, we examine its mechanisms and underlying theory with diagrams and concrete examples.
We tested a thought experiment on FP6 quantization using a small image super‑resolution DNN. Here we present the findings from exploring quantization conditions, a comparison of 174 images divided into separate capture sequences, and an evaluation using 12 unused images.
Using the spacing between adjacent FP6 values as a starting point, I conducted a thought experiment on how to distribute limited bits in LLM quantization. This line of inquiry then branched into discussions about precision allocation in JPEG and the optimization strategies employed by Zopfli.
Before building the physical circuit, we will verify the circuit configuration using a microcontroller, DAC, and op‑amps through mathematical analysis and simulation.
Since it would be fascinating if machine learning could be done with analog circuits, let's first consider the underlying principles.