Last time you heard from me I had to pivot due to the some technical limitations of the Unity3D engine and my choice for neural processor (Encog).
Today I pivot again. The difference between my original plan and this new one is significant, as my original plan was an unsupervised network, and this new approach is a supervised network.
I thought I could make it work.. and I could.. technically – even if it is a bit of a shoe horn, but I thought of something that fits the model a lot better: an automated supervisor – or Robovisor. I will build a literal supervisor that will provide feedback to the network based on whether the Robovisor likes the action of the system- or in this implementation if the number that the system returns is the actual number. If it does, then it will be given positive feedback, and if it is wrong, it will receive negative feedback. The goal of the system is to continue to have positive feedback.
I thought of this feedback sensor previously as I was brainstorming ways to coach an embodied system with Synapse. I imagined that if I liked the way that the truck took the turn in the game, I would have an app that I could “thumbs up” the truck providing it positive feedback. I am going to expand this concept and instead of me providing feedback for the truck, I will build a supervisor that will know the number written in the MNIST image and provide positive or negative feedback based on the action of the system.
I’m not sure if this will work or not, but this is a lot more interesting to test than the standard labeled dataset supervised learning examples that exist everywhere. I think that this is a novel approach and am looking forward to how it works out!
New layout for MNIST Implementation
- Number Sensor (0-9) [This is for the Supervisor]
- Image Sensor (28×28 pixel images)
- Feedback Sensor (-1, 0. 1)
- Interpreted Number (0-9)
- Positive Feedback (Goal of 1 = Correct Answer)
Do you think that this new approach will work? Have you seen something like this implemented before?