Adding Supervised Learning to Synapse

I started my AI platform (Synapse) with the understanding that I wanted to make an AI that paralleled some human constructs, as the field of AI has too many of it’s own terms that makes learning AI more complicated than it needs to be.

This meant that Synapse would be an unsupervised system. The difference between a supervised, and unsupervised system is labeled data, versus unstructured data. An example of labeled data for supervised learning would be CAPTCHA tests you have to pass to make sure you are not a robot when logging into your favorite service. By selecting the images that all have bikes in them, you are helping label image data. Without the label, the system can not learn what a bike is. Labeled data provides you the correct answer, while unsupervised learning doesn’t have any right or wrong – it just learns the features of the data being processed. [What’s the difference between supervised and unsupervised?]

I made a definition for AI only slightly modified from a techmergence.com definition, and made a statement that all AI’s could be defined with it – so does that definition apply to unsupervised AI alone, or supervised as well?

“Artificial intelligence is an entity, able to receive and process inputs from the environment in order to flexibly act upon the environment in a way that will achieve goals over time.”

With this definition it is important to identify the new environment (MNIST data) and determine what each of the pieces are.

  • Environment:
    • MNIST Dataset
  • Inputs:
    • 28×28 pixel images
  • Actions:
    • Print a Number (1, 2, 3…9)
  • Goals:
    • Accurately Interpret the Numbers

The trick for a supervised system in Synapse, is that in the unsupervised implementation, the goal is usually an ideal sensor state. For example; in my Super Truckin’ AI, the vehicle’s speed has a sensor, and there isa goal represented in the system of maximum speed. The system (theoretically at this point) would identify the relation between hitting the gas, and getting closer to the goal of top speed, and learn to act by hitting the gas.

In a supervised system, if I were to provide the actual number as the input goal in addition to the pixels of the image of the number, then it would learn to ignore image pixels, and would just repeat the goal as the number, which is essentially useless. That setup is like taking a Jeopardy test where you need to answer with a question when you are given the question already.

Since the goal is explicit in a supervised system, the system needs to optimize the output (action) with a dynamic goal (each image is a different number), and not a static goal (go fast), since the goal is different and explicit for each set of numbers.

This implementation of MNIST is not bringing anything new to the world of AI, but I plan on using the MNIST dataset as a test of my neural network. I’ll start with version 1 of the network using some basic parameters and as the system evolves, I can use this data as a benchmark of progress.

I’ll let you know after I implement the “pivot” in the system and add supervised learning  if I have to revise the AI definition or not, but I think I have re-framed the problem in a way that solves it for a supervised implementation even if it breaks some of my architectural constructs.

Do you disagree? I hope so, because then one of us is going to learn something…

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