1. Examining the Mechanisms of the Human Brain with Computer Science Arvind Ravichandran

2. Purpose Attempt to simulate a test for a possible mechanism for learning in the human brain. Simulation required because you cant get into a human brain and find out for sure Understanding how people learn will address many mental illnesses such as Alzheimer's and Autism

3. Scope Scope was probably a little too large Simulate parts of the brain (message processing, learning, and memory) Test the simulation with learning in tic tac toe

4. Similar Research Most research works the other way – using what we know about the brain to simulate intelligence Many different robotic and mathematical endeavors to attempt to simulate intelligence All have failed to different extents

5. Development Program the “core” of the program – message processing and memory Use “layers of abstraction” (explained later) “Plug-in” tic-tac-toe game with a perfect AI and test the computer against itself

6. Layers of Abstraction Through the development of this project, the code was programmed with layers of abstraction Idea – as data moves up (towards the brain) it becomes more and more abstracted Allows for enormous flexibility as we see in plug-and-play compatibility

7. Plug-and-play and Modules Since data is abstracted, base level data will all eventually be numeric Thus, we can use “modules” – independently developed programs These modules simply plug into the program setup They do this by passing messages to the message handler

8. Simulating the Brain Unfortunately Algorithm did not get very complex at all All it does is look for a string of digits of a certain length (easily adjustable) that are repeated Numeric Abstraction – Since we believe neuron’s fire “all-or-nothing” convert all data to 1’s and 0’s

9. Testing Four major tests Tested the message processing unit – does data flow through the program Test numeric conversions – input alphabet and see if it converts Test pattern recognition – controlled number of “01s” Test tic-tac-toe – does the AI learn?

10. Tic-tac-toe Results

11. Problems AI doesn’t Learn! Ultimately, my reasoning was that there is no sense of “morality” in the AI – it can not distinguish loss from win, so it has no direction Numeric conversion can be extremely inefficient for expressing certain kinds (mostly visually complex) data

12. What I learned The Nature Vs Nurture Debate – There MUST be some nature, we MUST have so idea of right and wrong, or else learning has no context Pattern Recognition is, alone, insufficient to account for all of human learning Layers of Abstraction and module plug and play is very efficient for coding, but requires a sort of “giant” company to create the over-arching program