Adaptive Neural Network Usage in Computer Go

For decades, computer scientists have worked to develop an artificial intelligence for the game of Go intelligent enough to beat skilled human players. In 2016, Google accomplished just that with their program, AlphaGo. AlphaGo was a huge leap forward in artificial intelligence, but required quite a lot of computational power to run. The goal of our project was to take some of the techniques that make AlphaGo so powerful, and integrate them with a less resource intensive artificial intelligence. Specifically, we expanded on the work of last year’s MQP of integrating a neural network into an existing Go AI, Pachi. We rigorously tested the resultant program’s performance. We also used SPSA training to determine an adaptive value function so as to make the best use of the neural network

Sustaining the miwelt Project

The purpose of our IQP is to provide recommendations for sustaining the miwelt project. In order to accomplish this goal, we identified three primary objectives. The first objective was to review the current financial state of the miwelt project. This involved two major aspects: aggregating the current financial data and performing a financial analysis of the miwelt project’s activities at the Hochschulspektakel. The second objective was to investigate non-corporate methods of funding. Our primary areas of focus for this objective were crowdfunding and the feasibility of using an electronic book (e-book) to generate revenue. Our third and final objective was to evaluate how corporate sponsorship would affect the perception of the Swiss public towards the miwelt project