GitHub Copilot is an artificial intelligence model for automatically generating source code from natural language problem descriptions. Since June 2022, Copilot has officially been available for free to all students as a plug-in to development environments like Visual Studio Code. Prior work exploring OpenAI Codex, the underlying model that powers Copilot, has shown it performs well on typical CS1 problems thus raising concerns about the impact it will have on how introductory programming courses are taught. However, little is known about the types of problems for which Copilot does not perform well, or about the natural language interactions that a student might have with Copilot when resolving errors. We explore these questions by evaluating the performance of Copilot on a publicly available dataset of 166 programming problems. We find that it successfully solves around half of these problems on its very first attempt, and that it solves 60\% of the remaining problems using only natural language changes to the problem description. We argue that this type of prompt engineering, which we believe will become a standard interaction between human and Copilot when it initially fails, is a potentially useful learning activity that promotes computational thinking skills, and is likely to change the nature of code writing skill development.

In a previous module, we examined language models and explored n-gram and neural approaches. We found that the n-gram approach is generally better for higher values of N but this may be constrained by available compute resources. There was also the concern about the lack of representation for n-grams not present in the training corpus. On the other hand, applying subword tokenization methods such as Byte Pair Encoding and Wordpiece, recent neural approaches are able to resolve the issues with n-gram language models and show impressive results. We also traced the development of neural language models from feedforward networks that rely on word embeddings and fixed input length to recurrent neural networks which allowed for variable length input but struggled to capture long term dependencies.

DiPaola's and Yalcin's extensive research on empathy in AI is also addressing issues in a variety of industries, including e-health. In a collaborative project with the national AGE-WELL initiative, a helper AI conversational bot is being developed to assist the elderly in staying independent at home. Other applications are geared to the entertainment industry. After premiering at the NeuroIPS conference, the AI Empathic Painter system will travel to Europe to be showcased in Florence in May 2020. Formerly from Stanford University, DiPaola lead SFU's Interactive Visualization Lab (iVizLab), which strives to make computational systems bend more to the human experience by incorporating biological, cognitive and behavior knowledge models.

It wasn't that long ago that Deepmind's AlphaGo proved it could play the game better than the best humans. From the standpoint of the range of possible future moves, the game of Go is not a searchable problem. It represents search spaces that are astronomically larger than all the potential moves in chess. Yet, the individual moves are far simpler and more atomic than chess (and almost any other game) partly because of the incredible simplicity of the rules combined with a giant catalog of hundreds of thousands of human played games. Because it was relatively easy to have it play a large number (countless millions) of games against itself, the game is a good fit for deep learning.