The Michigan Student Artificial Intelligence Lab (MSAIL) is a student organization for discussion of artificial intelligence and machine learning. Andrew Ng said:

...if you read research papers consistently, if you seriously study half a dozen papers a week and you do that for two years, after those two years you will have learned a lot... But that sort of investment, if you spend a whole Saturday studying rather than watching TV, there's no one there to pat you on the back or tell you you did a good job.  —  Andrew Ng
MSAIL is a community in which motivated students can read and discuss modern machine learning literature together. We welcome students of all backgrounds and ability. To join MSAIL and stay up to date, simply join our Slack team! Also be sure to check out our sister organization: the Michigan Data Science Team! We are both graciously sponsored by the Michigan Institute for Data Science.

Upcoming Events

TUE
Oct 08
Tuesday (2019-10-08) at 18:00 in BBB 3725

Traditional NLP models view language as a set of fixed conventions. Pragmatics is the study of how language is used for communicative purposes. We explore the rational speech act (RSA) model, which is a probabilistic model that formalizes many intuitive aspects of language and enables us to predict human behavior.

Recent Events

TUE
Oct 01
Reading Group Neural Machine Translation
Tuesday (2019-10-01) at 18:00 in BBB 3725

Neural networks are dense, parametric, and continuous, while language is sparse, non-parametric, and discrete. So how can the former process the latter? Famously, one uses one-hot embeddings and softmax sampling to translate between continuous and discrete domains. One uses word embeddings to represent sparse sets of words as dense clouds of semantic vectors. One use recurrent neural networks to reduce variable-length sequence problems to local, parametric ones. But there has been another breakthrough recently: one can use Attention Mechanisms to model long-distance relationships between words! Attention lies at the core of this week's papers.

TUE
Sep 24
Reading Group Generative Adversarial Networks
Tuesday (2019-09-24) at 18:00 in BBB 3725

Discriminative models have several key limitations, namely they cannot model the probability of seeing a given input example and therefore cannot generate new examples. Generative Adversarial Networks (GANs) are an application of generative models in which a generator and discriminator are trained to compete against one another in a 2-person game. The generator attempts to create samples that deceive the discriminator into believing they are true samples, and the discriminator attempts to determine which samples are real and generated. We explore the motivation behind GANs, basic theory of how they work, and dive into the future of generative models.

TUE
Sep 17
Tuesday (2019-09-17) at 18:00 in BBB 3725

Self-supervised learning has been a key data source for many recent state-of-the-art natural language processing models. We explore a new use case for self-supervised learning with VideoBERT, an attempt to jointly train a visual-linguistic model to learn high-level features without any explicit supervision.

Active Leadership

The following awesome people plan MSAIL's activities. If you would like to help out as well, contact Sean via email. Our constitution codifies our roles.
Laura Balzano
Assistant Professor in EECS
Faculty Mentor
Nikhil Devraj
BS '21 Computer Science
Speakers
Yashmeet Gambhir
BS '19, Computer Science
Tutorials & Projects
Danai Koutra
Assistant Professor in CSE
Faculty Mentor
Patrick Morgan
BS '22 Computer Science
Administrivia
Zach Papanastasopoulos
BS '21 Computer Science
Tutorials & Projects
Sean Stapleton
BS '20, Computer Science
Administrivia
Jenna Wiens
Assistant Professor in CSE
Faculty Mentor