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Agent Iris

by C. Ross Jam on July 16, 2017

What is Iris?

Iris is designed to help non-expert programmers who understand what kinds of analyses they need to run (for example, creating a logistic regression model, or computing a Mann-Whitney U test) but not how to write the code to accomplish these goals. Iris also allows expert programmers to accomplish data science tasks more quickly.

Iris supports a broad set of functionality available in popular Python scientific libraries such as scipy and scikit-learn, and we intend to open source the system upon release.

And from a deeper explainer:

Iris supports interactive command combination through a conversational model inspired by linguistic theory and programming language interpreters. Our approach allows us to leverage a simple language model to enable complex workflows: for example, allowing you to converse with the system to build a classifier based on a bag-of-words embedding model, or compare the inauguration speeches of Obama and Trump through lexical analyses.

Iris is an academic research project led by Ethan Fast of the Stanford CS department. I’ll be interested to see how far this gets. Conversational agents that are domain specific, vertically integrated with an environment, and targeted at complex activities seem a bit more promising than the low bar tasks industry currently seems to be focusing on (cough, meeting scheduling, cough). Also feels like a “right moment” with Siri, Cortana, Alexa, Slackbots, Twitterbots, Xiaoice, Tay, and friends establishing a beachhead but bigger wins coming down the road.

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