Off The Shelf AI (Part 1)

Presented by: Jordan Thayer, Robert Herbig

Hearing about artificial intelligence is unavoidable these days if you’re watching the news or staying abreast of the technical sector. We frequently hear about the power of AI-enabled tools, and are shown soundbytes of experts extolling the virtues of their approach.

While these stories inform and entertain, they also create the perception that AI i s extremely difficult and exclusively the realm of experts, which simply isn’t true! These days we do not need to be an AI expert to reap the benefits of the research community. Off-the-shelf open source tools exist which are powerful enough to solve many industrial problems.

In this workshop we will map business problems to tools and show how to translate a problem domain into the expected input of the tool. Using these tools will help us identify development opportunities that we might have otherwise missed and save time by not re-implementing common solving techniques.

The workshop is split between lecture portions, where we talk about common problems, formalisms, and historical applications and hands on sections where we work through demos and exercises of open source tools solving the kinds of problems we just discussed.

We’ll cover the following broad topics and tools:

Three major types of AI problems:
* Processing Natural Language
* Making Sense of Data
* Building Controllers for Automated Systems (e.g. Robots)

Several off-the-shelf tools that can tackle these problems:
* ChatGPT
* VADER Sentiment Analysis
* scikit-learn
* PyTorch
* Gymnasium (formerly OpenAI Gym)

Tags: Python, Programming Principles, Machine LearningLevel: Introductory and overview