Indy.Code() Sessions tagged artificial intelligence

When is a Regular Expression Better Than Artificial Intelligence?

Natural language processing is a technique for taking human understandable text and wresting machinable information from it through a variety of techniques. When we think about NLP, we typically think about tasks like assessing the tone of a passage of text, answering questions stated in natural language, or summarizing a large amount of text. We recently helped a client build a system for scoring pre-interview screening assessments using AI & ML.

Typically, we spend a great deal of effort trying to convince our clients that there are AI techniques relevant to their problems, and that those approaches are mature enough for prime-time use. Here, we had the opposite problem: Our client knew AI was appropriate for their problems, and they were absolutely convinced it was ready for deployment. However, they wanted to use AI to solve all of their automated scoring problems. Over the course of building the system, we found several situations where simpler non-AI techniques could provide comparable or better performance than state of the art AI.

In this talk, we’ll discuss:

  • Why automating scoring was a fundamental business need for our client
  • What their technical approach to automated scoring was
  • How we improved their existing AI models
  • How we identified situations where AI wasn’t the best approach

By the end of the talk, the audience will:

  • Have been introduced to several models for AI-based natural language and code understanding
  • See a 10,000 foot view of how to automate scoring rubrics for assignments that include both programming and human communication
  • Have some rules of thumb for deciding when AI is necessary or when a simpler technique is likely to exist or be more desirable.

Speaker

Robert Herbig

Robert Herbig

Lead Software Engineer, SEP

Artificial Intelligence For Lumber Mill Optimization

We’re all familiar with the notion that software is everywhere, and that in some way it touches nearly every product you’ll ever own. One such product is dimensional lumber, like a 2x4 or 4x4. There are a number of steps between a tree in a forest and a piece of lumber you buy in a store. One of those is ‘edging’, the process of removing the living edge from a flat section of raw material, and producing a board of an appropriate width with straight sides.

This talk is a post-mortem of a prototype system we built for optimizing the potential value of material coming out of an edger. While the AI for optimizing produced material was an important part of our system, it wasn’t the only part of our system! In this talk, we’ll cover:

  • The general problem of dimensional lumber extraction
  • How the client’s brand influenced which AI techniques we used to solve the problem
  • How AI is just a part of a larger software product, including
  • How we took an agile approach to AI development
  • How we handled estimating the cost of building the solver (and the rest of the software)
  • How AI integrated with the rest of the team

I’m hoping the audience takes away: Sometimes the best technical solution is not the best overall solution Even when AI is required for a product, it is never the whole product AI software isn’t ‘special’ from a best-development-practices perspective * Folks interested in using AI on a project, but especially * Individual contributors who wonder how to build AI * Product owners who wonder how AI integrates on a product level * Project managers who wonder how AI impacts development rituals and team composition

Speaker

Robert Herbig

Robert Herbig

Lead Software Engineer, SEP

Avoiding False Starts With Artificial Intelligence

Artificial Intelligence (AI) is no longer science fiction; it’s here today, and it’s here to stay. It is in the products you use every day: home automation, digital assistants, or credit card fraud detection, just to name a few.

All businesses will be affected by AI in the coming years, and the impact will be significant. The only remaining question is, how will you influence its effect on your company?

Getting started with AI is a daunting task, but necessary for businesses who want to stay competitive. During this session, we’ll discuss:

  • How to determine if, where, and how to use AI effectively within your organization
  • When and how to build an AI team
  • Common early mistakes and pitfalls when getting started with AI
  • Typical misconceptions around AI and its application
  • What to look for in an AI partner or potential hire

Speaker

Robert Herbig

Robert Herbig

Lead Software Engineer, SEP