But Agile techniques can be applied to fields outside of software development if practitioners focus on the principles of Agile rather than specific methodologies and practices. Agile for AI is a mindset that emphasizes outcomes, embodied by practices focused on establishing rapid feedback loops based on intense collaboration around small increments of work. It requires a detailed examination of available data, thorough analysis of solution alternatives, repeated hypothesis testing to determine the best approach, and an emphasis on research and learning that does not naturally fit into a software development time-box.

In this session, Dr. Doing-Harris will provide an overview of neural networks and machine learning, to expose the differences between developing AI solutions and traditional software development. She will reveal the points during the process where AI development teams can use Agile approaches to explore different high-risk solution approaches, validate (or disprove) relevancy, and refine AI product designs early, quickly, and iteratively.

Agile for AI is in many ways what “Agile for anything” should be: the result of intelligently applying an Agile mindset to diverse environments.

About the Speaker:

Kristina Doing-Harris Specializes on Ontology Development and Cognitive Science as it relates to workflow, decision making, learning and memory.  She has done Linguistics Research in Artificial Intelligence (Machine Learning and Natural Language Processing). She’s currently working at Nuance Communications, the creators of Apple’s Siri.