Live Webinar February 20th, 2018 1:00 PM – 2:00 PM EST
Activity Type: Education – Course or Training  1 Hour  1 PDU free
Provider: O’Reilly

We’re experiencing a 2nd wave of machine intelligence.

Today, traditional artificial intelligence (AI) and machine learning (ML) largely depend upon data scientists to formulate labels associated with feature vectors that take into account model attributes such as entity, action, or relationship.

This process can result in implementation difficulties and errors over time.

A key challenge is that a given feature vector could be a blended version of multiple labels, and many algorithms force a choice of a single label.

A feature vector may also need a percentage of a given class label (e.g. 60% friendly and 35% extroverted).

There is also the chance that the data scientist did not choose the best set of class labels for the data to reflect contextual change, motivating the need to retrain the algorithm models.

In this webcast, Brian Womack will discuss:

• Challenges of labeling data for next-generation ML applications, and suggestions for mitigating them
• Instance-based learning methods from cognitive science and how they can create associations that can be grouped into a dynamic set of labels from which models can re-learn
• Example uses cases to illustrate the challenges for the community to address

Presenter: Brian Womack PhD (LinkedIn profile) is a veteran of defense intelligence & operations with a combination of data science, analytics algorithm development, robust signal processing, and software engineering experience. He stays involved with the details of adaptive computing technologies to advance the state of the art of human machine intelligence (HMI). Brian’s R&D focus is on third wave of machine intelligence (3MI); which increases system autonomy by creating a true partnership of human and machine. His integration of traditional statistical learning methods from the 2MI and instance learning methods from cognitive memory-based computing, make high impact decisions faster with more relevant data. Dr. Womack received his Ph.D. from Duke in robust signal processing and his M.S. from Texas A&M in adaptive control systems.

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Creating Data Labels To Adapt To Contextual Change

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