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IA in the Age of AI: Embracing Abstraction and Change

About the Talk

March 23, 2018 11:15 AM

Chicago, IL

Chicago, IL

Artificial intelligence (AI) is when machines exhibit intelligence, perceive their environment and take actions to maximize their chance of success at a goal. (Wikipedia - Artificial_intelligence - Russell & Norvig (2003)) These systems take an existing body of knowledge (typically complex requiring a 1000’s of pages of reading) and apply that set of knowledge to new data to help humans to discover patterns and find unknown information in the data. In this session attendees will learn about the initial analysis that must be done in order for machine learning to be effective. High quality systems require training by humans to enable machine learning and AI systems. The ‘ground truth’ content must be curated, identified, ingested and maintained. The training involves taking a set of unstructured content (a dozen documents for example) and creating what are called annotators. There are different ways to teach these systems – some require months of programming and others are easier to use but still can take weeks of effort. In all cases, to create a supervised learning system subject matter experts are required for the training - either with a programmer or through a software system. Once this baseline is established the presenter will show a variety of use cases where organizations are using AI to build on the data they already have. This includes successfully understanding human speech (extremely difficult), competing at a high level in strategic game systems (such as Chess and Go), self-driving cars, and interpreting complex data (Wikipedia - Artificial_intelligence). AI technology has already achieved many of these and some technologies that were previously considered AI are now considered routine technology applications such as optical character recognition (OCR). The final portion of the session will focus on the IA work that is necessary to make great cognitive computing systems. The presenter will also discuss the challenges of AI and issues of ethics about machine learning. The benefits of AI are seemingly endless which computers helping us to find cures for cancer, and allowing our best people to focus on the next problems. UX as a practice must evolve to deal with the new issues these technologies bring and the new information that is created.

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