Designing Curiosity for Thinking Machines
Curiosity is a powerful motivator that enhances learning, memory and engagement, but it's far more sophisticated than previously understood. Recent neuroscience research reveals that curiosity is not a single cognitive phenomenon, but rather a five-dimensional scale. Furthermore, there are four unique subgroups of people with overlapping but distinct motivations.
In this talk, Julian Scaff introduces a "curiosity matrix" that can be used as a powerful tool for designing different types of curiosity in machine learning systems that can make AI more effective at solving different problems and engaging with a diverse group of human users.
Humanizing AI & Machine Learning
What is lacking in most discussions around AI, machine learning and other intelligent algorithms is how they can help solve people's problems. Any team that is working on hard, meaningful, and purpose-driven problems could benefit from AI/ML, but without the right focus you may end up building toys.
During the workshop, you will go through Empathy Mapping for the Machine exercise. Immediately after the workshop, our panel discussion will explore and discuss how to focus on the most important aspect in building these systems: human purpose.
Building Minds with Patterns
Since the 1956 Dartmouth Workshop where John McCarthy coined the term Artificial Intelligence (AI), the goal of this community has been to create generally intelligent machines who think. After 60 years of research are we closer to engineering such systems, and if so, what approaches seem the most promising?
In this talk we'll define terms, ask how these systems can be built, and explore possible answers.