UPDATED
Tuesday 28 January • 15:15 – 17:15
Course Length: 2 hours
Course Level: Introductory/Intermediate
Instructor:  Mónica López-González, La Petite Noiseuse Productions

Learning Outcomes
This course enables the attendee to:

• Identify the major, yet pressing, failures of contemporary autonomous intelligent systems.
• Understand the challenges of implementation of and necessary mindset needed for integrative, multidisciplinary research.
• Review the latest findings in the cognitive and behavioral sciences, particularly learning, attention, problem solving, decision-making, emotion perception, and spontaneous creative artistic thinking.
• Explain how relevant findings in the cognitive and behavioral sciences apply to the advancement of efficient and autonomous intelligent systems.
• Discuss various research solutions for improving current computational frameworks.

An increasing demand of machine learning and autonomous systems research is to create human-like intelligent machines. Despite the current surge of sophisticated computational systems available, from natural language processors and pattern recognizers to surveillance drones and self-driving cars, machines are not human-like, most fundamentally, in regards to our capacity to integrate past with incoming multi-sensory information and creatively adapt to the ever-changing environment. To create an accurate human-like machine entails thoroughly understanding human perceptual-cognitive processes and behavior. The complexity of the mind/brain and its cognitive processes necessitates that multidisciplinary expertise and lines of research must be brought together and combined. This introductory to intermediate course presents a multidisciplinary perspective about method, data, and theory from the cognitive and behavioral sciences and the arts evermore imperative in artificial intelligence research and design. The goal of this course is to provide a theoretical framework from which to build highly efficient and integrated cognitive-behavioral-computational models to advance the field of artificial intelligence.

Intended Audience
Computer and imaging scientists, mathematicians, statisticians, engineers, program managers, system and software developers, and students in those fields interested in exploring the importance of using multidisciplinary concepts, questions, and methods within cognitive science, a fundamental and necessary field to build novel mathematical algorithms for computational systems.

Mónica López-González, a polymath and visionary, is a multilingual cognitive scientist, educator, entrepreneur, multidisciplinary artist, public speaker, and writer. Using her original cross-disciplinary research work in the science of creativity, imagination, learning, and human intelligence, López-González uniquely merges the cognitive, brain, behavioral, social, and health sciences with business strategy and artistic acumen to integrate human-centered factors, like cognition and behavioral insights, into artificial intelligence development and competitive business solutions. She’s the chief executive & science-art officer of La Petite Noiseuse Productions, a unique consulting firm at the forefront of innovative science-art integration. She is also faculty at Johns Hopkins University. López-González holds BAs in psychology and French, and a MA and PhD in cognitive science, all from Johns Hopkins University, and a Certificate of Art in photography from Maryland Institute College of Art. She held a postdoctoral fellowship in the Johns Hopkins School of Medicine. She is a committee member of HVEI.