Colloquia, Workshops, Dialogues And Tutorials
*All Tuesday events are LIVE @4:00pm unless specified otherwise, held in Swift 107*
Peter Cheng (Local Host: Steven Franconeri)
Website: About Peter Cheng
Date: November 12, 2021, Friday 10:00am, Zoom
Title:Representing knowledge and thought: insights from the design of radical representational systems
Abstract:As citizens of a technological society ours is a world of representational systems. They substantially determine what we can think, how easily we solve problems and the difficulty of learning. To maximise the theoretical and empirical leverage available for the study of representations, I design novel notations for conceptually challenging topics (e.g., circuit electricity, probability theory, algebra, logic), novel user-interfaces for information intensive problem-solving (e.g., scheduling, planning), and novel diagrammatic systems for everyday activities (e.g., transit system navigation, dancing). From the task analytic and experimental contrast of these novel representations with extant conventional representations, various insights have been gleaned. STEM subjects should be easy to learn. In representational terms, conventional notations that encode STEM knowledge are typically conceptually incoherent. In contrast, effective representational systems possess semantic transparency and are syntactically plastic. Such representations promise a factor of two performance improvement for problem solving and learning. For the routine engineering of effective representational systems, in addition to the application of cognitive science, I propose (i) a theory that embraces the full conceptual richness of knowledge and (ii) a language to enable the systematic modelling of the full complexity of the concept-encoding functions of representational systems.
Melanie Mitchell (Local Host: Jacob Kelter)
Website: About Melanie Mitchell
Date: March 1, 2022, Tuesday 4:00pm, Zoom
Title:Why AI is Harder Than We Think
Abstract:Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI Spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI Winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars, housekeeping robots, and conversational companions has turned out to be much harder than many people expected.
One reason for these repeating cycles is our limited understanding of the nature and complexity of intelligence itself. In this talk I will discuss some fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I will also speculate on what is needed for the grand challenge of making AI systems more robust, general, and adaptable—in short, more intelligent.
Speaker Bio: Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).
Dialogue series: Cognitive Science in Practice and in Theory (Local Host: Matt Goldrick)
Overview: The time is ripe for cognitive scientists to critically examine how we conceptualize our field – to move beyond the theoretical conceptualization of the field at its foundation to examine how multiple disciplinary/research traditions actually interact in the practices of working cognitive scientists. Three speakers will examine this issue, grounded in discussion of their own work. Following the three talks we will host a panel discussion seeking consensus on what constitutes a coherent discipline of cognitive science and how we should train the next generation of cognitive scientists.
Website:About Virginia de Sa
Date: April 5, 2022, Tuesday 4:00pm, Live
Dr. de Sa’s research aims to better understand the neural and computational basis of human perception and learning. The driving philosophy behind her work is that studying both machine learning and human learning is synergistic. Insights from human learning and brain physiology are used to guide the development of novel machine learning algorithms, and ideas from computational algorithms are used to motivate new models of human and animal learning as well as to analyze neural and behavioral data in new ways.
Iris van Rooij
Website: About Iris van Rooij
Date: April 8, 2022, Friday 10:00am, Zoom
Professor van Rooij’s research lies at the interface of psychology, philosophy and theoretical computer science. Using formal modeling and complexity-theoretic proof techniques, she studies the scope and limits of computational explanations of cognition. She pursues, among other things, meta-theoretical questions like ‘how can explanations scale from toy domains to the real world?’ and `how hard is cognitive science?’
Website: About Asifa Majid
Date: April 15, 2022, Friday 10:00am, Zoom
Dr. Majid investigates categories and concepts in language, non-linguistic perception and cognition, and the relationship between them. She adopts a large-scale cross-cultural approach to establish which aspects of categorization are fundamentally shared, and which are language-specific. Her work combines psychological experiments with in-depth linguistic studies and ethnographically-informed description.
>>>>Panel Discussion (Dialogue Series)-Date: April 22, 2022, Friday 10:00am, Zoom<<<<
Phil Wolff (Local Host: Matt Goldrick)
Website: About Phil Wolff
Date: May 3, 2022-Tuesday 4:00pm, Live
Preliminary title: Using AI to investigate the mental lexicon: Implications for digital phenotyping
Dr. Wolff's research focuses on the use of language semantics, machine learning, and big data to predict human thinking and mental health. His research group also examines causal reasoning, future thinking, intentionality, and cross-linguistic semantics.
Mina Cikara (Local Host: Mary McGrath)
Website: About Mina Cikara
Date: May 10, 2022 -Tuesday 4:00pm, Zoom
Title: Causes and consequences of coalitional cognition
Abstract:What is a group? How do we know to which groups we belong? How do we assign others to groups? A great deal of theorizing across the social sciences has conceptualized ‘groups’ as synonymous with ‘categories,’ however there are a number of limitations to this approach: particularly for making predictions about novel intergroup contexts or about how intergroup dynamics will change over time. Here I present two projects that offer alternative frameworks for thinking about these questions. First I review some recent work elucidating the cognitive processes that give rise to the inference of coalitions (even in the absence of category labels). Then I'll discuss an ongoing project on the effects of social group reference dependence--which falls out of coalitional reasoning--on hate crimes in the U.S. between 1990 and 2010.
Date: Spring- May 24, 2022- Tuesday
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