Time: Every other Friday from 20 January until 28 April 2023, 14:15–15:45 EET
Place: University of Helsinki main building, Unioninkatu 34, 4th floor, room U4078, and online (Zoom link)
Learn more about the presenters and their topics below.
In everyday life we carry out many tasks that are apparently easy and simple, but are in fact underpinned by surprisingly sophisticated cognitive and neural mechanisms. This impressive adaptability of human perception and cognition is starkly revealed, for example, in artificial intelligence and robotics where everyday tasks have turned out to be huge challenges.
To understand why we can have a tremendous human advantage for these sorts skills, yet “superhuman AI” for others, we need to understand how the human mind and brain cope with the complexity and ambiguity of real-world tasks. This can only be achieved by taking a properly ecological approach to how we do research in cognitive science, both at the level of theory and methodology. But what does an “ecological” approach amount to, and what counts (and does not count) as an “ecological” approach to Cognitive Science?
In this talk, I present ten simple rules to evaluate the “ecological” credentials of empirical research in the cognitive sciences, discuss their rationale, and the ways we researchers all too often violate them.
Otto Lappi is an Academy Research Fellow and a Senior Lecturer in Cognitive Science at the University of Helsinki. He is interested in all aspects of the cognitive basis of everyday & expert performance – driving performance most especially. He leads a lab at the University of Helsinki, and his empirical work is mostly based on eye tracking & experimental work in challenging naturalistic tasks, especially combining observations "in the wild", simulator studies, and controlled lab tasks.
Besides his empirical research, he also has a background in philosophy, where he is interested in the conceptual and methodological foundations of cognitive science.
This talk is based on the recent book with the same title, freely available at www.painfulintelligence.info . The book uses the modern theory of artificial intelligence (AI) to understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind. This book intends to make the theory accessible to a relatively general audience, requiring only some relevant scientific background.
Aapo Hyvärinen studied undergraduate mathematics at the universities of Helsinki (Finland), Vienna (Austria), and Paris (France), and obtained a Ph.D. degree in Information Science at the Helsinki University of Technology in 1997. After post-doctoral work at the Helsinki University of Technology, he moved to the University of Helsinki in 2003, where he was appointed Professor in 2008, at the Department of Computer Science. From 2016 to 2019, he was Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London, UK. Aapo Hyvärinen is the main author of the books "Independent Component Analysis" (2001), "Natural Image Statistics" (2009), and "Painful Intelligence" (2022).
It has been claimed—perhaps most famously by Bertrand Russell—that the concept of cause has no place in the empirical sciences. This is arguably an exaggeration, but with a grain of truth. Undeniably, it has not been easy to see how philosophical accounts of causal connections fit with the scientific understanding of particular physical phenomena. This is understandable for so-called standard approaches to causation (i.e. in terms of regularity, conditions, counterfactuals, and intervention), since they are arguably attempts to elucidate how people think and reason about causation rather than attempts to figure out what causation really is. However, in the last 30 years or so there has been a surge of interest in developing realist accounts of causation that are informed by—or at least compatible with—the theories and findings of the empirical sciences. In this talk I will present four realist accounts of causation and discuss whether they really succeed in explaining two kinds of physical phenomena—collisions between billiard balls, and how water dissolves salt—in a manner compatible with the received scientific understanding of said phenomena. The four accounts are: (i) transmission accounts, (ii) mechanistic accounts, (iii) powers-based accounts, and (iv) the powerful particulars account. I will argue that the first three accounts fare badly in this exercise, mainly because they all assume that physical influence is unidirectional, while the natural sciences insist all interactions are reciprocal. The notion of “reciprocity” is admittedly ambiguous, and I will outline three different senses in which it currently understood. I will argue that the fourth account fares much better than the other. This is not surprising, because it was developed in the first place to address the flaws of the other three and develop an understanding of causation in terms of reciprocal action.
Jukka K. Nurminen is a professor of computer science at the University of Helsinki. He has worked extensively on software research in the telecom industry at Nokia Research Center, in academia at Aalto University, and in applied research at VTT. His key research contributions are on energy-efficient software, mobile peer-to-peer networking, and cloud solutions but his experience ranges widely from applied optimization to AI, from network planning tools to mobile apps, and from software project management to tens of patented inventions. He has e.g. led the Green Big Data project with CERN and many research activities on mobile phone and cloud energy consumption. Currently, his main interests are in the engineering of machine learning systems, combining data science and high performance computing, and software development for quantum computers.