Keynote Talks
University of Helsinki
Painful intelligence: What AI can tell us about human suffering
This talk introduces my recent e-book with the same title, freely available at https://www.cs.helsinki.fi/u/ahyvarin/painintl/. 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. The book starts with the assumption that suffering is mainly caused by frustration. Frustration means the failure of an agent (whether AI or human) to achieve a goal or a reward it wanted or expected. Frustration is inevitable because of the overwhelming complexity of the world, limited computational resources, and scarcity of good data. In particular, such limitations imply that an agent acting in the real world must cope with uncontrollability, unpredictability, and uncertainty, which all lead to frustration. Such computational theory is finally used to derive various interventions or training methods that will reduce suffering in humans. The ensuing interventions are very similar to those proposed by Buddhist and Stoic philosophy, and include mindfulness meditation.
Donders Institute
Probabilistic representations in the human visual cortex
Whether we are judging traffic in foggy conditions, estimating a ball’s trajectory when playing tennis, or interpreting radiological images to diagnose and treat disease – virtually every choice we make is based on uncertain evidence. How do we infer that information is more or less reliable when making these decisions? How does the brain represent knowledge of this uncertainty?
In this talk, I will present recent neuroimaging data combined with novel analysis tools to address these questions. Our results indicate that sensory uncertainty can reliably be extracted from the human visual cortex as the width of a probability distribution, and moreover, that observers rely on this probabilistic representation when making perceptual decisions.
Gatsby Unit, UCL
What's the question, and how do we answer it?
We have been gathering facts about the brain at an alarming, and accelerating, rate, at a cost of around 10 billion US dollars/year. In 2022 alone over 160,000 neuroscience related papers were published -- about one every three minutes. All with the goal of "understanding how the brain works". Missing, however, is a definition of "understand", making it hard to generate a coherent research program, and even harder to measure progress. Here we provide a definition. Based on that, we review the current trends in systems neuroscience, and make suggestions for interesting and answerable questions.