My guest is Michael C. Frank, better known as Mike Frank, who runs the Language and Cognition lab at Stanford. Mike’s main interests center on how children learn language – in particular he focuses a lot on early word learning, and what that tells us about our other cognitive functions, like concept formation and social cognition.
We discuss that, his love for developing open data sets that anyone can use,
The dance he dances between bottom-up data-driven approaches in this big data era, traditional experimental approaches, and top-down theory-driven approaches
How early language learning in children differs from LLM learning
Mike’s rational speech act model of language use, which considers the intentions or pragmatics of speakers and listeners in dialogue.
My guest today is Andrea Martin, who is the Research Group Leader in the department of Language and Computation in Neural Systems at the Max Plank Institute and the Donders Institute. Andrea is deeply interested in understanding how our biological brains process and represent language. To this end, she is developing a theoretical model of language. The aim of the model is to account for the properties of language, like its structure, its compositionality, its infinite expressibility, while adhering to physiological data we can measure from human brains.
This is the first in a mini-series of episodes with Alex Gomez-Marin, exploring how the arts and humanities can impact (neuro)science. Artistic creations, like cinema, have the ability to momentarily lower our ever-critical scientific mindset and allow us to imagine alternate possibilities and experience emotions outside our normal scientific routines. Might this feature of art potentially change our scientific attitudes and perspectives?
Panayiota Poirazi runs the Poirazi Lab at the FORTH Institute of Molecular Biology and Biotechnology, and Yiota loves dendrites, those branching tree-like structures sticking out of all your neurons, and she thinks you should love dendrites, too, whether you study biological or artificial intelligence. In neuroscience, the old story was that dendrites just reach out and collect incoming signals for the all-important neuron cell body to process. Yiota, and people Like Matthew Larkum, with whom I chatted in episode 138, are continuing to demonstrate that dendrites are themselves computationally complex and powerful, doing many varieties of important signal transformation before signals reach the cell body. For example, in 2003, Yiota showed that because of dendrites, a single neuron can act as a two-layer artificial neural network, and since then others have shown single neurons can act as deeper and deeper multi-layer networks. In Yiota’s opinion, an even more important function of dendrites is increased computing efficiency, something evolution favors and something artificial networks need to favor as well moving forward.
