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DTSTART;TZID=America/New_York:20260907T150000
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DTSTAMP:20260715T182801Z
CREATED:20260708T151148Z
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UID:2573-1788793200-1788796800@arni-institute.org
SUMMARY:Speaker:Jarod Lévy and Lucy Zhang– ARNI Frontier Models for Neuroscience and Behavior Working Group
DESCRIPTION:Title: Brain2Qwerty: Noninvasive decoding of typed sentences from human brain activity \nAbstract: Restoring communication for people who have lost the ability to speak or move after a brain injury is a major challenge. While intracranial implants now enable high-performing brain-computer-interfaces\, non-invasive alternatives are still lagging behind. Here\, we present Brain2Qwerty v2\, a model that can decode the production of natural sentences solely from real-time magnetoencephalography (MEG) recordings. By collecting 22\,000 sentences typed by nine subjects\, each recorded for 10 hours\, our model leverages character\, word and sentence-level representations to achieve an average word error rate (WER) of 39%. For our best participant\, the model accurately decodes half of the sentences with one word error or less. Critically\, decoding accuracy log-linearly improves with data volume\, suggesting that the performance gap with intracranial approaches could be partially bridged through data scaling. We show that AI enables this performance in three main ways: the substitution of hand-crafted pipelines for event detection with deep learning\, the finetuning of large language models to extract semantic representations\, and the deployment of AI agents to iteratively refine our decoding pipeline via automated code development. Together\, these results show that non-invasive brain-to-text decoding starts to operate at a level of accuracy previously thought exclusive to surgical implants\, opening a path toward safe and efficient brain-computer-interfaces. \nZoom: Upon request @arni@columbia.edu
URL:https://arni-institute.org/event/speakerjarod-levy-meta-fair-paris-and-inria-mind-arni-frontier-models-for-neuroscience-and-behavior-working-group/
LOCATION:Virtual
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DTSTART;TZID=America/New_York:20260909T150000
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DTSTAMP:20260527T143526Z
CREATED:20260501T134305Z
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UID:2476-1788966000-1788969600@arni-institute.org
SUMMARY:Speaker: Alexandre Pouget - ARNI Distinguish Seminar Series
DESCRIPTION:Alexandre Pouget \nDate and Time: May 21st at 3pm \nLocation: Zuckerman Institute Kavli Auditorium 9th Floor \nTitle: Neural Models of Compositionality \nAbstract: Compositionality is widely regarded as one of the cornerstones of general intelligence. It refers to the ability to rapidly generate or learn new concepts by combining simpler ones according to an underlying syntax\, as exemplified in natural language. Compositionality was long thought to be primarily a human capacity and widely considered incompatible with artificial neural networks. Recent neural models\, however\, have begun to challenge this view. I will present two such models: one focused on simple cognitive tasks\, the other on the control of complex motor trajectories. In both cases\, few-shot learning emerges through the discovery of compositional solutions. Remarkably\, the latter approach captures key\, and often counterintuitive\, aspects of rodent behavior in escape tasks\, precisely the kind of setting in which animals exhibit near zero-shot learning. I will also discuss how these findings connect naturally to more sophisticated forms of compositionality in humans\, particularly the use of language to support zero-shot learning and inference. \nZoom link: Upon request @ arni@columbia.edu
URL:https://arni-institute.org/event/speaker-alexandre-pouget-arni-distinguish-seminar-series/
LOCATION:Zuckerman Institute- Kavli Auditorium 9th Fl\, 3227 Broadway\, NY
ORGANIZER;CN="ARNI":MAILTO:arni@columbia.edu
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