The Algonauts Project: A Platform for Communication between the Sciences of Biological and Artificial Intelligence

In the last decade, artificial intelligence (AI) models inspired by the brain have made unprecedented progress in performing real-world perceptual tasks like object classification and speech recognition. Recently, researchers of natural intelligence have begun using those AI models to explore how the brain performs such tasks. These developments suggest that future progress will benefit from increased interaction between disciplines. Here we introduce the Algonauts Project as a structured and quantitative communication channel for interdisciplinary interaction between natural and artificial intelligence researchers. The project's core is an open challenge with a quantitative benchmark whose goal is to account for brain data through computational models. This project has the potential to provide better models of natural intelligence and to gather findings that advance AI. The 2019 Algonauts Project focuses on benchmarking computational models predicting human brain activity when people look at pictures of objects. The 2019 edition of the Algonauts Project is available online: this http URL.

[1]  Omer Levy,et al.  GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.

[2]  D. Hassabis,et al.  Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.

[3]  Radoslaw Martin Cichy,et al.  Resolving human object recognition in space and time , 2014, Nature Neuroscience.

[4]  Thomas E. Nichols,et al.  Scanning the horizon: towards transparent and reproducible neuroimaging research , 2016, Nature Reviews Neuroscience.

[5]  K. Grill-Spector,et al.  The human visual cortex. , 2004, Annual review of neuroscience.

[6]  James J DiCarlo,et al.  Neural population control via deep image synthesis , 2018, Science.

[7]  Leo Breiman,et al.  Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author) , 2001 .

[8]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[9]  Jörn Diedrichsen,et al.  Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis , 2017, bioRxiv.

[10]  Bolei Zhou,et al.  Semantic Understanding of Scenes Through the ADE20K Dataset , 2016, International Journal of Computer Vision.

[11]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[12]  J. Gallant,et al.  Complete functional characterization of sensory neurons by system identification. , 2006, Annual review of neuroscience.

[13]  Brian A. Nosek,et al.  Promoting an open research culture , 2015, Science.

[14]  T. Yarkoni,et al.  Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning , 2017, Perspectives on psychological science : a journal of the Association for Psychological Science.

[15]  J. DiCarlo,et al.  Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.

[16]  Li Fei-Fei,et al.  CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[18]  Antonio Torralba,et al.  Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence , 2016, Scientific Reports.

[19]  Jonas Kubilius,et al.  Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like? , 2018, bioRxiv.

[20]  Nikolaus Kriegeskorte,et al.  Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.

[21]  Luc Van Gool,et al.  The Pascal Visual Object Classes Challenge: A Retrospective , 2014, International Journal of Computer Vision.

[22]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.