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Lessons from infant learning for unsupervised machine learning

  • Paper
  • #Learning #ArtificialIntelligence #MachineLearning #Neuroscience
Tarek R. Besold
@jeublanc
(Author)
Rhodri Cusack
@rhodricusack
(Author)
Lorijn Zaadnoordijk
@LorijnSZ
(Author)
www.nature.com
Read on www.nature.com
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The desire to reduce the dependence on curated, labeled datasets and to leverage the vast quantities of unlabeled data has triggered renewed interest in unsupervised (or self-superv... Show More

The desire to reduce the dependence on curated, labeled datasets and to leverage the vast quantities of unlabeled data has triggered renewed interest in unsupervised (or self-supervised) learning algorithms. Despite improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning and clustering optimizations, unsupervised machine learning still falls short of its hypothesized potential as a breakthrough paradigm enabling generally intelligent systems. Inspiration from cognitive (neuro)science has been based mostly on adult learners with access to labels and a vast amount of prior knowledge. To push unsupervised machine learning forward, we argue that developmental science of infant cognition might hold the key to unlocking the next generation of unsupervised learning approaches. We identify three crucial factors enabling infants’ quality and speed of learning: (1) babies’ information processing is guided and constrained; (2) babies are learning from diverse, multimodal inputs; and (3) babies’ input is shaped by development and active learning. We assess the extent to which these insights from infant learning have already been exploited in machine learning, examine how closely these implementations resemble the core insights, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning

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Yann LeCun @ylecun · Jun 24, 2022
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It's a very nice overview paper. Indeed, my proposal is aligned with many of the conditions you state in the paper. Just in time for me to cite it 😀!
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