Connectionists: New paper: "Lessons from infant learning for unsupervised machine learning" (Nature Machine Intelligence)

[Tarek R. Besold]

Together with Lorijn Zaadnoordijk (@LorijnSZ) and Rhodri Cusack
(@RhodriCusack) from Trinity College Dublin we published a Perspective in
Nature Machine Intelligence on "Lessons from infant learning for
unsupervised machine learning".

Here is the link to the (read-only) online version of the text:
https://rdcu.be/cQbm1
...and here is a (starting) summary and discussion thread on Twitter:
https://twitter.com/LorijnSZ/status/1539651017662287873

ABSTRACT: 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.

-- 
Dr. Tarek R. Besold
http://www.tarekbesold.com
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20220623/b4ecf004/attachment.html>