From Associative Memories to Powerful Machines

TitleFrom Associative Memories to Powerful Machines
Publication TypeCBMM Memos
Year of Publication2021
AuthorsPoggio, T
Date Published01/2021
Abstract

Associative memories were implemented as simple networks of threshold neurons by Willshaw and Longuet-Higgins in the '60s. Today's deep networks are quite similar: they can be regarded as approximating look-up tables, similar to Gaussian RBF networks. Thinking about deep networks as large associative memories provides a more realistic and sober perspective on the promises of deep learning.
Such associative networks are not powerful enough to  account for intelligent abilities such as language or logic. Could evolution have discovered how to go beyond simple reflexes and associative memories?  I will discuss how inventions such as recurrence and hidden states can transform look-up tables in powerful computing machines.

DSpace@MIT

https://hdl.handle.net/1721.1/129402

CBMM Memo No:  114

Associated Module: 

CBMM Relationship: 

  • CBMM Funded