recent debate

[Juergen Schmidhuber]

A side note on what Jon Baxter wrote:

> In contrast, in a "learning to learn" setting where a learner is faced
> with a (potentially infinite) *sequence* of learning tasks ...

A more appropriate name for this is "inductive transfer." The traditional
meaning of "learning to learn" is "learning learning algorithms." It
refers to systems that search a space whose elements are credit assignment
strategies, and is conceptually independent of whether or not there are
different tasks. For instance, in contexts where there is only one task
(such as receiving a lot of reward over time) the system may still be
able to "learn to learn" by using experience for continually improving
its learning algorithm (more on this in my home page).

A note on what Bryan Thompson wrote:

> If we consider that the primary mechanism of recurrence in a
> distributed representations as enfolding space into time, I still have
> reservations about the complexity that the agent / organism faces in
> learning an enfolding of mechanisms sufficient to support symbolic
> processing.

There is a recurrent net method called "Long Short-Term Memory" (LSTM)
which does not require "enfolding space into time". LSTM's learning
algorithm is local in both space and time (unlike BPTT's and RTRL's).
Despite its low computational complexity LSTM can learn algorithmic
solutions to many "symbolic" and "subsymbolic" tasks (according to the
somewhat vague distinctions that have been proposed) that BPTT/RTRL
and other existing recurrent nets cannot learn: Sepp Hochreiter
and J. Schmidhuber. Long Short-Term Memory.  Neural Computation,
9(8):1735-1780, 1997

Juergen Schmidhuber, IDSIA                       www.idisia.ch/~juergen