Download Chapter 4. Discussion and Conclusions

Chapter 4.
1.
Discussion and Conclusions
Introduction
Christiansen and Chater, in reference to the same problem state that networks “...
will generalize in unexpected ways ... if [they have] too many degrees of freedom
(i.e. too many weights and biases) relative to the size of the data set [i.e. the
training set of examples] and hence [do] not need to find interesting [i.e. relevant]
regularities ...”1 Indeed both they and Dreyfus refer to just such a problem with
one particular network. In the early days of connectionism (i.e. perceptrons in the
mid-1960’s) the U.S. army attempted to train a network to distinguish a forest
with tanks from one without. They used photographs of the two different
situations to train the network. The network duly learned to discriminate the
exemplar set of photographs successfully. It even managed to generalise so that it
successfully categorised photographs which were not among the original training
set but which were a part of the same batch of photographs. However, on being
shown new photographs, which were taken on a different day than those of the
training set, the network failed to distinguish correctly. The problem was that the
original set of photographs of the forest without tanks were taken on a cloudy day
and the others on a sunny day. The network had learned to discriminate based on
(what we consider) an entirely irrelevant set of factors (amount of cloud cover or
shadows of trees). Dreyfus’s conclusion from this example is “... that a network
must share our common-sense understanding of the world if it is to share our sense
of appropriate generalization.”2
Bechtel and Abrahamsen ask “What is necessary to get a network to determine
similarities as humans do and so generalize in the same way?”3 In answer they
suggest that the network may need the same architecture as humans as it is hardly
surprising the simple architectures tried thus far will tend to generalise differently
from humans. Their reasons for this observation are based on Dreyfus’s claims.
Dreyfus claims that, for a network to display human-like generalisation, its
architecture would have to be designed in such a way that it responded to
situations in terms of what is relevant for humans. The features responded to “...
would have to be based on what past experience [of the network] has shown to be
important and also on recent experiences that determine the perspective from
which the situation is viewed.”4 This means that what is taken as relevant is
largely determined by perspective, and perspectives change over time. What is
relevant is not solely a feature of the world but is largely directed by one’s
perspective. Thus relevance is variable and dynamic.
1
Christiansen, M. & Chater, N., Connectionism, Learning and Meaning, 1992, p.237.
Dreyfus, H., What Computers Still Can’t Do, 1992, p.xxxvi.
3
Bechtel, W., and Abrahamsen, A., Connectionism and the Mind., p.121.
4
Dreyfus, H., Op. Cit., 1992, p.xxxviii.
2
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In any case, it should be noted that there will doubtlessly be a huge amount of
work involved in this research. As Pfeifer and Verschure admit in their concluding
remarks,
One could argue that our proposal may be suited to modelling the
intelligence of a fly but does not pertain to human intelligence.
Although it is an open question whether this approach will
eventually ‘scale-up’ to the levels of human cognition we are
convinced that in order to understand human cognition we need to
understand the basis into which it is embedded, i.e. the dynamics of
system-environment interactions.5
etc…
Two final examples: first a reference to a book...
Jones states that “metaphors are the basis upon which users think through the
effect of an action in an interface.”6 He is arguing that if we are to understand how
to structure an interface to ensure that users are successful in using them then we
must use the familiar metaphors to allow the user to ‘think through the effect’ of
their decisions to use what we have put in the interface.
If it was a web page it might work as follows:
Jones states that “metaphors are the basis upon which users think through the
effect of an action in an interface.”7 He is arguing that if we are to understand how
to structure an interface to ensure that users are successful in using them then we
must use the familiar metaphors to allow the user to ‘think through the effect’ of
their decisions to use what we have put in the interface.
5
Pfeifer, R. & Verschure, P., Beyond Rationalism, p.323.
Jones, P., Metaphors, p148.
7
Jones, P., Metaphors, at http://completely.silly.org/~Jones/HCI/metaphors, accessed Jan. 2001.
6
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Bibliography
Bechtel, William & Abrahamsen, Adele (1991), Connectionism and the Mind: An
Introduction to Parallel Processing in Networks, Basil Blackwell, Cambridge,
MA., USA
Christiansen, Morten H. & Chater, Nick (1992), Connectionism, Learning and
Meaning, Connection Science, Vol. 4, No’s. 3 & 4, pp227-252.
Dreyfus, Hubert (1992), What Computers Still Can’t Do: A Critique of Artificial
Reason, MIT Press, Cambridge, MA.,USA
Pfeifer, Rolf & Verschure, Paul (1992), Beyond Rationalism: Symbols, Patterns
and Behaviour, Connection Science, Vol. 4, No’s. 3 & 4, pp313-325.
Jones, P. (1999), Metaphors: Their importance to HCI, MakeyUppy Books Ltd.,
Timbuktoo.
Jones, P. (1999), Metaphors: Their importance to HCI, at
http://completely.silly.org/~Jones/HCI/metaphors, Completely Silly Organisation,
Somewhere Nice, Accessed Jan. 2001.
[NOTE:
Bechtel et al., Jones, and Dreyfus are books
The other two are journal articles.
Editors are usually signified by “(Ed.)” after the name.
Op. Cit. is used in footnotes when the work referred to has already
been cited above.]
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