  Despite the several successes of deep learning systems, there are concerns
about their limitations, discussed most recently by Gary Marcus. This paper
discusses Marcus's concerns and some others, together with solutions to several
of these problems provided by the "P theory of intelligence" and its
realisation in the "SP computer model". The main advantages of the SP system
are: relatively small requirements for data and the ability to learn from a
single experience; the ability to model both hierarchical and non-hierarchical
structures; strengths in several kinds of reasoning, including `commonsense'
reasoning; transparency in the representation of knowledge, and the provision
of an audit trail for all processing; the likelihood that the SP system could
not be fooled into bizarre or eccentric recognition of stimuli, as deep
learning systems can be; the SP system provides a robust solution to the
problem of `catastrophic forgetting' in deep learning systems; the SP system
provides a theoretically-coherent solution to the problems of correcting over-
and under-generalisations in learning, and learning correct structures despite
errors in data; unlike most research on deep learning, the SP programme of
research draws extensively on research on human learning, perception, and
cognition; and the SP programme of research has an overarching theory,
supported by evidence, something that is largely missing from research on deep
learning. In general, the SP system provides a much firmer foundation than deep
learning for the development of artificial general intelligence.
