  This paper describes problems in AI research and how the SP System (described
in an appendix) may help to solve them. Most of the problems are described by
leading researchers in AI in interviews with science writer Martin Ford, and
reported by him in his book {\em Architects of Intelligence}. These problems
are: the need to bridge the divide between symbolic and non-symbolic kinds of
knowledge and processing; the tendency of deep neural networks (DNNs) to make
large and unexpected errors in recognition; the need to strengthen the
representation and processing of natural languages; the challenges of
unsupervised learning; the need for a coherent account of generalisation; how
to learn usable knowledge from a single exposure; how to achieve transfer
learning; how to increase the efficiency of AI processing; the need for
transparency in AI structures and processes; how to achieve varieties of
probabilistic reasoning; the need for more emphasis on top-down strategies; how
to minimise the risk of accidents with self-driving vehicles; the need for
strong compositionality in AI knowledge; the challenges of commonsense
reasoning and commonsense knowledge; establishing the importance of information
compression in AI research; establishing the importance of a biological
perspective in AI research; establishing whether knowledge in the brain is
represented in `distributed' or `localist' form; how to bypassing the limited
scope for adaptation in deep neural networks; the need to develop `broad AI';
and how to eliminate the problem of catastrophic forgetting.
