IEEE Technology and Society Magazine - September 2022 - 8

Editorial
built by Computer Recognition Systems, but it was
not a commercial success. On the other hand, deep
machine learning, as originally pioneered by 2019
Turing Award winners Bengio et al. [8], has undoubtedly
been a colossal commercial success-although
it also required considerable and complementary
advances in sensors, networks and computing, and
has raised concerning secondary issues of application
[9], sustainability [10], privacy [11], monetization
[12], and ethics [13].
Aleksander's research had two aspects, the second
being machine consciousness. Noticing the similarity
in the way that, on the one hand, computer scientists
thought in terms of " neural networks, " " internal
states, " and " state structures " and, on the other hand,
brain scientists spoke in terms of " the nervous system, "
" mental states, " and " the mind, " Aleksander and
Morton [14] suggested that artificial consciousness
might be possible if a robot were to be endowed with
a neural state machine with neurons as learning state
variables, in parallel to the biological version.
A subsequent " manifesto " on the possibility and
use of conscious machines gave rise to an interdisciplinary
meeting at Cold Spring Harbor in 2001, bringing
together researchers in neuroscience, philosophy,
and computer science known for their contributions
to " the science of consciousness. " While, perhaps
unsurprisingly, no precise definition of consciousness
was produced, there was agreement on the following
closing proposition: there is no known law of nature
that forbids the existence of subjective feelings in artifacts
designed or evolved by humans. Consequently,
a kind of formal structure for attributing the state of
" being conscious " to a machine was produced-the
Aleksander-Dunmall test [15], perhaps?
At the same time, and largely in the same building,
that Aleksander was developing his ideas on
machine learning and machine consciousness, David
Mayne's early research contributed to many fields at
the beginning of the " revolution " in control triggered
by the results of Bellman (dynamic programming and
optimization) and Kálmán (signal processing and the
eponymous filter). However, Mayne's outstanding
contribution was in model predictive control (MPC), a
control method that controls a process while satisfying
constraints on independent (control) and dependent
(output) variables. One particularly significant breakthrough
established secure theoretical foundations
which were otherwise lacking at the time [16]. These
foundations ensured that MPC has had substantial and
8
significant industrial application over time, originally in
" traditional " processing industries but more recently in
electrical power systems. However, this slow development
of applications is at least in part due to the limitations
of available computing technology compared to
the complexity of the control problem, and the theorists
had to wait for the computing power to " catch up "
to apply practically their algorithms.
It is perhaps possible that there is some profound
mathematical theorem demonstrating the dual nature
of deep learning and MPC (i.e., a transformer that converts
a deep learning network into an MPC, and vice
versa), but what these exemplars really demonstrate
is the need for diversity in research (would it be so
wise to use deep learning to control a chemical plant,
if the program cannot explain itself) and the difficulty
of predicting a particular technological development's
future " impact " -and not just because defining
" impact " (and on what, exactly) is so imprecise.
Asking for impact
One question that could be asked-and increasingly
is asked for, by some national science funding
bodies-is that applicants should specify the
" expected impact " of their proposed research. Taking
a retrospective look at Aleksander's research programs
on machine learning and machine consciousness, and
Mayne's research program on MPC, it could be argued
that the impact of Aleksander's work on machine
learning was to be part of and help grow a research
community and create a practical (if commercially
unsuccessful) breakthrough, while the commercial
impact was a consequence of other people's breakthrough
(those people being part of the same community,
of course). The impact of Aleksander's work
on machine consciousness was to be part of and help
grow a research community, and to add a layer of
knowledge to the field commensurate with the modern
understanding of the brain and computer sciences,
while commercial exploitation remains in the future (if
at all). The impact of Mayne's work on process control
was to be part of and help grow a research community
that created a theoretical breakthrough, and this,
in turn, led to continual advancement and a range of
significant industrial applications.
There are, perhaps, at least three conclusions
that could be drawn from these exemplars. The first
is that measuring the impact of research depends
on when the sample is taken, bearing in mind that
both positive and negative impacts can be delayed
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine - September 2022

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