The long title of the book is Rebooting AI: Building
Artificial Intelligence We Can Trust.
In the aftermath of the introduction of ChatGPT, a chatbot
by OpenAI, and the attendant controversy which the site stirred up, this book
can serve as a great introduction to, and provide an explanation of Artificial
Intelligence as it is being designed right now.
The reason for the longer title is that the authors do not
believe that AI community, specifically the Deep Learning contingent, are ever
going to give the world Artificial Intelligence which will come close to replicating
human intelligence. Indeed, the authors make a bold statement about WHY the
Deep Learning Approach will never come close. That argument is the premise of
this book.
The authors are well respected members of the AI movement. Their
credentials are impeccable and they straddle the academic and the commercial
spheres of AI.
The authors lay out their problems, and the problems with
how the current approach to creating AI as it is practiced today, is flawed.
They proceed to explain, in qualitative details, how Deep Learning and the
statistic based AI methods are just very advanced curve fitting: no more, no
less; they also point out why this approach will not ever create anything that
will even crudely approximate human intelligence.
They do so in eight chapters. The
first two lay out the problem and gives us a vision of what our expectations
should be. The third chapter details the process: Deep Learning, that all the
AI world use to create their systems. Chapters four and five make the argument against
the premise that Deep Learning is the sole method that is necessary to create
human like artificial intelligence. The authors call what is necessary Artificial
General Intelligence (AGI). The distinction between what we have versus
what we truly want, i.e. human intelligence, is the distinction between AI and
AGI.
Chapters six through eight is where the authors make their point
about what is missing in just using Deep Learning to build AI, and what is
critical and necessary for being able to approximate human intelligence: AGI. They
offer up the examples of human traits which are non-numerical, intuitive,
causal, and elusive. They also delve into why these traits can not be
replicated through programming the AI engine with infinite amounts of training
data.
It is necessary to point out that the authors are not
naysayers when it comes to the idea of artificial intelligence, they do not
outright argue against the enterprise of creating artificial intelligence
systems, indeed, they are steeped in the AI world, they are AI enthusiasts,
they are AI believers. They are also critical thinkers and cognitive scientists,
very well grounded in the human mind and its knowns and unknowns. They give
credit where credit is due when they allude to the impressive results of Deep
Learning creations; their objection is that the Deep Learning proponents are using
just a single note to create the polyphonic sound we hear when we imagine
machines that think; because they are neglecting the cognitive and
psychological aspects of being human.
Their argument is convincing, because they are clear, concise,
and their combined knowledge on the subject is impressive. They lay out their
case with an impressive combination of technical papers and study results mixed
in with imaginative weaving of common sense. Their logic appeals to what humans
have always longed for when we speak of robots and our technical ability to
design our dream robots.
As the news of ChatGPT has made the rounds of popular social media,
the mix of concern, amazement, and bemusement has fed the interest of the lay
public in the subject, an interest that has even surprised those who created ChatGPT.
Yet the discussions have not gotten beyond the superficial layer of AI. This
book makes a very fine primer for those who wish to dig in the depths of the
subject, at least a little bit. Fortunately, the authors have also provided a
substantial list of references for the reader, in case they are curious.
