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One thing that stands out for AI compared to other types of
technology is the confusion and myths it has created in public
discourse. Too many, AI seems like this magical thing that,
variously, can solve all problems or take over the world and doom
the rest of us. The more moderate version of this in the field of
IP is the notion that somehow AI can independently solve problems
and make inventions. In “YOU LOOK LIKE A THING AND I LOVE
YOU”, Janelle Shane gives short shrift to these
notions and debunks the myths. With examples ranging from
optimising cockroach farming to a magical sandwich hole, via a
quick stop to generate some pick-up lines, Janelle shows how AI
(read machine learning) works and what it can and cannot do. While
Janelle mentions some specific technologies such as CNN,RNN, and
GAN, the stories she tells illustrate the broader point of how
computers learn from data guided by an objective function, a narrow
process devoid of any common sense or human-like cognition.
Understanding this is crucial to appreciate issues such as bias and
explainability and the caution that must go into developing AI
The book’s central message (if you ask me) is to debunk the
myths and enable us to understand the circumstances under which AI
works well and can generate tremendous value, as well as the
circumstances in which this is not the case. AI is good at
answering narrow, specific questions, like recognising objects in
images, completing sequences of words or playing games with set
rules. AI is terrible (really terrible) at answering broad
questions like should you trust this person as your babysitter, how
likely is someone to re-offend, would this person be a good hire?
These points are illustrated by silly and amusing, as well as
serious and thought-provoking, examples as you compulsively turn
the pages. And while Janelle talks about AI in humanising terms -
it learns, helps, tries etc., which I think generally contributes
to many of our misconceptions, in the context here, it works well
and makes the book very readable.
One thing to be cautious about, though, is the pace of
innovation in this area. While the book was written when GPT-2 was
state of the art, some of the issues like memory constraints and
catastrophic forgetting are now less visible as GPT3, BERT, and all
kinds of gigantic transformer models produce ever more impressive
feats not only in language generation but also generating images
(DALL-E, Imagen) and learning a variety of tasks at the same time
(GATO). However, while it would be nice to expand the discussion to
these huge transformer models, the fundamental points Janelle
drives home have not changed.
In short, if you are into AI (I assume you are if you are
reading this) and you are looking for something at the same time
amusing and enlightening to read on the beach this summer, add this
book to your reading list. Alas, having just finished the book, I
will need to find something else to read, but I am looking forward
to the sequel – Janelle seems to be creating plenty of material for
one on her blog.
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