Medhat ullah’s Reviews > How Smart Machines Think > Status Update

Experts in the robotics, AI, and machine learning communities will
often know the implementation details of some of the algorithms I will
describe; but the remaining narrative and the design of entire systems
will still probably be new to many of them (except when that is their
area of research). My hope is that there is something new in this book for
everyone.

This book is meant to be accessible to a
broad audience—from a curious high school student to a retired mechanical
engineer. Although it will help if you know a little computer science, the
only real prerequisites for this book are curiosity and a bit of an attention
span. And I have intentionally kept the math in this book to a minimum
to communicate the core ideas without alienating casual readers.

I’ve written How Smart Machines Think with the hope that it will be
helpful for tech enthusiasts young and old who are curious about sci
ence and technology in general, or to industry leaders who hope to learn
more about whether machine learning and artificial intelligence might
be useful for their companies.

The curious thing to me was that I’d spent countless hours studying and
practicing machine learning in academia and industry, and yet I still couldn’t consistently answer that question. Perhaps I didn’t know AI and machine learning as well as I should, I thought, or perhaps college courses didn’t teach us the right material

But over time, I found myself repeating this process again and again.
Whenever I saw another breakthrough in artificial intelligence or machine
learning hit the press, I came back to the same question: How does it work?

The seed for this book was planted in an AI research lab on the top floor
of a computer science department one night in 2010. Having attended
some recent talks about self-driving cars, and curious about how they
worked, I did a few web searches

With the paucity of approachable, technically sound introductions
to machine learning, this book is an ideal way to bootstrap your under
standing of the underlying technologies, and to help you make better
determinations about what to believe and what to discard as hyperbolic
nonsense.

How Smart Machines Think assumes relatively little back
ground in machine learning or computer science, and hence is entirely
approachable to a broader audience.

Given that machine-learning systems are sometimes able to reproduce aspects of human intelligence, poetic license can be stretched near its breaking point. Sean’s careful, pragmatic descriptions of these technologies reflect his years in the trenches, where one learns through a sometimes-painful process of trial and error that machine learning isn’t magic

Sean leverages over a decade of experience in industry and academia solving some of the toughest problems that machine learning has to offer