Status Updates From How Smart Machines Think (M...

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

How Smart Machines Think was borne out of Sean’s desire to understand
what makes modern machine learning tick. And in describing the essence
of these systems in a clear, approachable way

Microsoft, Amazon, Google, Apple, Baidu—
provide APIs, toolkits, and cloud computing infrastructure to put machine
learning development in the hands of tens of millions of developers across
the world

Understanding how machine learning works is a smart career bet for
developers and researchers alike. Expertise in these technologies is already
in high demand at the world’s biggest technology companies.

Understanding how machine learning works is a smart career bet for
developers and researchers alike. Expertise in these technologies is already
in high demand at the world’s biggest technology companies.

Such as labeling objects in images, recognizing speech, playing strategy games, and translating between human languages—we are still in the early days of the development of these technologies with decades of innovation and discovery ahead of us

AI platforms for developers and researchers; and a rush to embed real-time
intelligence into mobile devices, cars, consumer electronics, and the increas
ingly ubiquitous computing devices connected at the edge of the cloud.
Even though this rapid progress has included achievements that have sur
prised many onlookers as machines have approached or exceeded human
capabilities in a number of narrow domains—

wokring on the statistical machine leanring at goolge for large scale machine learning.The rapid progress being made at the moment in machine learning is being driven in part by an explosion in data; a renaissance in high performance computer architecture;

This book is for the engineers and reseachers who concieved of and then built thee smart machines

Foreword by kevin scott, CTO, microsoft

Sean Gerrish is a software engineering manager at Google. He has worked in various capacities on machine learning and data science projects at his current company and in a previous role as a quantitative engineer at Teza Technologies. He holds a PhD in machine learning from Princeton University.