Superintelligence – Paths, Danges, Strategies – Nick Bostrom

Inside your cranium is the thing that does the reading. This thing, the human brain, has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that we owe our dominant position on the planet. Other animals have stronger muscles and sharper claws, but we have cleverer brains. Our modest advantage in general intelligence has led us to develop language, technology, and complex social organization. The advantage has compounded over time, as each generation has built on the achievements of its predecessors. If some day we build machine brains that surpass human brains in general intelligence, then this new superintelligence could become very powerful. And, as the fate of the gorillas now depends more on us humans than on the gorillas themselves, so the fate of our species would depend on the actions of the machine superintelligence.

We do have one advantage: we get to build the stuff. In principle, we could build a kind of superintelligence that would protect human values. We would certainly have strong reason to do so. In practice, the control problem—the problem of how to control what the superintelligence would do—looks quite difficult. It also looks like we will only get one chance. Once unfriendly superintelligence exists, it would prevent us from replacing it or changing its preferences. Our fate would be sealed. In this book, I try to understand the challenge presented by the prospect of superintelligence, and how we might best respond. This is quite possibly the most important and most daunting challenge humanity has ever faced. And—whether we succeed or fail—it is probably the last challenge we will ever face. It is no part of the argument in this book that we are on the threshold of a big breakthrough in artificial intelligence, or that we can predict with any precision when such a development might occur. It seems somewhat likely that it will happen sometime in this century, but we don’t know for sure. The first couple of chapters do discuss possible pathways and say something about the question of timing. The bulk of the book, however, is about what happens after. We study the kinetics of an intelligence explosion, the forms and powers of superintelligence, and the strategic choices available to a superintelligent agent that attains a decisive advantage. We then shift our focus to the control problem and ask what we could do to shape the initial conditions so as to achieve a survivable and beneficial outcome. Toward the end of the book, we zoom out and contemplate the larger picture that emerges from our investigations. Some suggestions are offered on what ought to be done now to increase our chances of avoiding an existential catastrophe later.

Related posts:

Deep Learning with Keras - Antonio Gulli & Sujit Pal
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Intelligent Projects Using Python - Santanu Pattanayak
Python Machine Learning Eqution Reference - Sebastian Raschka
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning with Applications Using Python - Navin Kumar Manaswi
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Foundations of Machine Learning second edition - Mehryar Mohri & Afshin Rostamizadeh & Ameet Talwalk...
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Data Science and Big Data Analytics - EMC Education Services
Python Deep Learning Cookbook - Indra den Bakker
Deep Learning dummies first edition - John Paul Mueller & Luca Massaron
Python Data Structures and Algorithms - Benjamin Baka
Deep Learning with Hadoop - Dipayan Dev
Python Machine Learning - Sebastian Raschka
Introduction to Deep Learning - Eugene Charniak
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Deep Learning with Theano - Christopher Bourez
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Medical Image Segmentation Using Artificial Neural Networks
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Learn Keras for Deep Neural Networks - Jojo Moolayil
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke