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 dummies second edition - John Paul Mueller & Luca Massaronf
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Learn Keras for Deep Neural Networks - Jojo Moolayil
The hundred-page Machine Learning Book - Andriy Burkov
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Pattern recognition and machine learning - Christopher M.Bishop
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Medical Image Segmentation Using Artificial Neural Networks
Deep Learning and Neural Networks - Jeff Heaton
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning - Ian Goodfellow & Yoshua Bengio & Aaron Courville
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Machine Learning with spark and python - Michael Bowles
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Python Machine Learning Eqution Reference - Sebastian Raschka
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
TensorFlow for Deep Learning - Bharath Ramsundar & Reza Bosagh Zadeh
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Deep Learning with Hadoop - Dipayan Dev
Machine Learning with Python for everyone - Mark E.Fenner
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Introduction to Scientific Programming with Python - Joakim Sundnes
Deep Learning with PyTorch - Vishnu Subramanian
Artificial Intelligence - 101 things you must know today about our future - Lasse Rouhiainen
Neural Networks and Deep Learning - Charu C.Aggarwal