The humachine: humankind, machines, and the future of enterprise

Cover
Title Page
Copyright Page
Dedication
Table of Contents
List of Figures
List of Tables
Preface
Introduction: Defining the Humachine
1: The Fourth Industrial Revolution
	“Deep Blue” Blues
	Kasparov’s Law: Triumph of Process
	The New Kid in Town
	The “Big Bang” of Modern AI
	“Robot Proof” the Workforce: Embracing Moravec’s Paradox
	Machines Can’t Do Everything
	There Are Worse Things than Losing Your Job
	The Humachine: People, Process, Machines
	How to Get There
	Conclusion
	Notes
2: Pathways to Superintelligence
	Thousand Pound Gorilla Philosopher
	What Is Superintelligence?
	Using (Not Abusing) Neo-Darwinian Accounts of Intelligence
		Competition, Evolution, and Survival
		The Evolutionary Paths of Human and Machine Intelligence Split
		Case in Point
		Biological Cognitive Enhancement
		Neural Lace: Turning Humans into Cyborgs
		Whole Brain Emulation
		Collective Superintelligence
		Bostrom’s Blind Spot: The Promise of Collective Intelligence
		Why Is It a Problem?
		Collective Intentionality
			Does “Microsoft” Really “Intend” Anything?
			Collective Minds Play a Causal Role in the Real World
			Some Philosophical Points That Support Collective Intentionality
			Could a Corporation Actually Be Conscious?
		Humachines and the Noosphere
		Conclusion
		Notes
3: The Limits of Machine Capabilities
	When Robots Hang with the Wrong Crowd
	Let’s Not Indulge in Hyperbole Here
	Big Data, Algorithms, Cloud Computing, and Dark Data
		Big Data
		Algorithms
		Cloud Computing
		Dark Data
		Data Is the Foundation for AI and Machine Learning
	AI, Machine Learning, Deep Learning, and Neural Networks
		Artificial Intelligence
		Machine Learning
		Neural Networks
		Deep Learning
	What Machines Can Do
		Pattern Recognition
		Processing Velocity, Accuracy, Volume, and Consistency
		Connect Complex Systems
	What Machines Cannot Do
		Lacking Common Sense
		No Understanding of Context
		Data Hungry and Brittle
		No Intuition
		Local Optima
		Black Box Decision-Making
		Garbage in, Garbage Out
	Conclusion
	Notes
4: The Limits of Human Capabilities
	The Microwave’s Eureka Moment
	Thinking about Thinking
	The Conundrum of Originality
	In Defense of Homo Sapiens
		General Intelligence
		Intuition
		Creativity and Innovation
		Pleasure and Aesthetics
		Emotions
		Emotional Intelligence
		Care
		Play
		Ethical Convictions
	Managing When Human, All Too Human
	Dealing with Human Biases
		Anchoring
		Bandwagon Effect
		Attribution Bias
		Confirmation Bias
		Framing
	Cultivating Human Virtues in the Technology Era
	Changing What It Means to Be Smart
	Baby Boomers and Botsourcing
	Conclusion
	Notes
5: Integration of People and Technology
	Use It or Lose It
	Recipe, Formula, Alchemical Spell, Call It What You Will
	Jobs Are Going to Evolve but Not Disappear
	AI Is a Horizontal Enabling Layer
	Complementary Coworkers: Moravec’s Paradox Resolved
	Augmentation, Amplification, and Other Performance Enhancers
		Augmentation
		Co-bots
		Amplification
		Enhancing Creativity
		Improving Innovation
		Enhanced Learning Through Visual Analytics
	The Human-Machine Interface
	Developing Trust in the Machines
	Human Resources in the Artificial Intelligence Era
	Dangers of Overreliance: Atrophy
	Conclusion
	Notes
6: Legal Issues in the Humachine Era
	Cybersecurity, Deepfakes, and Generative Adversarial Networks
	There’s More Than Just the Control Problem
	Asimov’s Laws of Robotics
	Risk Management, Pandora’s Box, and the Chamber of Commerce
	Deepfakes: A Pernicious Permutation of Photo Fakery
		The High Stakes of Deepfakes
		Law’s Limited Remedy
		The Promise of Legal Solutions
	Consumer Privacy and the “Internet of Eyes”
	The Cost of Failing to Implement Privacy and Security Safeguards
	Beware of Algorithmic Bias
	Black Box Decisions: AI’s “Dark Secret”
	The Impact of AI on Lawmaking and Regulations
		Erosion of Public Trust
		Improving Public Participation
	The Spambot Lobby: Repealing Net Neutrality
		“Notice and Comment” Rulemaking
		Open Internet on the Electronic Frontier
		Betraying the Founders of the Internet
		Transparency versus “Openwashing”
	Conclusion
	Notes
7: Breaking the Paradigm
	Human Experience over Hierarchy
	Technology-Driven, Human-Centric
	A New Business Paradigm
	Intentionality
		Purpose
		Culture
		Authenticity
		Integrity
	Integration
		From Pyramids and Silos to Flexible Networks
		Humans and Machines: The New Coworkers
		Design Thinking
		AI-User Interface
	Implementation
		Have a Clear Message
		Hire the Right Talent
		Create a Work Environment That People Love
		Foster Freedom to Create, Experiment, and Fail
	Indication
	A Dynamic Model
	Conclusion
	Notes
8: Mutations
	If Refrigerators Could Talk—Oh Wait, They Can!
	A Different Species of Organization
		Mutation, Not Transformation
		Humachine Traits
		Kasparov’s Law Redux
	Focus on Human Needs
		The “Experience Economy”
		Customer Is the Co-creator
	Flat and Fluid Organizational Structures
		Flatness and Fluidity
		Porous Boundaries, Social Webs
		From Holacracy to Rendanheyi: One Size Does Not Fit All
	A Culture of Entrepreneurship and Innovation
		Entrepreneurial Spirit
		Innovation
	Self-awareness
		Organizational Self-awareness
		Intentionality Driven: Understand Who You Are
	Orchestrating the Mutation: Thoughtful Leadership
		Leadership and Failure
		Develop a Long-Term Vision
		Understand the Current State
		Allocate Sufficient Resources
		Embrace Transparency
		Start with a Pilot
	Conclusion
	Notes
9: Reflections on the Humachine
	Finding Your Organization’s Soul
	Foundations
	Are Humans Actually Getting Smarter? (No)
	With Great Power Comes… the Ability to Do a Lot of Damage
	We Need to Play to Our Strengths
	“Humanics”: Education for the Humachine Workforce
	Reflections on the Future of Work
	Conclusion
	Notes
Index