A01=Hector J. Levesque
A01=Ronald J. Brachman
Age Group_Uncategorized
Age Group_Uncategorized
ai
ai books
algorithm
algorithms
artificial intelligence
Author_Hector J. Levesque
Author_Ronald J. Brachman
automatic-update
automation
biology
brain
business
Category1=Non-Fiction
Category=UYQ
cognition
computer
computer books
computer science
computers
consciousness
COP=United States
critical thinking
culture
cyber
cyberpunk
Delivery_Delivery within 10-20 working days
dystopia
economics
eq_bestseller
eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
future
futurism
games
gaming
hacking
innovation
intelligence
internet
language
Language_English
machine learning
mental health
military
neuroscience
PA=Available
philosophy
Price_€20 to €50
programming
PS=Active
psychology
robotics
sci-fi
security
self help
self improvement
social media
society
sociology
softlaunch
superintelligence
tech
technology
war
Product details
- ISBN 9780262547321
- Weight: 414g
- Dimensions: 143 x 222mm
- Publication Date: 17 Oct 2023
- Publisher: MIT Press Ltd
- Publication City/Country: US
- Product Form: Paperback
- Language: English
Secure
checkout
Fast Shipping
Easy returns
How we can create artificial intelligence with broad, robust common sense rather than narrow, specialized expertise.
It’s sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what’s happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today’s AI systems.
Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.
It’s sometime in the not-so-distant future, and you send your fully autonomous self-driving car to the store to pick up your grocery order. The car is endowed with as much capability as an artificial intelligence agent can have, programmed to drive better than you do. But when the car encounters a traffic light stuck on red, it just sits there—indefinitely. Its obstacle-avoidance, lane-following, and route-calculation capacities are all irrelevant; it fails to act because it lacks the common sense of a human driver, who would quickly figure out what’s happening and find a workaround. In Machines like Us, Ron Brachman and Hector Levesque—both leading experts in AI—consider what it would take to create machines with common sense rather than just the specialized expertise of today’s AI systems.
Using the stuck traffic light and other relatable examples, Brachman and Levesque offer an accessible account of how common sense might be built into a machine. They analyze common sense in humans, explain how AI over the years has focused mainly on expertise, and suggest ways to endow an AI system with both common sense and effective reasoning. Finally, they consider the critical issue of how we can trust an autonomous machine to make decisions, identifying two fundamental requirements for trustworthy autonomous AI systems: having reasons for doing what they do, and being able to accept advice. Both in the end are dependent on having common sense.
Ronald J. Brachman is Director of the Jacobs Technion-Cornell Institute at Cornell Tech in New York City and Professor of Computer Science at Cornell University. During a long career in industry, he held leadership positions at Bell Labs, Yahoo, and DARPA. Hector J. Levesque is Professor Emeritus in the Department of Computer Science at the University of Toronto. He is the author of Common Sense, the Turing Test, and the Quest for Real AI (MIT Press), and other books.
