Chapter 1 Introduction
1.1 What Is AI?
Some definitions of artificial intelligence:
Thinking Humanly
Acting Humanly
Thinking Rationally
Acting Rationally
1.1.1 Acting humanly: The Turing test approach
The Turing test, proposed by Alan Turing, was designed as a thought experiment that would sidestep the philosophical vagueness of the question “Can a machine think?” The computer would need the following capabilities to pass the Turing test:
natural language processing to communicate successfully in a human language
knowledge representation to store what it knows or hears
automated reasoning to answer questions and to draw new conclusions
machine learning to adapt to new circumstances and to detect and extrapolate patterns
To pass the total Turing test, a robot will need
computer vision and speech recognition to perceive the world
robotics to manipulate objects and move about.
1.1.2 Thinking humanly: The cognitive modeling approach
We can learn about human thought in three ways:
introspection–trying to catch our own thoughts as they go by
psychological experiments–observing a person in action
brain imaging–observing the brain in action
The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind.
1.1.3 Thinking rationally: The “laws of thought” approach
Several Greek schools developed various forms of logic, which provides a precise notation for statements bout objects in the world and the relations among them.
1.1.4 Acting rationally: The rational agent approach
An agent is just something that acts. A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome.
AI has focused on the study and constructionof agents that do the right thing. This general paradigm is so pervasive that we might call it the standard model.
Limited rationality: act appropriately when there is not enough time to do all the computations one might take.
1.1.5 Beneficial machines
The standard model has been a useful guide for AI research since its inception, but it is probably not the right model in the long run. The reason is that the standard model assumes that we will supply a fully specified objective to the machine.
The problem of achieving agreement between our true preferences and the objective we put into the machine is called the value alignment problem*: the values or objectives put intothe machine must be aligned with those of the human.
We don’t want machines that are intelligent in the sense of pursuing their objectives; we want them to pursue our objectives. Ultimately, we want agents that are provably beneficial to humans.
1.2 The Foundations of Artificial Intelligence
1.2.1 Philosophy
Can formal rules be used to draw valid conclusions?
How does the mind arise from a physical brain?
Where does knowledge come from?
How does knowledge lead to action?
Descartes was a proponent of dualism. He held that there is a part of the human mind (or soul or spirit) that is outside of nature, exempt from physical laws. Animals, on the other hand, did not possess this dual quality; they could be treated as machines.
An alternative to dualism is materialism, which holds that the brain’s operation according to the laws of physics constitutes the mind. Free will is simply the way that the perception of available choices appears to the choosing entity.
The empiricism is characterized by a dictum of John Locke (1632–1704): “Nothing is in the understanding, which was not first in the senses.”
The logical positivism holds that all knowledge can be characterized by logical theories connected, ultimately, to observation sentences that correspond to sensory inputs; thus logical positivism combines rationalism and empiricism.
The confirmation theory attempted to analyze the acquisition of knowledge from experience by quantifying the degree of belief that should be assigned to logical sentences based on their connection to observations that confirm or disconfirm them.
Utilitarianism: that rational decision making based on maximizing utility should apply to all spheres of human activity, including public policy decisions made on behalf of many individuals. Utilitarianism is a specific kind of consequentialism: the idea that what is right and wrong is determined by the expected outcomes of an action.
In contrast, a theory of rule-based or deontological ethics, in which “doing the right thing” is determined not by outcomes but by universal social laws that govern allowable actions, such as “don’t lie” or “don’t kill.” Many modern AI systems adopt exactly this approach.
1.2.2 Mathematics
What are the formal rules to draw valid conclusions?
What can be computed?
How do we reason with uncertain information?
The theory of probability can be seen as generalizing logic to situations with uncertain information—a consideration of great importance for AI. The formalization of probability, combined with the availability of data, led to the emergence of statistics as a field.
The history of computation is as old as the history of numbers, but the first nontrivial algorithm is thought to be Euclid’s algorithm for computing greatest common divisors. The incompleteness theorem showed that in any formal theory as strong as Peano arithmetic (the elementary theory of natural numbers), there are necessarily true statements that have no proof within the theory.
Alan Turing tried to characterize exactly which functions are computable —capable of being computed by an effective procedure. For example, no machine can tell in general whether a given program will return an answer on a given input or run forever.
Although computability is important to an understanding of computation, the notion of tractability has had an even greater impact on AI. A problem is called intractable if the time required to solve instances of the problem grows exponentially with the size of the instances.
The theory of NP-completeness provides a basis for analyzing the tractability of problems: any problem class to which the class of NP-complete problems can be reduced is likely to be intractable.
1.2.3 Economics
How should we make decisions in accordance with our preferences?
How should we do this when others may not go along?
How should we do this when the payoff may be far in the future?
Decision theory, which combines probability theory with utility theory, provides a formal and complete framework for individual decisions (economic or otherwise) made under uncertainty—that is, in cases where probabilistic descriptions appropriately capture the decision maker’s environment.
Models based on satisficing—making decisions that are “good enough,” rather than laboriously calculating an optimal decision—gave a better description of actual human behavior.
1.2.4 Neuroscience
How do brains process information?
Neuroscience is the study of the nervous system, particularly the brain. A collection of simple cells can lead to thought, action, and consciousness.
Even with a computer of virtually unlimited capacity, we still require further conceptual breakthroughs in our understanding of intelligence.
1.2.5 Psychology
How do humans and animals think and act?
Wundt insisted on carefully controlled experiments in which his workers would perform a perceptual or associative task while introspecting on their thought processes. The behaviorism movement rejected any theory involving mental processes on the grounds that introspection could not provide reliable evidence.
Cognitive psychology views the brain as an information-processing device. Three key steps of a knowledge-based agent: (1) the stimulus must be translated into an internal representation, (2) the representation is manipulated by cognitive processes to derive new internal representations, and (3) these are in turn retranslated back into action.
Intelligence augmentation states that computers should augment human abilities rather than automate away human tasks.
1.2.6 Computer engineering
How can we build an efficient computer?
Moore’s law states that performance doubled every 18 months. Quantum computing holds out the promise of far greater accelerations for some important subclasses of AI algorithms.
1.2.7 Control theory and cybernetics
How can artifacts operate under their own control?
Modern control theory, especially the branch known as stochastic optimal control, has as its goal the design of systems that maximize a cost function over time.
1.2.8 Linguistics
How does language relate to thought?
Modern linguistics and AI, then, were “born” at about the same time, and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing.
1.3 The History of Artificial Intelligence
1.3.1 The inception of artificial intelligence (1943-1956)
The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943).
1.3.2 Early enthusiasm, great expectations (1952-1969)
The intellectual establishment of the 1950s, by and large, preferred to believe that “a machine can never do X”. John McCarthy referred to this period as the “Look, Ma, no hands!” era.
1.3.3 A does of reality (1966-1973)
In almost all cases these early systems failed on more difficult problems. The illusion of unlimited computational power was not confined to problem-solving programs.
1.3.4 Expert systems (1969-1986)
The picture of problem solving that had arisen during the first decade of AI research was of a general-purpose search mechanism trying to string together elementary reasoning steps to find complete solutions. Such approaches have been called weak methods. The alternative to weak methods is to use more powerful, domain-specific knowledge that allows larger reasoning steps and can more easily handle typically occurring cases in narrow areas of expertise.
1.3.5 The return of neural networks (1986-present)
In the mid-1980s at least four different groups reinvented the back-propagation learning algorithm first developed in the early 1960s.
1.3.6 Probabilistic reasoning and machine learning (1987-present)
In the 1980s, approaches using hidden Markov models (HMMs) came to dominate the area. Pearl’s development of Bayesian networks yielded a rigorous and efficient formalism for representing uncertain knowledge as well as practical algorithms for probabilistic reasoning.
1.3.7 Big data (2001-present)
Remarkable advances in computing power and the creation of the World Wide Web have facilitated the creation of very large data sets—a phenomenon sometimes known as big data.
1.3.8 Deep learning (2011-present)
The term deep learning refers to machine learning using multiple layers of simple, adjustable computing elements. Experiments were carried out with such networks as far back as the 1970s, and in the form of convolutional neural networks they found some success in handwritten digit recognition in the 1990s.
1.4 The Sate of the Art
ROBOTIC VEHICLES
LEGGED LOCOMOTION
AUTONOMOUS PLANNING AND SCHEDULING
MACHINE TRANSLATION
SPEECH RECOGNITION
RECOMMENDATIONS
GAME PLAYING
IMAGE UNDERSTANDING
MEDICINE
CLIMATE SCIENCE
1.5 Risks and Benefits of AI
LETHAL AUTONOMOUS WEAPONS
SURVEILLANCE AND PERSUASION
BIASED DECISION MAKING
IMPACT ON EMPLOYMENT
SAFETY-CRITICAL APPLICATIONS
CYBERSECURITY