The Quest for Intelligent Machines
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- TL;DR
- This chapter explores whether computers can achieve intelligence, introducing the Turing test as an unbiased method for evaluating machine intelligence.
- Problem
- Not specified in the abstract.
- Method
- The chapter covers several machine learning approaches, including neural networks, decision trees, and Bayesian learning.
- Results
- Not specified in the abstract.
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- The chapter concludes by viewing the brain as a sophisticated statistical machine evolved to improve its owner's chances of survival.
- Applications
- Advances in artificial intelligence have enabled numerous computer applications that are not possible with conventional, non-intelligent programs.
- Topics
- Artificial intelligence, machine learning, Turing test, neural networks, decision trees, Bayesian learning
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
This chapter addresses the question of whether a computer can become intelligent and how to test for that possibility. It introduces the idea of the Turing test, a test developed to determine, in an unbiased way, whether a program running in a computer is, or is not, intelligent. The development of artificial intelligence led, in time, to many applications of computers that are not possible using “non-intelligent” programs. One important area in artificial intelligence is machine learning, the technology that makes possible that computers learn, from existing data, in ways similar to the ways humans learn. A number of approach to perform machine learning is addressed in this chapter, including neural networks, decision trees and Bayesian learning. The chapter concludes by arguing that the brain is, in reality, a very sophisticated statistical machine aimed at improving the chances of survival of its owner.