While studying the typical range of tasks that we might expect an “intelligent entity” to perform, we need to consider both “common-place” tasks as well as expert tasks.
Examples of common-place tasks include
- Recognizing people, objects.
- Communicating (through natural language).
- Navigating around obstacles on the streets
These tasks are done matter of factly and routinely by people and some other animals. Expert tasks include:
- Medical diagnosis.
- Mathematical problem solving
- Playing games like chess
These tasks cannot be done by all people, and can only be performed by skilled specialists. Now, which of these tasks are easy and which ones are hard? Clearly tasks of the first type are easy for humans to perform, and almost all are able to master them. The second range of tasks requires skill development and/or intelligence and only some specialists can perform them well. However, when we look at what computer systems have been able to achieve to date, we see that their achievements include performing sophisticated tasks like medical diagnosis, performing symbolic integration, proving theorems and playing chess. On the other hand it has proved to be very hard to make computer systems perform many routine tasks that all humans and a lot of animals can do. Examples of such tasks include navigating our way without running into things, catching prey and avoiding predators. Humans and animals are also capable of interpreting complex sensory information. We are able to recognize objects and people from the visual image that we receive. We are also able to perform complex social functions.
Intelligent behaviour: This discussion brings us back to the question of what constitutes intelligent behaviour. Some of these tasks and applications are:
- Perception involving image recognition and computer vision
- Understanding language involving natural language processing, speech processing
- Solving problems
Practical Impact of AI
AI components are embedded in numerous devices e.g. in copy machines for automatic correction of operation for copy quality improvement. AI systems are in everyday use for identifying credit card fraud, for advising doctors, for recognizing speech and in helping complex planning tasks. Then there are intelligent tutoring systems that provide students with personalized attention
Thus AI has increased understanding of the nature of intelligence and found many applications. It has helped in the understanding of human reasoning, and of the nature of intelligence. It has also helped us understand the complexity of modeling human reasoning.
Approaches to AI
Strong AI: aims to build machines that can truly reason and solve problems. These machines should be self aware and their overall intellectual ability needs to be indistinguishable from that of a human being. Excessive optimism in the 1950s and 1960s concerning strong AI has given way to an appreciation of the extreme difficulty of the problem. Strong AI maintains that suitably programmed machines are capable of cognitive mental states.
Weak AI: deals with the creation of some form of computer-based artificial intelligence that cannot truly reason and solve problems, but can act as if it were intelligent. Weak AI holds that suitably programmed machines can simulate human cognition.
Applied AI: aims to produce commercially viable “smart” systems such as, for example, a security system that is able to recognise the faces of people who are permitted to enter a particular building. Applied AI has already enjoyed considerable success.
Cognitive AI: computers are used to test theories about how the human mind works–for example, theories about how we recognise faces and other objects, or about how we solve abstract problems.