Different AI techniques, such as neural networks and expert systems, can be used to develop strong or weak AI programs that mimic human thought processes. Neural networks use categorization layers to identify objects, while expert systems use logic and if/then statements to analyze information. Strong AI seeks to replicate human cognitive abilities, while weak AI focuses on a single function.
While many different artificial intelligence (AI) techniques have been developed, as new methods have been created, some forms of AI have become increasingly popular. Some of the more common techniques include the use of neural networks and the development of expert systems. These different AI techniques can be used to develop different forms of AI, usually based on how much ‘thinking’ the program can actually do, and these are known as ‘Strong AI’ or ‘Weak AI’.
AI techniques are methods that can be used to develop and create computer programs that are commonly viewed as forms of artificial intelligence. In general, artificial intelligence refers to a program that can mimic or recreate the thought processes demonstrated by the human brain. This usually involves solving problems, making observations or receiving input to use in analyzing or troubleshooting, and being able to classify and identify different objects and the properties of those objects.
There are many different AI techniques that can be used by an AI programmer, although two of the most common are neural networks and expert systems. Neural networks are computer programs designed around the cognitive processes used by the human brain. Essentially, a neural network consists of categorization layers and methods by which objects can be identified and categorized. This is similar to the idea of schema in human cognition, which allows people to identify objects based on the properties of those objects. The new information presented to the neural network can then be analyzed and identified based on previously entered criteria, allowing the system to “learn” new categories and identify known or unknown objects.
Expert systems are artificial intelligence techniques built around logic and “if/then” statements. This usually involves a large amount of information being “taught” to the computer system, which then makes the system an expert in a particular field. When new input is introduced, such as a financial reporting request, the expert system can analyze the information using these if/then statements to limit the outgoing response.
These various AI techniques can be used to develop systems that are considered “strong AI” or “weak AI”. Strong AI systems are those that most fully seek to emulate human thinking and cognitive abilities across a broad range of functions. These systems can analyze new information and provide outputs that potentially go beyond the limits of the input data. AI techniques that develop weak AI systems have a narrower focus and seek to replicate only a single function or aspect of human intelligence.
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