Artificial Intelligence

Artificial Intelligence means getting computers to behave or perform as if they were intelligent.

Some of the intelligent task that AI is expected to perform are:

Symbolic AI

From the mid-1950s to the mid-1990s, the dominant paradigm in AI research was Symbolic AI (also called Classical or Logical AI). This approach to AI was based on logical reasoning, using symbols and rules—hand-written by human programmers—to represent concepts and the relationships between them, as opposed to perfoming calculations on numerical data.

It relies on a top-down, knowledge-based approach, where human experts encode rules and knowledge into the system. Classical AI systems typically use techniques like expert systems, logic programming, and rule-based reasoning to solve problems.

Classical AI had many successes, including AIs that could play games, hold basic conversations, and answer queries using expert systems.

In Symbolic Artificial Intelligence, a symbol is a representation of a real-world item or concept. One simple type of symbol is a picture. A symbol can also be a group of other symbols, such as the letters that make up the name of an object. In symbolic AI, symbols embody the total sum of the relevant facts and information required for the system to understand what something is. To achieve this, data is labeled and attached to a symbol.

AI's are programmed to follow strictly logical rules, with the aim of producing reliable conclusions. One such rule is the syllogism, which states: If all As are Bs and all Bs are Cs, then all As are Cs. This simple principle enables AIs to know that all items of a particular class will always have a particular characteristic.

Heuristics

Some problems can be too complex for an algorithm to solve quickly. In such cases, all an AI can do is a brute-force search, which means to methodically work through and evaluate every possible solution. This is slow, however, and in some cases impossible.

A more efficient alternative is to use a heuristic. This practical method uses a common-sense approach, searching for an approximate solution by estimating a good enough choice at every decision point based on the information available.

Rule-Based Systems

A rule-based AI system uses instructions, consisting of IF-THEN statements, to draw conclusions based on an initial set of facts. In its simplest form, an IF-THEN statement says to the system: If this condition is true for the current facts, then do this; if it is false, do nothing. Adding an ELSE option allows for more complicated statements: If this is true, then do this; otherwise (else), do that.

Rule-based systems are predictable, reliable, and transparent, meaning it is easy to see which rules the AI applies. However, rule-based AIs cannot learn by adding to their store of rules and facts without human intervention.

Statistical AI

In contrast to Symbolic AI, statistical AI, also called machine learning, takes a bottom-up, data-driven approach. Learning is achieved from data through various machine learning algorithms. It involves training AI systems on large datasets, allowing them to learn patterns and relationships in the data, and then applying that learned knowledge to make predictions or decisions. Statistical AI systems use techniques like neural networks, decision trees, and support vector machines to uncover hidden patterns and relationships in the data.

Although statistical AI has since overtaken classical AI, the old approach has not been entirely abandoned; many of its techniques have been incorporated into modern AI applications, such as natural language processing and robotics.

Classical AI is better suited for domains with well-defined rules and structured data, while statistical AI excels in domains with complex, unstructured data where patterns are not easily discernible by human experts.

In recent years, there has been a growing emphasis on combining the strengths of both approaches, leading to the development of hybrid AI systems that incorporate elements of both classical and statistical AI.

Much of what we do with machine learning happens beneath the surface.

Jeff Bezos

Machine Learning

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