Learn how AI trained with machine learning can quickly calculate the best route between two points by performing rapid trial-and-error computations. Understand the key principles powering AI route optimization.
Table of Contents
Question
Which of the following explains why an AI trained with machine learning can rapidly map the best route between two points on a map?
A. It relies on a complete database of all possible routes through the city.
B. It uses predefined programming instructions to navigate through traffic.
C. It performs millions of tiny calculations quickly, trying different routes through trial and error.
D. It predicts traffic problems based on stored alternative routes for every possible situation.
Answer
C. It performs millions of tiny calculations quickly, trying different routes through trial and error.
Explanation
An AI trained with machine learning approaches the problem of finding the best route like climbing a tree, trying different routes and comparing successful ones to identify the shortest route. This process involves performing millions of tiny calculations quickly through trial and error.
An AI system trained using machine learning to find optimal routes does not rely on a complete pre-programmed database of all possible routes, predefined navigation instructions, or stored alternate routes for every scenario. Rather, the power of machine learning lies in the AI’s ability to rapidly explore a vast number of possibilities through trial-and-error computation.
The AI starts with a representation of the map and algorithmically tries out huge numbers of route permutations in quick succession. It assesses each candidate route using an objective function – a way of scoring routes based on criteria like total distance, estimated travel time, number of turns, etc.
Through this rapid iterative process of generating and testing route options, the machine learning model progressively discovers more optimal routes. The best-scoring routes are reinforced, while poor routes are discarded. Within milliseconds, the AI’s parallel processing capabilities allow it to converge on a set of routes that optimize the objective function.
So in summary, machine learning enables the AI to find optimal routes not through exhaustive storage of possibilities, but by harnessing its computational power to rapidly explore the solution space and uncover strong solutions through trial and error. The AI teaches itself from its own simulated experience.
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