Among the many capabilities of AI, we can list the ability to make predictions and inference, recognizing patterns in data, optimizing and automating arduous processes.
Although they are similar tasks, predictions are about forecasting something yet to happen, such as a high-precision weather forecast.
Whereas inferences focus on determining a target output based on a set of inputs known as predictors, for instance, suggesting a book you may like based on your preferences as a customer.
Other pattern recognition tasks made possible by AI include: Clustering: used in applications like customer segmentation for the discovery of groups of data with similar characteristics. Anomaly detection: used in security and finance domains to identify transactions or other data occurrences that deviate from the normal. And data generation or Generative AI,
AI can also solve plenty of optimization problems, consisting in finding the best and most efficient possible solution given a number of constraints
Examples of optimization problems solvable by AI include: finding the optimal route in logistics and delivery services; efficiently operating and controlling energy grids; building a dynamic pricing strategy in travel bookings that maximizes revenue; and launching discount campaigns to increment product or brand sales.
Automation itself is not AI, as it does not involve mimicking human intelligent processes, but AI can help improve automation, improving the efficiency of processes that would be time-consuming when performed by humans,
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