Evolution, Application And Auditing Of Artificial Intelligence [Forbes]
Alphabet Inc. - Class C Capital Stock (GOOG)
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Source: Forbes
This exploration traces AI's development, highlights its diverse implementations and underscores the importance of reviewing and managing emerging risks effectively. Modeling History Empirical analysis relied heavily on parametric estimation due to its computational efficiency and the small datasets of the era. Despite the existence of decision trees and neural networks, their use was limited by technological constraints. The new millennium ushered in significant advances in computing power and data handling, shifting focus toward non-parametric methods. The rise of open-source tools like Scikit-learn and high-profile competitions like the Netflix Prize showcased the effectiveness of new algorithms. In particular, ensembles, such as aggregation with variations of decision trees like Random Forests and Gradient Boosting Machines, became popular. Marked by the mainstream adoption of neural networks and deep learning, fueled by GPU-accelerated computations and a surge in data avail
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