Artificial Intelligence (AI) is driving the transformation of many industries, practically changing the way we conduct business operations. Its impact on humanity (past, present, and future) is the critical reason why understanding how AI evaluation has evolved is necessary for all.
As AI systems have developed, the requirement for robust testing mechanisms has also risen.
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Discover how the evolution of AI evaluation has occurred over stages.
New methods of testing assess the performance, capabilities, and ethical implications. It can be segregated into different stages.
The Conception of AI: The Turing Test
The concept of evaluating the “smartness” of a machine was first coined by British mathematician, Alan Turing in the 1950s. In one of his papers, he proposed a way to measure the machine’s ability to exhibit intelligence that he labeled the “Turing Test.”
It focused on how distinguishable the machine’s intelligence was from that of a human.
The test had a human tester interact with a machine and a human, without being aware of which is which. If the person fails to recognize the difference and tell them apart, the machine has passed the test.
Although it was a groundbreaking notion at the time, it primarily focused on how well AI can mimic humans.
Creating Performance Metrics for AI Evaluation
As research in the field progressed, particularly with machine learning (ML), the method of AI evaluation shifted towards measurable performance metrics. Here, benchmarks were added to assess various tasks such as recognizing speech, classifying images, and more.\
These metrics enabled a comparison across algorithms and models, which helped researchers understand how an AI system performs for specific applications. But these preliminary metrics still focused on performance and didn’t consider broader aspects of AI evaluation.
Rise of Deep Learning
The rise of deep learning in the 2000s created fresh challenges for AI evaluation. While deep neural networks displayed success in computer vision and natural language processing, conducting their evaluation required more sophisticated methods.
Researchers considered confusion matrices and advanced statistical methods to test the model’s robustness. But the critical issue of “black box” remained.
Ethics and Fairness of an AI Model
As AI systems began to impact greater aspects of society, AI evaluation expanded to include ethical considerations. People began recognizing the AI systems could perpetuate existing and new biases and inequalities if they are not properly evaluated.
This led to the creation of FAT (fairness, accountability, transparency) principles in evaluating AI. It tested how well a model tested users equitably, ensured privacy, and operated transparently.
Closing Thoughts
In a nutshell, the evolution of AI evaluation has moved from basic philosophical thought to extremely detailed and nuanced processes.
