The advent of big data and improvements in neural networks led to the development of deep learning, significantly enhancing the capabilities of AI systems and leading to the modern era of AI, characterized by self-driving cars, facial recognition, and real-time …
As computational power increased, the 1990s welcomed a shift from knowledge-based systems to machine learning, focusing on algorithms that could learn from and make predictions on data.
The 1980s saw a resurgence in AI with the development of expert systems, which were programmed to mimic the decision-making abilities of a human expert.
AI faced significant challenges and skepticism, leading to the first of several “AI Winters,” where funding and interest in AI research declined.
The development of the perceptron in 1958 by Frank Rosenblatt marked the early days of neural networks, laying the groundwork for what would become machine learning.
Alan Turing, often called the father of modern computing, introduced the concept of a machine that could simulate human intelligence, famously positing the question, “Can machines think?” in his paper “Computing Machinery and Intelligence.”