How did AI come into existence?
Artificial intelligence (AI), a multidisciplinary blend of mathematics, neurobiology, statistics, and computer science, has been a game-changer in the global technological landscape. Spanning over a mere six decades, this young discipline seeks to emulate human cognitive capabilities. It has seen tremendous progress since its inception during the Second World War, yet, the journey has been marked by alternating periods of intense development and relative stagnation. Let’s take a closer look at the intricate path of AI development, and understand why it’s such a pivotal part of our present and future. The Birth of AI: 1940-1960 In the two decades following the Second World War, the synergy between technological advances and the desire to link machine functionality with organic beings paved the way for the birth of AI. Founding figures like Norbert Wiener, Warren McCulloch, Walter Pitts, John Von Neumann, and Alan Turing introduced the concept of cybernetics, the biological neuron model, and laid the foundation of computer logic that drives contemporary machines. The Advent of AI: 1950-1960 The term ‘AI’ itself was coined during this period by John McCarthy at MIT, and Marvin Minsky of Carnegie-Mellon University. The famous Dartmouth College summer conference in 1956 is widely considered as the discipline’s cornerstone. This era also witnessed some early applications of AI, notably the LTM program that aimed to demonstrate mathematical theorems. AI’s First Winter: Late 1960s Despite the initial fascination with AI, technological limitations around memory capacity and computer language usability led to a decline in the popularity of AI in the late 1960s. AI faced what is often referred to as its ‘first winter’, a period of reduced interest and funding. Rise of Expert Systems: 1980-1990 With the introduction of the first microprocessors in the late 1970s, AI entered a golden era marked by the development of expert systems. They were logical mirrors of human reasoning, capable of providing high-level expertise responses to the input data. However, the complex programming and maintenance of these systems, alongside emerging simpler and less costly alternatives, led to a second decline in interest by the early 1990s. AI Renaissance: Post-2010 Around 2010, AI witnessed a massive resurgence due to two critical factors: the proliferation of data and significant improvements in computing power. This new era was marked by notable achievements like Watson (IBM’s AI) winning Jeopardy, Google’s AI recognizing cats on videos, and AlphaGO (Google’s AI) winning Go games. Deep Learning and the Future of AI Deep learning, a subset of machine learning techniques, has emerged as a significant breakthrough in AI, especially in areas like voice or image recognition. Noteworthy contributions by researchers like Geoffrey Hinton, Yoshua Bengio, and Yann LeCun have expedited the growth of deep learning, paving the way for the next chapter in the history of AI. From its conceptual birth to its present achievements, AI’s journey has been an incredible mix of ups and downs. Yet, with every step, it has inched closer to mimicking human cognitive abilities. The story of AI serves as a reminder that progress is often not linear, and the path to understanding our world more deeply, whether through human cognition or artificial intelligence, is a journey worth undertaking.
