Artificial Intelligence (AI) has been a topic of interest since its inception in the 1950s, but it was not until the 1980s that AI research saw a resurgence in interest and activity. This resurgence was fueled by several factors, including advances in computing power, the availability of large datasets, and new machine-learning techniques.

In the early 1980s, the field of AI experienced what is now known as the “AI winter.” This was a period of reduced funding and interest in AI research due to the inability of AI systems to deliver on their promises. However, by the mid-1980s, AI research began to see a resurgence, driven by new breakthroughs and approaches.

One of the key drivers of this resurgence was the development of new machine-learning techniques. In particular, the development of neural networks and backpropagation algorithms allowed researchers to train complex AI systems to perform tasks such as image recognition and natural language processing.

Another factor that contributed to the resurgence of AI research was the availability of large datasets. With the growth of the internet and the digitization of information, researchers had access to vast amounts of data that could be used to train and test AI systems. This led to the development of new data-driven approaches to AI, such as deep learning, which have become increasingly popular in recent years.

The increased availability of computing power was also a key factor in the resurgence of AI research. In the 1980s, the cost of computing power was prohibitively expensive, but with the advent of powerful GPUs and cloud computing, researchers were able to develop and train complex AI systems at a much lower cost.

The resurgence of AI research in the 1980s and 1990s led to several breakthroughs in the field, including the development of expert systems, decision trees, and natural language processing systems. These breakthroughs paved the way for further research and innovation in AI, setting the stage for the development of more sophisticated AI systems in the 21st century.

In the early 2000s, AI research saw another period of reduced funding and interest, known as the “AI winter 2.0.” This was due in part to the failure of some high-profile AI systems, such as IBM’s Deep Blue chess-playing computer. However, the field of AI research saw another resurgence in the mid-2010s, driven by advances in deep learning, big data, and cloud computing.

One of the key drivers of the recent resurgence in AI research has been the development of deep learning techniques. Deep learning is a type of machine learning that uses neural networks to simulate the human brain and enable AI systems to learn and improve over time. This has led to significant breakthroughs in areas such as image and speech recognition, natural language processing, and robotics.

Another factor that has contributed to the resurgence of AI research is the availability of big data. With the growth of the internet and the increasing digitization of information, researchers now have access to vast amounts of data that can be used to train and test AI systems. This has led to the development of new AI applications, such as personalised medicine, predictive analytics, and smart cities.

Cloud computing has also played a significant role in the recent resurgence of AI research. Cloud computing enables researchers to access vast amounts of computing power and storage, allowing them to develop and train complex AI systems at a lower cost. This has led to the democratization of AI research, making it accessible to a broader range of researchers and organizations.

In conclusion, the resurgence of AI research from the 1980s to the present day has been driven by a combination of factors, including advances in machine learning, the availability of large datasets, and the increased availability of computing power.