Over the past decade, significant advancements in artificial intelligence have been made through a method
known as “deep learning.” This technique is essentially a modern version of neural networks, which have
been a topic of interest for over 70 years.
Initially proposed in 1944 by Warren McCullough and Walter Pitts, neural networks gained traction in
both neuroscience and computer science until 1969 when they fell out of favor. However, they resurged in
the 1980s and have once again become prominent today due to the enhanced processing capabilities
of graphics chips.
Neural networks function as a form of machine learning, where a computer learns by analyzing labeled
examples. These networks, inspired by the human brain, consist of interconnected nodes organized into
layers. During training, the connections between nodes are assigned weights, which are continually
adjusted to ensure the network provides accurate outputs.
While the inner workings of neural networks can be complex, researchers have been making progress
in understanding and optimizing them. Ongoing studies aim to address key challenges such as enhancing
network computations, achieving global optimization, and preventing overfitting.
Despite the historical ebbs and flows in the popularity of neural networks, ongoing research
endeavors hold the potential to establish them as a stable and innovative technology in the
field of artificial intelligence.