From Biological Learning to Artificial Neural Network: What’s Next?

Back in the beginning of the 21st century, when I studied for MBA at NYU Stern, one class I took was called Data Mining, which introduced many algorithms to “mine” the data, meaning to automatically find the meaning of the data for forecasts and decision making. The neural network was one of them, but it was far from the top choices because it was slow, required a lot of data to train, and, hence, had minimal use cases. Twenty years later, neural network algorithms have thrived as the cornerstones of machine learning and artificial intelligence (AI) due to the tremendous computational power that removed the fundamental obstacle and, in turn, led to the invention of more advanced algorithms and models. <a href="https://towardsdatascience.com/from-biological-learning-to-artificial-neural-network-whats-next-c8cf0d351af5">Click Here</a>