For the AI enthusiasts of the world, it is always a wonder as to what makes an AI model great. Does it take a scientific mind or an artistic mind?

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During the last several years, the great scientific minds have researched to understand human’s cognitive abilities to understand how humans store, process, and access information. How is knowledge organized? How do we constantly learn from our experiences and apply as needed? This has resulted in a deeper understanding of human learning and to possibly answer the most enigmatic question of all as to what intelligence really means. There have been significant efforts to model that into machines to pave way for artificial intelligence revolution.

There is still a long way to go to teach machines to empathise and be creative and truly intelligent. But creativity is hard to explain. An idea originating in the mind is transpired from the seeds of knowledge acquired over time but we can’t explain how it got manifested. Greater knowledge gives more capacity to think of more ideas but we can’t explain the causal inferences. We can’t explain how we understand a language or recognise faces instantly even though we are very good at doing all that. In order to teach a machine to be as creative as humans, it would take huge amounts of training data as humans acquire over a long period of time. Ideas in the areas of storytelling, reciting poems, generating visual art, human-like conversations with understanding of emotions, generating music or even humour. The question arises, does it take a scientific mind or an artistic mind to build a great AI system that is creative, intuitive, and intelligent?

Developing an AI model requires good quality data and an iterative process to derive insights from it by understating complex relationship between the input and output variables encapsulated into a mathematical model. This requires constant monitoring and evaluation to come up with a good AI model that generalises to the existing input data so well that it gives meaningful predictions corresponding to the unseen input data. AI algorithms provide a way for the machines to learn and derive insights from data on their own. There is a feedback loop in the process to seek confirmation just like humans do when they learn something. Some of the more advanced AI techniques model a machine based on how human brain works to process information through millions of neurons. This science is the basis for deep…

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Source: towardsdatascience.com