Friday, June 18, 2021

    What are non-technology components of AI and ML by Dr. Jassim Haji

    There is a wide perception that Artificial Intelligence and Machine Learning is only about programming and Data science, but what are the other essential to complete the echo system of AI?

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    Math and Statistics for Data Science are essential because these disciples form the basic foundation of all the Machine Learning Algorithms. In fact, Mathematics is behind everything around us, from shapes, patterns and colors, to the count of petals in a flower. Mathematics is embedded in each and every aspect of our lives.

    Although having a good understanding of programming languages, Machine Learning algorithms and following a data-driven approach is necessary to become a Data Scientist, Data Science isn’t all about these fields. In this blog post, you will understand the importance of Math and Statistics for Data Science and how they can be used to build Machine Learning models.

    Mathematics is the bedrock of any contemporary discipline of science. Almost all the techniques of modern data science, including machine learning, have a deep mathematical underpinning.

    It goes without saying that you will absolutely need all the other pearls of knowledge programming ability, some amount of business acumen, and your unique analytical and inquisitive mindset—about the data to function as a top data scientist. But it always pays to know the machinery under the hood, rather than just being the person behind the wheel with no knowledge about the car. Therefore, a solid understanding of the mathematical machinery behind the cool algorithms will give you an edge among your peers.

    AI as a Technology

    Is AI the technologies people use to make machines intelligent, or, is it the movement towards the goal of achieving machine intelligence? According to some researchers, AI is actually a science. It’s a field of study. But it might be more helpful to think of AI as a goal. If AI is considered to be a collection of technologies, then you can argue all day about what is and what isn’t AI. Are software robots AI? Are self-driving cars AI? Is computer vision AI? Is character recognition AI? If you think about it as technology then it’s always subject to disagreement and interpretation. However, if you think about it as a goal, or a quest, then it’s something we’re always striving to achieve, even if we aren’t quite there yet.  Even if you think of AI as a field of study, like physics, those fields of study have goals. The goal of physics is to gain the true understanding of the nature of the universe. Everything we’ve developed in that quest for understanding in physics are technologies that are useful in our everyday lives. But those technologies aren’t physics — they are the byproducts of our quest to understand physics. In the same way, machine learning and computer vision and robotics aren’t AI, they are the technology byproducts of our quest to achieve or understand AI.


    By. Dr. Jassim Haji

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