Can bringing in artificial intelligence to high school curriculum bridge the skill-gap? Experts believe that such a move would help in building a sustainable workforce that can leverage the transformative technologies to ensure social and inclusive growth in line with the development philosophy of the government. In that respect, strengthening mathematical concepts at high school stage can help sharpen the understanding of future students. For example, statistics plays a crucial role as it used in data minimisation that increases the computation speed. So, what does a high school student need to master before studying AI in college? Matroid CEO and adjunct professor at Stanford Reza Zadeh tweeted that the concepts in maths like – calculus, linear algebra, probability and statistics can make up a good background for students who wish to pursue a career in AI.
A career in AI requires a strong foundation in mathematics. A common question asked by may beginners is, “What level of math competency should one be before attempting to learn AI?” Facebook’s Yann LeCun commented in 2016 that for students to excel in AI, what’s required is “math and just more math”.
A TechCrunch article cited a computer scientist mentioning that linear algebra, probability, statistics and calculus III should be incorporated in education — the sooner the better. Since high school curriculum offers a watered-down version of probability and calculus, it is high time the Indian think tank NITI Aayog revisits it to overhaul the existing curriculum if the country wants to make AI the engine of growth and economy.
Here’s why school curriculum should integrate higher level maths which is a prerequisite to understanding core computer science concepts:
- Given how calculus and linear algebra are key to understanding AI algorithms, high school curriculum should be ramped up to include Calculus III and Probability.
- Most programming languages are also dependent on algebra and a solid understanding of discrete maths.
- Building a solid maths foundation at school-level can help students decide what specialisation in AI they can aim for.
- At a minimum level, differential calculus, linear algebra, statistics and basic probability is needed for understanding the concept of optimisation.
- Meanwhile, Linear algebra is required for matrix algebra and operations and matrix multiplications
- It is not just AI that requires a solid foundation in mathematics, computer science courses also require its basic knowledge.
- Even if students are geared in the direction of machine learning and data mining, a refresher in calculus and probability theory is needed to get one started.
- In fact, the areas of maths in which the student excels, can decide which branch of AI he/she should specialise in
- Differential calculus — understanding how to take the derivative of a function — is basically the key to understanding the concept of optimisation.
China And Canada Introduce AI At High School-Level
So, how can one get high school students interested in one of the most buzzing industries? The first thing one should do is to make learning easy and accessible for students across the board — from practical as well as societal point of view. Maths and Physics curriculum should be redesigned and taught from an AI perspective. Teachers should try to make learning fun and interactive by drawing parallels with applications from the real world. Besides China, which has mandated AI learning at school level, Blyth Academy also created an AI course for high school students in Canada.
According to a NITI Aayog discussion paper, US, China and Japan are already leading in cutting-edge research. US universities such as CMU, MIT, Stanford and Berkeley are already leading in AI research and offering top-notch machine learning courses, led by stellar faculty. Similarly, Chinese universities like Tsinghua and Peking are now leading the race with the highest number of research papers published, have large-scale public funding and have forged research partnerships with Chinese companies. The think tank acknowledges for India to strive ahead in the AI race — a public-private-academia approach is required for AI talent development.
Another key point cited by the research paper is that countries across the globe are ramping up resources for building a future workforce by stepping up investment in STEM talent development through collaborations with universities and designing new courses. For example, UK plans to build a pool of 1,000 PhD researchers by 2025 through the Turing fellowship to fuel development of AI while China is addressing the problem at its root by training 500 teachers and 5,000 students working on AI.
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