A few weeks ago I had the opportunity to connect with a colleague who is the Chair of the Computer Science Department at one of the top universities in the United States. He told me that their entry-level AI class has 10,000 students. They are unable to keep up with the pace of interest. Similarly, an undergraduate student at another prestigious US university told me he cannot get into an AI class – they are all full.
What is the moving train?
I like to think of the AI world as a moving train. Innovations come out every day. Because many of these innovations are made publicly available as open-source software, as soon as a new entry emerges, others build upon it and release further innovations. From a research standpoint, a recent analysis suggests that, as early as 2019, 3 new papers were submitted to Arxiv (a respected repository of AI papers) every hour, over 148 times faster than in 1994. The same analysis suggests that over 5 researchers enter the AI field every hour since 2019. The AI train is moving very fast, and every day it gains speed.
How to get on the train?
This is the question most people ask me – whether they are the parents of a 6th grader or a professional with decades of technical experience but not in AI. They all see the train moving. Many of them sense that it is gaining speed. They are standing on the sideline, wondering how to get on a train that never stops. They wonder if they are ready, but they also sense that waiting is unwise. Every day they do not get on the train, it becomes harder to get started. So how does one ride the train?
- In a previous post, I provided a context to AI Literacy called the 4 C’s – Concepts, Context, Capability, and Creativity. The key idea is that AI is a large space and is not possible to understand everything deeply at the beginning. By understanding some of the core concepts, how they are used (Context), having some hands-on experience (Capability), and mapping it to problems of interest (Creativity), you can start to get a handle on the situation.
- Start as early as possible. K-12 is not too early. The anecdotes above suggest that K-12 may be exactly the right time – and waiting may be too late. A recent survey of AI projects in Science Fairs suggests that AI can be a very powerful complement to other STEM subjects at the K-12 level, creating an opportunity to learn early and thrive.
- Top-down (start by riding the train – do not start with the internals of the engine). A mistake some learners make is to feel that they cannot even start to learn AI till they learn more math or coding. This is similar to trying to learn how the engine works while the train is speeding away. By the time you learn how one type of engine works, the train will be moving even faster, and likely with an entirely new kind of engine.
Takeaways: Riding the Train
- Jump on. Try some AIs. Learn how they work conceptually. Dive in as deep as is appropriate for your interests and background.
- When a new type of AI comes out – give it a try! While AIs used to be hard to find and access, they are not so any longer. Many new AIs are instantly available on websites. Try them and form an opinion based on what you know.
- Pick and choose. As you try new AIs, you will develop an opinion of what is helpful to you – whether you are a K-12 teacher, a lawyer, a doctor, etc. The AI space is too big for everyone to learn everything. The key is to choose. It may be difficult at the beginning, but after you have tried a few AIs, you will have a stronger opinion on what is right for you.
- Keep learning. Success does not mean deeply learning a type of AI that is already outdated. Success means knowing how to parse the flow of new developments, pick what works for you, ask good questions, and use the new technology to thrive.