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How to Prepare the Next Generation for Jobs in the AI Economy

Most of us regard self-driving cars, voice assistants, and other artificially intelligent technologies as revolutionary.


For the next generation, however, these wonders will have always existed. AI for them will be more than a tool; in many cases, AI will be their co-worker and a ubiquitous part of their lives.

If the next generation is to use AI and big data effectively – if they’re to understand their inherent limitations, and build even better platforms and intelligent systems — we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level.

For example, consider how kids are currently interacting with AI and automated technologies: Right now, it might seem magical to tell Siri, “Show me photos of celebrities in orange dresses,” and see a photo of Taylor Swift pop up on a smartphone less than a second later. But it’s clearly not magic. People design AI systems by carefully decomposing a problem into lots of small problems, and enabling the solutions to the small problems to communicate with each other. In this example, the AI program divides the audio into chunks, sends them into the cloud, analyzes them to determine their probable meaning and translates the result into a set of search queries. Then millions of possible answers to those queries are sorted and ranked. Thanks to the scalability of the cloud, this takes just a few dozen milliseconds.

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This isn’t rocket science. But it requires a lot of components – waveform analysis to interpret the audio, machine learning to teach a machine how to recognize a dress, encryption to protect the information, etc. While many are standard components that are used and re-used in any number of applications,  it’s not something a solitary genius cooks up in a garage. People who create this type of technology must be able to build teams, work in teams, and integrate solutions created by other teams. These are the skills that we need to be teaching the next generation.

Also, with AI taking over routine information and manual tasks in the workplace, we need additional emphasis on qualities that differentiate human workers from AI — creativity, adaptability, and interpersonal skills.

At the elementary level, that means that we need to emphasize exercises that encourage problem solving and teach children how to work cooperatively in teams. Happily, there is a lot of interest in inquiry-based or project-based learning at the K-8 level, though it’s hard to know how many districts are pursuing this approach.

Ethics also deserves more attention at every educational level. AI technologies face ethical dilemmas all the time — for example, how to exclude racial, ethnic, and gender prejudices from automated decisions; how a self-driving car balances the lives of its occupants with those of pedestrians, etc. — and we need people and programmers who can make well-thought-out contributions to those decision making processes.

We’re not obsessed about teaching coding at the elementary levels. It’s fine to do so, especially if the kids enjoy it, and languages such as Snap! and Scratch are useful. But coding is something kids can pick up later on in their education. However, the notion that you don’t need to worry at all about learning to program is misguided. With the world becoming increasingly digital, computer science is as vital in the arts and sciences as writing and math are. Whether a person chooses to become a computer scientist or not, coding is something that will help a person do more in whatever field they choose. That’s why we believe a basic computer programming course should be required at the 9th grade level.

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