We’ve all heard the story of Elon Musk deciding he wanted to build rockets, realising he didn’t know how to build rockets, teaching himself with textbooks and the internet, and then becoming the world’s best rocket builder. This is an oversimplification of the tale (and not meant to take away from the amazing SpaceX team, or that Musk is clearly of above-average intelligence) but it makes you think — is identifying the problem enough to position you as a (potential) expert in the solution?
In another world (but the same simulation?) I re-entered the classroom in 2018 and soon began to wonder ‘where are the robots?’.
Now, I’m a teacher. I’m not an engineer. I’m not a computer scientist. I can code, but not like some people can code. I’m an armchair enthusiast of AI — I know they can beat us at games like Go and (kind-of) drive our cars better than us. But I’m not an AI expert, on an assessment rubric, I would be ‘emerging’ or ‘novice’.
But that is kind of the point. In a world of 7+ billion people, it is obvious that there are many people more qualified, intelligent, and experienced than me. Many people who could have taken on the issue of a lack of AI in education. But the movement wasn’t fast enough. 5/10 in ‘doing’ will always outperform 10/10 ‘knowing’. I’m reminded of a tweet I came across after entrepreneur Sam Altman retweeted:
So this is what I applied to the Innovator program with…
At the Innovator Academy, the amazing Monica Martinez took us through the design thinking process, and from this, a solution began to emerge. And so, over the next year, I will be working on Train My Dragon* a platform to allow educators to train AI to do the tasks we don’t want to.
Also at the academy, one of the coaches, Stuart Kelly, spoke about a lightbulb moment during his own project:
‘ its not my problem, its our problem ‘
It takes a village to raise an idea into reality. Some other takeaways from the Academy were:
1. You Need to Know What’s Needed.
To solve a problem you don’t need to know each detail of fixing it, you need to know what is needed. By becoming an expert in knowing what is needed you know what you need to learn, or the skills you need to recruit into your team. It is not important that you do everything. It is important that you make sure everything gets done.
2. Technology Changes Quickly.
This may seem like it works to the disadvantage of non-experts since they are starting on the back foot. But I see this differently. Within a few months of a technology being launched, there are blogs and YouTube tutorials showing us novices how to use it. To encourage us to use technologies, they are very well documented. Just because technology isn’t brand new doesn’t mean it isn’t incredibly useful, especially in a new application.
I’m currently teaching myself how to use TensorFlow, a Google machine learning library. There are endless applications for training this technology to help educators that are currently unexplored.
3. ‘Sell’ Your Idea to Allies and Early Adopters.
If your idea is going to work, you’re going to have to convince other people its worth their time. This might start with finding your first few users, or before that finding some allies or peers to help you develop the project. Needing help executing your idea is just an early opportunity to work on that elevator pitch. If you can convince someone to code/market/design your project with you, getting users on board will probably seem simple.
4. Your Job Is To Make It Work For Educators.
The point of the Google Innovator programme is not to make you a Google Classroom expert (you’ve already passed a test for that) or make you a computer programmer. You are selected because you are an educator. My job with Train My Dragon is not to make AI great, it is to make AI great for educators.
