AI Has Finally Woken Up to Its Power Problem

Photo by Kelly Sikkema / Unsplash

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Early in my electrical engineering degree, I learned the importance of testing and modeling. I quickly learned that any time you can effectively model and instrument a real-world outcome using software, you make your life easier.

Not only can you prototype good, bad, and frankly ugly ideas faster, but you can also save yourself tons of time as your project gets more and more complex.

However, I also learned that creating these modeling engines can be a pain in the *ss — especially at the start.

You have to create all sorts of different inputs and outputs.

Each input and output has to model a specific part of your environment. It also needs to record what happens before, during, and after each event so that when something inevitably goes wrong, you know what happened.

Then, each input and output has to interact with all the other inputs and outputs seamlessly. It doesn’t take long before you start feeling like you’re spending more time modeling the outcome than you are actually creating it in the first place.

Even though you eventually learn that the time spent now doing this modeling will…