Does it get the job done?

Some tech choices don’t matter much., because they sit on a smooth curve of cost and quality, and all, ultimately, get the job done. My base model car gets me from A to B just as much as the fanciest luxury model would. Not in as much style, but that’s okay.

Some tech choices matter tremendously, because the wrong choices fall on the “won’t even work, at all” side of a discontinuity: An airplane does not get you to the moon. Doesn’t matter that an airplane is cheaper than a rocket. (A favourite rant of mine: If the good solution costs $1000 and the bad solution costs $100, you don’t save $900 by going for the bad one. You waste $100.)

One of the challenges in an AI project is that many choices are of the latter type and you don’t necessarily know beforehand what the right answer is before you try it. That’s where broad experience and a history of experimenting with different approaches comes in handy. It’s unlikely that you encounter exactly the same problem twice, but you build up intuition and a certain sixth sense that will tell you:

• Ah, it feels like a random forest with gradient boosting would do fine here

• Hm, I feel that fine-tuning one of the BERT models won’t get us there, but a workflow with two LLama models working together will.

And so on. Is there a simple checklist? I wish. There’s no way around building up experience. Though the general principle is:

• The more nuance and context-dependence a task has, the more powerful of a model is required.

Concretely, if you pick a random person and they can make the correct decision for you task by looking at just a few lines of input, chances are it’s a simple problem: “Is this user review of my restaurant positive or negative?” and so on.

But if you need an expert, and that expert would consult not only their intrinsic knowledge but countless additional resources, you’re looking at a much larger, more complex problem. No matter how much data you throw at a simpler model, in this case it just won’t get the job done.

PS: Thinking about a challenging problem and not sure what approach would have a chance at getting it solved? Talk to us.