Welcome to AI! Welcome to machine learning! Does it matter if you don’t know the difference? Nope, because you’ll start applied projects in them the same way.
What way is that? Perhaps surprisingly, not any of these:
Look familiar? These tend to be favorite starting points, but they’re all traps. Many businesses fall for them and fail at machine learning, but not you. You’ll start right.
But first, why are these the favorites? It’s a story of comfort zones.
If you’re the studious type, your instinct might be to take a course or sign up for a degree. Watch out, though. The classic AI courses out there are probably wrong for your needs. I’d hate for you to wind up with the wrong degree!
If you’re a business leader, your instinct is to hire someone who sounds qualified. Great instinct! Except the person best qualified to start an applied AI project is not your garden variety AI PhD. It’s… you! Whoops. Hire yourself first and read on to find out what you’re supposed to do before you bring your champion nerd on board.
If you’re an AI researcher— recently hired to sprinkle some machine learning magic on top of the business, am I right?— you’ll want to start where you’re most comfortable. With the algorithm, naturally. You just spent 10 years of your life studying how to design new AI methods, so why would the leader want you to start elsewhere? Let’s pick an algorithm… mmmm,neural networksare all the rage. Maybe we can figure out how to make them even cooler? Let’s create a new approach! Now, what data can we shove into our new-ral network? (Here’s hoping we’ll wind up with something we can sell to the leader to justify the past six months spent inventing things.)
Or maybe you’re a data scientist. (Also a classic first hire, since today’s market thinks data scientists walk on water.) Perhaps you also have a PhD, but your Great Love isn’t methods. It’s data. Data data data! What data do we have? Let’s figure out what beautiful ingredients we can use!
Wait… use for what?
If you’re a data scientist or AI researcher and this sounds familiar, you just got handed a lemon by your leader. They let you down! Go on strike until they’ve done their part.
Leaders, figure out who’s calling the shots. If it’s you, then let’s designate you The Decision-Maker for this project. Otherwise, delegate the position to someone else and ask them to read the rest of this while you play outside in the sunshine.
Okay, Decision-Maker. It took a while to track you down, but here you are. You understand the business and you have plenty of imagination, so you’re qualified for this. Glad someone forwarded you this letter! Let’s get you oriented with how to set a machine learning (or AI) project up for success.
Imagine that this ML/AI system is already operating perfectly. Ask yourself what you would like it to produce when it does the next task. Don’t worry how it does it. Imagine that it works already and it is solving some need your business has. (That’s why you needed those qualifications. Someone fresh out of a PhD doesn’t understand your business yet, so they’re not qualified for this task.)
The problem with the approaches discussed previously is that the order of operations is all messed up. The right way to approach an applied project is to flip the algorithms-inputs-outputs order on its head, like so: think about outputs, then inputs, then algorithms!
A kitchen analogy comes in handy here. If you’re running a restaurant (as opposed to an appliance factory or food science lab), why would you think about buying — or, worse, inventing — a pizza oven before you’ve even considered whether adding pizza to your menu makes sense? That sounds like the rookie mistake of someone who doesn’t know what business they’re in. Instead, start with what your customers want and what food quality you’re willing to settle for.
Figuring out what success looks like can be nuanced. Which of these three is good behavior?
“All of them”? But surely you don’t want your police dog chasing sheep! Or vice versa, for that matter. A better answer is that it depends on what the owner wants. That’s you! Diving into algorithms and data before figuring out what outputs would count as good or bad behavior is a bit like putting a puppy in a basement with food and water, then being surprised what comes out isn’t good at being a police dog. You can’t expect to just sprinkle machine learning on your business, leave it brewing, and get something useful.
Applied AI requires you to have a very clear vision of what your model needs to grow up into (and why), then you have to train it towards that. If you don’t know what you want, head to the font of inspiration instead: analytics.
Besides, analytics uses some of the same math, so you wouldn’t be lying if you told your friends you’re using ML/AI algorithms (though you’re not building ML/AI systems). Many people who think they want ML/AI actually only need analytics. The latter is a great idea for all projects, while the former is good for only certain kinds. If you’re unsure, go for the sure thing.
Dogs aren’t born knowing that you don’t appreciate their couch-chewing inclinations. It’s up to you to think about what you’re looking for in a pet so you can train them towards your ideal… before you find out what the stuffing is made of.
What goes for puppies goes for ML/AI systems. To figure out what success looks like, you don’t need to understand how the puppy’s brain learns from sensory inputs. You don’t need to think about how those sensory signals are stored and processed (yet). What you do need is to figure out that you want a sheep dog (and what that means to you). To do your job thoroughly, you also need enough imagination to picture what behaviors you’re aiming for and what you’re trying to avoid.
Additionally, it helps to do a quick intuitive reality check: verify that relevant data is within your reach and that you have the hardware muscle to process it. If you’re training a sheep dog, are you confident you can get hold of enough actual sheep to show it? Even if you have sheep, your puppy’s brain needs to be able to take in and use information about them. If your “puppy” is actually a fly larva, it’s not going to be able to do good things with sensory data about sheep. (It can’t run in production either.) I don’t need to tell you that you’ll have a problem.
What’s obvious with dogs seems to elude many ML/AI teams I’ve seen. Some only ask what the dog is for when they retrieve it from the basement after a few years. Well, now you know.
Figuring out what problem ML/AI will solve for you is the first and most important step in your project, but unfortunately it’s quite often taken by the wrong people in an organization. While it’s supposed to fall squarely within the decision-makers’ remit, for some reason leaders try to avoid their duties by hiring a bunch of PhDs and sending them off to “Go sprinkle machine learning over the top of our business so… good things happen.” What could possibly go wrong?
It takes business savvy to properly think through what an ML/AI system is supposed to do for you and why it’s worth building. Focus on this first, before getting anywhere near the nitty gritty, including figuring out whether or not the algorithm that’ll solve your problem is considered AI or ML (you deal with that much later). If you have no ML/AI training, tackling this first part before you’ve hired a team or bought sci-fi kit might sound daunting, but I’ve got your back… here’s a step-by-step guide just for you!
If you had fun here and you’re looking for an applied AI course designed to be fun for beginners and experts alike, here’s one I made for your amusement:
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