What To Know About Big Tech Machine Learning And AI Advancements

big tech machine learning development linkedin ai microsoft artificial intelligence facebook technology

Recently on the Best Business Builders Blog we discussed how understanding the LinkedIn algorithm tells us what to do with our LinkedIn profiles to maximize the chances of us being found. It was part of a full-day article writing blog blitz, and was one of several conte pieces about LinkedIn artificial intelligence.

What was surprising was how many of the other LinkedIn articles out there don’t use insights gleaned from how LinkedIn’s AI functions. The company (owned by Microsoft) has not been shy about how their new technology system works; their engineers have been on podcasts and livestreams, their engineering blogs talk about their work in machine learning, and their patents, both pending and granted, are full of juicy technical details that tell us clearly how their systems make decisions.

Big Tech Explaining Their Technology

This new tech transparency isn’t only true of LinkedIn or Microsoft either. Facebook, Google, and many other tech companies publish academic papers, research, patents, and other documentation that states clearly how their systems work; for some things, there’s definitely more of a broad-strokes outline than nitty-gritty technical details, but overall, these companies do a decent job of explaining how their technology works. This is why it’s so vital to understand how these systems work, how the algorithms and models work, so that we tune our efforts to work with them, not against them.

The Cooking And Tech Analogy

It’s important that big tech explains its new technology to the masses, because when people don’t understand then things get ugly. Imagine not knowing how a microwave oven worked. You’d just randomly throw foods in it, pick random times, and hope that it worked for the best. Over time you might learn what did and didn’t work from anecdotal evidence (“hey, it was a real bad idea to put the leftovers in a foil container in there”), but if you don’t know the guiding principles of microwave energy, you’d still be flummoxed every time you ran across a new situation.

Contrast that with understanding that microwaves vibrate polar particles, like water. A microwave oven functionally boils food from the inside by vibrating the water particles so quickly that they heat up. Once you understand that a microwave is the same as steaming a food, it becomes clear what you should and shouldn’t use it for. Reheating something? Sure. That’s no different than putting the food over a pot and steaming it hot. Cooking vegetables? Same as steaming. Cooking meat? I can’t think of many recipes that call for steamed meat. Baking bread? Not many breads do well steamed (some buns do, though).

Do you smell what we’re cooking? Tech needs to be taught to the end users and developers and not just tampered with until eventually it’s eventually figured out.

LinkedIn Leveraging Graph Networks

When we look at how LinkedIn uses graph networks for everything, and we understand how graph networks work, suddenly it becomes clear that if we want to be known for something, we need to build our networks around that thing, connect with other people also in our space, and critically, create and engage with content relevant to what we want to be known for. If you’re posting cat memes and political rants on LinkedIn and your business doesn’t do either, then you’re diluting your reputation in its graph network, making the algorithm less likely to display your content to the kinds of professionals you’d want to have viewing it. Knowing how graph networks operate guides our strategies, tactics, and execution.


The takeaway is this: if understand how the AI behind your favorite modern marketing channels works, then you’ll have the wind at your back rather than in your face for 2021 and beyond.