I. Always Bet On Black

Have a look at that roulette table below. What you see is there are about 50% black sections and 50% red sections and 1 green section (because the house always has to win, right)? If we imagine that black represents Biologic Intelligence and red represents Artificial Intelligence, what you’ll see very quickly is that pretty much everyone in the tech world, whether corporations, investors, or startups are all placing their bets on red. There are very few, if any, folks outside of academia who have placed their bet on black.

Roulette wheel. Always bet on black, aka biologic intelligence.

But just like Wesley Snipes, that’s exactly what we’ve done. We bet the house on Biologic Intelligence because of one very simple reason. You’ll have to keep reading to find that one out.

II. Defining Biologic Intelligence

There are some things that AI can do. But there are many things that Artificial Intelligence cannot do that. One of the most important things it can’t do is generalize across different tasks. For example, a deep learning algorithm can get really good at winning Alpha Go, a game, or get really good at recognizing a cat.

But even when deep learning is really good at one thing, like recognizing a cat, it doesn’t mean it can use the same system to recognize toilet paper. That’s even in the same general ballpark. Computer vision. It’s not even trying to apply the same system to an entirely different problem set like driving a car or auditing financial statements.

Lets use an example to make the point. There was a photo that got shared around the internet earlier in 2016 showing some of the problems inherent in deep learning prediction engines. A computer vision deep learning algorithm was trained on data sets like toilet paper, pirate ships, etc. And so, when you give it a new image, the mathematical formula gives you the confidence of predicting toilet paper or a toilet seat.

As you can see from the example below, even though it’s getting an inherent shape right it’s not quite establishing context.

Deep Learning computer vision predicting the object inside a photo.

Artificial Intelligence requires a different system, different data, different programs and different equations to beat Alpha Go, to run a self-driving car, and to provide business intelligence tools.

The same Biologic Intelligence system works for self-driving cars, beating video games, and optimizing supply chains.

Here’s an example of what a biologic intelligence system looks like in nature. We refer to it as sensory-input-to-motor output. To translate that into terms you can Google, the Artificial Intelligence community might refer to it as “unsupervised, end-to-end learning”. Or more simply, general intelligence.