I have a rule. After every podcast, I write down 10 things I learned. I don’t know if anyone else does this. Do you do this? Some people make illustrations. They send me what they’ve learned. It’s a creation of a creation of a creation. A drawing of a podcast of someone’s life.But I broke my rule. It’s been over a month. And my brain is digging for the lessons from my interview with the creator of WordPress. I think I have Alzheimer’s. Matt was 19 years old when he started WordPress. It was 2003. Now WordPress.com gets more traffic than Amazon.com.The Wall Street Journal and The New York Times both use WordPress. I use WordPress.I wanted to know if it’s still worth the time and effort to make your own site. He said it is. That’s how you break out…“We’re trying to revitalize the independent web,” Matt Mullenweg said. He’s 33 now. “It’s not like these big sites are going anywhere. They’re fantastic. I use all of them, but you want balance. You need your own site that belongs to you… like your own home on the Internet.”This is part of Matt’s code. Not WordPress’s “code.” Matt’s like a robot. I mean that as a compliment. There are many signs of this: language, ability, he’s very exact.
Done? While there’s not necessarily a “correct” answer here, it’s most likely you split the bugs into four clusters. The spiders in one cluster, the pair of snails in another, the butterflies and moth into one, and the trio of wasps and bees into one more.That wasn’t too bad, was it? You could probably do the same with twice as many bugs, right? If you had a bit of time to spare — or a passion for entomology — you could probably even do this same with a hundred bugs.For a machine though, grouping ten objects into however many meaningful clusters is no small task, thanks to a mind-bending branch of maths called combinatorics, which tells us that are 115,975 different possible ways you could have grouped those ten insects together. Had there been twenty bugs, there would have been over fifty trillion possible ways of clustering them.With a hundred bugs — there’d be many times more solutions than there are particles in the known universe. How many times more? By my calculation, approximately five hundred million billion billion times more. In fact, there are more than four million billion googol solutions (what’s a googol?). For just a hundred objects.Almost all of those solutions would be meaningless — yet from that unimaginable number of possible choices, you pretty quickly found one of the very few that clustered the bugs in a useful way.Us humans take it for granted how good we are categorizing and making sense of large volumes of data pretty quickly. Whether it’s a paragraph of text, or images on a screen, or a sequence of objects — humans are generally fairly efficient at making sense of whatever data the world throws at us.Given that a key aspect of developing A.I. and Machine Learning is getting machines to quickly make sense of large sets of input data, what shortcuts are there available? Here, you can read about three clustering algorithms that can machines can use to quickly make sense of large datasets. This is by no means an exhaustive list — there are other algorithms out there — but they represent a good place to start!You’ll find for each a quick summary of when you might use them, a brief overview of how they work, and a more detailed, step-by-step worked example. I believe it helps to understand an algorithm by actually carrying out yourself. If you’re really keen, you’ll find the best way to do this is with pen and paper. Go ahead — nobody will judge!