Archive for April, 2020

Sandwiches

April 11, 2020

Today, from the writer’s point of view, and a while back from the reader’s point of view, I’m reading a person describing his making of a PB&J sandwich. He asks the person that wanted it, why can’t you make it? That person says, he doesn’t know the ratio.

Mathematicians see everything as being between some upper convergence and some lower convergence. This being a normal distribution, it’s mostly about a ratio about some average and a flight envelope. That average roughly bisects that envelope.

So working from before and moving to after, we start with a minimum usually below zero even if you don’t code on your own dime. And you exit at some max. That minimum is your right most convergence. The maximum is your left most convergence. Moving that middle horizontal line, moves your points of convergence. The Exit line does not indicate the when the VC forces you to exit. That is way earlier.

Notice that we have two similar, but different sandwiches. Call it a burrito and that problem goes away.

Let’s get with modern management theory. Everything has to be aligned. Obviously, our two sandwiches must be correlated, or mapped is some confusing way. Sandwich A is run by executive A. And, Sandwich B is run by executive B. But, we do have a solution, the Burrito. The Burrito invariant solves the the problem.

Actually, it causes problems. But, it is aligned. One executive job cut. But, every bite is the same. And, there is a problem with circles. Circles imply no correlation. So no big data, no machine learning hill climbing–it’s indifference all the way around. But, our data says that a correlation exists, aka an ellipse, and we are not talking about a bad rolling of the burrito.

Now, after adding a dimension, we have an ellipse. That blue ellipse results from the correlation of the new dimension with the prior data. We are adding the technology adoption lifecycle (TALC) as this dimension.

We’ve rolled up our envelope into a burrito. The layers have a varying thickness. We did not align the burrito rollup with the TALC rollup. The TALC is the timeline of the company. The exit is on the right where a real burrito would be sealed.

The statistical structure of the TALC is different from the ingredients in our burrito. The burrito rollup is shown in blue and rolls up from the center. The TALC rolls up from the outside in. This is shown or intended with the black spiral that starts at the tornado. The six early adopters we need are the tortilla. The annotations to the circle to the right end of our burrito hint at where TALC phases would be in to burrito. If only I had Autocad. The numbered circles are the six early adopter/vertical applications. The purple circle is the early mainstreet/aggregated application.

When you take samples of your customers, you would capture their physical location and their TALC phase indicators. That data is independent like our TALC-burrito mapping. When you roll up a burrito, the ingredients that you don’t have don’t get in the burrito. Like when a VC demands an existing market, you can only give them the last half of the burrito (dark green).

The light green represents the early mainstreet phase and a vertical application or two.

Beyond the Burrito

The technology adoption lifecycle is a process. Yes, Moore denies this. So throw in some asynchronous like we did with email, and the process remains. Enter here, enter there. It’s like a shopping mall. It has multiple entries and multiple exits. Yet, you park in your usual spot and take your usual door and then you walk down the mall, the loop. You can’t get back to your car without a loop or a bidirectional walk in the same part of the mall. After a decade of walking the same mall, it seems like a process. The series of stores only changes when one of the stores goes out of business, or moves.

The early mainstreet phase of the TALC, while it is being cannibalized by the cloud, is moving closer to the exits, or for the phobics the entry.

That average of our envelope is just a portion indicator. The TALC has more than one average. The TALC is an aggregate normal of many normals. Each phase is a normal distribution unto itself. Each phase has a front half and a back half. Each phase has an entry point and an exit point. That’s a lot of sandwiches, sandwiches with different and very specific fillings that get served to very particular populations of users. One sandwich satisfies a user in a specific phase, but in no other phases. It’s a Boolean. There are some parameterizations that get us across the TALC.

Sometimes you hear about doing a phase from back there, closer to the entries, after a phase from closer to the exits. Someone will talk about crossing the chasm when they were no innovative and never had a chasm to cross. They didn’t do the chasm’s parameterizations either. Who can blame them.

If they had walked up and pitched something that had no existing market, aka was not in the late mainstreet or later phases, they would not have gotten pitched. VCs don’t invest in that build a market proposition mainly because they were orthodox business people that began their careers in innovative companies that hired them once they were in the late mainstee phase. Nevermind the who statistical geometries problem that causes problems with the mathematics and understates future returns. Of course, they never faced those problems or resolved those problems. Hell, as it is, they exit their continuous innovation investments quickly, because they don’t see, should never see, and will never achieve the multipliers involved in successful discontinuous innovations.

A while back, a lawyer out in my twitter stream was involved with passing an innovation funding effort in the state of Minnesota. The pitch was all about the returns from discontinuous innovation, but most innovation these days is continuous, or not discontinuous. VC exits will follow. Wrong sandwich. Oh, well.

The ratio of continuous to discontinuous innovation is why we don’t have jobs for everyone, why we are not creating new categories, new worlds, new careers, and many more wealthy former programmers. Even they have that wealth inequality problem. The VCs want to blame the financial markets. But, the financial markets know what success is, economic wealth. That is not cash. The VCs are investing solely for cash.

The TALC is a metaphor. I’ll stop here with that here. The mathematical sandwiches are what made me write this post. Yeah, it’s well past lunch here. What gets us up and out of bed, or having lunch at a particular time during this pandemic? Hell, even the pandemic is a technology adoption problem.

Enjoy.

Holes

April 6, 2020

Back in school, we did a lot of math where the solution did not exist. Well, the solution existed, but it was beyond our reach mathematically. Then we ran into step functions where each step was defined by another function. Then, there were functions that had holes. “Tickets? Do you have a ticket?” “No.” “Well, I can’t let you through this hole. Sorry.” Yeah, that hole led to dimension m+1, whatever the hell dimension m happened to be. We could put it more abstractly, but our cognitive limit prevents us from dealing with more dimensions. “Yes, to enter this hole, you have to let another dimension go.”

That multidimensional function with the hole in it is like a step function, but when we move from on step to another, we are entering another dimension. That multidimensional function becomes an aggregate of single dimensional functions. We don’t blink an eye when a normal distribution does the same thing. A single dimension might be kurtotic, so maybe we should blink.

Then, you have the variable before parameterization. Does it exist, or not, aka the Boolean. That’s qualitative. Quantitative doesn’t happen until parameterization has happened. That’s like probabilities. One has to assert a random variable. We get a line to infinity at n0 generated by a Dirac function. There is no interval. But all the probability mass exists. It’s Boolean. The next two values, ni+1 and ni+2 give us an interval, where the probability mass spreads out and a probability, kurtotic, of course. Being kurtotic, those two modes give us two tails. If we only had one of those modes one tail would be short, and other tail would be long. But with two tails, I really don’t know about the tails.

Every dimension has its own probability mass, or not. And, we ignore the whole kurtotic issue. I am not talking dataset here. I’m talking data. Data science is really Dataset science. They are not the same thing. I am not assuming normality, or normalizing the data.

“All of you, up against the wall! Assume the position!
“Do you have your normalization paperwork?”
“Is this all about there not being enough of us?”

What a mess, yes? Well, mathematicians hide the mess under the rug. They like things simple. So the dataset.

“But, its a hole. Only one of us at a time will fit.”

“But, I have nothing to do with that dimension.”
“That’s the aggregate function’s problem, not mine.”
“Well, I’m calling my Do Not Exist. He better not be busy.”
“Yeah, sticking to that story until their lawyer shows up.”