Meta Play Tactic or Pop Up a Level. Heh Heh Heh

September 4, 2023

Vea4_thumb_thumb_thumb_thumb_thumb_tNote: This essay is the work of a real and still-alive dinobaby. No smart software involved, just a dumb humanoid.

Years ago I worked at a blue chip consulting firm. One of the people I interacted with had a rhetorical ploy when cornered in a discussion. The wizard would say, “Let’s pop up a level.” He would then shift the issue which was, for example, a specific problem, into a higher level concept and bring his solution into the bigger context.

8 31 pop up a level

The clever manager pop up a level to observe the lower level tasks from a broader view. Thank Mother MJ. Not what I specified, but the gradient descent is alive and well.

Let’s imagine that the topic is a plain donut or a chocolate covered donut with sprinkles. There are six people in the meeting. The discussion is contentious because that’s what blue chip consulting Type As do: Contention, sometime nice, sometimes not. The “pop up a level” guy says, “Let pop up a level. The plain donut has less sugar. We are concerned about everyone’s health, right? The plain donut does not have so much evil, diabetes linked sugar. It makes sense to just think of health and obviously the decreased risk for increasing the premiums for health insurance.” Unless one is paying attention and not eating the chocolate chip cookies provided for the meeting attendees, the pop-up-a-level approach might work.

A current example of pop-up-a-level thinking, navigate to “Designing Deep Networks to Process Other Deep Networks.” Nvidia is in hog heaven with the smart software boom. The company realizes that there are lots of people getting in the game. The number of smart software systems and methods, products and services, grifts and gambles, and heaven knows what else is increasing. Nvidia wants to remain the Big Dog even though some outfits wants to design their own chips or be like Apple and maybe find a way to do the Arm thing. Enter the pop-up-a-level play.

The write up says:

The solution is to use convolutional neural networks. They are designed in a way that is largely “blind” to the shifting of an image and, as a result, can generalize to new shifts that were not observed during training…. Our main goal is to identify simple yet effective equivariant layers for the weight-space symmetries defined above. Unfortunately, characterizing spaces of general equivariant functions can be challenging. As with some previous studies (such as Deep Models of Interactions Across Sets), we aim to characterize the space of all linear equivariant layers.

Translation: Our system and method can make use of any other accessible smart software plumbing. Stick with Nvidia.

I think the pop-up-a-level approach is a useful one. Are the competitors savvy enough to counter the argument?

Stephen E Arnold, September 4, 2023


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