Ah, the proliferation of abilities. Never ending. On the computer, that is, in all types variants. And, the complexity. Confounded seems to be everywhere extant. 

Deep learning is a misnomer. Let us go down into deep thinking and show how this is so. About time? One ML expert (long years of experience) mentioned that he is looking at the other history of AI. Yes, we’ll need some type of hybrid effort. I’m getting to that. Another noted that ML/DL will not do physics in the sense of sitting at a desk like the student or in the office as the theoretician or in the lab as the experimentalist or in the field as the real person (we need more people to get down and dirty – like the geologists?). 

Myself, I want to talk truth engineering. However, since the computer is the key, we have to use the cloud as dirty as it is. For me, it was stepping into GitHub the other day. I’ll be there for a while with reports out to the real world from time to time.

Initial reaction was delight. With respect to all of the work that got us to where we are now over the decades. My initial focus is html/css/js. Anyone who has watched has seen this explode in interest. It might even go beyond Python. Let’s discuss that. 

In the meantime, I have┬áseen a 3D graphics, some rule processing, OOP mimicking, and more on JavaScript, the scourge of the coder crowd (it seemed at one time). I’m starting with it and will be branching to other languages when appropriate.

As I work (getting familiar with the cloud’d development approaches) and look at issues for TGS plus truth engineering, I’ll have GitHub as the focal as it has support for project management and more. And, under PortalToTruth, there is a Rlog which looks at technical issues as they are addressed.

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