model_tar_gz

The “if you build it, they will come” strategy.

Why do I like, specifically, want this specific “knife” so bad?

Nope. Security risk, can’t let him leave. He can live out the rest of his days desperately clinging to any straw of attention he can get in mar a lago.

And CPD: Chronic Procrastination Disorder

Then download a NVIDIA 4090Ti and a 4K x 144Hz screen.

newegg hates this one simple trick.

Fucking work for once you piece of fuck. Fuck this day. Fuck this shit. Fuck this degree. Fuck.

Cool cool, we’re cool. I get a little triggered when I hear people say that NN/DL models are “fancy statistics”—it’s not the first time.

In what seems like another lifetime ago, my first engineering job was as a process engineer for an refinery-scale continuous chromatography unit in hydrocarbon refining. Fuck that industry, but there’s some really cool tech there nevertheless. Anyway when I was first learning the process, the technician I was learning from called it a series of “fancy filters” and that triggered me too—adsorption is a really fascinating chemical process that uses a lot of math and physics to finely-tune for desired purity, flowrate, etc. and to diminish it as “fancy filtration”!!!

He wasn’t wrong, you’re not either; but it’s definitely more nuanced than that. :)

Engineers are gonna nerd out about stuff. It’s a natural law, I think.

AI is a very broad term that also includes expert systems (such as Computational Fluid Dynamics, Finite Element Analysis, etc approaches.). Traditional machine learning approaches (like support vector machines, etc.) too. But yes, I agree—most commonly associated with deep learning/neural network approaches.

That said, it’s misleading and inaccurate to state that neural networks are just statistics. In fact they are substantially more than just advanced statistics. Certainly statistics is a component—but so too is probability, calculus, network/graph theory, linear algebra, not to mention computer science to program, tune, and train and infer them. Information theory (hello, entropy) plays a part sometimes.

The amount of mathematical background it takes to really understand and practice the theory of both a forward pass and backpropagation is an entire undergraduate STEM curriculum’s worth. I usually advocate for new engineers in my org to learn it top down (by doing) and pull the theory as needed, but that’s not how I did it and I regularly see gaps in their decisions because of it.

And to get actually good at it? One does not simply become a AI systems engineer/technologist. It’s years of tinkering with computers and operating systems, sourcing/scraping/querying/curating data, building data pipelines, cleaning data, engineering types of modeling approaches for various data types and desired outcomes against constraints (data, compute, economic, social/political), implementing POCs, finetuning models, mastering accelerated computing (aka GPUs, TPUs), distributed computation—and many others I’m sure I’m forgetting some here. The number of adjacent fields I’ve had to deeply scratch on to make any of this happen is stressful just thinking about it.

They’re fascinating machines, and they’ve been democratized/abstracted to an extent where it’s now as simple as import torch, torch.fit, model.predict. But to be dismissive of the amazing mathematics and engineering under the hood to make them actually usable is disingenuous.

I admit I have a bias here—I’ve spent the majority of my career building and deploying NN models.

It wants to sell my air quality data. How often I fart, what the dispersion rate is, geolocation correlations for wildfire air quality monitoring, how often I replace filters.

Why the fuck does my air purifier need to be cloud connected?

At this time of year?

Popeyes and Taco John’s at the Love’s.