The online version will remark free and available. The in-person, for credit course is being discontinued. Unless you are an incoming Harvard student, this won’t affect you.

For a fee and a background check, one can become a citizen of Mr. Lee’s Greater Hong Kong, Inc., which operates as a sovereign entity within the various territories around the globe in which it has franchises.

Militech will have the initial advantage but don’t count Arasaka Intelligence out, their strategic AI and logistics are second to none.

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(D)ead in the water.

Sorry about the ttrypo.

You misspelled pencil in this context. I believe you meant “a fuckin’ pencil”.

Poland is in NATO, that would trigger Article V. How much more “real” do you want it to be?

Up

Highlander, Inc. Where There Can Only be One^TM.

And you just reminded me of a movie from a show and let’s just say I recommend both.

https://youtu.be/X1DcKBwliAw

Anything Goes!

Sure, but $120k is definitely not FAANG-tier base comp in SF. Not even close. Maybe it’s on the low side of scrappy startup/scaleup comp.

The schools will be dismantled until critical thinking improves.

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I use cloud computing to run a lot of my computer stuff. Not a PC. I self-host some services on a home-server. Also not a PC. I can install a GUI on these if I want and RDP into them, still doesn’t make these PCs.

I can use my personal laptop as a server if I want (and I have!) with remote-access enabled; so it is both a PC and a not-PC?

I think we have to settle on PC being usecase-driven; not hardware-defined. Which is what I think you were trying to get at, but abstracting too far.

Religion.

That’s fair. I see what I see at an engineering and architecture level. You see what you see at the business level.

That said. I stand by my statement because I and most of my colleagues in similar roles get continued, repeated and expanded-scope engagements. Definitely in LLMs and genAI in general especially over the last 3-5 years or so, but definitely not just in LLMs.

“AI” is an incredibly wide and deep field; much more so than the common perception of what it is and does.

Perhaps I’m just not as jaded in my tech career.

operations research, and conventional software which never makes mistakes if it’s programmed correctly.

Now this is where I push back. I spent the first decade of my tech career doing ops research/industrial engineering (in parallel with process engineering). You’d shit a brick if you knew how much “fudge-factoring” and “completely disconnected from reality—aka we have no fucking clue” assumptions go into the “conventional” models that inform supply-chain analytics, business process engineering, etc. To state that they “never make mistakes” is laughable.

Absolutely not true. Disclaimer, I do work for NVIDIA as a forward deployed AI Engineer/Solutions Architect—meaning I don’t build AI software internally for NVIDIA but I embed with their customers’ engineering teams to help them build their AI software and deploy and run their models on NVIDIA hardware and software. edit: any opinions stated are solely my own, N has a PR office to state any official company opinions.

To state this as simply as possible: I wouldn’t have a job if our customers weren’t seeing tremendous benefit from AI technology. The companies I work with typically are very sensitive to CapX and OpX costs of AI—they self-serve in private clouds. If it doesn’t help them make money (revenue growth) or save money (efficiency), then it’s gone—and so am I. I’ve seen it happen; entire engineering teams laid off because a technology just couldn’t be implemented in a cost-effective way.

LLMs are a small subset of AI and Accelerated-Compute workflows in general.

We’re looking at this from opposite sides of the same coin.

The NN graph is written at a high-level in Python using frameworks (PyTorch, Tensorflow—man I really don’t miss TF after jumping to Torch :) ).

But the calculations don’t execute on the Python kernel—sure you could write it to do so but it would be sloooow. The actual network of calculations happen within the framework internals; C++. Then depending on the hardware you want to run it on, you go down to BLAS or CUDA, etc. all of which are written in low-level languages like Fortran or C.

Numpy fits into places all throughout this stack and its performant pieces are mostly implemented in C.

Any way you slice it: the post I was responding to is to argue that AI IS CODE. No two ways about that. It’s also the weights and biases and activations of the models that have been trained.