So because of the sheer number of interviews I’ve been doing I’m starting to get a bit brazen with them since I’ve started to really not give a fuck about most of them and I’ve started to notice patterns in common lines of questioning resulting in this unexpected gem today:

Interviewer: So we always start our devs off on the bottom end of our salary band.

Dev: Either give me the top or I’m not interested.

Interviewer: 😡. But if we start you at the top of the salary band we’ll have nothing to give you later. 🥺.

Dev: No need, I’ll take the money up front. Companies don’t give raises these days anyway, it’s just a carrot to dangle in front of the naive.

Interviewer: 😡. Well if all you care about is money so focussed on money you’ll just leave if a better offer comes around!

Dev: All the more reason to give me the highest number possible to defend against that possibility.

Interviewer: 😡. But there are other devs on the team with similar experience that will be making less than you.

Dev: Sounds like they fell for the negging and guilt tripping you are currently attempting on me in order to save a buck. Salary is not based on your skills or experience anymore, it’s based on your ability to negotiate. Here’s mine.

Interviewer: ………………. I’ll pass you along to the hiring manager.

Dev: ???? wtf

HOW THE FUCK DID THAT ACTUALLY WORK ARE YOU FUCKING KIDDING ME I WAS TRYING TO GET THEM TO HANG UP FOR SHITS AND GIGGLES AND NOW I’M LOOKING AT A 20K RAISE ALL BECAUSE I CONTINUALLY TOLD THEM TO GO FUCK THEMSELVES??? THIS IS ACTUALLY WHAT IT TAKES TO BE TREATED PROPERLY BY A COMPANY???

In today's episode of dR Hunger Games...

RIP in peace @Frederick

Long live District05!

*giggles* congrats to 16 upvotes

This is gonna be a long post, and inevitably DR will mutilate my line breaks, so bear with me.
Also I cut out a bunch because the length was overlimit, so I'll post the second half later.

I'm annoyed because it appears the current stablediffusion trend has thrown the baby out with the bath water. I'll explain that in a moment.

As you all know I like to make extraordinary claims with little proof, sometimes
for shits and giggles, and sometimes because I'm just delusional apparently.

One of my legit 'claims to fame' is, on the theoretical level, I predicted
most of the developments in AI over the last 10+ years, down to key insights.
I've never had the math background for it, but I understood the ideas I
was working with at a conceptual level. Part of this flowed from powering
through literal (god I hate that word) hundreds of research papers a year, because I'm an obsessive like that. And I had to power through them, because
a lot of the technical low-level details were beyond my reach, but architecturally
I started to see a lot of patterns, and begin to grasp the general thrust
of where research and development *needed* to go.

In any case, I'm looking at stablediffusion and what occurs to me is that we've almost entirely thrown out GANs. As some or most of you may know, a GAN is
where networks compete, one to generate outputs that look real, another
to discern which is real, and by the process of competition, improve the ability
to generate a convincing fake, and to discern one. Imagine a self-sharpening knife and you get the idea.

Well, when we went to the diffusion method, upscaling noise (essentially a form of controlled pareidolia using autoencoders over seq2seq models) we threw out
GANs.

We also threw out online learning. The models only grow on the backend.
This doesn't help anyone but those corporations that have massive funding
to create and train models. They get to decide how the models 'think', what their
biases are, and what topics or subjects they cover. This is no good long run,
but thats more of an ideological argument. Thats not the real problem.
The problem is they've once again gimped the research, chosen a suboptimal
trap for the direction of development.

What interested me early on in the lottery ticket theory was the implications.
The lottery ticket theory says that, part of the reason *some* RANDOM initializations of a network train/predict better than others, is essentially
down to a small pool of subgraphs that happened, by pure luck, to chance on
initialization that just so happened to be the right 'lottery numbers' as it were, for training quickly.

The first implication of this, is that the bigger a network therefore, the greater the chance of these lucky subgraphs occurring. Whether the density grows
faster than the density of the 'unlucky' or average subgraphs, is another matter.
From this though, they realized what they could do was search out these subgraphs, and prune many of the worst or average performing neighbor graphs, without meaningful loss in model performance. Essentially they could *shrink down* things like chatGPT and BERT.

The second implication was more sublte and overlooked, and still is.
The existence of lucky subnetworks might suggest nothing additional--In which case the implication is that *any* subnet could *technically*, by transfer learning, be 'lucky' and train fast or be particularly good for some unknown task.

INSTEAD however, what has happened is we haven't really seen that. What this means is actually pretty startling. It has two possible implications, either of which will have significant outcomes on the research sooner or later:
1. there is an 'island' of network size, beyond what we've currently achieved,
where networks that are currently state of the3 art at some things, rapidly converge to state-of-the-art *generalists* in nearly *all* task, regardless of input. What this would look like at first, is a gradual drop off in gains of the current approach, characterized as a potential new "ai winter", or a "limit to the current approach", which wouldn't actually be the limit, but a saddle point in its utility across domains and its intelligence (for some measure and definition of 'intelligence').