I came to the same observations; lots of experts not much expertise.

I think my wider team are on par with their ability and understanding so we now can sift through the BS a bit easier.

Nod, smile, accept that no one has a clear understanding.

I'm the AI expert for my org. Everyone else is more or less opposed to AI.

>One senior-dev (team-lead also) tried to explain to me that AI is a subfield of machine-learning, and always stochastic in nature (since ChatGPT responds differently to the same prompt).

machine learning is the sub field of AI.

Not really stochastic as far as I know. The whole random seed and temperature thing is a bit of a grey area for my full understanding. Let alone the topk, top p, etc. I often just accept what's recommended from the model folks.

>We/they are selling tailor-made "AI-products" to other businesses, but apparently we don't know how sampling works...?

Sales people dont tend to know jack. That doesnt mean they dont have an introvert in the back who does know what's going on.

>Am I just too junior/naive to get this or am I cooked?

AI for the most part has been out a couple years. With rapid improvement and changes that make 2023 knowledge obsolete. 100% of us are juniors in AI.

You're disillusioned because the "ai experts" basically dont exist.

This "senior dev" has it all mixed up and is incorrect.

"AI" is all encompassing umbrella term that includes other fields of "AI" such as the very old GOFAI (good old fashioned AI) which is rule-based, machine learning (statistical, bayesian) methods, and neural networks which deep learning and more recently generative AI (which ChatGPT) uses.

More accurately, it is neural networks which are more "stochastic" with their predictions and decisions, not just transformer models which ChatGPT is based on.

> Am I just too junior/naive to get this or am I cooked?

Quite frankly, the entire team (except you) is cooked, as you have realized what you don't know.

Early on in my career I was hyper-fixated on building features correctly at this particular company, according to what I thought was a proper way to build websites. I was probably right, but my job wasn't to be right, my job was to get things done in a certain period of time according to whatever people who controlled the money at the company thought was important, not what a nerd would necessarily care about.

When you're in school or just graduated, you're basically qualified to start learning (outside academia) and it's important to pay attention to what other people value, then do your best within that until you have the power to determine what's worth valuing.

Many times plum resume building assignments at big companies go to the best politicians rather than the biggest experts.

But I know enough to know neither AI nor machine learning are subfields of the other. AI just developed out of the very earliest days of electronic computing as an expression of the desire to get intelligent behavior out of computers by any means possible. Machine learning arose from the desire to express functions in which we know the inputs and outputs but not the form of the function itself, so we use various estimation methods that can be learned from the data itself. A whole lot of overlap and parallel efforts simultaneously developed the same or similar techniques between computer scientists and software engineers on the one side and statisticians and applied mathematicians on the other side. It seemed to have turned out that statistical methods generally seem to provide the best algorithms for machine learning, and machine learning has seemed to provide the best algorithms to get intelligent behavior out of computers.

So they've kind of grown together, stats, automated learning, and AI, but they're still distinct things that developed independently of one another and still exist independently of one another.

This is putting aside all the various "big data" technologies and efforts that grew out of the 2007 or so era of collecting enormous amounts of user or machine-generated data that required new tech to store, query, and new ways to perform parallel batch processing often married to the storage and query tech, all of which was necessary for and enabled statistical machine learning to become as successful as it has become, but is completely separate from the mathematical and algorithmic discipline itself.

Even the guys I named above are probably not really experts in all of these things separately. As with anything, it takes a village.

Similarly if they participated in all the early arguments about where your models would be located then they have no idea now that they are fed up with the endless thread of subtle change requests.

And then there’s of course career climbers playing politics and people getting into the field because of interest or resume building.

This part is honestly the most worrying to me, as compliance with customers and legal would really need you to not lie about this.

Mean? Sure. Reality? You betcha. It's incredibly rare these days to encounter truly competent professionals. Most are just hoping the guy below them doesn't know enough to spot their shortfalls and speak up.

This aligns shockingly well with Uncle Bob's rough stat: “The number of programmers doubles every five years or so. This means that half the programmers in the world have less than five years of experience.”

There's definitely a rush of people trying to upskill/reskill into this technology space despite having no formal training or background beyond basic dev skills. There's other people (such as myself) that came from the big data/NLP space (ads & search) that are trying to add AI to our extensive skillsets but aren't necessarily deep-math experts.

Unfortunately there's not a lot of room at the top and the vast majority of AI implementations at smaller companies are just OpenAI API wrappers. Essentially there's very little lived experience since it's expensive to experiment at home and smaller companies just aren't going to invest in self-hosted models that are expensive to run and quickly fall behind state of the art.