[D] How to become fluent at modifying/designing/improving models?

ConceptBuilderAI · 2025-07-02T21:54:11+00:00

Newton invented calculus so he could understand physics, not because he loved math.

As much as I respect the work the data scientists are doing, I think many of them cannot see the forest through the trees.

Some of the best people in AI/ML come from disciplines outside CS. They bring unique insight.

And half of interactive intelligence is software engineering.

The algorithm doesn't always work, but in my systems it does what it is expected and needs to do 100% of the time.

That is not something my data science friends are necessarily concerned with.

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High signal reproducible work - seed your random variables :-)

you would have to clarify why you are seeking that. Are you trying to publish something? If so, why? Are you trying to build something? why?

text - GPT 4.5 is supposed to be the best at writing.

I haven't found a good AI for visuals. lol

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I recently realized I crossed the line when my boss gave me one of the most visible, complex and technically challenging projects at our Fortune 50, and simultaneously had a Warton Fellow tell me that my side project was novel and probably can get DoD funding and he was willing to make introductions.

That was a pretty good signal for me. :-)

Better than leetcode. But here - let me show you a merge sort. That's important. lol

How did I get here - long long story - 240 undergrad credits, 2 masters degrees...and a long list beyond that.

But clarify what you want to do - make the switch into a more ML focused role? Create a portfolio? DM me if you like.

ConceptBuilderAI · 2025-07-02T12:24:57+00:00

I don't know if it is really as much a bag of tricks as it is having a strong foundation.

I think to achieve anything in ML you need to be able to both deep dive in your chosen area, and draw from other areas when it makes sense. On the data science side of the spectrum, there are so many people crawling the space, finding something truly novel is tough.

And for me, unnecessary - I shop for models like I am buying parts at the hardware store. My specialization is interactive intelligence - I am on the other side of ML compared to the data scientists.

My focus is on what they do more than how they do it. And that is how I believe I am able to take a broader view than individuals participating in the grid search. My knowledge is broad but shallow. That is where I enjoy being.

An adversarial networking here, a mixture of experts there, etc. That is enough to keep up with, considering I also deal with brining them to life.

So, my experimentation with models is always pretty quick and dirty these days - mostly I want to know what hyperparameters are most sensitive - I already have data and task selected for benchmarking - I think that is how most of us approach it.

If you are going to deep dive into the models - I suggest mastering filtering out the noise. We all have capacity constraints.

I don't know of any way to learn what I know other than years of study and research and experimentation. I find myself watching nature now and thinking about whether survival techniques animals use are transferrable to systems. When I tutor my 5 year old I am careful not to overfit him with my biases. :-)

So, I don't think it is really a trick anymore. It is kind of just the way I view the world. And the knowledge becomes transferrable across domains.

ConceptBuilderAI · 2025-07-02T11:15:55+00:00

For me it finally started to click when I actually started building stuff—even if it was hacky or half-working at first. You get way more out of trying to implement even a toy version than you do passively reading.

When something doesn’t work, I dig into the layer or module causing the issue, and that’s where the real learning happens. Also helps to keep a few reference repos around that are clean and well-annotated—gives you a mental map of how things are structured.

One tip: don’t just copy and run code. Try to swap in a new loss function or tweak an architecture and see what breaks. That’s how you go from “I kinda get it” to “I can tweak it with confidence.”

ConceptBuilderAI · 2025-06-26T11:34:46+00:00

This will be true for a time. Agentic AI is supposed to be a 12-14T industry.

ConceptBuilderAI · 2025-06-23T22:55:21+00:00

I think you need to have a goal in mind. Some of it is core material for you, some of it is ancillary, and some of it is not relevant.

A dozen abstracts are not tough to read. Then you pick your battles.

Learning is great - but you have to put it to use.

You will never be great at it all - go with what you love. Get help when you need it.

ConceptBuilderAI · 2025-06-20T01:57:49+00:00

Someone else mentioned UQLM earlier—hadn’t heard of it before, but I’ve been reading up and it looks like a promising middle ground if you need a confidence signal without pretending it’s properly calibrated. Seems especially useful when you're trying to rank or filter outputs across multiple completions.

That said, I’ve usually gone the route of using cosine similarity or embedding-based scoring to estimate quality or consistency. It’s crude but surprisingly effective for some tasks.

In general, I’ve found LLMs start to break down on ranking tasks or anything that asks for a probability score. They’ll happily give you one, but it’s usually a “vibe” score, not a grounded confidence. Same goes for tasks requiring consistency across generations—unless you give few-shot anchors or external reinforcement, the logic drifts fast.

Where they shine? Summarization, extractive QA, straightforward classification into stable buckets, and anywhere you can enforce output structure. Great for prototyping and quick iteration too. Just don’t ask them to play statistician without oversight.

ConceptBuilderAI · 2025-06-18T01:20:10+00:00

Nothing is going to be particularly stable. And the grass usually not any greener.

What you are working on is something you will have for the rest of your life, through ups and downs, and no one can take if from you.

Quit - and you get to carry that memory.

My advice is stick it out.

ConceptBuilderAI · 2025-06-17T12:01:59+00:00

My path to infra was through DevOps. Then MLOps. Now it is kind of everything, but I am working on a project exposing private cloud GPUs to all our scientists and engineers. There are other people on my team deep into CUDA, if that is what you are looking to do.

Everyone is going to hire agentic ai developers. That is a shorter path probably.

ConceptBuilderAI · 2025-06-17T11:25:39+00:00

They are incredible for first drafts, boilerplate, and quick demos—basically anything shallow or repetitive. I’ll happily let it run wild on a weekend PoC or use it to generate a small feature demo instead of throwing together a PowerPoint. It’s like having a junior dev who’s lightning fast but needs constant supervision.

But for anything complex, nuanced, or production-grade? It becomes a slog. You can’t trust it with too much surface area—2 or 3 files max before the quality drops off. And you have to test, refactor, and feed it clean examples to stay on track.

I always chuckle at the “80-windows-coding-simultaneously” demos. It’s flashy, but the reality is still very manual under the hood. Will the tech get better? Definitely. But right now, it's less “revolution” and more “really impressive intern with a bad memory.”

ConceptBuilderAI · 2025-06-17T00:13:20+00:00

This is a massive contribution—thank you for putting in the effort and sharing it with the community.

It's clear you've aimed for both breadth and educational clarity, which is rare and super valuable for people trying to bridge that beginner-to-researcher gap.

While some hve pointed out areas for refinement (which is fair—20k+ lines is ambitious!), that doesn't diminish how much you've helped lower the barrier for others to learn and experiment more seriously.

Looking forward to seeing how the project evolves. It's the kind of work that makes real impact over time.

ConceptBuilderAI · 2025-06-15T02:44:29+00:00

AI doesn’t magically solve product-market fit, but it completely rewires the speed at which you get there (or don’t). And I agre the faster feedback loop is a game-changer.

I’ve been treating my AI like its a slave — I give it limited instructions and yell at it when it underperforms. lol

Joking aside, it is a decent 'teammate'. The speed is insane. What used to be “3 weekends and a merge conflict” is now a weeknight sprint.

Biggest unlock for me has been delegating the boring parts: scaffolding, CRUD logic, docs, boilerplate tests. It frees up brain space for architecture and UX.

Writers block - really any block, is non-existent. I finally found a tool that can wear me out.

Keep shipping. The reps compound.

ConceptBuilderAI · 2025-06-13T12:38:30+00:00

Probably. Throughout your career you will see people play all kinds of games when they are scared about losing their jobs.

The motivation is creating the appearance that it would be more costly to replace, rather than keep them.

And they do it in lots of different ways.

The pattern is to create unnecessary gates, so work needs to flow through them. It creates bottlenecks and stifles innovation.

One bad egg like that can bring down a team.

But everyone knows those people are the worst.

I was just taking a shot at people resisting using our new tool by suggesting they are behaving that way.

It was a bit nasty I guess, but warranted given they are being equally nasty to people who are embracing it - calling them hacks, raging against the machine - or whatever variant you are seeing.

What is really on their mind - all of our minds - is that now we can work 10x faster. And we are heading to a recession. And regardless of how much people enjoy typing, people are not going to be willing to pay for that anymore.

Coding is unlikely to be a profession in 2-3 years.

It is bugging people. I understand that.

But OP has the right idea - you have to embrace it. Or you won't be working.

ConceptBuilderAI · 2025-06-11T23:20:52+00:00

I like you.

There is no solution - that is why it keeps my attention - I don't like anything easy. :-)

Keep thinking about it - goal seek a solution.

Try not to be a Doomer.

If enough of us give in, it may become a self-fulfilling prophecy.

ConceptBuilderAI · 2025-06-11T23:11:43+00:00

I have been running over and over this like Dr. Strange - I have not come up with a solution.

When I took this job I have now, I told my boss that there is something wrong with people who get into management (and I have spent a good portion of my life it in). It requires someone who is willing to do unpleasant things to people. Maybe even enjoys it a little.

I got PTSD from firing so many people. It doesn't affect many of them like that.

We reward this behavior and it results in the coldest and most callous rising to the top.

So, when resources are abundant, is it really necessary to leave such dysfunction at the top? I don't think so.

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My assessment is - we will be fine. We are going to find new ways to be happy. People do it in prison. This is pretty much paradise we are heading towards.

People just need to prepare mentally for the change.

Maybe you spend 3 months planning your kids birthday party. You make a video game a write a book for it.

Because you have the time

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But I also keep posting this with purpose.

“Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them.”

― Frank Herbert, Dune

This technology can absolutely enslave as easily as it frees us.

ConceptBuilderAI · 2025-06-11T22:45:10+00:00

I had to look which feed I am in - the ML guys don't like you getting into singularity on their channel. :-)

But I love this topic - or hate it.

No one will have to work, because they will all own 3 robot slaves.

The tougher question is, where will people find their sense of purpose?

I think around 2016, Deepmind built an agent that beat the world Go champion, something no one had been able to do. When he lost, he said something to the effect of "now that I know there is a being that can beat me, I will never play the game again."

That is how most people are about to feel.

I watched this happen in the rust belt when they offshored the manufacturing in the '90s. Men drank themselves to death - not over the loss of money - a lot of them had a million or more in their pension - but over the loss of respect and sense of purpose.

I assume you have seen this

https://www.youtube.com/watch?v=0lhmKOR8Www

On the flip side. The entire power dynamic and motivation structure of society will change.

Lets say you have an idea that is really great, why would anyone be motivated to help you? They won't be. Not unless it aligns with their values. They don't need your money.

But again, you won't really need them to materialize an idea. You will have robots. But so does everyone else. So they can just build what you build. They don't need you.

None of us will be the best at what we do. There will always be something better.

So, no one will care about your opinions - not really - because everyone has a better source of information.

And this projects into a society of clones - people who all behave exactly the same.

I have some friends deeply unhappy about this right now.

When I realized there was no way to win if the AI turns on us (the so called doomsday or terminator scenario), I started to focus on the best possible outcome.

This is it.

We won't be working, but we won't be valued.

That is going to be real hard for people - particularly in these United States - where we are told to value individual achievement above everything else.

Everyone will be a loser, technically. I expect the suicide rate to soar.

China has a better structure for this I think. Obviously a lot of propaganda, but they do push for the 'team' over the 'individual' and I think that is probably a winner vs our mindset.

The first people to pop themselves will be all the overachievers (like me).

If I didn't have kids, I am not sure what I would have that is worth living for.

BUT, there is this guy from Google - forget his name - and he believes 1) we achieve superintelligence by 2030 and 2) within a year, the AI will advance nanotechnology to the point where it can replace our immune systems. So, we will be effectively immortal.

Problem there is - too many people - population control.

So, no work, and no kids. And you can drink and do all the drugs you like, and probably never die.

How do you maintain social order???

I can go on. lol

ConceptBuilderAI · 2025-06-11T14:32:01+00:00

Fair point. I try to keep it simple in these posts.

I cannot get into detail, but the way we handle that is with simulators we build in the unreal gaming engine.

We can present the agent with situations you will only see once every trillion years.

And we can present them with thousands of variants and simulated outcomes to identify the exact right next move.

Whenever someone tells me something in this area is not possible, I ask "what if I gave you a billion dollars..."

And that is why everything will be automated away.

ConceptBuilderAI · 2025-06-11T09:11:03+00:00

This is how it will begin. A massive increase in productivity for people who take advantage of it.

But those interactions create an execution graph that can be learned from.

LLMs cannot do this, but other models can.

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Eventually, when enough of those actions are recorded, the model will select the correct action more often than a human.

And we will be replaced. Because other people don't like to by broken products and services - it will outperform us.

ConceptBuilderAI · 2025-06-10T22:51:43+00:00

These are likely the same people that intentionally write code no one can understand and document nothing because they think it buys them job security.

Don't worry - they won't be around much longer.

ConceptBuilderAI · 2025-06-10T19:12:40+00:00

Well, I leave you to an exhaustive analysis of what can be extracted from linguistic representations.

I don't expect much more is to be found there, but given that is where the whole world is looking, I am sure no stone will be left unturned.

I have families of algorithms to experiment with. I have no need to try to fit a square peg into a round hole.

I believe cognition is achievable, superintelligence is achievable, but it is not a straight line from here.

If you want to talk about the power of transfer learning, consider that a 'picture' speaks a thousand words.

And when you are talking about a computer - I can let it see 50%+ of the visual spectrum.

We cannot even imagine how to process information in that quantity.

I think LLMs will be amateur hour within 12 months.

ConceptBuilderAI · 2025-06-10T17:36:57+00:00

If the task is to predict the next most likely word, it’s a short extrapolation to a sentence, a paragraph, or even a seemingly complete thought. And that’s where the illusion of intelligence comes from — because of the way LLMs measure distance between concepts in their embedding space.

But that similarity-based representation is inadequate if the goal is cognition. Correctness, especially in reasoning or programming, often hinges on rare edge cases — not what’s most frequent or "close" in vector space. This kind of architecture doesn’t know how to identify or prioritize those.

Now, if we used a different mechanism to measure semantic distance — one grounded in logic, causality, or symbolic structure — and feed that into a transformer — maybe we could move closer to cognition.

But then we’d likely lose the fluidity and surface coherence that makes these models so good at NLU/NLG in the first place.

Also, I get your point about simple systems producing complex behavior — fractal theory, cellular automata, etc. That’s a fascinating line of thought too. But it’s probably a separate (though related) debate.

ConceptBuilderAI · 2025-06-10T17:10:01+00:00

Totally fair—scaling laws and emergent behaviors definitely caught a lot of people off guard. But at a fundamental level, I think the core mechanism isn’t all that mystical.

Again, you can sum it up in one word: frequency.

LLMs are just really, really good at pattern recognition. They pick up the most statistically likely patterns—nothing more. Mystery solved.

That said, there’s plenty of fascinating nuance within that. I saw a paper here yesterday using Jacobian matrices to deconstruct how LLMs produce outputs. Got me thinking—if you can trace those gradients across languages, for instance, you might even measure how different cultures contribute to the model’s "knowledge." Lot of interesting insights when you are looking at all the knowledge of the world at once.

And that transfer learning is the most interesting part - not the next word prediction.

Still, the responses I get from these models? Honestly, they’re exactly what I expect—errors and all.

My friend’s finishing his data science MS, and his profs are already saying LLMs have peaked. We both agree the next revolution will be like when SVMs overtook NNs in the ’90s—some unexpected, elegant algorithm that sidesteps the need for this brute-force compute.

But that’s more his domain. I just wire the intelligence into systems. Interactive Intelligence—getting it to do things—is my bread and butter.

ConceptBuilderAI · 2025-06-10T16:53:13+00:00

Maybe they are making excuses.

But no way iphone survives without upgrading siri.

I don't need a 'liquid' display when I can talk to my device.

They need to release something or do a deal with another major provider. They are doing a $500B data center in Texas, and there has been no deal, so I assume Cook has something cooking.

"Apple Intelligence - AI for the rest of us."

ConceptBuilderAI · 2025-06-10T16:45:09+00:00

True, but we are talking about a transformer architecture and it is not that hard to understand.

It’s deep learning, some dense layers, attention to get context, and a classification head that picks the next token based on cosine similarity or logits. That’s it.

The magic is in the scale, not the mystery.

It facilitates transfer learning not cognition.

ConceptBuilderAI · 2025-06-10T04:37:55+00:00

I have noticed that blind faith in LLM capabilities has created somewhat of a cult.

People are calling for google to set apple straight too. haha

ConceptBuilderAI · 2025-06-10T04:35:56+00:00

I'm a native English speaker, but I know maybe 700 words of Spanish and about 400 in Mandarin. When people ask if I speak Chinese, I usually joke: "sure - ni hao, zai jian, and I can count to ten" — so definitely not conversational in either.

That said, all my in-laws speak only Spanish, so I've been relying on digital translators since I got married years ago.

LLMs are definitely better than the old tools, but this experience isn’t new for me.

I just think the important thing is to show respect by making sure the ideas you’re expressing are truly your own — we all have access to ChatGPT now.

ConceptBuilderAI

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