> Modern coding AI models are not just probability crunching transformers. (...) The complete package includes things like highly curated training data, specialized tokenizers, pre and post training regimens, guardrails, optimized system prompts etc, all tuned to coding.

It seems you were not aware you ended up describing probabilistic coding transformers. Each and every single one of those details are nothing more than strategies to apply constraints to the probability distributions used by the probability crunching transformers. I mean, read what you wrote: what do you think that "curated training data" means?

> Put it all together and you get one shot performance on generating the type of code that was unthinkable even a year ago.

This bit here says absolutely nothing.

>The complete package includes things like highly curated training data, specialized tokenizers, pre and post training regimens, guardrails, optimized system prompts etc, all tuned to coding.

And even with all that, they still produce garbage way too often. If we continue the "car" analogy, the car would crash randomly sometimes when you leave the driveway, and sometimes it would just drive into the house. So you add all kinds of fancy bumpers to the car and guard rails to the roads, and the car still runs off the road way too often.

Except we should aim to reduce the boilerplate through good design, instead of creating more of it on an industrial scale.

>In current coding models the transformer bit is just one part of what is really an expert system. The complete package includes things like highly curated training data, specialized tokenizers, pre and post training regimens, guardrails, optimized system prompts etc, all tuned to coding. Put it all together and you get one shot performance on generating the type of code that was unthinkable even a year ago.

This is lipstick on a pig. All those methods are impressive, but ultimately workarounds for an idea that is fundamentally unsuitable for programming.

>That's >90% of all code in the wild. Probably more.

Maybe, but not 90% of time spent on programming. Boilerplate is easy. It's the 20%/80% rule in action.

I don't deny these tools can be useful and save time - but they can't be left to their own devices. They need to be tightly controlled and given narrow scopes, with heavy oversight by an SME who knows what the code is supposed to be doing. "Design W module with X interface designed to do Y in Z way", keeping it as small as possible and reviewing it to hell and back. And keeping it accountable by making tests yourself. Never let it test itself, it simply cannot be trusted to do so.

LLMs are incredibly good at writing something that looks reasonable, but is complete nonsense. That's horrible from a code maintenance perspective.

>hallucinations

This is the first word that came to mind when reading the comment above yours. Like:

>They can't, despite marketing, really reason

They aren't, despite marketing, really hallucinations.

Now I understand why these companies don't want to market using terms like "extrapolated bullshit", but I don't understand how there is any technological solution to it without starting from a fresh base.

> Not to disagree, but "non-english" isn't exactly relevant.

how so? programs might use english words but are decidedly not english.

Does speaking in your native language, speaking in a second language, thinking about your life and doing maths feel exactly the same to you?

> Prove to me that human thought is not predicting the most probable next token.

Explain the concept of color to a completely blind person. If their brain does nothing but process tokens this should be easy.

> How can you tell a human actually understands?

What a strange question coming from a human. I would say if you are a human with a consciousness you are able to answer this for yourself, and if you aren't no answer will help.

That is ill-posed. Take any algorithm at all, e.g. a TSP solver. Make a "most probable next token predictor" that takes the given traveling salesman problem, runs the solver, and emits the first token of the solution, then reruns the solver and emits the next token, and so on.

By this thought experiment you can make any computational process into "predict the most probable next token" - at an extreme runtime cost. But if you do so, you arguably empty the concept "token predictor" of most of its meaning. So you would need to more accurately specify what you mean by a token predictor so that the answer isn't trivially true (for every kind of thought that's computation-like).

Just a couple of weeks ago on mid-range models. The problem is not implementation or refinement - the core idea is fundamentally flawed.

>from a neuroscience perspective, there's a growing consensus among neuroscientists that the brain is fundamentally a token predictor, and that it works on exactly the same principles as LLMs

Hahahahahaha.

Oh god, you're serious.

Sure, let's just completely ignore all the other types of processing that the brain does. Sensory input processing, emotional regulation, social behavior, spatial reasoning, long and short term planning, the complex communication and feedback between every part of the body - even down to the gut microbiome.

The brain (human or otherwise) is incredibly complex and we've barely scraped the surface of how it works. It's not just nuerons (which are themselves complex), it's interactions between thousands of types of cells performing multiple functions each. It will likely be hundreds of years before we get a full grasp on how it truly works - if we ever do at all.

> The only difference between a brain and an LLM maybe the size of its memory, and what kind and quality of data it's trained on.

This is trivially proven false, because LLMs have far larger memory than your average human brain and are trained on far more data. Yet they do not come even close to approximating human cognition.

> and that from a neuroscience perspective, there's a growing consensus among neuroscientists that the brain is fundamentally a token predictor, and that it works on exactly the same principles as LLMs

Can you cite at least one recognized, credible neuroscientist who makes this claim?

Look you don't have to lie at every opportunity you get. You are fully aware and know what you've written is bullshit.

Tokens are a highly specific transformer exclusive concept. The human brain doesn't run a byte pair encoding (BPE) tokenizer [0] in their head. anything as tokens. It uses asynchronous time varying spiking analog signals. Humans are the inventors of human languages and are not bound to any static token encoding scheme, so this view of what humans do as "token prediction" requires either a gross misrepresentation of what a token is or what humans do.

If I had to argue that humans are similar to anything in machine learning research specifically, I would have to argue that they extremely loosely follow the following principles:

* reinforcement learning with the non-brain parts defining the reward function (primarily hormones and pain receptors)

* an extremely complicated non-linear kalman filter that not only estimates the current state of the human body, but also "estimates" the parameters of a sensor fusing model

* there is a necessary projection of the sensor fused result that then serves as available data/input to the reinforcement learning part of the brain

Now here are two big reasons why the model I describe is a better fit:

The first reason is that I am extremely loose and vague. By playing word games I have weaseled myself out of any specific technology and am on the level of concepts.

The second reason is that the kalman filter concept here is general enough that it also includes predictor models, but the predictor model here is not the output that drives human action, because that would logically require the dataset to already contain human actions, which is what you did, you assume that all learning is imitation learning.

In my model, any internal predictor model that is part of the kalman filter is used to collect data, not drive human action. Actions like eating or drinking are instead driven by the state of the human body, e.g. hunger is controlled through leptin and insulin and others. All forms of work, no matter how much of a detour it represents, ultimately has the goal of feeding yourself or your family (=reproduction).

[0] A BPE tokenizer is a piece of human written software that was given a dataset to generate an efficient encoding scheme and the idea itself is completely independent of machine learning and neural networks. The fundamental idea behind BPE is that you generate a static compression dictionary and never change it.

You seem to be an LLM