Nothing new here, this is called tool embodiment. A little time after assimilation, you stop consciously thinking about the tool. You are the hammer, the hammer is you.
So what is being missed here is that the brain operates on, well, mental constructs. Ideas and ways of thinking. But those are not the world or the brain, those are tools.
The higher level processes into your mind use ways of thinking the same way it uses the body. Its unconscious (because it has been doing this for enough time) and automatic. The brain just gets guided by the tools. It wants to hammer that nail.
So, what does it have to do with crossing the street and not being able to transmit this knowledge?
You can’t transmit the incorporation. You can describe how to do things, how to think about things, but you can’t reconnect other people’s neurons to establish a way of thinking or a tool as part of the brain image of the self. Yet.
You can’t teach a baby how to embody his spine. You can’t teach someone how to become his thoughts. But you can’t certainly guide then on the use and this usage will build the neural networks. Once established, they’ll get it.
The book was on 3d modeling in Rhino 3d. I was really good at Rhino3d at that time, to the extent that using it felt like a natural extension of my hands. IMHO every other 3d modeling program has a trash UI compared to the absolutely amazing UI that Rhinoceros 3d has.
I had to learn how to translate my absolute love of Rhino3D onto a page and explain it to other people. It was hard. It made my brain work in ways it was not used to, but it was an incredibly valuable experience.
The only remaining copy of the book sits behind me on a bit rotted CDR.
I have had 3 types of math teachers in life. American teachers, who generally teach rules from a book according to a curriculum. Russian teachers, who have a passion and a love for the field and who teach how to intuitive the answer to math problems first before going all in on the formulas. And East Asian math teachers who show off the beauty of the equations themselves.
I had one math teacher who couldn't speak English. He didn't need to, he had an incredible ability to communicate math through pure equations. It was lovely, one of the best math classes I've ever had. Math was truly used as a universal language.
I had another teacher (Russian) who got so excited solving equations and explaining DiffEq that he'd break his chalk in half and he'd go diving under desks to pick up the pieces.
But it is artists who are some of the best at transmitting intuitive knowledge. They have centuries of best practices of how to train students to rewrite their brains to literally see the world differently. (And yes a lot of it does involve drawing boring still lives of fruit bowls! But, hey, it works)
It's not just you. There is something about it that is qualitatively different.
EVERYTHING is awful compared to Rhino3d. Viscerally painful to use bad in comparison.
That's why when private equity buys a company, the first thing they often do is shut down any new product development or R&D. They want to run the machine and extract profit from what it does now - a cash cow - without taking risks on changing a working model. In this model, new product development is for startup ventures, not mature companies whose DNA doesn't tend to be a good fit for it anyway.
tl;dr: What you're describing is the system working as designed and intended. For better or worse.
I've never seen the word calibration used this way:
different modes of learning. The first is instruction: the transfer of explicit models, rules, and relationships from one person to another through language. The second is calibration: the development of internal models through repeated exposure to feedback in a specific environment.
Judgement is learnable through calibration. It is not transmissible through instruction.
A little bit is natural and expected, but this kind of change in meaning feels like a consequence of a culture that in the last decade has accelerated the practice of re-framing specific words and concepts as something that's "actually a positive" or "actually quite negative if you think about it".
Part of this is a result of our (in the US) culture wars and hijacking of popular terms, but it's also a symptom of social media culture that's always seeking a hot take and creators who are looking to distinguish themselves with (what seems to me) clever re-framing.
The result is a culture that is increasingly fragmented and in which a word can have dramatically different meaning and insinuations depending on it's use in certain social groups or intellectual cliques.
It increasingly feels like I need to download a massive amount of linguistic context before I step into the world of a niche online community because their tight-knit dialogues and shared experiences have now re-framed a word or concept that was largely understood to mean something else.
By reading and re-reading old books. You learn the original meanings and usage of words and then recognise when someone tries to twist them.
We don’t engage. It’s the only shot we have.
There was a useful article at 404 Media recently about our failure to prevent those on the extreme edges of culture from normalizing their language and behavior: We Have Learned Nothing About Amplifying Morons[0]. See the article, but essentially by engaging we cede ground. Sorta like how both-sides journalism gives space to anti-science nuts and lets them spread falsehoods.
0. https://www.404media.co/we-have-learned-nothing-about-amplif...
Choosing a good abstract dichotomy is hard (mine is also faulty, as you have noted).
They chose "instruction" versus "calibration" which I feel is a terrible splitting plane (muddying whatever they are trying to articulate).
I have been fascinated listening to a smart nursing friend of mine explain some of the intuitions they learnt through observation (not explicitly taught). I believe they had an outlier skill for noticing patterns. They might have been able to teach the patterns they saw, but they probably couldn't teach the skill of discovering patterns ≈intelligence.
Intuition and other forms of knowledge are stock quantities while calibration and instructions are types of flows which change the stock. I'd love to know if there a better word for learning through trial and evaluation than calibration.
Analytical or reasoned would be better opposites (of intuitive) for the split in meaning I was looking for.
Inductive and deductive reasoning are usually analytical.
I disagree to a degree. Yes, what the author says is accurate about people dismissing street-smarts as a lower level of intelligence than it deserves. But a sufficiently skilled communicator can absolutely articulate many of the factors being evaluated when they judge a situation and how their descision-making process works.
> They evaluate intelligence through the lens of articulacy
There was an earlier instance of the author using a word such as unability (or similar) and it should have been inability and I let it go, but this misuse of language is making my head hurt. However, I confess that I thought the word should have been articularity and it turns out that’s not a real word either. But I at least pay attention to spellcheck. I don’t understand how someone could take the time to write a long and thoughtful essay about intelligence and not use spellcheck to proof it.
LLMs ain't gonna do sheeeeeeeit if this is still where we are at...
That sounds right but I suspect it is wrong. Watching smart intuition has been a personal interest of mine for years. Few people avoid the manifold traps.
1: people hallucinate their reasoning or are self-deceptive (or even intentionally deceptive). Watching AI has helped hone watching people.
2: you need to be sufficiently close in skills and language for someone to be able to communicate the nuances. E.g. sportspeople.
3: Judging whether an intuitive statement is true is hardhard. We need to identify a correct intuition (and ignore incorrect intuitions) before judging whether some explanation is valid.
1. Formally calling out a concept for judgment-based skills that cannot be easily taught. I think everybody understands this, but having a word for this would be useful.
2. Opening up the conversation on the topic of which types of skills can/should be codified and how.
That said, everything else in the article is suss. "Dimensionality" is largely a distraction to try to sound smart, most of the claims in the article are unwarranted (e.g. processes & checklists can be great, even for disciplines with true experts like airline pilots).
For example saying that skill learning cannot be accelerated is just patently false in many domains -- take something like learning chess. If you have a coach and other tools you'll learn a LOT faster. But certainly I've worked places that wished they could automate away reliance on experts because it gives organization power to a few non-management individuals.
One of the implications is that at any given point in time, the vast majority of human knowledge is living in people's brains and cannot be stored. The seemingly ineluctable and almost mechanical progression of technology is happening on a thin margin between generational losses.
For example the design of a machine may have it tolerate inputs way outside spec & work fine. It may be built to take a beating, while no manual mentions using it in a rough environment. There may be subtle or not-so-subtle tweaks done to it over the years.
So that machine embodies knowledge, that may be 're-discovered' (by observing machine in action) long after its original designer is gone.
Another example: the design of traffic systems, the layout of cities (mostly organic growth), and how it affects the flow of people & goods through that city.
That's just a few examples. In short: knowledge is stored in other ways besides books/videos etc, or people's heads.
Science is empirical knowledge and processes which can be transferred, art is gut feeling and subconscious knowledge applied automatically, which can’t be transferred.
Roughly I think this corresponds to how our minds perform cognitive offloading of repeated tasks. New tasks that require instruction following occupy our attention, but the more we do them, the more our minds wire the behavior into our “muscle memory”. Practitioners of the arts (or even the art of science, one might say) have a built a neural network that offloads tasks so that higher cognitive functions can focus on applying those tasks in expert ways.
It’s sort of like when we start out our brains have to bitbang all tasks (muscle movement, speech, etc.) but over time our brains develop their own TCP offloading, or UART peripherals. And you can’t just download a TCP offloading engine, it has to be built into the silicon. Hence why “expert knowledge” isn’t transmissible.
Which is why spaced repetition is an effective learning method. You’re hacking your brain to wire facts into the hardware.
Kinda has me wondering about the implications of the BAR being the end all be all of a law school. Contrast it with a Doctor's residency and i think law school is very much crafting an overly binary right/wrong profession, and perhaps they should have something beyond it more akin to something like officiating a sports game, where they see potential implications of being too stringent applying their rule system when there is certainly room for being charitable. It is a complex issue though, because the charitable interpretation of a law gives way for bad actors to abuse that interpretation.
Now bridge this all with all the weird 1st level and 2nd level stuff surrounding medicine that is placed there by people outside of the field of medicine and imposed on an expert of the medical field. They have to apply their expertise to the patient, decide a course of action, and then describe that action in those 1st/2nd level terms to a non expert who for some reason is the deciding authority, despite downgrading the expert's actual thoughts by design. I know im all over the place, but it was a pretty good article that made me think about a lot of different applications of the ideas.
What we are generally getting though is a network with extremely high dimensionality trained on many domains at once, at least as far as the commonly used ones like LLMs and VLMs.
We do have mixture of experts which I guess helps to compress things.
Going back to the idea that this stuff just can't be represented by language, I wonder if someday there could be a type of more concise representation than transmitting for example a LoRA with millions of bytes.
Maybe if we keep looking at distillation of different models over and over we might come up with some highly compressed standardized hierarchical representation that optimizes subdomain or expert selection and combination to such a degree that the information for a type of domain expertise can be transmitted maybe not orally between humans but at least in very compact and standard way between models.
I guess if you just take something like a 1B 1 bit model and build a LoRA for a very narrow domain and then compress that. That's something like the idea. Or maybe a quantized NOLA.
But I wonder if someday there will be a representation that is more easily interpretable like language but is able to capture high dimensional complex functions in a standard and concise way.
"But I wonder if someday there will be a representation that is more easily interpretable like language but is able to capture high dimensional complex functions in a standard and concise way"
Perhaps we can train AI experts to show us what parameters they found most useful in contrast to what human experts used. That could be a start at filling in the human knowledge gaps.
That said, procedural knowledge remains suspicious because it could hinge on a cheap mental shortcut. A very experienced pedestrian may unconsciously make terrible decisions based on overfitted train data or causally irrelevant variables. Expressing the model "formally" can help expose those terrible decisions (e.g. I feel good about 1980s Fiat Pandas so I cross more confidently when I see them...). Problem is that introspection does not always work well.
"Language is a serial, low-bandwidth channel. It transmits one proposition at a time, sequentially. Each proposition can relate a small number of variables: “if X and Y, then Z.” Complex conditionals can extend this to perhaps five or six variables before the sentence becomes unparseable: “if X and Y but not Z, unless W and V, then Q.” ... "This is not a claim about human cognitive limitations. It is an information-theoretic claim about the channel capacity of natural language relative to the complexity of the models being transmitted."
No.
This definitely is about human cognitive limitations. Consider *why* that 6-variable rule is unparseable: Human working memory is typically about 7 items. His unparseable example has by a generous accounting 10 entities--6 variables and 4 relationships. (And by a strict accounting you need to count the "then", for 11 entities.) Very, very few humans could learn that. Knowledge must be chopped up into chunks small enough to fit into our working memory to be understood and incorporated into our models.
Once one chunk has been incorporated into our model we can then add another chunk to it, repeating until we have built up a complex model. And it's not just read, store, read another--each bit must be worked with to actually be modeled. (But this process does not need to be strictly linear--to take his stupid pedestrian, they can separately learn speed, distance, stopping distance etc and tie them together. But if his pedestrian cares if the driver is inattentive his model is wrong--why are you doing something that involves a moving driver being aware of you in the fist place??)
Manufacturing in the USA is slowly improving.
"The Manufacturing PMI® registered 52.7 percent in March, a 0.3-percentage point increase compared to the reading of 52.4 percent in February. The overall economy continued in expansion for the 17th month in a row."
https://www.prnewswire.com/news-releases/manufacturing-pmi-a...
active knowledge i can produce on command. passive knowledge only comes up when it is triggered from the outside.
a lot of things we know are only accessible when they are triggered. i could not describe the path through a forest, but i know it when i walk it. same for the process to solve a particular problem. i could not describe the whole way. only if i follow the steps can i remember the next steps. and if there are multiple possible steps at some point, only for the choices that are actually triggered will i remember what comes after.
you can follow me and learn the steps by observing me, but you will only learn the steps that we actually do. and i couldn't possibly give you a list of all other potential steps.
in the latest video by tom scott, he watches people creating bells. the guy observing the molten metal knows when it has the right temperature by just looking at it. he learned that from years of practice, and his future replacement will learn it by observing him.
Or how many people know that many CEOs and leaders are idiots, but cannot say it, as they would face retribution.
Yes.
"This is the deepest reason why experience cannot be compressed."
No. Finding relevant features and compressing their valuation is what ML systems do. Especially in vision systems. Early attempts at machine vision had human-chosen features and detectors for them. There used to be papers with suggested features - horizontal lines, vertical lines, diagonal lines, patterns of dots, colors, and such. That's where things were in the 1990s. Those have now been replaced by learning-generated low level feature detectors, which work better.
This does need a lot of training content. The training process is inefficient. It definitely compresses, though.
I agree with the article’s main point, but it’s flagrantly AI and (as usual with AI) way too verbose.