And most importantly you have to write. A lot. Writing allows our brain to structure our thinking. Enables us to have a structured dialogue with ourselves. Explore different paths. Thinking & pondering can only do so much and will reach the limits soon. Writing, on the other hand enables one to explore thoughts nearly endlessly.
Given that thinking is so intimately associated with writing (could be prose, drawing, equations, graphs/charts, whatever) and that LLMs are doing more and more of writing it'll be interesting to see the effect of LLMs on our cognitive skills.
I’d love to see some sort of study on people who actively particulate writing their stuff on social media and those who don’t.
If u want to spare your mind from GPT numbness - write or copy what it tells you to do by hand, do not abandon this process.
Or just write code, programs, essays, poems for fun. Trust me - it is and you’ll get smarter and more confident. GPT is a very dangerous convenience gadget, is not going away like sugar or Netflix, or obesity or long commutes … but similarly dosage and counter measures are essential to cope with the side-effects.
Like not only do I cosign all said above, but I will also add to this: brevity is the soul of wit and none of these fucking things are brief. No matter what you ask for you end up getting just paragraphs of shit to communicate even basic ideas. It's hard to not think this tool was designed from go to automate high school book reports.
I would only use these programs to either create these overly long, meandering stupid emails, or to digest ones similarly sent to me, and make a mental note to reduce my interactions with this person.
It's no wonder the MBA class is fucking thrilled with it though, since the vast majority of their jobs seem to revolve around producing and consuming huge reports containing vacuously little.
There's a lot of talk about AI assisted coding these days, but I've found similar issues where I'm unable to form a mental model of the program when I rely too much on them (amongst other issues where the model will make unnecessary changes, etc.). This is one of the reasons why I limit their use to "boring" tasks like refactoring or clarifying concepts that I'm unsure about.
> it'll be interesting to see the effect of LLMs on our cognitive skills.
These discussions remind me a lot about this comic[1].
I find this to still be true with AI assisted coding. Especially when I still have to build a map of the domain.
Hitting the keys is not always writing.
* Describing the purpose of the writing
* Defining the format of the writing
* Articulating the context
You are writing to figure out what you want.
I feel like to goes beyond writing to really any form of expressing this knowledge to others. As a grad student, I was a teaching assistant for an Electrical Engineering class I failed as an undergrad. The depth of understanding I developed for the material over the course of supporting students in the class was amazing. I transitioned from "knowing" the material and equations to being able to generate them all from first principles.
Regardless, I fully agree that using LLMs as our form of expression will weaken both the ability to express ourselves AND the ability to develop deep understanding of topics as LLMs "think" for us too.
Not to be pedantic, but I’d still argue that thinking is the most important. At least when understanding the nature of learning. I mean, writing is ultimately great because it facilitates high quality thinking. You essentially say this yourself.
Overall, I think it’s more helpful to understand the learning process as promoting high quality thinking (encoding if you want to be technical). This sort of explains why teaching others, argumentation, mind-mapping, good note-taking, and other activities and techniques are great for learning as well.
Why do I still know how to optimize free conventional memory in DOS by configuring config.sys and autoexec.bat?
I haven’t done this in 2 decades and I’m reasonably sure I never again will
Now think about the effect on those humans currently using LLMs at that stage of their development.
I did this for a living at a large corp where I was the 'thinkpad guy', and I barely remember any of the tricks (and only some of the IBM stuff). Then Windows NT and 95 came out and like whoo cares... This was always dogshit. Because I was always an Apple/Unix guy and that was just a job.
The last phone conversation you had with a utility company, how did they greet you exactly?
There's lots that we do remember, sometimes odd things like your example, though I'm sure you must have repeated it a few times as well. But there's so much detail that we don't remember at all, and even our childhood memories just become memories of memories - we remember some event, but we slowly forget the exact details, they become fuzzy.
People do stuff like that all the time, bringing up past memories in spontaneity. The brain absolutely does remember things it "doesn't need".
It's very tempting to let it write a lot, let it structure things, let it make arguments and visuals. It's easy to let it do more and more... And then you end up with something that is very much... Not yours.
But your name is on it, you are asked to explain it, to understand it even better than it is written down. Surely the report is just a "2D projection" of some "high dimensional reality" that you have in you head... right? Normally it is, but when you spit out a report in 1/10th of the time it isn't. You struggle to explain concepts, even though they look nice on paper.
I found that I just really have to do the work, to develop the mental models, to articulate and to re-articulate and re-articulate again. For different audiences in different ways.
I like the term cognitive debt as a description of the gap between what mental models one would have to develop pre-LLMs to get a report out, and how little you may need with an LLM.
In the end it is your name on that report/paper, what can we expect of you, the author? Maybe that will start slipping and we start expecting less over time? Maybe we can start skipping authors all together and rely on the LLM's "mental" model when we have in depth questions about a report/paper... Who knows. But different models (like LLMs) may have different "models" (predictive algorithms) of underlying truth/reality. What allows for most accurate predictions? One needs a certain "depth of understanding". Writing while relying too much on LLMs will not give it to you.
Over time indeed this may lead to a population "cognitive decline, or loss of cognitive skills." I don't dare to say that. Book printing didn't do that, although it was expected at the time by the religious elite, they worried that normal humans would not be able to interpret texts correctly.
As remarked here in this thread before, I really do think that "Writing is thinking" (but perhaps there is something better than writing which we haven't invented yet). And thinking is: Developing a detailed mental model that allows you to predict the future with a probability better than chance. Our survival depends on it, in fact it is what evolution is in terms of information theory [0]. "Nothing in biology makes sense except in the light of ... information."
Yes definitely!
I'd say that being able to turn an idea over in your head is how you know if you know it ... And even pre-LLM, it was easy to "appear to know" something, but not really know it.
PG wrote pretty much this last year:
in a couple decades there won't be many people who can write.
So a world divided into writes and write-nots is more dangerous than it sounds. It will be a world of thinks and think-nots.
Sounds very plausible, though how does that square with the common experience that certain skills, famously 'riding a bike', never go away once learned?
OTOH, IME the quickest way to truly forget something is to overwrite it. Photographs being a notorious example, where looking at photographs can overwrite your own personal episodic memory of an event. I don't know how much research exists exploring this phenomenon, though, but AFAIU there are studies at least showing that the mere act of recalling can reshape memories. So, ironically, perhaps the best way not to forget is to not remember.
Left unstated in the above is that we can categorize different types of memory--episodic, semantic, implicit, etc--based on how they seem to operate. Generalizations (like the above ;) can be misleading.
The skills that leave: arguments, analysis, language, creativity often seem abstract and primarily if not exclusively sourced in our minds
I remember hearing about some research they'd done on "binge watching" -- basically, if you have two groups:
1. One group watches the entire series over the course of a week
2. A second group watches a series one episode per week
Then some time later (maybe 6 months), ask them questions about the show, and the people in group 2 will remember significantly more.
Anecdotally, I've found the same thing with Scottish Country Dancing. In SCD, you typically walk through a dance that has 16 or so "figures", then for the next 10 minutes you need to remember the figures over and over again from different perspectives (as 1st couple, 2nd couple, 3rd couple etc). Fairly quickly, my brain realized that it only needed to remember the figures for 10 minutes; and even the next morning if you'd asked me what the figures were for a dance the night before I couldn't have told you.
I can totally believe it's the same thing with writing with an LLM (or having an assistant write a speech / report for you) -- if you're just skimming over things to make sure it looks right, your brain quickly figures out that it doesn't need to retain this information.
Contrast this to riding a bike, where you almost certainly used the skill repeatedly over the course of at least a year.
I worked with some researchers who specifically examined this when developing training content for soldiers. They found that 'muscle memory' skills such as riding a bike could persist for a very long time. At the other end of the spectrum were tasks that involved performing lots of technical steps in a particular order, but where the tasks themselves were only performed infrequently. The classic example was fault finding and diagnosis on military equipment. The researchers were in effect quantifying the 'forgetting curve' for specific tasks. For some key tasks, you could overtrain to improve the competence retention, but it was often easier to accept that training would wear off very quickly and give people a checklist instead.
Except when it does-- for example in the abstract where it is written that Brain-to-LLM users "exhibited higher memory recall" than LLM and LLM-to-Brain users.
Indeed the paper doesn’t provide a reference or citation for the term “cognitive debt” so it is a strange title. Maybe a last minute swap.
Fascinating research out of MIT. Like all psychology studies it deserves healthy scrutiny and independent verification. Bit of a kitchen sink with the imaging and psychometric assessments, but who doesn’t love a picture of “this is your brain on LLMs” amirite?
And also DUH. If you stop speaking a language you forget it. The brain does not retain information that it does not need. Anybody remember the couple studies on the use of google maps for navigation? One was "Habitual use of GPS negatively impacts spatial memory during self-guided navigation"; another reported a reduction in gray matter among maps users.
Moreover, anyone who has developed expertise in a science field knows that coming to understand something requires pondering it, exploring how each idea relates to other things, etc. You can't just skim a math textbook and know all the math. You have to stop and think. IMO it is the act of thinking which establishes the objects in our mind such that they can be useful to our thinking later on.