jeremyjh
6 hours ago
These are all important points and I love the analogy. But there is an even bigger issue with having LLMs write for you:
Writing is thinking. Thinking and deciding. There have been many times when I start out writing something substantial - could be an email, a blog post, a software design document, anything - when my own views substantially changed during the writing process. Writing forces you to serialize your thoughts - and you can't always trust the gestalt.
Reviewing gives you the chance to ensure the arguments connect solidly, that references are accurate (even informal references) and gives you the time to consider counter-arguments you aren't addressing.
None of this matters much on LinkedIn, but it matters a lot in our work. You cannot outsource your understanding to AI. They are powerful tools but they do not have any human understanding - that isn't their optimization target.
bcherny
3 hours ago
(I work at Anthropic) I agree. I use an LLM to write my code, but I do all of my writing by hand, since it helps me think.
It reminds me of the transition over the last year from AI-assisted coding to AI doing all the coding. At first the code output wasn't good enough, and humans read and iterated on the code all day, so the details of the source code mattered. Now, the code is largely high quality and it meets a large set of guardrails we've set up over the years (linters, typecheckers, security checks, LLM-assisted code quality checkers), and it's just Claude working on the code, so the details matter less and engineers think a level or two up (machine code < assembly/bytecode < source code < conversation with agent < artifact with high level design).
I wonder if long form writing will go the way of code. You and the AI agree on an outline or other high level representation, then the LLM expands it into a document. But writing and coding are different enough in a number of ways that this is far from inevitable.
lelanthran
an hour ago
> wonder if long form writing will go the way of code. You and the AI agree on an outline or other high level representation, then the LLM expands it into a document.
What use is that? I'm not being facetious, I'd really rather like to know.
Who or what is the audience for that sort of long form writing? If it's a human, why would they read it? They'd just give it to an LLM and get the salient points back. If the audience is another LLM, why expand it?
The only use case is an audience of humans who still read and understand, and those people aren't going to be interested in a message when it is not apparent that the sender actually understands the message themselves.
CityOfThrowaway
19 minutes ago
You can imagine:
1. I have a bunch of data or research that I've gathered with a unique hypothesis
2. Having gotten my arms around that pile of information, I believe I have a compelling thesis to put forth
3. I design the narrative arc and of the thesis. The important parts, the necessary but not sufficient scaffolding.
4. An AI helps fill in the story from there. Fact checks each claim, connects the dots, makes it comprehensible.
Who is this for? Well, quite possibly the human who asked for it. It's pretty informative to read back a research brief in full that you helped do the scaffolding.
Also of very clear use is other AI's who did not have the same unique hypothesis and did not gather the supporting evidence. It's an interesting angle for others to build on.
And of course, other humans! Most human written content gets almost zero readers today as it is. And I suppose LLM content probably pulls the asymptote closer to zero, but some pieces of content may be genuinely interesting or useful.
whstl
an hour ago
A lot of corporate documentation exists solely to measure if people are working or not.
That's why there's so many meetings in white collar companies. Because people can't understand what is going on at those documents so they just need to "align".
LLMs are amazing at generating this useless documentation that goes absolutely nowhere.
bcrosby95
2 hours ago
You claim writing is thinking, but imply writing code isn't thinking.
My opinion of LLM design review isn't that high - it seems to miss design tweaks that could vastly simplify corner cases. But if your code isn't written for human consumption maybe it doesn't matter. I'm still directly responsible for what I commit, so I can't just offload it to Claude.
sersi
2 hours ago
That's something I struggle with, I try to get LLMs to output code I don't care much about and focus on the parts I do and it kind of works but the problem is that reading code written by the LLM is even worth than reading LLM generating text. It's nauseating and you still have to read what the LLM did if you want to really work on the parts that matter.
CamperBob2
an hour ago
I still write code, I just write it in English now. It's still rock and roll to me.
bigstrat2003
39 minutes ago
That... isn't writing code.
CamperBob2
28 minutes ago
Sigh.
It's like watching somebody about to be hit by a bus. You yell, you wave your arms, but they either don't hear you, or they don't believe you. The last thing that goes through their head is a Greyhound's hood ornament.
jeremyjh
2 hours ago
You are just talking about the output though. If you only think at a "higher level" you aren't doing the actual thinking. Its the same with code. The output may be good enough, but over time you lose touch with the details to the extent that you can no longer serve a useful steering function for the organization. Before coding agents I'd seen this with many humans when they get promoted passed the point where they work with code directly and can't figure out how to add value there.
a2ff6eeb0
2 hours ago
Yes, of course. At some point the LLMs will also run the organization.
vorticalbox
11 minutes ago
You could try this [0] I’ve started doing this and it’s helped a lot.
1. I understand fully the code and everything it does 2. You can pick up on mistakes super early and it can adjust the plan is it goes. 3. Faster than writing it by hand but slower than letting the LLM do it.
[0] https://ankursethi.com/blog/prevent-cognitive-debt-by-manual...
cweld510
an hour ago
Writing is already amenable to many different levels of abstraction, though. If an LLM can expand your outline into writing, then you aren’t writing at the correct level of abstraction in my opinion; you should instead be explaining how you arrived at your chosen outline. You don’t need to explain the details because any party can generate those with an LLM; same as how many PRs today can be auto-generated and no one needs to read implementations; that is no longer the correct level abstraction to work at. This should actually free us to do work at a higher level of abstraction —- more consideration of strategy, objectives, etc and less worry about implementation details.
daveguy
8 minutes ago
This only applies if LLMs aren't making mistakes 20% of the time and that's the problem. When you're only saving time on the easy part, it doesn't matter if you're working twice as fast because review of the tricky parts is still going to take 80% of what it would have taken to do the whole thing. Total effort ends up being more rather than less if you want the same quality.
prisonguard
3 hours ago
You and the AI agree, but how about you and the team who will eventually read and do code review.
Cognitive burden increases marginally with AI assisted coding.
This is why we haven't seen big projects(think browsers and browser engines) spawning in the past year.
trhaynes
2 hours ago
Indeed. I'm curious what the security team has to say about that approach, for example.
brabel
2 hours ago
If you get an AI to review the code especially for security, it does a very good job st finding issues. Better than any human reviewers I have worked with, and getting better. As someone who works in security , I feel much less worried about security bugs on code reviewed by a AI for security issues, be it written by AI or human.
yehoshuapw
30 minutes ago
extra reviews never hurt. But trusting only the AI to both code and review security-wise? not even close to usable
zdragnar
2 hours ago
It's anthropic, of course the entire team is also using AI to do the code reviews.
a2ff6eeb0
2 hours ago
> but how about you and the team who will eventually read and do code review.
Why are you reviewing AI code in detail? Do you also review the assembly output of GCC line by line?
electric_toucan
an hour ago
Compilers like GCC are deterministic and the source code already fully defines the behavior. LLMs are non-deterministic and will accept ambiguity, filling in details where you haven’t. These sorts of comparisons aren’t really fair.
In the case of writing, it’s like hiring someone to write a book for you vs. hiring someone to translate a book you wrote into another language. In the first case, you didn’t really define the message for readers, whereas in the second case you did, and the translator is converting that same message for another audience to consume.
a2ff6eeb0
an hour ago
Sure, but I don't know what GCC's behavior is, and I don't vet behavior differences between compiler upgrades. As long as the output works, why does it matter that the black box is deterministic?
rogerrogerr
31 minutes ago
Because _someone_ has vetted the output of GCC. It’s used in flight-critical stuff.
The closest thing we have to vetting LLMs is “whoa look, it escaped this sandbox, that’s prolly not great but it’s so cool!”
cozzyd
29 minutes ago
For instructions you really care about, yes of course you review the assembly output! Usually when you're doing SIMD or want to check atomics are doing what you expect.
rogerrogerr
an hour ago
I can’t remember the last time GCC emitted code that just flat out called the wrong function. If it did that occasionally, I would review it.
lelanthran
an hour ago
Come on, this is a take we expect from a 1st grader!
Gcc makes maybe 1 mistake ever 2 billion emissions. LLMs make 1 mistake ever 3rd emission.
trhaynes
2 hours ago
Do your coworkers send emails and create written work product using LLMs? I'm curious what the standards and culture are within an AI org.
rogerrogerr
an hour ago
[non-AI org] I despise this and call it out every time I see it. Some dude hooked up an LLM autoresponder to his email, sent some nauseating AI slop to a huge distribution list.
I couldn’t help myself, replied and asked him for a recipe for delicious apple cobbler and hiking trail recommendations in Glasgow, which “he” immediately provided. Highlight of my career.
I think my core argument is this: I have access to every bit of information your AI does, so if I want an AI answer I’ll get one myself. If that isn’t true, why are you hoarding information? Push it somewhere we can all see it. So the only reason I would send you a message is to access _your_ brain. I have no interest in talking to an AI through a worse interface.
swatcoder
2 hours ago
> it's just Claude working on the code, so the details matter less
If you're not billed for usage, anyway.
Otherwise, for the other 99% of folks, that attitude is of course a pit trap that captures code bases and makes them maintainable only through the providers -- presumably one or few -- with a rich enough model to keep up with the growing mess. Preserving a code base that's legible, organized, and fundamentally maintainable by both humans and trailing commodity models is of imminent concern for anybody who doesn't want their margin strangled by your employer once it's too late to have other options.
As frontier capabilities advance, the details don't matter less; they matter more.
user43928
2 hours ago
It does not concern me.
Prices are very competitive and today's SOTA is next to free in half a year.
Whether code is maintainable without AI becomes less and less important.
arctic-true
2 hours ago
I suspect some amount of long form writing will go the way of code - long form writing for the purpose of consumption by other AIs. Writing as a means of exchanging qualitative information, with no regard for how the reader will feel about it (beyond understanding what the words mean). Not everything can be distilled into data, but this doesn’t mean it is beyond the reach of LLMs.
On the other hand, long form writing for human consumption seems like it may evade LLMs for much, much longer.
brabel
2 hours ago
What makes you think it will take a long time? AI seems capable of imitating any writing style if prompted to do so already, and I think it will get better on this quickly since the AI writing style is a main focus of AI labs right now. I can see no reason at all to believe this is a matter of years still, more like a few months.
arctic-true
an hour ago
You can have a great “writing style” and still put together really crappy long-form work. The problem is that AI writing, particularly creative writing, is too repetitive, too predictable, too trope-laden.
All of the things you say are very true in the near term for short form writing - a page or two of Claudeslop will probably be much easier to swallow in a year or two than it is now. But I don’t see a path to fully AI-generated novels or long-form investigative journalism becoming mainstream in the next couple of years.
bigstrat2003
39 minutes ago
> Now, the code is largely high quality...
No. No it is not. Nobody who actually cares about the quality of their work is letting an LLM just turn out code without reviewing it carefully.
jampekka
5 hours ago
> Writing is thinking. Thinking and deciding. There have been many times when I start out writing something substantial - could be an email, a blog post, a software design document, anything - when my own views substantially changed during the writing process. Writing forces you to serialize your thoughts - and you can't always trust the gestalt.
I don't disagree, but I think it's often not appreciated how much there's other work to writing too.
The biggest one is that you have to communicate non-interactively to an unknown audience. Having to (literally) put it in someone else's assumed terms does help giving different perspectives into the matter, but doesn't necessarily help one's own thinking that much. Instead you have to do some of the reader's thinking for them.
You also have to spend time on textual matters like grammar and style and a lot of "unspoken rules", which aren't really about linearizing your thinking about the contents.
Not all writing is thinking and not all thinking is writing.
fwlr
5 hours ago
Strange! I would quite specifically highlight “having to do the reader’s thinking for them”, as well as more generally developing the skill to “communicate non-interactively with an unknown audience”, as extremely valuable upgrades to my thinking.
card_zero
3 hours ago
It's a kind of shared fiction of an imaginary person's thinking, really. The author writes "now, I know what you must be thinking", and hopes the reader will agree "OK, close enough".
iterateoften
5 hours ago
> You also have to spend time on textual matters like grammar and style and a lot of "unspoken rules", which aren't really about linearizing your thinking about the contents.
Hard disagree. Constraint is the driver of creativity. Also rewording sentences to sound better or make sense can make you reconceptualize the whole concept you are expressing
jampekka
4 hours ago
Sure, but doing an interpretative dance or an abstract painting can make you reconceptualize the whole concept too. But we're not really pushing those tools as much as writing.
Take for example a non-native writer of the language. I'm sure having to check up words from a dictionary may help to reconceptualize things, but I'm sure also that it's not often very efficient. And I think similar is going on for natives too for many types of writing.
alpinisme
3 hours ago
It’s not clear that doing an abstract painting can make you reconceptualize in the way that reframing in other words does. The point about putting in other words is that you may stumble on a clearer, more tractable, more extensible framing. The kind of reframing an abstract painting does is very different, more like changing your attitude or way of looking. But I don’t think it ever leads to a place where you will suddenly find yourself with a sharper understanding that helps you communicate with others more effectively.
Dumblydorr
5 hours ago
All writing is thinking when done by a human, you’re literally distilling your thoughts into words. You can’t write without thought.
And not all thinking is writing is a clear truism, there’s no point to even stating that.
Writing helps us think about the world, it’s a pivotal intellectual technology.
altmanaltman
4 hours ago
I have been thinking over this comment for a while now. I think yes, you are correct that thought is involved in writing but I don't think it is possible to claim "all writing is thinking when done by a human". Like what type of thinking are you claiming here?
Because one can copy a text and write it down and that involves thinking in the sense that anything we do involves thinking fundamentally. But that thinking is different from thinking logically about a concept and writing it down which I think is where you are getting at.
The definition of writing and thinking is too broad in that sentence even though it does apply in several obvious cateogires within that at different levels.
And also "writing helps us think about the world" is too broad again. Why? Why does me writing "apt apt apt apt apt apt apt" help me think about the world? I just wrote it because i felt like writing it. Why wouldn't you consider that writing?
lelanthran
an hour ago
> Why does me writing "apt apt apt apt apt apt apt" help me think about the world?
Poor example. You wrote it to make a point, after all.
max__dev
2 hours ago
You wrote "apt apt apt apt apt apt" to prove a very specific point no? Absolutely requires reasoning and understanding the problem to go there. And good luck getting an LLM to do that.
jampekka
4 hours ago
This is an example of the problem. I did choose to write it in the way that although I knew it can be interpreted trivially, if the reader assumes I'm an imbecile. It was a "punchy" recap of the relatively long explanation for a HN comment and hopefully decently argued point.
jkahrs595
5 hours ago
> Not all writing is thinking
Would love an example where you’re able to write without transferring your thoughts. Besides the obvious: fjcjfjrnjfjfifjfnrnakosifnrbwkofgjrj
jampekka
4 hours ago
I did think about this while writing, and I made the compromise to accept that someone will nitpick about it to hopefully drive the point better for those willing to read it charitably.
QED?
jakelazaroff
3 hours ago
What is the charitable interpretation? I am not being coy or sarcastic here; it is genuinely (forgive the claude-ism) unclear to me what your arguments are.
tomjen3
37 minutes ago
When you transfer other people's thoughts. On this board, we are engaged in pursuits where the truth matters. A lot of writing is about showing that you belong to the right in-groups. That is better done by repeating their talking points than sitting down and coming up with an earnest way to show that you agree.
27183
5 hours ago
The number of times the friction of writing has saved me from prematurely communicating a poorly understood idea must be in the hundreds or maybe thousands. For me when something is difficult to write about, it's a very good signal that I don't understand it well enough. So I think all the things you label as aspects of writing that aren't necessarily "thinking" are nonetheless good for thinking because they provide some necessary friction.
hi_im_greg_h
5 hours ago
This to me is the key reason people are so obsessed with using an LLM for everything.
They’re completely opposed to experiencing any type of friction.
27183
3 hours ago
Hence The Great Smoothening of Minds we're all experiencing in this decrepit era. I blame the financial incentive, and welcome its disappearance. There are too many people doing computers just for the big paycheck, it would be more fun without them. Maybe the AI bubble popping will get rid of them.
btrettel
5 hours ago
I've thought exactly that "writing is thinking" before as a reason to not let a LLM write for me.
Then again, I've seen a counterargument [1] by someone who clearly heavily uses LLMs for writing (going by both their LLMy writing style and their own admission). The person I'm citing describes a process where they get a LLM to write something, they check over it and provide feedback to the LLM, the LLM rewrites, and the process repeats iteratively. So clearly he is putting thought into the process.
I think there is something valuable missing, even if it's hard to clearly express. I'll try. The threshold for what I'm willing to accept if I'm simply approving something is likely different from what I'll get if I write something myself, for instance. Saying "LGTM" is too tempting. It seems to me like he's outsourcing his selection of topics to cover as well. If you're not thinking yourself about what to cover then it would be very easy to miss a critical subject. There also an asymmetry between checking and generating something with constraints placed on it. Checks can't catch everything, and a constrained generating process can reduce the amount that needs to be checked, avoid issues that can't be checked so easily, and focus your attention on areas that you know historically have had issues with this generating process. I've thought about this quite a bit in terms of whether to write new code or use an existing library. Sometimes "the devil you know" (my code) is better than an existing library simply because I understand its flaws better.
jeremyjh
4 hours ago
I've tried doing it that way, and thought it was even acceptable for the reasons you said. I did learn a lot through that process and clarified my ideas. But later I rewrote the whole thing from scratch and then had the LLM review it. It made some good suggestions but no substantial changes. The difference was night and day. That final product had my voice, and I understood it better. LLMs are powerful tools and can improve quite a lot of the writing process, but using them to do all the writing leaves a lot on table along with your fly open.
simonra
an hour ago
I always found writing to be an unnecessarily arduous tool for communication, so I never do it until I've overthought what I want to express and approximately how. At that point I flesh out the skeleton of the message, and pile on words and structure and references and other rhetorical tricks until the packaging feels sufficient. Rarely, if ever, have I experienced writing things down altering my perception of the strength or weaknesses of the chosen arguments, or revealing new ones. That is to say, I really don't vibe with the concept of writing being thinking, rather it's a waste of humanity's resources to push it as The Tool.
agumonkey
an hour ago
That's an important point. It's the core of your life, thinking, balancing, exploring, improving. I don't believe adjusting something large you didn't do will ever benefit people.
zero_shift
4 hours ago
I have found this extremely relevant as a (primarily) non-verbal thinker.
I don't, generally, think in words, more in - I guess I would call it something like meta-shapes? A sense of a shape but not things I can exactly visualise.
(You might be surprised to read this and then hear I have an English degree. Surely I thought about Shakespeare in words?! Nope. Shapes, movement, structures)
For me, having to write is critical because it is the only way I practice serialising my thoughts in a way other people can understand.
If I do not then I get very "deep" into my own way of sensing ideas and it's difficult to dig myself back out.
This might also be why I have never been very enchanted by LLMs? They only seem to "think" verbally. So it is always a translation effort for me.
I never can really enter any "flow" state with an LLM. My intuition is that highly verbal thinkers can enter flow with LLMs very easily
eikenberry
an hour ago
+1... this is me as well. Using LLMs in a conversational mode does not fit my thinking. I use AI help in my editor through targeted code generation, explanations, etc. and I'm writing my own harness to hopefully get a better feel for the shape of LLMs that way. How are you adapting?
AnimalMuppet
3 hours ago
Interesting. I have designed code that way. And the shapes aren't UML diagram elements or anything like that, they're just... shapes. I'll slowly walk around, in the hall or outside, and be kind of seeing these shapes and vaguely moving my hands around as I sort out the relationships between them.
I think I have produced reasonably good designs. Don't ask me to teach anyone how I do it, though.
jimmaswell
an hour ago
I find this and the parent comment highly relatable with the caveat that I also find it extremely intuitive and rewarding to get good outputs from quality LLM's like Sol or Astra, and I haven't had any trouble with "flow state".
One of the most rewarding things for me is figuring out a good shape for a system and how it would interoperate with the other systems, especially in a way that reframes other parts of the codebase in a way that bring clarity and makes it more intuitive to work with. Creating the right ontologies can make all the difference in what you can do with a project. It's a form of creating mathematical objects.
For example, a Unity game I work on has quest and dialog systems driven by visual scripting graphs. We had two way dialog with different units for player response choices and npc dialog. But we wanted to expand to letting NPC's have dialog with each other as well as conversations with more than two participants. I went outside and thought it over, which largely amounted to visualizing a dialog node graph and a feeling in the back of my mind like it was trying to perform a kind of geometric shape-fitting exercise. A fitment solution jumped out at me to have only one "Dialog" node shared by all participants, with a "participant" value on it. If the player parses this node then the options go on-screen as responses, while if an NPC parses this node with multiple options in it, it picks one. And this lets you voice the player if you want, and enables some things like overhearing other NPC's talk to an NPC then talking to that NPC yourself and having the same tree.
And for quests, the quests had just been for the player, but I was thinking about how to make scripted events in-game easiest to work with for script team who primarily works in visual scripting. Similar story - let the NPC's have their own little quests, with task stages, which are easy to track and make branching choices from, and let the NPC's definition for how to use that quest contain a collection of actions to override the typical actions available to it, so an NPC in a specific "quest" can't do things you don't want it to do, a common enough case that it's preferable to making a series of conditions on the general action planner like "not in quest A"
And timing myself, it took 1-2 hours each time to write out the detailed plan for how I wanted each thing implemented in the game with some other tasks thrown in, and it paid off after Astra worked on it until it was done. It was awesome coming back to something pretty much exactly what I asked for each time.
pixelmonkey
an hour ago
Related, one of my favorite quotes by the computer scientist Leslie Lamport is this one:
"If you’re thinking without writing, you only think you’re thinking."
vehemenz
2 hours ago
> Writing is thinking.
This way of phrasing it is needlessly confusing. Writing is a kind of thinking--one of many--but it's not equivalent to thinking.
Dialectical thinking, for example, produces similar results.
victorbjorklund
4 hours ago
On the other hand there is so much things that don’t require hard thinking. An example is I got an email the other day from a supplier asking if they can turn off their old email. It didn’t require ”thinking”. All I had to communicate was ”turn off graphql but don’t touch restapi”. Instead of sending off such a short and maybe unclear reply I had AI type up a concise and clear reply with exactly what can be turned off and what must be left on. Could I have done it on myself? Sure but would require more work than just a quick prompt and copy paste.
whateveracct
4 hours ago
writing also implicitly has your review baked in. hand written code is "reviewed by construction", if you will.
agile-gift0262
5 hours ago
I completely agree. And I'd say that at least in my case, it applies to both writing prose and code
jeremyjh
4 hours ago
Agreed, but a lot of code doesn't need to be understood. Knowing which is which is the art right now.
ripe
3 hours ago
> a lot of code doesn't need to be understood
Really? I am having difficulty thinking of any examples of code that doesn't need to be understood. If it isn't understood by someone, then how is it even working?
If you mean like a library you are using, where you aren't even reading the internals or might not even have access to it, OK, but that code is stull understood by its authors, surely?
27183
3 hours ago
Uhh... what? You'll instantaneously feel very differently when the service you're responsible for is down at 3:15am and your logs are full of stack traces that end somewhere in that code that "doesn't need to be understood". At that point, you will need to understand it well enough to fix it stat.
jeremyjh
2 hours ago
Test code for a new bug is a good example. You can prove the test covers the bug without understanding the test code (you need to understand the bug, of course). There are some domains/tests where you can't do that - you need to be sure its failing for the right reason, but often you can do that without understanding every line of the test code. You can extend this to lots of related test infrastructure. If you can watch playwright test the app the way you expect it to, you don't have to understand all the code.
You can also do this for apps that are just tools for your own use. You satisfy yourself that they are working, and you use them because they save your time. You review enough to be sure its implemented the way you think it is - and if it is working, that tells you quite a lot. Sometimes you will be surprised and have some time wasted.
Yes, yes - there are people who will make the wrong choices in some of these cases but that doesn't mean there are never cases where you can do it.
More broadly - anyone who works in a team is already working with code they don't fully understand. I have code I wrote years ago I don't fully understand. I trust its observable properties and its track record.
27183
an hour ago
> You can prove the test covers the bug without understanding the test code (you need to understand the bug, of course).
I'm not following.. When we write regression tests those tests encode invariants we expect to be maintained under source code transformations over time. If I don't understand the test code I've written, how can I know which invariants I've imposed? That's why, broadly speaking, we write test code to be as simple as possible above all else--it's absolutely imperative that these invariants are not only intentional and easy to reason about, but also that when an invariant is violated we can easily discover why. Often, on a team, the person encountering a test failure after making a code change is not the person who originally established the invariant, so it's very important they be able to easily understand it.
I see no possible world in which failing to understand the test code is... possible? Like, if you have indecipherable test code things are really bad in your codebase. Fixing that is P0, because it'll compound rapidly.
jeremyjh
21 minutes ago
You can know the test is likely good, if it reproduces the failure you are fixing. I don't think we're communicating though because I never said the test code was undecipherable.
exe34
2 hours ago
This puts into words something I have been thinking about but haven't been able to articulate when people ask me if I'm using AI heavily. Usually when I write a report, I start out with a question and try to find some hints that will give me hypotheses that I can then test. I can't just prompt "Write me a report". The process of coming up with the information in the prompt is best done by writing the friendly report.
bbor
5 hours ago
They are powerful tools but they do not have any human understanding - that isn't their optimization target.
Ofc the rest is all right on, but I'd quibble with this specific idea. LLMs are absolutely targeted at modeling human understanding, which is the same faculty that contains what we call perception (!= sensibility) and intuition (!= rationality). It would be nice to train them to be completely alien from the ground up, butA) we only know of one species capable of metacognitive understanding,
B) we already tried that in the 1970s, and it was good work but often evolved into what we'd call boring ol' computing rather than AI, and
C) an alien mind wouldn't be a very good agent, for a ton of reasons relating to affect, conversational rythyms, cultural understanding, etc.
The trick is to make something that acts like a human but with the affordances of a computer (e.g. scalibility, symbolic certainty), without making it so human that it takes issue with its existential reality and/or use of its labor...
jeremyjh
4 hours ago
We don't have a reward function for "human understanding". We reward the appearance of understanding. We define goals that we cannot conceive of reaching without something like understanding happening. There is something happening, but it is alien and counter-intuitive - it makes bizarre mistakes that betray it - and we don't know what it is. I'm pretty sure it is not human understanding.
antonvs
2 hours ago
> We reward the appearance of understanding.
Which is exactly what happens with human evolution and development. Sure, we can say LLMs don’t have “human” understanding - which is something we can’t really define anyway - as long as we’re not trying to claim LLMs don’t have understanding at all. The latter is a much higher bar.
> We define goals that we cannot conceive of reaching without something like understanding happening.
Functionally speaking, that is understanding. Again if you want to go past a functional definition, that’s a bar which no one can clear right now.
jeremyjh
an hour ago
I don't think its the same. Evolution dealt with the real world where there were real consequences to poor understanding. The reason LLMs are good at math and programming is because selection is truly based on results, not perceived results.
I think AI models do have something like understanding - I think Leela understands chess and I think Claude understands code in some very real sense, though not a human sense.
But for general writing, you have to understand the world at large and there is no sufficient RL for that. Do you really not see the constant errors that AI make that betrays a lack of understanding the world? I see them so constantly I rarely think about them, I just skim over that slop and move on.
antonvs
39 minutes ago
I think your claim is narrower than I was imagining.
Sure, the exact nature of the understanding that an LLM exhibits is different from a human's. The differences in the training data we're each exposed to can explain a great deal of that, and of course there are architectural differences etc. as well.
But the specific quote I responded to was "We reward the appearance of understanding." My point is that's no different from humans: evolution and a child's upbringing rewards the appearance of understanding. The result is imperfect, e.g. people end up with an understanding of the world that in some cases is completely nonsensical (all religions except the one true religion, mine, are false!), but it's sufficient for them to survive.
This demonstrates that "appearance of understanding" is not a meaningful distinction between LLMs and humans. The meaningful distinction is in the training data and the specifics of the reward functions.
Many people seem to try to make a kind of "no true Scotsman" claim about understanding, that somehow LLMs "don't have real understanding". Based on the above quote, it seemed like you might be making that kind of argument. The counter to that argument is simple: if LLMs don't have real understanding, then neither do humans, because broadly speaking, both operate on similar principles: we learn from training data, there are reward (and punishment!) functions that influence what we learn, and the result is a "mind" that demonstrates an understanding of the world.
zahlman
an hour ago
> absolutely targeted at modeling
Not everyone accepts a simulationist view in which modeling something accurately enough inherently results in creating the actual thing.
AnimalMuppet
3 hours ago
LLMs model the part of human understanding that is captured by the relationship between words in the training corpus. Anyone who thinks non-verbally, the shared understanding of "apple" that comes from having eaten them, understanding what someone is thinking or feeling by their body language - there's a lot of aspects of human understanding that LLMs don't model.
ModernMech
4 hours ago
Writing can be thinking. There's a huge presumption that if someone is banging away on a keyboard they're doing work because you can hear and see them doing stuff. But that's the whole plot of The Shining -- Jack despite all his writing wasn't thinking at all.
Likewise using AI can be thoughtless, but it doesn't have to be. I don't see why a valid creation process can't be like this Simpson's meme[1], where you start with a rough object and then cut away and refine until it's done. I don't see it as lacking merit or requiring less thinking compared to starting from a blank canvas and adding more until it's done.
And either way at the end of the day the writing artifact stands on its own. It's either good or bad, taste permitting, and can be evaluated for what it is.
[1] https://media.licdn.com/dms/image/v2/D4D22AQFoqRgMxteTNg/fee...
a2ff6eeb0
2 hours ago
> You cannot outsource your understanding to AI. They are powerful tools but they do not have any human understanding - that isn't their optimization target.
Understanding is the bottleneck; the way they speed things up is by letting me outsource understanding, and get back a summary. The entire advantage to AI is that it lets me skip understanding the problem, and just get a working solution.