My go-to example is if conditions. When I start with the first case, it almost always figures out the rest of the cases correctly.

But I can understand why some workflows may be a liability rather than help. Similar to chatgpt sometimes allusinate code with reserved words that don't exist in the language (but exists in others), or uses old code from a deprecated api / version of your framework.

In other words, somtimes the cleaning process can take as much time, as just writing the code yourself.

With prose it's all your manual work again to check. If your text is mundane it might be trivial but for anything a pg would write I guess an AI is pretty useless in its current state.

Structural Alignment: Modifying Transformers (like GPT) to Follow a JSON Schema

The whole thing reminds me of the blockchain hype train. Still using and loving databases here for the foreseeable—still writing things by hand for the foreseeable, and loving every moment.

Here on HN, a few days ago, there was a post about Microsoft publishing a GitHub repo that contained a "table recognizer AI". Basically, you feed it PDFs that contain horrible scanned images of finance records, and it spits out Excel spreadsheets. For some reason, Microsoft had just "thrown this over the fence" and released it to the public for free. This, despite man-years of effort developing the thing. It was working, and everything.

I made a comment wondering if Chat GPT 4 with the vision extension could solve the same problem. One of the devs that had worked on the aforementioned AI (for years!) mentioned that yes, yes it can.

Game over.

Those years of effort had just been replaced with a one-sentence English-language prompt that starts with "Please output a table from..."

If this doesn't blow your mind, then... I don't know how to help you understand just how much has changed, virtually overnight.

OmniPage Ultimate is an entire workflow that's turn-key and ready-to-go.

Generally speaking, Kofax -- or any other off-the-shelf OCR tool -- can't process tables properly. They get confused by headings, total rows, and the like. Hence the R&D effort by that Microsoft team to develop a purpose-built AI-driven tool that can specifically identify these elements and then output the result not just as ASCII text, but as a spreadsheet.

Either way, whether you are talking about the Microsoft AI or the Kofax tool, the effort is measured in man-years, man-decades, or perhaps even man-centuries.

You can just ask ChatGPT to do similar tasks for you in seconds.

A real-world use case I had for GPT is to fix up the formatting of badly copy-pasted tables. E.g.: I had created a cloud VM and pasted the summary tab into text editor, and only then noticed that the cells ended up on individual rows, interleaved with the headings. Even if I had used a regex to undo the damage, the headings mess up the alternating row interleave. E.g.:

    Overview
    Hardware
    SKU
    A2
    Zone
    2
    Software
    Operating System
    Windows
    Image
    Licensed
    Yes

Sit down for a second, and ask yourself: How much time and effort would you have to invest to solve this style of problem, in general. E.g.: given some random, broken table formatting for an arbitrary but well-known subject, fix up the formatting.

Years of effort?

Decades?

Whilst to the untrained eye, sure, the NGINX config looked great, but it didn't function, and it didn't include the URL rewrites as instructed, hallucinated a bunch of rewrites that didn't exist and would serve no purpose, and despite refining the prompts over many days, and consulting with self-proclaimed "prompt engineers", it still didn't give the expected output.

It's neat. Reliable? Not in my experiences for my needs, but I am genuinely glad you're making it work for what you need, that's definitely cool, provided it doesn't hallucinate in an unnoticed capacity. It's a lot of trust to place in an LLM.

The trick is to know what they can and can't do, and use them where they're useful enough.

Someone here on HN quipped that ChatGPT is "isn't intelligent" because it couldn't come up with a revolutionary new battery chemistry.

Like... what are you expecting? A god?

LLMs are useful when you can validate the output yourself. Similarly, they're useful when the output doesn't have to be precise but the inputs are English.

For example, they're awesome "filters" for human input as measured against human metrics. "Is the following text rude? Output YES or NO only?" is very useful and works well enough, right now.

They're also useful when you need to iterate to find something, or when your search parameters are incredibly vague but have a narrow Venn diagram intersection.

I've been using ChatGPT as a replacement for the /r/tipofmytongue sub-Reddit. It knows everything well enough to be able to interactively find what I'm looking for to an extent that is super-individual-human, and in some ways beyond what even a very large collection of humans can achieve.

In my opinion, the mental model you should use when evaluating LLMs-vs-Human capability: When asked a question, the LLM has to answer without being able to iterate, back-track, or even use a scratch pad such as pen&paper or a text editor. Basically it's the same as an "oral exam", where you have to stand in the middle of a room and get grilled by a professor to determine your knowledge on a subject.

Don't compare "human with tools and unlimited time" to "LLM with no tools and seconds of time". Compare "human being interrogated in an empty room" and then it is much more clear where an LLM rates.

GPT 4 is definitely super-human in some areas, such as general knowledge and translation between languages.

No human knows as much, or can speak as many languages.

Ask yourself this: can any human, when asked to "invent something", just do it, then and there?

Can you share the prompt and your expected output? I'm interested to see where it goes wrong.

https://pastebin.com/8FwQzDiE

TL;DR. I start by asking it to generate sentences where subject-object inversion yields a meaningful sentence where the verb's meaning is shifted metaphorically. For instance "I smoke the cigarette vs the cigarette is smoking me". After some back and forth it comes up with:

The painter captures the landscape vs. The landscape captures the painter. The gardener shapes the garden vs. The garden shapes the gardener. The chef creates the dish vs. The dish creates the chef.

Maybe that will change your mind.

And how well does it do them? Correct me if I'm wrong, but for the time being all you know about its ability to replace that other MS tool is a "yes, yes it can" by a MS dev? Don't you want to withhold judgement until after you've spent some time actually using ChatGPT to do OCR?

This is like saying you could do CAD via ChatGPT, sure, but it's an objectively crappy experience versus an actual piece of CAD software with an appropriate UX.

Using different pieces of software isn't a problem for most adults.

For now I'd treat it more like a sparring partner who can help you with your ideas and support you throughout your process, rather than a magical genie that can magically solve your problem for you. And in this manner I find it to be very useful indeed.

[0] https://github.com/paperswithcode/axcell

I agree that there is currently way too much hype. I mean GPT4 is certainly cool and probably going to be useful in some limited use cases, but it's not the new electricity and it's not going to take over the world.

An example I had last night: how do you split a pdf into separate pages on the command line, and how do you put them back together again (I replaced one of the pages with a scanned signed version).

This is a basic task, a beginner's task in the world of pdf munging, but I didn't know the answer off the top of my head. ChatGPT gave me command lines straight away.

That's what they're good for right now. Beginner / common tasks in areas you're not familiar with.

And a further implication, they're great for one man bands, generalist roles where simple things need to be done in lots of different areas. For specialist deep roles where you have all the tacit knowledge in mind already, not nearly as useful.

I recently had to do a 'git pull' on 120 repositories, and of course we already had an in-house tool to do that. Of course I could google a script to do it. But it saved me 5 minutes to just have ChatGPT write it for me, and I saved the overhead of a more thorough context-switch.

Even the guy sitting next to me. "Hey, check this out, ChatGPT wrote this script for me and saved me 5 minutes". "Oh, but we already have a tool for that". "Yeah, I figured. Where is it?" "Ummmmm, I don't know, let me ask someone. Frank maybe?" And 5 minutes spent chasing Frank.

I think I'm still better off starting with clChatGPT, I can always follow up with "list 5 alternative solutions".

Interesting, because I still find it faster/easier to do a quick DDG search, with a click on the first relevant SO post, and scrolling straight to the top answer. Asking the same question to ChatGPT means waiting for its output, which may be objectively a few ms faster but creates the perception of slowness ( = irritation). (That is: waiting for X seconds is more disruptive than taking several actions for 1.5X seconds.)

And then it's definitely going to be much more verbose, which is worse 99% of the time. SO is nicely folded - if I want a deeper explanation, I look at the answer's comments, or I can read other answers, or I can even read the full question. ChatGPT doesn't have the same guarantee of such a clear information hierarchy in its answers.

Here's a GPT4 response to a (poorly written) prompt from me to answer the parent question and try to satisfy your desire for a straight-to-the-point response.

https://i.imgur.com/OmsHaXO.png

> Using only the command line, answer the following question (also, keep it terse, no extraneous narrating please): how do you split a pdf into separate pages on the command line, and how do you put them back together again

For example, sometimes the top answer is long and requires a few minutes of reading, sometimes there are corrections in the replies that need to be read, and sometimes I don’t fully understand it and therefore need to read multiple replies.

This can easily take up 5 - 15 minutes, versus 1-5 minutes for ChatGPT.

Additionally, I find value in NOT having to keep myself from getting distracted by the tons of other highly interesting things on Stack Overflow. It can be nice to have that focused, text-only, distraction-free dialogue that ChatGPT provides.

1. Time — for me to search Google and then read anywhere from 1-10 links to get an answer typically takes anywhere from 5 - 30 minutes. The average is probably 10-15 minutes. But using ChatGPT takes less than 1 minute.

2. Specificity — ChatGPT gives me answers to more specific questions, with no fluff text to read through, no ads, no redirects, no links that appear to answer my specific question but actually don’t when I click on them and read them. I get a specific answer to a specific question.

3. Interaction — If I have follow-up questions and/or need clarification or alternative options/solutions I can simply ask ChatGPT and it answers. I often find that getting answers to followup questions via web search takes even longer than the original search.

These all make it worth it for me. And that still doesn’t touch on all the other use cases that search engines don’t do at all.

Especially in refactoring or working with poor codebases. I do notice with copilot specifically some people seem to get wildly different response times with it, for me it gives suggestions almost instantly so as im typing ive got an ongoing suggestion I can accept at any time as it gets closer to what I intend or ignore if not. A common really useful thing is when I have to set up a series of something (recently in a power plant code I set up parameters for a certain kind of battery) and then I just start writing for each other kind of battery it suggests the whole boilerplate for every case, I can go back and fill in the details and exceptions.

As for chatgpt its more useful at interpreting existing code. I often give it a giant function and say "I want you to split off this part and put it into its own thread, get the output with a queue" or something like that. Again, output can often need a little bit of massaging, but I find it reduces cognitive load involved in doing such a task. The trick is to be naturally skeptical of the output but appreciate how much of your life is spent writing nonsense.

I've also found that learning to put what I want exactly into words can be a particular skill that has helped my communication. Making sure you know the name and function of every tool you use. I think if I had said that to myself before I started using these things id have been incredulous, "of course I know the name of things" but no, its a different thing to be talking about them often, especially when you have an idiot savant that takes everything you say too literally. You have to learn to be consise and precise

People want to use LLM hammers like screw drivers and are disapointed.

Also, since the quality of the context matters a lot for completion, copilot will be way more useful on clean code with beautiful variable names and nice structure flows than on code-golf snippets. Same got chatgpt: write good prose, and it will help you better.

IMHO, the reason people do so is because LLMs are advertised as such.

I can't tell any more what is satire and what is not.

But in the end, it's just a tool. If it isn't working for you, it isn't working for you. For me, I prefer thinking in what structure to me as bigger building blocks.

So I prefer vim bindings because I used up teenage time learning it. And now I don't think in terms of characters but in terms of text-blocks.

I prefer writing Java in IntelliJ and Rust in Clion because I don't think in char terms since I can use the built-in refactor tools. E.g. I write functions inline and then extract them rather than writing them as functions and calling.

Likewise when I'm at the shell I think of pipelines as less than pieces. Being an experienced shell user I can usually write a parallel pipeline with conditionals and loops correct first time verbally with no computer usually.

But my copilot flow is improved even better. I talk to the computer in components:

# get all private ip addresses in Alibaba region cn-zhangjiakou ips=...prompted text... # ssh to each and check last long line ...Prompted text...

It's faster than me and this is great for r-i-search with fzf.

Overall, quite happy. But just a tool.

It only needs to save 10 minutes of my time a month for it to be worth the $20 - I would estimate it saves hours.

"Hype" can also mean marketing.

I only use the free 3.5+ ChatGPT and it's written some nice mapping code and some fun poems for me. Poem subjects ranged from rheology to texas hold'em.

I came to the sad conclusion that perhaps what I write and what I do is not as repetitive or as generic as others. Over the years when I have had to do more repetition, writing once well and ctrl-c / ctrl-v.

Couple hours work done in a few minutes.

Most people are terrible writers, and never read anything at all. So they have no way to judge if the wordy informationless crap gpt wrote is good or not. It's long, thus is good.

It's also somewhat structured, which puts it at above average for writing tasks.

The differentiator is, I guess, expectations: I fully expect my own code to strike a certain balance of readability / optimization / correctness. In this context all but the most trivial code suggestions, sloppy as they usually come, instantly fall out of place.

On the other hand, there is a group of people (e.g. those spamming GitHub pull requests...) who expect the output to be kind of visually similar to some code. When they realize that the result also sometimes works and gives sane-looking results, they are absolutely blown away.

Nonetheless it's better than Google at many things. I find it really good at giving paragraph-long introductions to things, instead of pages-long blogspam. It's also good at suggesting improvements when writing in a second language.

But I don't see what those AI startups see when they want to replace all sorts of skilled workers with this bullshit generator.

The trick is to use it for what it's good at.

For example, I had this vague, fuzzy memory of a talk I saw online about a decade ago. It was about a database modelling paradigm. All I remembered was that the country it was developed in was in northern Europe, the methodology was based on splitting out columns as individual objects, and that it had funny little icons such as diamonds on the edges connecting things together in a graph.

Good luck finding that on Google. I had tried and failed. It was bugging me, because I needed something like it for my work, but for the life of me I couldn't dig up the reference.

Chat GPT 4 found it just three prompts: https://www.anchormodeling.com/

That's insane.

I read for probably 8-12 hours a day. I write every day. I think every day. I’ve been doing this for decades. GPT has been like a talking dog. It’s an insane achievement, but he’s not the worlds best conversationalist.

But to be 17 again and struggling all night to write a three page paper filled with unoriginal ideas, it would have been a game changer back then.

I find GPT very useful in that I can quickly become mediocre in any domain. Need a legal filing? I can get a good-enough legal filing. Need some code in a framework I've never used? I can get it instantly. Need something written in the language of some esoteric field of science? I can get that instantly from a bullet point list.

It doesn't do the hard parts well: What's being written, and how it's structured. That's where I fit back in.

I agree that GPT isn’t much of a conversationalist. But it is an exceptionally good tool some of us. It has already proved invaluable for me in helping diagnose a medical issue with a family member — one that escaped several ICU doctors and nurses. And a couple of weeks ago helped me fix an issue with my car.

A good analogy might be a calculator. There are some people who can do calculations in their heads near instantly. And some of these people could do arithmetic of numbers too large to be represented by calculators even. But for most of us, calculators were immensely useful.

The generation after me is hardly able to reset their iPhone if it freezes without going to a help desk for help. Meanwhile, that same generation is using the cameras in their phones, readily available video editing software and processing power to create content like no generation has ever before. They are able to entertain millions of people around the globe while doing it and some build incredible wealth at the same time.

My grandfather built his own house. My generation gets their house delivered in prefab parts that are then welded together. Nobody really cares that we've lost (the majority of) the ability to build houses or blacksmith katanas.

I think you meant hoard incredible wealth?

There's a limit to how much wealth you can actually create with immaterial entertainment alone. Even a best seller book: it takes time to read, so while it provides value to millions of readers, it also removes value in the form of opportunity cost. No way around choosing what to do with your own time.

Other alternatives have more potential. Educational content could lift some people out of incompetence and help them build actual wealth (say a very good programming or sawing tutorial). Writing useful software could also create wealth, even more so if it's Free (and free). And of course, building stuff (while taking care not to deplete our resources or burning up our planet…).

We say that people "make" money, but that's a dangerously misleading idiom. They don't actually make money, they extract money. Hopefully this money is actually earned in proportion to the value they actually injected into society (make a chair, get paid for the chair, all fair and square). But never forget that the people who "make" the most money generally do so by taking it from other people. Employers, landlords, stakeholders… who get most of their "earnings" not from what they do, but from what they own.

Do you know anybody who owns zero chairs?

The Japanese generally didn't use them (I think?) before they came into contact with European civilization.

I don't think any civilization could survive without entertainment though.

But I think this is a false dichotomy: A comfortable armchair and a good book go well together.

https://ourworldindata.org/energy-production-consumption

That will catch up to us. It already has, in many parts of the world, and it's not looking like it'll get better soon.

And let's not forget, it's not just people who have paid the price.

> The contemporary rate of extinction of species is estimated at 100 to 1,000 times higher than the background extinction rate, the historically typical rate of extinction (in terms of the natural evolution of the planet); also, the current rate of extinction is 10 to 100 times higher than in any of the previous mass extinctions in the history of Earth.

- https://en.wikipedia.org/wiki/Holocene_extinction

The ecocide which we've inflicted on the planet for the last 100 years will some day be seen for what it is - an atrocity.

https://www.newyorker.com/cartoon/a16995

All of which is to say nothing about the inequality in how that shareholder wealth is distributed, which is incredibly short-sighted for literally billions of reasons. 8 Americans own more wealth than 4 billion humans. It's perverse beyond comprehension.

When the going gets tough...

About the inequality : it is really bad, but hundreds years ago, the poor were starving because of lack of food, in stark contrast with landlords and knights and kings.

Today an equal amount of or more, people die from too much food than of too little.

My point is, you need to take a step back and look from a bit more distance to see the real trend. Things are bad now, because we are at a maximum.

The plateau is not coming fast enough, not even close. Scientists are very clear on this point.

We used all that oil and gas to get more work done, but we're still working more hours per year than those feudal peasants.

> people die from too much food than of too little.

We still have thirteen million hungry children in America. 700 million hungry people worldwide.

It's all very solvable, we just don't. We could end world hunger by eating like, two or three rich [0]. Knights and Kings have nothing on our oligarchs.

These trends of irresponsible emissions and rising inequality won't fix themselves. Not in time. Not without radical action.

[0] - https://www.theguardian.com/global-development/2020/oct/13/e...

See, the harmful confusion right there.

Making money and creating value are two separate things. There are people who create value without receiving any money in return. And there are people who receive an obscene amount of money while creating very little value — and sometimes destroying more value than they create.

Understanding that the two are separate is the only way you can question the legitimacy of our richest people. I’ll give you a hint: the most impressive CEOs journals routinely praise as geniuses, are very, very unlikely to produce as much value as the money they actually receive.

If you want to even stand a chance at critically looking at our current economic system, you absolutely need to properly separate the notions of "making money" and "creating value".

Even a more thought out form doesn't do anything class analysis didn't already do.

Not quite: bullshit jobs are when the workers themselves say their own job isn't doing anything useful. And apparently they comprise a sizeable portion of all workforce.

Of course, jobs people say are useless, jobs the workers themselves say are useless, and actually useless jobs, are 3 different sets. But I think we can confidently say something is wrong when so many workers say their own job is useless, even if they aren't: working a job you think is useless just isn't healthy.

Americans in particular always have pretty narcissistic ideas about what their jobs should be. You can see this from all the examples of hippies trying to start communes, which then fail because everyone appoints themselves official poet instead of farmworker.

Yeah, I'm gonna need a citation for that one.

I grew up in a council house. I.e. a house created by pooled money in the form of taxes.

Houses must exist. Landlords are an inefficiency to be optimized out.

Though for what it’s worth, the government at least has the advantage that it can print its own money, so it doesn’t need to take it from somebody else, unlike investors.

Strictly speaking the answer is zero and zero. They use the money they have to have construction workers build the houses.

> from the money they actually took from somebody else?

Is this supposed to imply someone is taking money from others and the other does not? I know the view that government taxes is basically theft, and I think this view is ludicrous: citizen get that back in the form of infrastructure and public services, and in a functioning democracy it's basically their collective will that decided how to allocate that money. (How functioning the democracy really is is another debate.)

Investors on the other hand… well there are two kinds: those who worked for their money, and those who took it from workers. And it's a spectrum too, it depends how much of your income comes from your work, and how much comes from your possessions. Now I can guess most small landlords like yourself probably paid their houses with money they earned through actual work. Some even have constructed the houses themselves. But if that Second Thought video https://www.youtube.com/watch?v=m1m7WmKJZyQ has any accuracy the majority of rented houses are rented by big landlords, and those definitely did not earn their money through their own work (the clerks that work under them do, but they don't own the houses and are paid a meagre salary, compared to the renting money they manage).

I can't speak for government constructed houses, but given the above, your average investor-owned house was constructed or bought with money that was taken from people working for the investor. (Well strictly speaking the worker creates value for their boss, which gives a salary in return, but there's always a difference between value created and salary returned. That difference is the exploitation/theft part. And I'm glossing over the fact that managing a company is valuable work unto itself.)

> And which houses are nicer, safer and better maintained?

Am I supposed to answer "the investor's houses"? A citation is needed for that answer, and it'd better control for when the house is build (construction norms tend to evolve over time), how much it actually cost, and in some cases who it actually cost. Suburbian houses may be nice for instance, but the car dependency and the unsustainable infrastructure costs definitely are not.

A marketplace of ideas does not necessarily lead to the best outcomes, or even good outcomes.

Suppose the marketplace of ideas is an unregulated marketplace. Then the wealthy and powerful can use their wealth and power to determine the world that other people see, getting those people to mistakenly fight for things that help the wealthy and powerful.

But suppose it's kept a fair marketplace. Fairness is not natural – it's a human value that we have to actively maintain. But suppose we succeed. Even then, the marketplace may not select the best ideas, because people have emotional needs – like belonging to community – that will cause them to stubbornly reject good ideas. But we accept this because it's the only way for us to live with each other; if I want you to let me disagree with you, I have to let you disagree with me.* The price of intellectual freedom is very, very steep, even if it's worth it.

So I don't believe the fact that capitalism is dominant is good evidence that it's the best idea so far. In fact, I think we have little evidence of it at all.

* There are of course complications of the Popperian, paradox-of-tolerance sort, where we do have to fight to the death, so to speak. If someone repeatedly demonstrates an unwillingness to compromise or act fairly, they've left you the choice of letting yourself perish or eliminating them, which is a shitty situation to be in.

FWIW, Adam Smith has argued that this is unlikely. The world seems to prove him correct.

That said I do disagree with the way GP's argues the point. While the dominant economic system is likely more effective at creating dominating economies, it's not a given that it is good at being fair. Shutting down discussion while claiming that "capitalism is the best so far" because nobody else has come up with an alternative reeks of willful ignorance. It's easy to believe something is the best if you don't actively look for alternatives and demand others to serve it to you on a silver platter.

I didn't realize that Adam Smith had argued on this point, though! Thank you for letting me know.

Beyond that, the only reason they give you more rent money is because you own the house. But you got your ROI so by now they have paid for the house. And yet it's not theirs, so they still have to pay you. And when they do, any additional money you get, you basically took from them. As for your son, who presumably did not work for the house at all, will definitely take money from people through that house. Just because he had the right dad.

Don't get me wrong, you investing for your son and securing his future is you being a good dad. You just can't wave away how the current system works: people who own stuff have the power to take money from people who don't.

People who have invested capital, hedged risks, setup maintenance, did marketing etc.

Now we can agree that housing is very specific branch of economy where if you have upper hand(capital) you can be dealing cards to those who don't. There are ways to address that without the canvas of grand narratives(extracting labour etc).

Struggling as a learning tool is important, but there's a good chance the fastest path to expertise is to start with examples of how to do it, and delay that struggle a bit.

What you don't want is to keep the crutch forever and never struggle. That would be a sure path to stagnation.

The rejection of LLMs for education by otherwise intelligent people is disheartening.

Also, some things are not need to be known anymore, like phone numbers or the inner working of a car. There are plenty of things to learn even if automation made some of knowledge beneign.

- they wouldn’t have yielded all the knowledge of the little kirks, know how, edge cases, etc… they now have to know (and the Pareto rule 20% vs 80%),

- created an intuition from all the hours spent just struggling to understand what is wrong or find a solution and little by little honing their intuition from that hard work they would have done by themselves for themselves (compared to having ChatGPT, a teacher, or an expert telling them most of time what to do),

- and getting a will to find a solution by themselves even after hours looking at a shell prompt (or logs or whatever else) that is taunting them and will tell them nothing.

Edit: I've had conversations with people who write. We have come to an agreement that there are writers who love the process of writing. They want to BE writers. Then there are people who want to HAVE BEEN a writer. ChatGPT was meant for these people.

I disagree with this take. My personal anecdote is that I am far, far better at mental math than I was in high school because I ended up in a job where doing a lot of ad-hoc calculations was just a day-to-day thing. I started out using a calculator, but eventually got to where I didn't need it most of the time just through sheer repetition.

I think AI tools (the kind people are worried about right now, anyway) will be used in a similar fashion to calculators. After learning the basics, it will be a tool taught as an assist for streaming ideas into and getting out a well-formatted and coherent output. People who write a lot will eventually find the hassle of using the tool to be more trouble than it's worth. People who don't will still know how to use it to and know how to recognize when the output doesn't make any sense.

LLMs are the single biggest advance in mass learning since the printed book. They are infinitely patient and can be reprogrammed, or repurposed with three sentences. Debate partner, editor, quiz master, literally anything. Every new tool can be abused, of course we can't structure education the same, but we should always be re-evaluating our methods and techniques in the face of new technology and knowledge.

I am dreaming when physics students can have an electronics TA that can projection map over a breadboard to help kids understand and debug their circuits. For 70% of physics undergrads, hands on electronics classes are the hardest classes they took.

If those things are a problem, we have already failed. Because if your current education system is built upon taking things at face value, the harm is not in the shoveled knowledge but in building a student who accepts what is presented as unquestionable fact.

My child is an phenomenal bullshit discriminator. So much so that they have been correctly correcting all of their teachers in every grade they have been in. The real education is learning how to use the tools we have available to learn anything you want. And in that way, the LLM is most powerful thing we have since the printed book.

The problem is if there's just one teacher tutoring, one quiz master who points to sources it controls or simply invents as its "proof". My concern is that AI will be trusted more than a single book or a single teacher, precisely because it claims to neutrally encompass a consensus already obtained through all those sources. [It's a tertiary source masquerading as one offering primary and secondary sources, without the ability to differentiate or trust one source more than another].

This is exactly why the frequent comparison to calculators is so misguided: Because calculators are always right. To whatever degree LLM proponents believe LLMs are simply useful tools like calculators, that will be the same degree to which they fail to check for bullshit.

(I hated it, but it's still there 2000+ years after books were invented.)

People aren’t great at writing cursive with fountain pens anymore because we type. Our mental math skills aren’t as strong because we have calculators. Even our sense of direction and ability to navigate is probably weakening due to decades of reliance on GPS.

Are there still people who can memorize whole books, ride horses, write cursive, calculate big numbers in their head, and navigate by the stars? Of course, but they are outliers. Most of us just use our smartphones.

On balance, it's been hugely positive.

Jealous. What's your job?

My worry is the innovator's dilemma. Given a few generations (software generations, not human) of continuing exponential improvement in capability of LLMs (or whatever replaces them), I worry that those who have spent much more time learning to co-work with a GPT-alike (or replacement) will move to the fore.

I'm not sure about "17 year old me struggling to write a short paper full of unoriginal ideas," but it sure could help me write that paper titled "Bullshit, bullshit, etc., etc., bullshit" that I always wanted an excuse to turn in for some college course or another. ;)

I use chatgpt everyday and am getting better and better at asking questions to get the answers I want.

I used copilot when it was free, and it was best at boilerplate, and sometimes not bad at auto completing the next few lines, I didn't think it was worth paying for at the time. I use Codeium now, that works in a similar way, probably not quite as good as copilot in that it doesn't understand as many languages, but still does a reasonable job with boilerplate type code.

The genius of it is that you could give it the notes of a meeting and get it to rewrite them in a different form. Nothing else can handle language like that.

I can see it guide me in the right direction if I have a more generic question but even then, if I am not specific enough I fear I might go down a rabbit hole that ends no where.

It may be better than google in finding something but when I manage to find something on google the information is usually correct and not totally wrong. Especially technical problem that someone wrote somewhere on a forum regarding a specific device.

Finding the things I am looking for on google allows me to judge the correctness of the data. When I find answers in what is clearly blog spam I know that the info is probably wrong. Which ChatGPT I have no idea how it arrived at the conclusion it gives me.

A good example is if I want to know the max current rating for a chip. If I search google and find the manufacturers datasheet, I know the information is correct. If however the same information is on stack overflow or some forum I may not take it as a fact but more as a guidance.

ChatGPT would be 100 times more useful is it cited a source. Like "The max rating of chip ... is ... according to the manufactures datasheet dated ... which you can find here ...". I guess Bing and Google are trying to do something like that.

So here's one that saved me a bunch of time:

https://gitlab.com/-/snippets/2535443

Basically, I knew the package could do what I wanted; I knew it was almost certainly in GPT-4's data set; I could do it myself, but it would involve searching through all the documentation and figuring out the right bits. It just did it for me.

Now there were a few minor bugs: It duplicated a buggy error message, and at some point later it called a "Subtree" method rather than a "Tree" method. But those were a lot easier to fix than writing the code from scratch.

Once I had a list of 27 book names I wanted put into "canonical order" and in the form of a golang array. I could have done it myself but it would have been tedious; I just pasted the list into GPT-4, asked it what I wanted, and out popped the result.

Here's another place it was helpful recently; I prompted:

"We're hiring a new community manager for the $PROJECT. I'd like you to help me develop some interview questions. What information to you need about the role to help you develop more useful questions?"

The questions it asked me about the role were really good; just answering the questions was quite a useful exercise, and I'm sure the resulting document will be a good intro to whomever we hire. I wouldn't say the resulting interview questions were brilliant, but they were solid, and I used a couple of them.

There are times when "the form" is there for a reason; if you want something re-written in a specific form, GPT-4 can do a good job. I wrote an email recommending something to somebody's managers in a different company in a different country; then I pasted it into GPT-4 and asked if it had any suggestions. It did a fair amount of rewording, of which I decided to take about half. In this case, the "polite form" is there to avoid offense, and it's exactly what I wanted.

I've also asked it to write some Tweets highlighting some specific aspects of an upcoming conference I'm planning. It did a good job coming up with the sort of punchy, tweet-length messages which seem to do really well.

Connecting it to the context of the article: My day job is basically arguing with people on the internet. :-). I do read and write all day every day; but I don't write messages where diplomacy is critical, nor do I write tweets. Perhaps I could get better at those, but I don't think it's worth the effort. Am I the worse off for that? Probably not in the way the author thinks; I don't think being diplomatically polished would change my thinking that much -- much less being able to write punchy tweets.

If I started relying on it for the core writing, however, I'd certainly be selling myself short.

FWIW, this use case of writing a core message, then using an LLM to polish it into, as you put it, the polite form, is what I had in mind with Nicer.email, a Gmail extension that lets you do exactly what you described with one click, rather than copying/pasting/writing prompts. Trying to optimize the common case! https://chrome.google.com/webstore/detail/niceremail-easiest...

Okay, now I have to know what this job is.

GPT-4 Passed the Bar Exam, so I think there's something to it https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4389233

1. Use it when you know how to do something but don't want to.

2. Use it when you don't know something, and don't know how to find the answer.

3. Never trust. Verify.

Clearly, this means that what impresses others will often not impress you.

I chalk it up to "hype aversion" -- an internal heuristic that anything with so much hype around it must be bullshit. For some, there might also be good old resistance to change, to the unknown consequences of all these developments.

Industry outsiders also tend to take ML for granted, even when the 'experts' can see it's completely inappropriate. (My GF, a few years ago: "Why do we still need judges and juries? Can't computers judge cases and author legal opinions?" Me, at the time: "LOL, no, that would be ridiculous, and here's why..." Me, today: "Uh, about that argument we were having...")

Especially when folks are already out there LLM-generating SEO-spam, so even "well the answer also came up in a Google search" isn't a sign of accuracy.

This doesn't always work, as some stuff is harder to verify. But what's valuable to me is it allows me to ask a vague question and have follow-ups. I usually learn just by having the conversation.

It is not revolutionary for areas you specialize in, but if I try using it for something like setting up a web scraper with python (something I understand conceptually but have never done) it feels pretty amazing.

My one concern is that if I use it for JS I can immediately spot issues with it, but I am too ignorant of python to spot subtle problems.

Copilot wasn’t useful for me because I want a back and forth conversation to straighten out concepts. With Copilot you don’t get that and it just sprays out little messes of code.

I use GPT4 like a rubber duck in the sense of “rubber duck debugging” and it can be surprisingly supple and creative in its solutions… many bad ones, but again, push back on those and you get to good ones.

Give it a Django model and ask it to create a data visualisation. It'l perform complex ORM queries with filters, annotations and aggregations, then load it into a pandas dataframe, then create more columns in the dataframe, does more aggregations, then spits out matplotlib code to draw the chart. The time typing the prompt is time I would have needed even in the pre-GPT-era, since I would have to think about what I want to achieve anyways, now I'm typing it into ChatGPT, which makes the thinking process more structured as well!

I have built more tools in the last three weeks (for work and personal) than I have in the last three years combined. All thanks to OpenAI's brain constantly pushing me into the right direction.

If you asked chatgpt to generate a document, its generates one that reads well but has terrible content. Eg. Super broad or just plain contradictory. But given some not so well written piece of writing, it can clean that up fairly well.

I’d love to see which of the following might have any correlations with reported usefulness of chatgpt/copilot:

1. How articulate a person is.

2. How much of an expert they are in what they are trying to do with the tool.

3. How successful they are at giving instructions to a human to perform the same task.

4. How much experience they have managing/coaching junior devs/interne/newbies.

5. How much experience someone has at decomposing a problem into smaller parts and identifying the simple parts and complicated parts

There are huge consequences to either answer to the question: “Is using an AI tool effectively a coachable skill?”. I’m sure someone has already looked into this or is looking into this - if it turns out to be a coachable skill, and we can identify what those are, there’s a lot of money to be made in bringing a coursework to market.