It's not ChatGPT what we should be putting in our forecasts, but its successors. And, from what I have been reading, our AI folk are coming with better performers all the time by chaining multiple neural networks, "making modular brains", so to speak. Any argument that starts by saying "ChatGPT is very bad at X, humans will always do X," is flawed IMO.
So I think that any job that can be done by a non-human is up for grabs, but for the sake of the argument, let's restrict it to manual jobs where being human doesn't matter. For example, a kitchen clerk at a burger joint doesn't need to be human, as long as it does all the tasks it needs to do and doesn't put anybody in danger. A security guard at an airport can be a machine gun with wheels, it is more intimidating that way. Picking up coffee cherries from a coffee tree can be done by a machine which is smart enough.
But assume that there are jobs "high in the ladder," for amazing engineers and creatives super-charged with AI powers. The problem is that not everybody can climb that ladder. And that's a problem we are already seeing. Right now, if you are highly qualified, you have no trouble getting a decent job. But if you are not, you are fighting for 10 USD/hour shifts somewhere. Imagine if 90% of those shifts are gone. Compound that with our social media (and AIs optimizing for engagement there in) making our kids a little dumber by stealing their attention for long periods of time.
"A security guard at an airport can be a a machine gun on wheels" - are you serious? That is a job which is 99.99% about understanding human intentions in confusing and ambiguous situations, and where making a mistake will literally kill people. It's the last job you want to automate with "AI".
It's a very good example to understand how "AI evangelists" think. Anyone with even slightest common senes would immediately understands why it's a bad idea, but AI evangelists just can't.
It's also very... American to not think mention the absurdity of machine guns at airports, even in the hands of humans. Even if lethal force is required, would you want a security person (not a trained policeman) to "start blasting" and spraying bullets with a machine-gun?
> It's also very... American to not think mention the absurdity of machine guns at airports
Now you mention it, despite the two respective national stereotypes, the only airports where I can remember seeing machine guns on the security team were one of the London airports.
If I saw machine guns in SFO or JFK, I was too tired from the flight to remember.
From what I can remember, it's an everyday occurrence in various european cities to see police armed with machine guns. The first time I saw it it was very surprising and I wondered what was going on. Definitely not a uniquely american thing.
Indeed, but the UK in particular is one of the few places where police aren't routinely armed, and yet my aforementioned experience in some of the London airports…
The reason is that we have very few armed police and not that many police and you often (comparatively) want police with arms at an airport, so suddenly the only police at airports are the armed ones and they have the most lethal kit.
The dispatch time for armed police has gone up because they are doing a regular police job, while armed, rather than waiting for the call up.
Machine guns are in a lot of airports these days, in Europe as well. It is assuredly not strange anymore (though it does come across as hostile and extremely unsafe).
My experience it’s far more common to see them in airports outside the US. Shortly after 9/11 it was pretty common to see heavily armed guards in the US because there was an overt military presence at that time.
What I see now is occasional police officers with their standard sidearms, but rarely see anyone more armed than that.
AIUI, the difference in training is not that much in the UK between sidearms and machine guns, so anyone who is certified on one probably also has the other.
Whereas in the US, every cop is armed but there's more checks on the more strict stuff.
Your airport police want to be armed but there's no need for them to be heavily armed. So in the UK they all are, because it's the same people. In the US the heavily armed folk are deployed elsewhere (SWATing someone's Twitch stream if the media tells me the truth...)
That's a particular case of the general idea that there are a lot of completely clueless people, whether tech bros, business leaders, politicians, etc that have only the most superficial understanding of various problems and are arrogant enough to think that their super simple solution is novel and can address the problem.
I think "doomers" is a better description in this case. "Evangelists" are "AI will do everything and that's great" and doomers are "AI will do everything and that's terrible". The OP is a representative of the middle way: AI is an important new tool and that's a mixed bag but net positive.
>> It's the last job you want to automate with "AI".
Have you been at the Charles de Gaulle Airport in France? The security people there look like paratroopers[^1]. It's terrifying, and it's a reason I avoid that particular airport every time I fly. It sends the message "Paris is an unsafe place to be, we are very paranoid, blame the others, don't come here." The machine guns have no business among civilians, being with robots or people, period.
>> That is a job which is 99.99% about understanding human intentions in confusing and ambiguous situations, and where making a mistake will literally kill people.
Yes, if you ever are allowed to pull the trigger. And in that case, I wouldn't trust machine nor person; I have been profiled on the basis of my physical appearance more times that I can count.
But in some cases, like the airport above, the goal and effect is to simply terrify civilians. You can't deny that a machine would do a better job at that[^2].
[^1]: Just search Google images for "Security at Charles de Gaulle airport." I've been there, and that's exactly how they look.
>Have you been at the Charles de Gaulle Airport in France? The security people there look like paratroopers[^1]. It's terrifying, and it's a reason I avoid that particular airport every time I fly. It sends the message "Paris is an unsafe place to be, we are very paranoid, blame the others, don't come here." The machine guns have no business among civilians, being with robots or people, period.
In europe there's 1-2 orders of less police brutality and issues overall, but the tradeoff is you randomly have military police walking around with rifles.
They're quite friendly actually, I've ended up getting directions from them once or twice when trying to catch a connection.
Also the FAMAS is a beautiful piece of French Engineering
What does suck about CDG is how horrifically unorganized they are. Like "print your boarding pass, but the printer was out of ink, so the barcode didn't generate, and they just *wave you through* the check with an invalid boarding pass because it happens all the time
that is what scares me, not European cops with guns
A bit ironic to talk about how ok it is to have police with guns in France given the enormous portests still underway caused by a policeman with a gun...
> In europe there's 1-2 orders of less police brutality and issues overall, but the tradeoff is you randomly have military police walking around with rifles
I doubt any relation of these two facts, or cannot see it.
> They're quite friendly actually, I've ended up getting directions from them once or twice when trying to catch a connection.
You're a braver person than I. I cannot imagine a circumstance where I'd be willing to even approach officers armed with machine guns, let alone ask them for anything. I'd just be itching to get out of that place ASAP.
I actually feel quite safe knowing that someone with more rigorous military training than police training, and with more familiarity with their service weapon, is ready to act in case of a bomb, knife or vehicle attack.
I think you're over-estimating the quality of military firearms training and under-estimating how soldiers and marines are trained for war rather than policing.
Friendly-fire military incidents are common; while it's associated with inexperienced troops, the reality is that experienced and "elite" troops still have many friendly-fire incidents. And then there are all the civilians that are killed as collateral damage.
Yeah, areas where there's a high liability and potential loss of life are the last ones that will be fully automated. On the other hand, most office jobs are easy targets because work can be verified in non-realtime before using it.
Trains and planes already operate completely autonomously 99.9% of the time. Yet we still have pilots and train engineers there to handle the edge cases because having one or two people to keep an eye on a million dollar machine that can kill hundreds of people and cause billions in property damages if handled incorrectly works out to be a pretty good deal.
This is the main problem with self driving cars. They can kill people.
To be honest, if we're talking about most minimum wage jobs being performed better by AI (I would imagine being armed security, at least in the UK is a lot more than min wage, because of the responsibility and trust of course), I'd worry less about their AI machine gun at airports vs the existential questions around there being a working class (in the original sense).
I'd be quite surprised if AI (as we've seen so far) threatens manual jobs as these have already been under quite intense pressures to automate for well over a century.
The low-hanging fruit IMO are most likely to be anything that is done entirely via/inside a computer. Because interacting in the digital world is much less of an issue than having to deal with digital + physical world like manual jobs do.
That's not to say it will take all computer jobs but those intuitively seem like they will be the easiest for computer-based AI to do.
Something like working in a kitchen is a long way off (or at least will require a redesign of kitchens from the ground up or humanoid robotics that are effective, reliable, and reasonably cheap). Whereas if your job is already entirely done via computer with little real world engagement, then it will be much easier for AI.
Not really afraid of that at all. On the contrary, you cannot just take an AI and make it do stuff. You need experts to parameterize and train it. It is also a bit more complicated than a pid controller where you turn a few knobs. For training you would need domain experts.
The same reason we didn't see too widespread automation in industry. You actually need a lot of engineers to automate systems for their specific purpose. Today robots sold the most primitive parts but even there rework is needed. There are exceptions where scale and value of the good allows for it.
An AI currently isn't even capable of doing excel business analysis without the data being checked. This check has to be done by an analyst as well. It cannot even do book keeping without errors and the work to check for errors surpasses doing it yourself.
We aren't there where anyone has to fear about their jobs from AI compared to general technological advancement. If so I think the first impact will hit the click generators (good) and artists (bad). Although in both cases you need people to generate new concepts. True that you cannot get rid of cooks ever though.
I agree that engineers will likely be fine (if at all decent). But there are many more jobs done on computers that are fairly routine which are the ones that will be threatened.
> An AI currently isn't even capable of doing excel business analysis without the data being checked. This check has to be done by an analyst as well. It cannot even do book keeping without errors and the work to check for errors surpasses doing it yourself.
Here I think you are thinking about this wrong. AI is going to be built directly into Excel so I think it will harm a lot of especially entry level type work for analysts. If any part of your job is using microsoft/Mac software I think you will see a radical shake up in work (that will still enable more people to be entrepreneurs etc so on balance a good thing).
> Here I think you are thinking about this wrong. AI is going to be built directly into Excel so I think it will harm a lot of especially entry level type work for analysts.
I'd go further and say:
Excel is the automation: "computer" used to be a profession rather than a machine, and a linear regression line-of-best fit is basically the same maths as a single (no backprop) perceptron, which we used to have to calculate by hand.
> Here I think you are thinking about this wrong. AI is going to be built directly into Excel so I think it will harm a lot of especially entry level type work for analysts.
I think AI will probably eliminate almost all future knowledge worker jobs through simple attrition. In 20 years, perhaps 1 human knowledge worker will be doing the work of 100 (measured by todays productivity) augmented by integrated AI in the tools they use.
I am convinced that short of certain trades, we will watch AI erode everything. Personally, I am on a mission to convince my 2 year old grandson that becoming a plumber is the key to his future wealth.
"I think AI will probably eliminate almost all future knowledge worker jobs through simple attrition. In 20 years, perhaps 1 human knowledge worker will be doing the work of 100 (measured by todays productivity) augmented by integrated AI in the tools they use."
Yea, I don't think this will happen over the longterm.
I do see worker dislocation in certain sectors though, can't see how LLM's won't substantially shake-up office work especially as Microsoft is already busy building them into the Office Suite.
Which was my point. Knowledge workers (and the types of people who gravitate to knowledge work careers) will be dislocated and what work remains may not be as rewarding both financially and personally to these kind of people. People of course adapt, but would that person sitting on the autism spectrum who can thrive and make exceptional income as a SWE be able to adapt to a less cerebral career? Will there be mentally rewarding work for those displaced? Those are real concerns and a possible butterfly effect of AI.
My hope for my grandchildren is to pursue careers that can weather the automation and AI storm. Becoming a plumber might be literally shit work, but it’s one trade that is AI and automation proof for many years to come.
I would heavily recommend becoming an SWE if they have an inclination for it. It won't be deprecated by AI any time soon and there is still so much work...
AI will become a tool instead of it replacing SWEs. Chances are that software engineer even become more essential through that if AI is integrated in every system.
I disagree, I think SWE will be one of the first careers displaced by AI. There is a whole “jobber” class of lower quality SWEs nowadays who are bright enough to do the job, but just barely. The extra pair of hands we needed to help digitize the world.
Once the bulk of the coding can be offset by AI, you probably just need elite SWEs as the check to the system. The lower quality SWEs that fill that extra pair of hands role that we need now will go away because the code they produce will be of inferior quality to the code that AI will produce. They will be an anti-productive variable to the equation and will be gradually lost to attrition.
>I agree that engineers will likely be fine (if at all decent). But there are many more jobs done on computers that are fairly routine which are the ones that will be threatened.
So you mean like something you can wheel in and install or a humanoid style robot you could use in any kitchen? Cause in theory you're just talking about a dishwasher lol.
If you mean a robot that could clean in any kitchen without breaking anything, I'd say at least 20 years. As in you could move it from kitchen to kitchen with no install needs.
I suppose another way to look at it is this: how many businesses use robot cleaners to vacuum/mop their floors? Because as far as I can tell that is already solved (basically) but it isn't done because the cost/benefit/efficacy just doesn't add up.
I can imagine a "box", say 1x1x2 meters, that you get in your home, it will have a plexiglass on it, and inside, it will have several AI-controlled arms, and you will be able to command it to fix anything in that space. Not really sure about cooking, but you could ask for example to fix your trousers, or repair a notebook, or make a fitting for water tap, and so on, and it would autonomously do it (it would ask to order some tools and materials and you would bring them to it). These could also be modular so that they could repair themselves in a limited way.
There would also be a larger "boxes" available for fixing larger things. For example, your washing machine broke up, you put it into the "communal" box that is like 6x6 meters, and inside it, it will repair it. Or you could have a communal box that works like a kitchen (but IMHO the biggest problem with AI kitchen is that we cannot easily automate the human taste and smell).
I find bipedal robots more plausible. Our living spaces are configured for humans; ultimately, something that looks like a human will be best suited to navigate them.
Things are so disposable now that I don't think most households would have a use case for a Repair-Bot; I can see it in offices and factories, OTOH.
I think we're pretty far away from a robot that can take an arbitrary problem and fix it accurately. I'll be excited when I can take my car to the mechanic and he's supervising a robot changing my oil.
> I suppose another way to look at it is this: how many businesses use robot cleaners to vacuum/mop their floors? Because as far as I can tell that is already solved (basically) but it isn't done because the cost/benefit/efficacy just doesn't add up.
At least in the US, the parasitic labor unions are explicitly fighting such automation:
A dishwasher isn't autonomous. I'm talking about the second. I think if we don't solve general intelligence first, we should be able to get there in three to four years.
"An autonomous robot cleaning the dishes on its own in an unfamiliar kitchen without breaking anything"
" I think if we don't solve general intelligence first, we should be able to get there in three to four years"
Very unlikely. I mean you can build something that roughly does that already today, but it will break things and/or it will be so slow it is unusable.
Also you have to take savety into account. The robot may not wash the cat or the kid and not accidently break them. But the level of required autonomy for the task requires a machine that can be dangerous. And the recent rise of LLMs really don't help with reliable doing tasks in an unfamiliar environment.
Making cars drive save everywhere is an equally hard target and despite all the investment, I don't see us getting there anywhere soon. And there is not the investment in making robot dishwashers, because we have common dishwashers, that do require manual labour, but they work. A humanoid robot on the other hand, that can put the dishes into the dishwasher, but requires some learning, this might be possible in a few years.
Right. I think the kitchen scenario is more constrained than self-driving (for one, you don't have to continuously keep moving to be safe), and the people who I think are at the forefront of self-driving are also doing robots, and "perform tasks in an unfamiliar kitchen" is a ML classic. My main assumption for "don't injure humans" is that even small animals routinely manage to avoid doing actions that would be potentially harmful, so it doesn't seem to require full general intelligence.
And of course, if we crack an AI that can work on AI better than humans can, that solves - or at any rate obviates - all other AI problems by proxy.
> Something like working in a kitchen is a long way off (or at least will require a redesign of kitchens from the ground up or humanoid robotics that are effective, reliable, and reasonably cheap)
Factories that make food are already a thing.
I didn't know there was an entire TV series dedicated to just this topic until 5 minutes ago, but I have seen at least one short segment about the topic, specifically Viennetta ice cream: https://en.wikipedia.org/wiki/Food_Factory
> but for the sake of the argument, let's restrict it to manual jobs where being human doesn't matter.
This is the biggest misconception about AI and automation: manual jobs will be the last to be automated, because making robots (which is specialized hardware) is much, much more expensive than making software. That's why our 70s dreams about robots haven't materialized, despite Japan going all in on robots, and why Besos aims to “algorithmically control workers' body”.
> It doesn't look good.
Having machines do work isn't a problem in itself, in fact that's what the industrial revolution was about, the problem is power and whealth grab by a minority of people owning the means of productions…
It has the potential to devastate economies like mine (India) where the entire middle class was built on outsourced tech labor. I can’t see how Infosys and TCS can keep employing 500k people when this tech matures within 5-10 years.
I love how RoboCop is this extremist satire against capitalism, authoritarianism, rogue technology, etc and we are actually living in a timeline where millions of citizens are plotting their way to be the bad guys in it. Millions of others are desperately trying to recreate the matrix. We're really in for it.
What would an economy like the one you describe look like in practice?
What would be the use of an AI picking coffee beans if there is no one with a job to buy the coffee beans? There is an unresolved issue here for sure. Unless the machines are self-sustaining, there's not going to be much use for multi-billion dollar data-centers and millions of dollars in electricity bills and expensive robots if there is absolutely zero need for anyone to be using these systems.
Tesla has robots making cars, ok, who has a job therefore money to buy a car? Many, many of those who are out of work from the Tesla factory will no longer participate in the economy, and that Robot cop at the airport? That will be pretty lonely once people stop flying for their profession, which would be a large customer base for air travel.
A shock this big to the economy could happen quite quick and it would be fairly devastating.
I love to think about all this and play with thought experiments, we have very litte clue where this is going. I actually think Robots are more of a human problem than we realize. AI systems probably have, much, much less need for robots than biological species because we actually heavily depend on manipulating the environment for survival.
Maybe it's like in the free-to-play video game business: Having one whale (person who spends more on the product) outweighs having lots of smaller customers.
The IMO dystopian move is to just let the AI employ humans as 'hands' for subsistence.
> But assume that there are jobs "high in the ladder," for amazing engineers and creatives super-charged with AI powers.
IMHO such jobs won’t exist. Once AI have reach AGI level there is nothing that a human can do that the AI can’t. Might as well just add more AI.
Frankly, what scares me is everyone rushing to advance AI but no one seem to be putting any effort into figuring out what to do about society once humans can no longer trade labor for money.
> Frankly, what scares me is everyone rushing to advance AI but no one seem to be putting any effort into figuring out what to do about society once humans can no longer trade labor for money.
Folks pay for handcrafted items today, even when there are cheaper and better mass-manufactured items available.
No matter how advanced AI is, humans will pay for things to be done by other humans.
I'm not saying things are great now, but I am starting to understand the position of some cultures that have a "it's better to die out than change in a particular direction" attitude.
When I read your post it makes me think the best course of action for most people is to go set fire to the data centers and reclaim their ability to make some cash. Was that intentional ?
three simple words that are key here. as long as nothing changes, the algorithms that look to the past to predict the future will remain accurate. as long as.
No one who claims to automate testing ever seems to have a sophisticated understanding of testing. I suspect the urge people have for automating anything comes not from the love and skill they possess for that activity, but rather for the imagined fruits of it.
Software testing is easy to fake, which means it’s particularly easy to claim automation accomplished.
Therefore, all these people thinking they are going to automate what I do when I test? No. You have no idea what I do. But what you can do it convince ignorant managers to buy fake software testing. That’s the real danger.
Imagine if a company came around claiming to have revolutionized blood testing with radical tech, labeling its competitors slow and obsolete. The sober and responsible players in that industry may experience real damage before people figure out that Theranos is a fraud.
What's the gap between a Co-pilot written test and a human-written test? Are tests written by co-pilot better than no tests?
Developers generally hate writing tests, so I suspect more and more unit tests will be generated. I've worked with someone who does this and it seems insane to me, although I never got to see the generated tests they were referring to, so I can't give specifics.
> What's the gap between a Co-pilot written test and a human-written test?
IMHO the gap is that human-written test understands the (unwritten) specification (which might be as simple as "program doesn't crash"), and tests the program against it.
I am of the (pretty radical, it seems) opinion that the (especially unit) tests that just take the code as the specification, instead of having specification coming from elsewhere to test against, are useless. So you cannot build a useful test just out of the code, because you always need to test an implementation against a specification, or, two implementations against each other.
I agree with you about ideal approach to tests and spec but sometimes the "specification coming from elsewhere" is pretty vague, incomplete or simply lost in time.
In that case, if I have code that satisfies that specification as demonstrated by say acceptance testing or years of use, I will happily opt for test that take the code as specification. I am not using those tests to prove the code "correct" but to prevent regressions during refactor or new feature development later on. Worse case scenario they should keep the program as broken as it is now or has been for years.
> I am not using those tests to prove the code "correct" but to prevent regressions during refactor or new feature development later on.
Then you don't really need to have the tests written, what you need to do is to compare the output and behavior of the two code bases before and after refactoring. I mean why use a low fidelity approximation of the original (i.e. test that only works in some cases), if you already have the original at hand? (After all, how can you predict ahead of time, which features of the original version will need to be tested as preserved in the new version? Isn't writing regression tests in advance a form of premature optimization?)
From the view point of testing, you're comparing two implementations. So again there needs to be some understanding which one is the correct specification - the original one or the new one?
It would be more useful if the industry actually treated the two cases - i.e. testing against specification and regression testing of the refactored code - as completely separate, instead of trying to push unit tests for everything. Because in the latter case, ideally you can prove that the refactored code is doing identical thing, which is stronger than testing.
Completely agree with this approach - recently I find that most of the tests I write are so-called 'integration' tests. I find these tests not only get more code coverage for fewer tests, but they more closely represent the real or implied external specification. In other words, they do a better job of verifying 'is the code correct' rather than 'is the code we wrote the code we wrote'.
I think the specification from elsewhere is all about the business value that the code delivers. At the end of the day, the business doesn't care about how exactly your code is decomposed into classes/functions/etc, the business cares about 'can I register a customer' and 'are customers prevented from writing to resources they only have read access to'. Of course there is some requirement for the code not to be a complete mess, as this has impacts delivering features and reducing bugs down the line, but with tests that sit closer to the value the code delivers than the structure of the code, you're free to refactor and the tests will tell you what you've inevitably broken. I find this considerably more valuable than unit tests.
I only know very little. But we had a great guy in our company who was a big proponent of rigorous methods even for non-critical software and he made a very interesting training about it. Here's the list of his recommended resources:
The first fallacy is that you can't "write" a test, because a test is an event, not an artifact. What coders mean when they say they "write a test" is that they have written an output check. You can write a test procedure, but the test procedure is not the test itself. If you sit down and watch a tester in the act of testing (try videoing it and analyzing 2 sec. slices, while labeling everything that is done, as I have) you will see that testing is a very rich process. It has a heuristic, rather than an algorithmic nature. It involves substantial social competence and judgment, which is a tacit skill set. It is not encodeable.
The next problem is that ChatGPT is not thinking critically about risk. It thinks (well, no it doesn't think... let's say it "treats")... it treats testing as a process of demonstration. Demonstration is not testing. Demonstration proves that functionality is possible, but not that it is reliable. Thus, it produces checking code that is shallow.
The next problem is how are you going to explain to ChatGPT what your product does? Do you give it all your source code? Do you give it all your Jira tickets? The demos I have seen are toy examples. Nothing on a realistic scale.
Let's say you feed it a whole spec and source code, somehow. The next problem is omissions. ChatGPT arbitrarily stops producing output. You then have to carefully check its work to see what it has left out.
What you are asking ChatGPT to do, really, is to write code that will maximize the probability of spotting a real bug, while minimizing the probability of a false positive. But it will arbitrarily focus on only those kinds of bugs that it can easily discover. This is the oracle problem.
The next problem is that ChatGPT halucinates or misunderstands, so you have to correct its mistakes. Sometimes going through frustrating iterations of prompts, like a man trying to pull a mule through town.
You also have trouble with test data and data setup. Only in toy examples is this not a significant problem.
ChatGPT produces conventional results, and that does have some value. But it's not enough for professional work.
I just think the people who casually say that ChatGPT is going to easily handle these things are pretty stupid, under the definition of stupidity as "the refusal to think."
Someone could’ve made the same arguments about automating horse-drawn carriages. Even the safety arguments would fit: going faster would be much more dangerous.
I think it’s a good idea to keep an open mind and see what happens. You might be right, but if you’re wrong, you’d be better served to be ready for it.
Which seems more likely: that it’s impossible to automate testing with AI, or that it might be possible?
I had bought into the GPT-4 hype. I used GPT-4 on ChatGPT Plus for two months, but then I unsubscribed because I wasn't using it as much as I thought I would.
It's certainly a very interesting technology. But in it's current form, I just don't see it replacing most jobs. Will it replace jobs if we scale further? I don't know, but my intuition says no. I feel that we will need some other major breakthroughs in AI to get there.
The combination of ChatGPT and Phind helped me, a noob with 8 months of coding knowledge, build an app with real paying users and revenue. I would have never accomplished this on my own, and now I have a working prototype to find co-founders and possible investors.
The Pair Programmer feature is absolutely fantastic and a massive gamechanger. It helped me figure out the architecture for a new app I'm planning. I showed it to a senior developer in my coworking space and he said - in his own words - "yep, that's how I would approach it too".
Unfortunately that would mean doxxing myself. But its largely a somewhat complex CRUD app with a user dashboard. The stac is nextjs, tailwind, and nodejs, express on the backend.
I built most off my UI off of FlowBite (which is a tailwind UI library). Did not need to use complex prompting resources, though I would sometimes switch between Phind and chatGPT-4 to get a better answer.
I think it works because its not a very complex app and since it's gated with a sufficiently high entry, I don't expect a ton of traffic, hence scaling is not an issue. I also chose the easiest options to deploy (Vercel for the frontned, Heroku for the backend), which might end up costing me in the long run - but I figure I'll get that fixed later
I've tried phind and it's almost always worse than a Google search. It often has a reasonable answer, but with Google I typically find more complete information and sometimes better rebuttals and alternatives.
I view the thing as a neat toy. Can you learn from it? Maybe. But if you think this allows you to learn something you couldn't otherwise, I wonder if you're really being honest with yourself.
I reckon the kind of questions I’m asking are way more basic than an experienced developer would. GPT-4 is likely way better at answering simpler questions since there is usually far more tutorial content geared towards noobs online than.
I have no delusions that this is making me a good coder, or that I can carry this beyond the prototype stage - I fully intend to find a CTO/co-founder. But for someone going from 0 to 1, this has been a fantastic tool.
It’s been incredibly helpful to me. It helped me figure out how to get docker containers setup. It’s even helped me figure out how to run data analysis on graph data. It’s not just made things simpler – it’s made things possible.
100%. I do know it might come bite me in the ass at some point.
Already I didn’t realise my docker container was saving data to the NON-PERSISTENT data volume. But that was a greater reflection of Chatgpt taking me beyond my knowledge limits, and me being in a place where my ignorance of my ignorance can lead to not-great consequences.
Don't feel too bad about that one. I've seen Docker's default of non-persistent storage cause disaster even for professionals with years of experience. It's just a really bad bit of design.
Lol just an attempt at being pithy. It’s true though. Something about ease of functionality curves making whole classes of new use cases / customers possible.
The strange thing is, search engines used to be able to do this. But they have gotten progressively more terrible at finding a very specific result based on a very specific query.
My ChatGPT experience is not that different from the "good old days" of searching the internet. This time the search is in latent space which has up- and downsides.
Same for me. The benefits are obviously not going to be uniformly distributed because intelligence is not uniformly distributed.
If I was more intelligent I would be less impressed with it because I would probably already know what it has taught me. I understand the thermodynamics of that submarine implosion now but if I had taken any kind of physics I would have already known all that. I didn't take physics though. This pretty much extends to all subjects.
I will have to have it teach me to setup docker containers.
The exercise I really need to get on is to try make something in a language that I have no clue about. Like Rust or Go I know nothing other than they are programming languages. Then it is an exercise in the skill of going from zero to not zero with the help of AI and that not zero will hopefully keep scaling up and up over time.
someone just offered feedback on their development experience for free, and all you can say is, "...are you so dumb you need an AI to help you with Docker?"
They might not need it, but it might reduce the friction dramatically.
I use GPT to learn things occasionally, but it’s almost always something where I don’t have the language on hand to navigate the problem space. GPT is great at opening windows and giving you the tools you need to figure something out.
In my case, I usually get to a point where I think I get the topic well enough to leave ChatGPT and go out on my own. Something interesting here is that you can ask GPT to tell you where good resources might be found on the internet. Sometimes it’ll actually come through, providing you some decent places to investigate the topic even further. Whether it’s blog posts, long form articles, or research papers.
It’s mostly a matter of eliminating friction and connecting dots faster. It’s definitely not making something possible that wasn’t possible before.
This applies to me. GPT4 increases the speed of simple tasks significantly.
I needed a WordPress plugin but I'm not a WordPress developer and it's been a while since I spent much time in PHP. Ultimately I don't have time to come up to speed on this and I just want the plugin now.
I wrote a ChatGPT prompt explaining the plugin in great detail, and it responded with the entire plugin. I installed it and it worked on the first try. I went back and forth for a few iterations to adjust things like css and button titles and now it's running on a live site.
Had I assigned the project to someone else, I'd still have needed to write the "prompt" and I'm not sure it would have saved me any time.
Yes, my experience is that most hype comes from people that don't actually work in the jobs they claim will be replaced. Writers, for example.
While generative AI does make writing some things more efficient, it almost always has a bland, generic style that is immediately recognizable. The only jobs it will be replacing are ones that didn't require much effort in the first place.
Isn't that almost always the case, and in truth the beauty of automation? Replacing simple, repetitive tasks that, if you have to do that, you say "I wish I had a machine doing this for me"?
Of course, that might impact the number of available jobs and it's something that need to be taken under control.
But, if talking about writers, while AI won't realistically replace imaginative authors and investigative journalists (at least, certainly not in the short term), it will likely replace (some) copy editors and dollar-per-thousand-words "journalists" that have to fill tabloids and free subway "newspapers". While it might be a financial loss for the people involved, the society will hardly sink due to that. In fact, if I feel optimist, in theory it might reduce costs for publishing houses and newspapers, to invest more into quality (human-made) content.
ChatGPT will definitely be used for rote tasks that are currently fulfilled by low-end writers, but realistically could already be automated by a competent programmer. Stuff like generic product descriptions or content farm product reviews. But I don't see it actually replacing flesh-and-blood writers that have a viewpoint anytime soon.
This is the main thing I'm curious about, as with any technology: fad or paradigm?
I can't see anything on Twitter anymore, but before that happened I think I hadn't seen many of the entertaining ChatGPT conversation screenshots anymore. That entertainment use case had the hallmarks of faddishness.
It seems far more useful as a productivity aid though, but as the OP discussed, I doubt the chat prompt UI is going to be the paradigm for that. Copilot is already mostly a more useful UI for me, and I suspect that's where this is headed, with a bunch of different specializations (and even more failed attempts).
I agree. It may be helpful under certain circumstances, but under different ones it might actually slow you down, thereby yielding a net productivity gain of zero. At least, that's what it did for me personally.
What I worry about is human greed in relation to AI, be it GPT or anything else. The incentives are lined up to produce something akin to AGI and the tools are becoming more tangible. The goal is to tap the honey pot of automating current knowledge labor first and foremost. Later, other areas of labor.
I am pretty concerned about AGI. I love computer science and programming. I would love to get into research after I finish my Masters in a couple of years.
I think it's fair to say that _some day_ we will achieve AGI. It might not be in 10 years, or 1000, but eventually we'll get there.
When we do have AGI, what would that mean for someone like me? Would there be any reason to have programmers (or similar) at all? Would computer science research (or scientific research at all!) exist, or is that something a computer could do?
I think humans might start pursuing more creative/leisure activities, but... I like learning and working!
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The anxiety of the above has made me feel like it's not worth being ambitious. What's the point of research if some AI might do it better than me in 10-20 years? What's the point of joining a startup or... doing anything? I realize this sounds a bit absurd, but it is something that I've really feared since ChatGPT came about.
Chess engines overtook human chess players more than a decade or two ago and human chess is thriving. Activities don't become less rewarding for you simply because someone else is better at it, even if that's a machine. A robotic arm can probably lift more than I do, doesn't take my enjoyment from working out away. And people enjoy the human aspect and narrative in any activity in its own right.
And that's the worst case in a way, because a chess computer is a really stupid machine. Getting beaten by minimax is kind of annoying. AGIs on the other hand if they ever exist will be very fascinating entities indeed and I wouldn't mind if they're better at anything than I am. And the same goes for scientific research. I haven't stopped doing math because Terence Tao is better than I'll ever be. How many people can seriously claim to be at the frontiers of science? The value in learning isn't in being the best guy or girl there is.
Chess is a leisure activity, not work, you cannot compare the impact of automation of the two.
Also, chess engines did kill chess. Chess used to be a spectacle on the national stage, from 2000 up until Queen's Gambit and the following streamer boom, chess was largely dead.
I'm extremely skeptical of the claim that engines killed mainstream interest in chess. The trend seems to only go back to 2004, which is not long enough.
My sense growing up in the 90s was that chess was a niche thing for nerds. I think it seems somewhat more mainstream now than it was then, while remaining fundamentally a niche thing for nerds :) But it seems very clear to me that it never underwent the broad collapse that I remember being broadly predicted after Kasparov vs Deep Blue.
My anecdotal recollection is that Deep Blue is a big part of what put chess into everyone's mind and rejuvenated the mainstream interest in playing what had previously been a nerd niche.
Even today, there's a handful of people who make a full living off chess. The owners of chess.com, a few streamers, a handful of super GMs, and chess teachers, and the latter will be automated away.
And prior to the explosion in popularity? Barely anyone.
The original article gets into this at the end. It's easy to get into a cyclic argument here, where you posit a tool that can do "anything" and then try to derive consequences from that. What we don't know is what its actual capabilities will be, or how transformative those will be.
There's a prayer about knowing the difference between what you can do and what you can't, and setting aside the things you can't do to focus on what you can. To me, this seems like the right way to approach this.
10 vs 1,000 years before AGI is a lifetime of difference. I liken it to "not knowing the future". All humans face the same anxiety of an unknown future. In this way, I don't think AGI is a _special_ concern to worry about any more than when your death date will be.
And if the concern is "I want to do something meaningful" well... that's a whole other realm of philosophy of meaning. TLDR: I don't think meaningful work is reserved to "influences the thousandth generation after non nonexistence".
> TLDR: I don't think meaningful work is reserved to "influences the thousandth generation after non nonexistence".
Sorry, I put that 10 vs 1000 year generation just to focus the conversation on AGI and not whether or not it's possible in some timeframe.
I personally feel like we could be _very_ close in 10-20 years, which is within my lifetime.
> All humans face the same anxiety of an unknown future. In this way, I don't think AGI is a _special_ concern to worry about any more than when your death date will be.
This is actually very reassuring, and I think a really good way for me to frame it for myself.
There's research of the lab variety, and research of the library variety. It's valid to feel a sense of loss if all that remains for us is the library kind.
The thing that makes me more optimistic about this specific question is chess. Chess has been, from a human perspective, fully solved by AI. But it has never been more popular. Why? I find it both hard to explain and kind of intuitive. Even though we can't beat computers anymore, we can still learn from them, but remain human, and so human play remains its own unique thing, but infused with insights from the super human computer players. And on net, human chess is now just better.
I think science and math could be the same, which is really exciting! That is, maybe humans would be learning from what the super-human computers are doing, but using that to do different, more human, things.
I agree with the article; GPT is not AGI, it's ABSI (automated BS intelligence). That's certainly useful and it will automate some jobs, but a) it's just an enabling technology for new applications, b) we haven't seen these applications yet, so we have no real idea what the impact will be.
To put it another way: the enabling tech for iPhone was (mainly) multitouch capacitive displays. I dont think anyone predicted Uber or taxi drivers being at risk because of multitouch when it was unveiled (late 90s?), but here we are.
But that's just it: there are as we speak an infinite number of chess "players" who are better than Magnus Carlsen. And chess has never been more popular.
There are many ideas that have not been covered by programming or predictive AI.
AGI sounds like Skynet, the Holodeck, the super information highway and all the buzzwords.
Wake me up in 20yrs, cause I will probably be using c++ and COBOL to sticky tape bank mainframes back together, when the boomers have finally given up on keeping those beasts alive and staving off the thirty seventh crypto revolution.
Luckily capitalism solves that bit for them. Just gradually automate jobs so people can't make a living and they'll slowly die off in the gutter bit by bit. The slower it, is the less chance of a revolt.
To paraphrase that old quote:
>First they automated the artists, and I did not speak out because I was not an artist.
>Then they automated the writers, and I did not speak out because I was not a writer.
>Then they automated the office workers, and I did not speak out because I was not an office worker.
>Then they automated me and there was no one left to speak for me.
> I'm concerned about who AGI will serve? Rich people surely!
Almost everything invented in the past 200 years was originally sold to rich people, because they were the only ones who could afford it -- then the price came down and middle-class folks could buy it, then the price came down more and virtually everyone was able to afford it.
I think the "sparks of AIG" presentations are compelling. It does feel like there are a just a few remaining big problems away from AGI level capabilities where it was more futuristic unclear projections in the past.
If the issues as synthetic data conversations like alpha go but for LLMs; setting much longer token contexts (or dramatically cheaper training on custom data sets) And scaling via software and hardware a few more orders of magnitude.
I think then we have some big waves that will wash over any observations we could make at this point in time.
It utterly befuddles me that so many people still can't (or refuse to) sense what's coming. Be it bad or good the magnitude of what is now unfolding is beyond everything that homo sapiens have ever witnessed. Unless we think that MI advancement will miraculously stall and never improve beyond current capabilities, I cannot conceive of future, even a near future, that we recognize as real.
>I think then we have some big waves that will wash over any observations we could make at this point in time.
This is spot on. Exactly as how we didn't know what the internet would be, the wiser amongst us realized it was a new and strange era. "An alien lifeform. Unimaginable, both amazing and terrifying." - Bowie. MI will far eclipse the internet.
I don't know, that's quite the romantic take. I can easily admit I don't know what's coming, but _homo sapiens have ever witnessed_... _cannot conceive of near future that we recognize as real_. Come on, lol
Imagine yourself in the movie Apocalypto and I'm using a Hollywood film on purpose here - the entire Universe of being as you see it all the sudden explodes its Universe with new Gods/demons different and more powerful _everything_ coming to kill you. And then there's the boring discovery of "we're just one planet in one solar system in one Galaxy...
LLMs gonna be orders of magnitude beyond that (in)comprehension? I mean maybe it will be more powerful and maybe it will come to kill us, but that's not an inconceivable new story line.
Funny that both of us can accuse one another of hubris.
I see what you did there gpt-"2". at least you don't dislike enough to downvote.
not sure what's not understandable. I think parent is overly hyperbolic. i get that chat GPT is unprecedented, but come on "impossible to conceive of near-term reality" ???
edit: oh! you're the parent. yeah i think you're wildly hyperbolic. There's more drastic historical precedents but of course the future is by definition going to be inevitably more inconceivable over a long enough arc, so I don't think of this as a debate. I just think you're being overly dramatic for effect.
That was not meant as an insult or downvote, I literally do not understand your comment.
>Imagine yourself in the movie Apocalypto and I'm using a Hollywood film on purpose here - the entire Universe of being as you see it all the sudden explodes its Universe with new Gods/demons different and more powerful _everything_ coming to kill you. And then there's the boring discovery of "we're just one planet in one solar system in one Galaxy...
Honest question, not meaning to disagree with you: how do you view previous predictions of the success of AI, such as Marvin Minsky predicting in the 60s that AI “would substantially be solved in a generation”? What reasons would there be that the experts are correct this time?
Current Capability would be the biggest one. We're at the point where any testable definitions of GI that the sota LLM fails (GPT-4) is also failed by a good chunk of humans. You couldn't say that a few years ago nevermind 60.
What we have now (so no hypotheticals) coupled with the fact that scaling hasn't yet shown any performance walls makes a pretty good shout that things will probably be different this time.
I'm curious as to your source, or particular examples, since they (or at least GPT-4) seem to me to be rather decent at planning. E.g., for writing code.
Ask a random college freshman in computer science to write a backtracking sudoku solver with coroutines and/or fibers in their language of choice. Oof, only 1 out of 10 can do it...? I guess 9 out of 10 college freshmen aren't generally intelligent? (I mean, I certainly wasn't very intelligent as a college freshman, but I'm pretty sure that's not the way you're using that word here).
Anyways, these kinds of strawmen always baffle me in regards to AI.
Insert random pseudo trivia that most of the general population wouldn't be able to do, see the AI fail at that specific task. "What did I tell you? The AI definitely isn't generally intelligent yet!"
Everybody is out to prove AI isn't intelligent without first defining what intelligence even is. And when other people rightly point out it can do a lot of stuff, they point to some specific task that it gets 90% of the way there but doesn't get perfect and then triumphantly declare AI isn't intelligent. Crazy.
I think everyone should have a test in their back pocket for testing claims made by companies making AI tools. Mine is backtracking sudoku solver with constraint propagation but that's because I know LLMs are incapable of recursion and backtracking. Usually people get the point I'm making but sometimes I have to elaborate further.
LLMs are cool toys but calling them intelligent is stretching the definition of "intelligent" way too far. It's important to be clear about what the words actually mean because if people start thinking these software systems can be substituted for their own thinking then we end up with all sorts of unnecessary confusion around what they're actually capable of achieving.
What exactly do you want me to state up front? A general intelligence can solve general problems when those problems are formally specified. Sudoku is a good example of a formally specified problem that can be solved by most people but not by LLMs. This is because LLMs and all neural networks are simply DAGs of function which do not support recursion or backtracking.
Adding recursion to neural networks has been tried a few times but no one actually knows how to stabilize their dynamics so the industry has settled on feed forward networks with constrained function blocks which have stable dynamics with respect to back propagation of errors.
Exactly what was in your comment that I told you to state up front.
Most people in a discussion about AI and replacing people are working with a definition of general intelligence that includes humans to a large degree.
> This is because LLMs and all neural networks are simply DAGs of function which do not support recursion or backtracking.
Without adding external state I can't solve a sudoku puzzle.
Ok great, next time I run into you I'll make sure to remind you that large language models are not intelligent as I conceive of the word "intelligent".
If you're saying it's not perfect, then sure: it required one iteration before the Python program it spat out worked. It also when asked gave a 2-page description of the code.
And how many humans can do this? Those poor exhausted goalposts.
I have strongly believed the best way of evaluating the state of AI is to look at what people complain it can't do.
It's taken a few short years to go from "keep a coherent story over more than a sentence" to "write a multithreaded sudoku solver in one shot".
Many programmers would fail to do this. I'm not even sure a randomly selected human would understand the question. And I'm wondering if you've setup any loops letting it write tests, search the internet, inspect and debug? If not what you see as an output is essentially it whiteboarding off the top of its "head". Programmers routinely fail to solve simpler things in interviews, and their skills are much narrower than current llms (you can easily argue deeper, but I feel comfortable saying broader).
I did not say anything about threads but I did hint at the fact that coroutines can be used creatively to solve problems which require backtracking and constraint propagation. The fact that this is still controversial means a lot of people are unaware LLMs do not support backtracking, recursion, and constraint propagation. Sudoku is just an obvious example of a problem that is easy to solve if you know about these concepts and next to impossible if you don't. Such problems can also be expressed as integer programs but even fewer people know how to do that so I don't usually bring it up but it would be another good test for any software system that is claimed to be intelligent by the corporate marketing department.
Llms don't have to support something internally to know about those concepts, those are entirely different things.
> Such problems can also be expressed as integer programs but even fewer people know how to do that so I don't usually bring it up but it would be another good test for any software system that is claimed to be intelligent by the corporate marketing department.
It's very telling if the level of testing is "can it do something few people in the field can?".
What exactly are you confused about? Plenty of people can learn about constraint propagation and integer programming but LLMs do not have the capability to solve integer programs because LLMs (and neural networks in general) are not generally intelligent.
I'm not confused I'm saying you're conflating two things - understanding something and being able to perform it in a single pass. Neither llms nor humans have to be able to solve a sudoku puzzle to write code that can. I can be awful at mental arithmetic and write code for a calculator.
No prompting on backtracking or recursion, that was chosen by the LLM. All I did was tell it to break the problem down and reason about it, then afterwards asked it to write the code. Examples were added by me at the end, from a random sudoku site for "easy" and "hard".
I was curious where GPT-4 would come up short on the problem and I was surprised -- it seemed to solve it pretty well whether or not using coroutines. (I dropped both solns into a python interpreter and both appeared to solve the problem.)
That's a good attempt but the coroutine solution is incorrect. See if you can figure out why and how to improve it. You can also ask it to propagate constraints and see what happens.
Sigh classic LLM -- without you, the expert, I can't quickly tell from the code / output how the answer the LLM produced is wrong. I also asked it to solve sudoku by "propagating constraints" and the answer seemed to work for me :/ Again, I'd guess the soln produced is wrong because I trust you more than the LLM but I don't have the mental horsepower to figure it out without resorting to tests & debugging.
If you are happy with the tool then continue using it. My point was a simple one, any generally intelligent software system would have given you an optimal solution by accounting for what people know about backtracking and constraint propagation but the solution presented did not account for how to optimally use coroutines to propagate constraints.
I'll repeat what I said previously in a different way, LLMs are useful but they are nowhere near what is required to achieve generally intelligent software systems. I'm sure they will continue to improve as engineers and companies learn how to utilize them in their workflows but let's temper the hype a little bit because statistical autocompletion is not enough to achieve general intelligence.
Having seen state of the art neural networks in the 70s and 80s, I am both in awe at the progress that has been made since and deeply unconcerned about AGI.
Yes, storage capacity and compute have made major leaps, but it's still only lossily compressing a semantic space. It won't generate things from thin air, and it's certainly not sentient, no matter how far you scale it up.
That said, it may be a useful tool in some cases, and we've also seen its numerous limitations, only some of which may be alleviated in the future.
As always, the problem is not artificial intelligence, but natural stupidity.
> As an analyst, though, I tend to prefer Hume’s empiricism over Decartes - I can only analyse what we can know. We don’t have AGI, and without that, we have only another wave of automation, and we don’t seem to have any a priori reason why this must be more or less painful than all the others.
This is a good way to look at it. The deployment of the LLM technology we currently have is one thing. The impact of AGI, when it's created in the future, is a different thing. And people are often not clear on which of the two they're discussing.
> But if it becomes cheaper to use a machine to make, say, a pair of shoes, then the shoes are cheaper, more people can buy shoes and they have more money to spend on other things besides, and we discover new things we need or want, and new jobs.
This sounds really similar to trickle down economics.
> So the shirts of humble servants at Henry VIII’s court cost between 3 and 10 days’ income. That would be similar to someone who earns 10 dollars or Euros an hour spending 240 to 800 dollars or Euros on an item today. (Of course, in the 15th and 16th century, people spent much more of their incomes on food, fuel, and clothing than they do in Europe or European settler societies today, and much less on rent, transportation, and medical care … but it seems that most people could make or obtain one or two new shirts every year or so).
> In the Edict, the simplest linen tunic could be sold for up to 500 denarii, whereas a linen weaver was to be paid 20 or 40 denarii per day plus maintenance. Fine linen tunics could be sold for up to 7,000 denarii. Elsewhere in the Edict, workers without maintenance (food and possibly fuel and shelter) earn about twice as much as those with. So a linen weaver would need to work for (500 / 2×40 to 500 / 2×20) 6 to 12 days to earn the price of the simplest linen tunic. That is not so different from the 3 to 10 days’ income for a worker to buy a shirt at the court of Henry VIII of England, considering that the ancients did not have spinning wheels. The linen tunics in 301 CE were probably woven as one rectangular or cross-shaped piece and sewed up the sides and under the arms, whereas the English shirts were cut and sewed from long pieces of cloth, but that is another story.
These days, you can buy a shirt for a much smaller amount of money, so you have more money to spend on other things, and the economy can grow. This isn't a new concept; in fact, it's so old, we tend to forget how things were before it took hold with the birth of industrialization and automation.
I agree with you that innovation "works". But whether it's through innovation or tax cuts, the result is that the business has more capital to work with. So I'm really wondering why tax cuts don't work.
Because of businesses vs individuals. Tax cuts for businesses are indeed generally beneficial (in my opinion; I know this is not an uncontroversial objective truth or anything) for this reason, but there is a far more tenuous connection between wealthy individuals having more cash and technological progress. The issue is that the lines between businesses and individuals for the purposes of taxation are fiendishly hazy.
And yet the wealthy still spend 3 to 10 days of low wage income on designer clothing.
It's interesting that in some domains there is not diferencial there is no 10k iphone and you can get a massive TV fairly cheaply. And yes commodity clothing is cheep.
If it was not already obvious we create new forms of value that shift as commodity product become common place or mas market items.
That shift to new forms of value inherently depends on scarcity and that scarcity can only exist as a sum of non-commodity goods, creating new opportunities to fill that pursuit of diferencial value and experience.
The claim of "trickle down" was that giving money to rich people, specifically, would somehow have the same effect as making the average household better off.
It turns out that rich people buy things that don't employ many people, and they buy them from other rich people: high-priced art works, famous jewellery, and other collectibles, existing mansions or apartments at exclusive addresses, and so on. So the prices of those things go up, but no-one else has any more money.
I think this is a straightforward circle to square: technological advances do indeed "trickle down", but the financial benefit from them doesn't, necessarily.
The main fallacy with this article is assuming that generative AI is just another tool that we use. But what happens when the tool becomes far more capable intellectually than the person wielding the tool? Why have that person around? What would they do that the AI can't do? We're not there yet of course and short term this dynamic plays out of tools enhancing productivity and lowering cost. But at some point it flips around to the point where an AI can now do anything that we do faster, better, and cheaper.
Of course that kind of thinking is also a bit of a fallacy because there is a third way of some people enhancing themselves with AI to level up their own capabilities. These hybrid/enhanced individuals would be able to complement each other and be able to interface with both the real world and the digital world far more efficiently. We'll be competing with our enhanced future selves. Maybe they'll ditch the wetware at some point but the near future is a hybrid of hardware and wetware.
The really fundamental objection to everything I’ve just said is to ask what would happen if we had a system that didn’t have an error rate, didn’t hallucinate, and really could do anything that people can do. If we had that, then you might not have one accountant using Excel to get the output of ten accountants: you might just have the machine. This time, it really would be be different. Where previous waves of automation meant one person could do more, now you don’t need the person.
This keeps happening in these discussions, instead of discussing LLMs, people want to skip to discussing this other, totally speculative, thing. It's like people are already bored with this new technology and have nothing to say about it, so they talk about this other thing that isn't boring yet.
The question we should be asking is why do we want AI that can do anything faster, better, cheaper than us? Corporations would like to not have to pay labor costs, and some would argue a society where nobody has to work is utopian. But do you trust governments like the US to figure out UBI? Do we really have any sort of sociological understanding of what such a society would be like? Do we have an understanding of what war will look like?
One could argue that AI should only ever be tool to enhance human capabilities, not something to replace humans. Why do we want to do that? For whose benefit?
Because we can use them to eliminate tedious tasks and make better use of our limited time on this mortal coil.
The article covers this. Is it bad that we don't have teams of people spending their lives pulling barges up rivers or writing out copies of documents by hand? No, it isn't bad.
We lack a socio-economic system where common people truly benefit from automation.
We fear automation because it may replace our jobs. No worries, we'll make up some new bullshit jobs. Or maybe you can keep your job, and we raise the expectation of your output. It's all "running to stand still", not to mention sustaining yourself becoming ever more complicated and stressful.
I wish we could escape this "job for job's sake" and to always output more in perpetuity. At what point do we have enough stuff and can start utilizing automation to improve people's lives, for example with less work hours, more economic security? This is an area in which zero progress is made in decades, if not negative progress.
Or it could be that the time/effort required to craft a prompt and check the output is not worth it for most/all use cases, and thus after the initial excitement ChatGPT and friends gather dust much like blockchain and Apple Vision.
Yeah this hype has opened my eyes to the fact that there are so many people who haven't already found a flow for internet searches that's more streamlined than this AI stuff. Don't we all do this every day?
I'll be the odd man out here - I teach special education and GPT has been a lifesaver. I feed it a complex task or text, ask it to simplify it down to my students' reading levels, and then just do a super quick read/edit.
Similarly, I've been using Dall-E to add pictures to the books we read in class.
Compare this to retyping everything at a 4th grade reading level and then painstakingly searching Google for pictures (and usually ending up with mismatched pictures with varying artistic styles or watermarks).
I don't know that we will have true artificial intelligence soon a lá I Robot, but I feel nervous and optimistic about what's to come either way.
By the way, call me crazy but I think the Apple Vision thing is going to be a game changer for business and consumers.
This works way better for me for many kinds of things I previously did with search, and also does not at all replace other kinds of things I did (and still do) with search.
To a large extent, it has demonstrated to me that wading through stack overflow or blog posts or canonical documentation to figure out how to do some technical thing was actually a way worse workflow than I thought it was. But searching "what to do in my town this weekend" or "what are the best books about xyz" remains the best workflow.
I don't think this is a good analogy. A better analogy is the multiple previous AI hype cycles. They played out similarly. They brought some truly new capabilities that are used in production, just like LLMs will become a widely used tool, but failed to live up to the AGI hype even though everyone was convinced we're just a step away back in the 80s already and in a few years machine will do all our work.
The only reason people don't see this is because they weren't around back then. It's not going to be different this time.
>The only reason people don't see this is because they weren't around back then. It's not going to be different this time.
There are people who've been around who think it's going to be different this time.
Look it's fine if you don't. Maybe you'll be right and they'll be wrong but this "anyone who disagrees is ignorant" rhetoric is uncalled for. You shouldn't need to result to ad hominems.
Agreed, I see this automating 20-30% of my work in the next 10 years. Then, I'll spend that time on the unending pile of work that's not completed.
I see a major issue for people entering the industry in a decade because ChatGPT/Copilot I can get 10 mediocre answers and verify them in the time it takes a junior eng to respond to PR comments.
I tend to agree with the comment you replied to, but I also agree with this. It isn't too be ignored that lots of people see something different this time. But I don't see the same thing as they do...
This is what it looks like to me too. Except I do think the tools are significantly more useful than in previous cycles. But I do think the hype cycle model is the right one. And I find it super odd that people keep jumping to the AGI thing. I honestly struggle mightily to see what they're seeing in gpt-4 that makes them feel like we're on the brink of a general intelligence.
I think you make a good point. Anecdotally, I haven't yet found a good use for it in my workflow, despite trying. A few shell scripts, but mostly my existing workflow has proven more effective.
I am keeping up with the technology, though, to see how it evolves.
"We don’t have AGI, and without that, we have only another wave of automation, and we don’t seem to have any a priori reason why this must be more or less painful than all the others."
We don't need AGI to eliminate 200+ million jobs. The output of a network driving a car can be literally two signed floating points, left-right/accelerate-brake; in hindsight we will understand there is little intelligence in going from A to B without crashing, a bee does it, in 3D even. To eliminate 1+ billion jobs you take the driving network and give it hands: flipping burgers, feeding a CNC machine, handling packages, all these jobs will be gone forever, as they should.
The only question is what will politicians do when their solutions so far have been to raise the retirement age.
The article speaks more about the "knowledge workers", the CC Baxters of the world performing more or less bullshit jobs [1]. The 2-3 billion unemployables of the next 10-20 years are not knowledge workers and will all be replaced forever by embedded robotics with good enough networks and statistical learning for following given goals while generating sub-goals.
Boston Dynamics' Spot costs $75K today and Atlas is around $150K, once the price gets closer to $10K there is very little reason to ever employ persons as assembly line workers, construction workers, warehouse workers, machine operators, truck drivers, janitors and cleaners, agricultural workers, security guards, food service workers, garbage collectors, and so on.
And even if 3 billion people could be forced to do spreadsheets to "earn" a living, "by the sweat of [their] brow", that would be an even sadder world than our currently sad world.
My argument is that we need a new metaphysics for what work and life means, one where the right of a person to have food and shelter is not tied to the economic value they produce. But I have no hope for this world: the trillionaires of tomorrow will share even less than the billionaires of today.
Shadow IT is an unsupported, unendorsed IT process. Like when an office (without official support, and possibly against official rules) uses MS Access + VBA + whatever to automate a business process. ChatGPT is not endorsed by a lot of offices (in fact it's discouraged by many because it's an easy way to exfiltrate proprietary or otherwise confidential information since OpenAI retains those inputs), but it's become a part of a lot of people's workflows.
Salesforce is oddly popular as a Shadow IT platform for web applications because it has a database and a programming language, but is often controlled by "non-IT" teams such as finance or whatever.
I'm seeing people migrate perfectly fine ASP.NET or JSP applications to Salesforce just to get away from their internal IT teams, which can stretch out the paperwork to 2-3 years just to spin up a VM for a legitimate purpose.
God. Migrate to Salesforce was exactly the idea brought up by the sales side at a job of mine. I was on the IT side. Thank god we talked them down (plus they got sticker shock).
Some sales asks were legit and we got 'em done. Some were legit but low ROI and never done/priority even as sales had majority say in those priorities. Some were "please take this customer-hostile, flaky, human-explicitly-in-the-loop CRM workflow that has the thinnest veneer of plausibility above pure gaming of KPIs and insert it into a core product flow that's working well".
I think low code tools will continue to proliferate and I'm not against it. I do think they will be an interesting test of how much and in what contexts consumers are willing to accept half baked solutions. The capital environment this past decade has meant corporate leadership has been able to touch hot stoves without getting burned. Often they've been rewarded for it.
That entire list is bananas. Not one mention of PowerAutomate or BI? Sticking within the typical Microsoft stranglehold offers a fair amount of tooling with multiple integration points. They come bundled with everything else in a corporate environment, so they are "free" and less intimidating for a non-technical person to dip their toe into than any other low-code tool.
This list is shadow IT, which means the things that the corporate IT department isn't paying for and often doesn't even know about. The tools you mention are in the Productiv (and Okta) data in a different category.
Probably won't, people don't ask for what they don't know, and you'd be surprised at how even in the field of software engineering people don't actively looks for ways to automate stuff.
Then what use cases are the general public using LLMs for other than automation for it to have exploded in popularity? Tho anecdotally it seems to be used for “wisdom” of the crowds coupled with blind trust in the objectivity of a language model
> This is one reason I think the future of LLMs is to move from prompt boxes to GUIs and buttons - I think ‘prompt engineering’ and natural language’ are mutually contradictory.
Yes. Just like search. But that didn't mean that search was replaced by a bunch of radio buttons and click boxes. On the contrary, we learned how to use freeform search. And we will do the same for LLMs.
What I'm seeing is that the systems are most powerful when driven through and conversational interface. They are strong translation systems. They can ask questions and incorporate the answers into their outputs. The way to use them is only clear to a few people who are deep in the experimentation process now, but it will become common knowledge and their outputs will improve to the point that what we are doing with them now will seem primitive.
I'm excited to "why not both?" this. I'm excited for "UIs with buttons and boxes" enhanced by a conversational interface.
Just to illustrate by way of one example I've had bouncing around in my head: imagine a zotero-like interface for accumulating tagged information, where it's both built from pulling out information from conversations and can be explored conversationally. I want to click a button that says "show me all the information I've accumulated about LLMs (or whatever)", which includes both links and conversations I've had about it, and I also want to be able to say that same thing to the conversational interface.
In truth, I think we already have examples of this paradigm, in the various "copilot" interfaces, where you can do both things.
So I think that any job that can be done by a non-human is up for grabs, but for the sake of the argument, let's restrict it to manual jobs where being human doesn't matter. For example, a kitchen clerk at a burger joint doesn't need to be human, as long as it does all the tasks it needs to do and doesn't put anybody in danger. A security guard at an airport can be a machine gun with wheels, it is more intimidating that way. Picking up coffee cherries from a coffee tree can be done by a machine which is smart enough.
But assume that there are jobs "high in the ladder," for amazing engineers and creatives super-charged with AI powers. The problem is that not everybody can climb that ladder. And that's a problem we are already seeing. Right now, if you are highly qualified, you have no trouble getting a decent job. But if you are not, you are fighting for 10 USD/hour shifts somewhere. Imagine if 90% of those shifts are gone. Compound that with our social media (and AIs optimizing for engagement there in) making our kids a little dumber by stealing their attention for long periods of time.
It doesn't look good.