> The current wave of AI companies did this to themselves. Had things moved more slowly and actively worked with all the affected industries, I suspect people would be far less interested in seeing the technology fail.
The goal was to raise as much money as possible as fast as possible before the curtain is pulled back to reveal the Wizard's empire of lies.
A lot of designers and artists were not hired for their particular style or skills. AI allows "good enough" results for less. Same with coding. It no longer takes skill to meet minimum requirements, just some money. Code monkeys and junior graphic designers will have to upskill somehow, or find other work. Maybe it will balance out eventually, with the AI costing about the same as hiring a junior designer.
> The goal was to raise as much money as possible as fast as possible before the curtain is pulled back to reveal the Wizard's empire of lies.
There may be some lies, but a big part of the backlash against AI is that it is too effective. The backlash is growing precisely because people are finally getting out of the "it's just a tool and there will always be a place for humans!"-denial phase and into the "they took our jobs!"-anger phase. They're seeing the writing on the wall many (although surprisingly not all) in the tech sector saw long ago.
Additionally, I'm quite sure it's not backlash against slop, as some might think. People have disliked spam and ads forever, but all in all they'll happily stomach loads of it just to watch some badly written Hollywood or Netflix human slop.
I had to use MacOS recently and wasn't impressed by Finder. I am convinced that the best file manager on the market bar none is Dolphin from KDE software suite.
Agreed. KDE apps are slowly getting feature parity between crossplatform builds, Kate's nearly there but Dolphin is still missing some features on macOS.
Hope there's a day I can just use Dolphin on any system
If a real person gave them this advice, like a doctor or pharmacist, there would be standing for a lawsuit, might even be criminal.
Looking past "drugs bad mkay", the same ChatGPT that gave this advice is just as capable of giving the same, or worse, advice to someone wondering if they can take an allergy medication like Benedryl with their MAOI antidepressant.
Yes but if you're wondering about drug interactions you shouldn't ask AI, because there's always a risk of hallucination. You should ask your pharmacist. You can just call them, I promise they won't reject a consult.
How will kids bootstrap that information? If you ask LLM vendors we’re right behind the corner of AGI and mass replacement of human labor, surely they would be better at telling us about drug interactions than mere human doctors?
Yes, but if chokemegently420 on some random sub Reddit gave them that advice, nobody would be the wiser. It's not like ChatGPT is a certified clinician
It's an algorithm (albeit an expensive one) designed to produce engaging output. It's not a doctor, it's definitely not more capable than experts in their fields. It's not replacing anyone, same way pocket calculators didn't replace people 50 years ago.
Unless you're being sarcastic because of all the fearmongering in news? In that case, the joke went over my head lol
If hammer companies were suddenly the most valuable international companies, and spent millions on ad campaigns and lobbying about trusting the hammer interface, then you can assume a large amount of people might trust the hammer interface
Even if your tool learns to talk and to make decisions, it's still a tool, not a person. You're the person and the one responsible for the decisions you make based on your tools.
Going back from the analogy, the problem is that we conflated software <engineers> with "coders".
A lot of people thought their job was to create code, we gave them a tool to generate a lot of code fast, and they truly think that "more code" = "more good"
Literally saw a video ad the other day which went like "I've always been cautious using Google's AI because it sometimes gets things wrong, but this time, it got it right!"
> I thought it had potential but I don't know how you'd fund it.
The same way we fund other social services here in Europe. If an individual is incapable of caring for themselves, the state is expected to care for them.
If I had a hammer robot that I told to go hammer some nails in a birdhouse and it goes "Sure, I'm on it!" then it nails a cat to the wall and says "Here's you new complete birdhouse, it's perfect in everyway and will make everyone jealous", then yes, that is a tooling issue.
That's not a good analogy then. What benefit is provided by a hammer that just tells the operator (who has eyes and can see) that there is a nail under it (and I assume to swing)?
If software engineering wants to progress past being an "art" and be considered an engineering discipline, then it should adopt methods and practices from engineering. First and foremost, one of the universal methodologies is analysis of root cause in faults, and redundancies to avoid that. e.g. the FAA has two pilots for planes, and each system is built in redundantly so if an engineer misses a bolt or rivet, the plane won't crash. intersections are designed such that there is a forcing function[0] on the behaviour of the motorists to prevent fault. Or, to take your tool analogy, nail guns are designed to be pressed against something with a decent amount of pressure before you can fire them.
All of these systems are designed around the core idea of "a human acting irrationally or improperly is not at fault" and, furthermore, that a human can have a bad day and still avoid a mistake. They all steer someone around a possible fault. Hell, the reason why we divide the road into lanes is itself a forcing function to avoid traffic collisions!
So, where is the forcing function in large language models? What part of a large language model prevents gross misuse by laymen?
I can think of examples here and there, maybe. OpenAI had to add guard rails to stop people from poisoning themselves with botulism and boron, etc. But the problem here is that the LLM is probabilistic, so there's really no guarantee that those guard rails will hold. I seem to remember there being a paper from a few months back, posted here, that show AI guardrails cannot be proven to work consistently. In that context, LLMs cannot be considered "safe" or "reliable" enough for use. Eddie Burback has a very, very good video showing an absolute worst case result of this[1], that was posted here last year. Even then, off the top of my head Angela Collier has a really, really good video demonstrating that there's an absolute plethora of people who have succumbed, in large ways or small, to the bullshit AI can spew[2].
I feel like if most developers were actually serious about being an engineering discipline, like we claim, then we wouldn't have all jumped on the LLM bandwagon until they'd been properly tested and had a certain level of reliability. Instead there are a sizable chunk of people saying they've stopped coding by hand entirely, and aren't even reviewing the code! i.e. They've thrown out a forcing function that existed to prevent errorenous PRs being committed! And for some bizzare reason, after about 2 decades of people talking about type safety and how we need formal verification to reduce error, everyone seems to be throwing "reduction of error" out the window!
> I feel like if most developers were actually serious about being an engineering discipline, like we claim, then we wouldn't have all jumped on the LLM bandwagon until they'd been properly tested and had a certain level of reliability
Development can’t be a “serious” engineering discipline because the economics of tech companies doesn’t allow for it. But this has a lot less to do about developers, and significantly more to do with the severe pressure company executives are putting on everyone to use AI, no matter what.
But let’s be honest, many companies have adopted things like root cause analysis and blameless postmortems to deal with infrastructure reliability and reducing incidents. Making systems resilient to human mistakes, making it impossible for the typo to blow up a database, etc. are considered best practices at most places I’ve worked. On the product side, I think it’s absolutely normal to make it hard for a user to take an action that would seriously mess up their account.
The core problem happens when your product idea (say, social media) has vast negative externalities which the company isn’t forced to deal with economically. Whereas in other engineering disciplines, many things are actually safety related and you could get sued over. I’m imagining pretty much anything a structural engineer or electrical engineer works on could seriously hurt or kill someone if a bad enough mistake was made.
That just doesn’t apply to software. There is a lot of “life & death” software, but it’s more niche. The reality is that 90% of what the tech industry works on is not capable of physically harming humans, and it’s not really possible to sue over the potential negative consequences of… a dev tooling startup? It’s a very, very different industry than those other engineering disciplines work in.
But, software engineering has actually been extremely successful at minimizing risk from software defects. The most likely worst software level mistake I could make could… crash my own program. It likely wouldn’t even crash the operating system since it’s isolated. That lack of trust in what other people might do is codified everywhere in software. On an iPhone, I’m downloading apps edited by tens of thousands of other engineers, at essentially no risk to myself at all.
When their precision mismatches their accuracy (or your expectations as driven by their design), just like with any other metrology tool.
Now you might say: "but the datasheet will give you the tolerances, and the manual will tell you to mind it!"
And yes, that's true. Just like how LLM providers also do: they tell you that outputs may be arbitrarily wrong, and that you should always check for mistakes.
Is this bullshit? Yes. So are metrology tools that have a mismatching precision and accuracy, need calibration, and have designs that fail to make you mind either of these, sending you to reading duty instead. Which just so happens to be a whole lot of them.
It is also absolutely not bullshit of course, because it is a fundamental limitation, just like those properties are for metrology devices. LLMs produce arbitrary natural language. Short of becoming able to perfectly read and predict the users' mind, they'll never be able to make any hard assurances, ever.
Defective devices also exist, and so do incorrect / incomplete documentation.
If investors invest heavily in lemon juice, then go around hyping it and selling it with the promise it makes you invisible to cameras (which it doesn't), it doesn't matter how stupid and gullible the rubes who fall for that are, the investors bear the responsibility for giving them that idea, when people start attempting to rob banks with lemon juice on their faces.
Hype is bad. Unwarranted hype is worse. Enabling people who can't do a thing to do what they think the thing is, but isn't, because they don't know any better, is inflicting a pox upon the world.
Why do you think this analogy is even remotely correct? It’s well-known that LLMs produce non-deterministic results. It’s also well-known that they hallucinate. To make it even clearer, all the top LLM players make sure to remind everyone of that behavior. If calculators had similar effects and warnings, it would have been a valid analogy. Instead, you're comparing apples and oranges.
I think it's a valid analogy in some contexts. Like when talking to a person that is not aware of non-determinism and hallucinations. Which happens on this website very frequently.
Many people here tell you to use AI like you use a calculator. With minimal or no oversight, with full access to production systems, etc.
To let a non deterministic tool communicate on your behalf, or give it access to critical systems is evidence enough that a good number of people are not aware of these facts.
Isn't part of the problem that these tools are advertised as allowing non-coders to code? How are you gonna recognise that the code is wrong when you don't know how to code and the product is telling you that you don't even need to?
Technical analysis tells you that a stock is in its upwards trend. You invest all your money on it without thinking twice. The price goes down and you lose thousands of dollars. Is it a tool problem?
LLMs spit out a sequence of tokens that is the most probable continuation of the input. LLMs don't lie any more than technical analysis does when it predicts the most likely trend of stock prices. It's up to you how to use this information.
All modern PCs ship with Pluton coprocessors. The end-to-end remote attestation hardware infrastructure is all already there, waiting for someone to flip a switch and turn it on.
When it first shipped out, Secure Boot was used to lock other OSes out on early devices, it was after pushback that it was implemented such that it allowed you to enroll your own keys.
That said, there are countless mobile devices with locked bootloaders and and boot integrity attestation that will never run anything other than OEM OSes. That's equivalent to a locked Secure Boot + UKI-like system on PCs and it's already here.
All of the "solo green field projects" I let LLMs mostly write, despite supplying the scaffolding, structure and specific implementation details as code, prompts or context, I can't tell you much about 6+ months later, except for the parts I did write.
It's like I never wrote them, because I didn't. I've got the gist of them, but it's the same way I get the gist of something like Numpy: I know how it works theoretically, but certainly not specifically enough to jump in and write some working Fortran that fixes bugs or adds features.
I now have a bunch of stalled projects I'm not very familiar with. I no longer do solo green field projects that way.
