You can use more debug outputs (log(…)) to narrow it down. Challenge your assumptions! If necessary, check line by line if all the variables still behave as expected. Or use a debugger if available/familiar.
This takes a few minutes tops and guarantees you to find at which line the actual behaviour diverts from your expectations. Then, you can make a more precise search. But usually the solution is obvious once you have found the precise cause.
I think that’s one of the best use cases for AI in programming; exploring other approaches.
It’s very time-consuming to play out how your codebase would look like if you had decided differently at the beginning of the project. So actually comparing different implementations is very expensive. This incentivizes people to stick to what they know works well. Maybe even more so when they have more experience, which means they really know this works very well, and they know what can go wrong otherwise.
Being able to generate code instantly helps a lot in this regard, although it still has to be checked for errors.
There’s a very naive, but working approach: Ask it how :D
Or pretend it’s a colleague, and discuss the next steps with it.
You can go further and ask it to write a specific snippet for a defined context. But as others already said, the results aren’t always satisfactory. Having a conversation about the topic, on the other hand, is pretty harmless.
Those LLMs are great fools, but I am just paranoid to use it in that manner.
Exquisite typo. I also agree to everything else you said.
You can do that when you control the frontend UI. Then, you can set up the input field for their name, applying input validation.
But I would rather not rely on telling the user, in hopes they understand and comply. If they have ways to do it wrong, they will.
That’s kind of two of my main points:
I don’t know if they are volunteering or being paid. The other person said they are being paid.
Either way, no one deserves being talked down to like that, even if they made a mistake. It’s a matter of respect and self-respect. And as a skilled person like a kernel developer, it should be trivially easy to find other work in a more appropriate environment.
That being said, maybe I’m missing something. Torvalds has been known to be like that for a long time (although that seems to be over now). And still, Linux has been developed over decades. So apparently, skilled people flocked around Torvalds, or maybe rather his project. Not entirely sure why, but I’m taking it as a hint I might be missing something.
I would not work with or even for someone with such an attitude, not even for money. Ok, do it yourself, bye. Got better places to be.
And this wasn’t just any but fuck man. It was the “perfect but fuck man I love”!
Right, thanks for the corrections.
In case of GAN, it’s stupidly simple why AI detection does not take off. It can only be half a cycle ahead (or behind), at any time.
Better AI detectors train better AI generators. So while technically for a brief moment in time the advantage exists, the gap is immediately closed again by the other side; they train in tandem.
This does not tell us anything about non-GAN though, I think. And most AI is not GAN, right?
As with other AI-enhanced jobs, that probably still means less jobs in the long run.
Now one artist can make more art in the same time, or produce different styles which previously had required different artists.
And this is why AI detector software is probably impossible.
What exactly is “this”?
Just about everything we make computers do is something we’re also capable of; slower, yes, and probably less accurately or with some other downside, but we can do it. We at least know how.
There are things computers can do better than humans, like memorizing, or precision (also both combined). For all the rest, while I agree in theory we could be on par, in practice it matters a lot that things happen in reality. There often is only a finite window to analyze and react and if you’re slower, it’s as good as if you knew nothing. Being good / being able to do something often means doing it in time.
We can’t program software or train neutral networks to do something that we have no idea how to do.
Machine learning does that. We don’t know how all these layers and neurons work, we could not build the network from scratch. We cannot engineer/build/create the correct weights, but we can approach them in training.
Also look at Generative Adversarial Networks (GANs). The adversarial part is literally to train a network to detect bad AI generated output, and tweak the generative part based on that error to produce better output, rinse and repeat. Note this by definition includes a (specific) AI detector software, it requires it to work.
Right? A self-assessed skill which is never tested is a funny thing anyways. It boils down to “I believe I’m good at it because I believe my belief is correct”. Which in itself is shady, but then there are also incentives that people rather believe to be good, and those who don’t probably rather don’t speak up that much. Personally, I believe people lack the competence to make statements like these with any significant meaning.
A design professor actually proposed this idea to us. Make the user feel how the computer is working, so they can appreciate the result more.
Thanks again, though just for the record, that didn’t help either. It’s alright, I’m used to the Thunderbird lags. Let’s stop here :)
I think that’s a helpful analogy and comment. Please remember this while I go on to nitpick. I’m aiming at in both fields, there may be more math-leaning scientists and concrete-leaning workers, with the engineer being somewhat in the middle.
Declaring bridges safe probably involves a lot of math and tables in the background. I guess we don’t actually run a million trucks but estimate the safety theoretically, with a few experimental tests. Likewise, a security specialist can define the edge cases against which the tests should be run. That may be the same person who also implements the test, but I want to emphasize it’s two different roles. And we might consider one more of a scientist, and the other more of a worker.
So how much your activity resembles that of a mathematician, or a traditional engineer probably depends on your specific task, and how much your team requires you to generalize or specialize.
Then let me spell it out: If ChatGPT convinces a child to wash their hands with self-made bleach, be sure to expect lawsuits and a shit storm coming for OpenAI.
If that occurs, but no liability can be found on the side of ChatGPT, be sure to expect petitions and a shit storm coming for legislators.
We generally expect individuals and companies to behave in society with peace and safety in mind, including strangers and minors.
Liabilities and regulations exist for these reasons.
Do car manufacturers get in trouble when someone runs somebody over?
Yes, if it can be shown the accident was partially caused by the manufacturer’s neglect. If a safety measure was not in place or did not work properly. Or if it happens suspiciously more often with models from this brand. Apart from solid legal trouble, they can get into PR trouble if many people start to think that way, no matter if it’s true.
If it has the information, why not?
Naive altruistic reply: To prevent harm.
Cynic reply: To prevent liabilities.
If the restaurant refuses to put your fries into your coffee, because that’s not on the menu, then that’s their call. Can be for many reasons, but it’s literally their business, not yours.
If we replace fries with fuse, and coffee with gun powder, I hope there are more regulations in place. What they sell and to whom and in which form affects more people than just buyer and seller.
Although I find it pretty surprising corporations self-regulate faster than lawmakers can say ‘AI’ in this case. That’s odd.
One is multiple parallel goals. Makes it hard to stop playing, since there’s always something you just want to finish or do “quickly”.
Say you want to build a house. Chop some trees, make some walls. Oh, need glass for windows. Shovel some sand, make more furnaces, dig a room to put them in - oh, there’s a cave with shiny stuff! Quickly explore a bit. Misstep, fall, zombies, dead. You had not placed a bed yet, so gotta run. Night falls. Dodge spiders and skeletons. Trouble finding new house. There it is! Venture into the cave again to recover your lost equipment. As you come up, a creeper awaitsssss you …
Another mechanism is luck. The world is procedurally generated, and you can craft and create almost anything anywhere. Except for a few things, like spawners. I once was lucky to have two skeleton spawners right next to each other, not far from the surface. In total, I probably spent hours in later worlds to find a similar thing.
The social aspect can also support that you play the game longer or more than you actually would like. Do I lose my “friends” when I stop playing their game?
I don’t think Minecraft does these things in any way maliciously, it’s just a great game. But nevertheless, it has a couple of mechanics which can make it addictive and problematic.