Because the technology has been making big leaps over the past couple of years, the comparisons are now being made not with what used to be before, but with what is being promised. And in that regard, things do fall short.
Basically the hype builders hyped things up so much they started hurting the hype.
I mean post ad hoc the machine could do that too. In fact, that is the only way your brain can explain anything.
I've created music and art in a visual medium and there's a lot of post hoc justification that your brain likes to present
(I’m also Not an illustrator)
And was a photographer long ago, double luck huh.
AI isn't drawing like a human artist does. It's a completely distinct process from someone putting marks on a page. The caveats, or turds as the author of the original article put them, are pretty difficult to fix without having actual training and skills in art, and acquiring those skills is a lot harder than learning to take photos.
-- Roy Amara
I see a potential risk here.
This is HN, so let's consider programmers in isolation for a moment. Today, I can issue the following prompt to ChatGPT and get a solution that works:
> Using Python, generate an example unit test that uses monkey patching.
That gives me a runnable, valid example test. I can then ask it to refactor it to use pytest instead of unittest:
> regenerate the above code sample using pytest instead of unittest.
Here's the resultant code:
# A simple class that we want to test
class Calculator:
def add(self, a, b):
return a + b
# Monkey patching the add method to subtract instead
def mock_add(self, a, b):
return a - b
# Test the monkey patched method
def test_add_with_monkey_patching():
# Create an instance of the Calculator class
calc = Calculator()
# Monkey patch the add method of the Calculator instance
calc.add = mock_add.__get__(calc, Calculator)
# Test the monkey patched method
result = calc.add(5, 3)
# Assert that the result is what we expect
assert result == 2
Here's where I think there is a potential major issue: As someone who is currently a "Senior Staff Software Engineer" and has been doing this sort of work for two decades now, if you had given me the same prompt my first response would have been "use monkey patching to do what?". ChatGPT doesn't do that. Instead, it gives you a response that best fits the question. It includes a half dozen paragraphs explaining the code, what it does, and what monkey patching is, sure... but it doesn't even touch on why you'd want to use monkey patching, when it is or is not the best approach, or any alternatives. In short, ChatGPT lacks the intuition that a human programmer will develop over the course of a career.
It's getting better. A few months ago I could have asked ChatGPT the same question and probably 30% of the time gotten a result that would even run. In the past couple of months I don't think I've run into that at all.
The "intuition" thing is a big deal, though. ChatGPT is a great tool in the hands of a competent engineer who has the experience to examine the output with a critical eye. Today, I could see it replacing a junior engineer in a professional setting. We should not use it in that way.
If we do, in a few years we'll get to the point where the current generation of senior engineers are retiring. If we've replaced the majority of our junior engineers with AI, we'll be left without people who can use the AI-generated results effectively.
There has to be a way to balance the savings represented by these tools (in terms of both time and money) with the fact that the people who are able to most effectively use it today only got to that point because they've spent much of their adult lives developing the exact skillset that we're proposing to replace with them.
Have you tried telling it who it is? You're coming at it with context about how it should act without telling it - it can role play a lot of things. I spoke to a teacher dealing with kids doing their homework too well with it, and showed you can just say "write X as a student with middling English" and get realistic mistakes.
> as if that actually stops this thing from hallucinating bullshit all the time.
This has nothing to do with hallucinations though. It's about giving something a poorly specified problem and then complaining that it gives a valid answer but doesn't act like a particular persona when that persona was not explained. This is a very general problem with a general approach to fixing it. Want it to challenge you on what you're asking it to do? Tell it that.
That raises the question of why they used it ineffectively in the first place then. They asked it to do something, it did, but they say it misses the point that a senior engineer would spot. My point is that if you want to judge how well it operates in replacing someone you have to actually specify who it's supposed to be imitating. It's not idle speculation, as I said I did this and it worked. Asking it for an example of monkeypatching gave back a proper example, along with a warning about maintainability.
Telling it I was a junior engineer and wanted to use monkeypatching did the same thing. Asking a followup of why I should be so careful with it and what I could do instead it gives explanations about several problems and then a few other approaches. Telling it simply to be a senior and I'm a junior, and that it should come up with followup questions for me it not only explains those problems but gives me the kind of followups a junior should ask but may not know to. Exactly the kind of intuitions about what's going on that are said to be missed here.
So I think the example used is significantly weaker than it appears, and the argument hinges on what GPT4 is lacking.
Now, a very solid response to the problem posed is that if it can approximate a more senior role for discussions it can make junior engineers more effective and teach them those skills.
> I told it that it was a senior engineer and I was a random client and asked the same thing.
I’m aware that ChatGPT is capable of performing the task I chose. I agree that it’s a relatively weak example, especially if it was my goal to show that AI can’t do this.
That’s not really what I was trying to show, though. My point is that you knew two things that many/most people probably wouldn’t:
1) That crafting your prompts to get the LLM to adopt a “persona” impacts the results significantly
2) That monkey patching has downsides, and that understanding when it is appropriate is important
I strongly suspect that you knew both of those things because you are a senior engineer yourself. My point is that some level of domain knowledge is necessary to get good results from it; my fear is that one day in the not too distant future, far fewer people will be around with that knowledge, because we’ve slowed the “senior engineer pipeline” down to a trickle through the use of these tools.
Here's a relevant paragraph
> That said, it's crucial to understand that monkeypatching can make your tests harder to understand and maintain if used excessively or improperly. When you monkeypatch, you're essentially altering the normal behavior of your code. This can lead to situations where tests pass because of the specific setup in the test and not because the actual code is correct, making it less effective at catching real bugs.
More than that, I could instead tell it at the start to tell me the questions I should ask as followups, here's the list when I told it I think I need to use monkeypatching:
1. When is it appropriate to use monkeypatching and when should I avoid it?
2. What are some common pitfalls or mistakes to avoid when using monkeypatching?
3. Can you give me more examples of how to properly use monkeypatching?
4. Are there any other techniques or tools for mocking or stubbing that I should consider?
I could then just say "answer those". No prompting about assuming downsides or problems.
I agree on point one but it's a very easy lesson to learn - try telling it that it's a pirate. It's also not actually required because someone like me can create the persona prompting and then everyone else can just use it. I'm not subtly crafting a weird prompt, it's just telling it who to be for the most part.
> my fear is that one day in the not too distant future, far fewer people will be around with that knowledge, because we’ve slowed the “senior engineer pipeline” down to a trickle through the use of these tools.
On the other side, what we have here is potentially an infinitely patient, always available mentor. You don't have to crash through mistakes on your own to learn, though some lessons are learned harder because of that.
I mean, you might not look at job postings for Jr. Engineers anymore, but they all ask for someone who can lead and mentor teammates less senior than they are so it seems like there won’t be many replacements for senior engineers retiring in the next decade regardless of ai’s existence.
It's a very complicated subject with several meta-conversations, and things being the way they are, a lot of people need to project arguments onto "They"
Here, we see a misinterpretation: its not "it's going to replace profession wholesale", it's a simple causal chain "hey you do 30% more work with GPT" => well then you need ~30% less workers => affect employment.
And we can run out a centithread of chastising any number of groups under the sun of maximalist claims, ex. from here, directly spin into "well _some_ people argue for complete replacement of artists", spin that into AGI doomers, etc.
I'm a natural contrarian and always used the worst first. I feel like I've warmed up a lot to generative AI (text particularly) but understand the limitations. People who came in from the other end thinking it was more than it is are naturally going to be let down when they see its performance in the sober light of day. I think more people are in this category because a big strength I see is for generative AI to produce stuff that superficially looks really great, but doesn't hold up if you actually want to do your own thing with it.
... is doing a lot of work here.
This is why expectations are exceeding reality. That said, the reality of these generative AIs is pretty damn impressive. I just wouldn't put it up against computers, cars, planes, boats, nuclear energy, medicine, chemistry or any of the other technological advances that did change the world. Not until the grounding problem is solved. After that, maybe.
But they did pave the way for the examples that did change the world.
No different here. Generative AI will change the world. Doesn’t mean these models will in and of themselves.
Like, nothing like this was remotely possible a year ago. I’m sure we’ll hit a wall at some point, but you certainly shouldn’t dismiss things as not possible because they’re not currently possible.
As I've gained more experience with it I feel I've seen the boundaries of what it's good at, what it isn't good at, and what it isn't useful for. It's good at like 80% of something. But there's so many things in that other 20%.
In the right places it will make certain individuals more productive in may ways but I don't feel the way I did 6 months ago.
Moreover, it's not at all strange that people learned what generative AI looks. When I see it in "in the wild", it instantly in my mind becomes "generated thing". And exactly because the images are created by a process of averaging, once I'm used to them, they have a "nothing special or interesting here" feeling, which isn't a good feeling for many thing, for example accompanying illustrations.
I'd bet good money you couldn't achieve more than say 60% in a properly controlled test
I would claim to be able to tell an AI-generated image from illustration hand-draw by a person if that illustration had roughly the characteristics of standard AI-generated images. IE, sort of curvilinear magic realism feel, crude knowledge of anatomy and perspective, uniform soft lighting etc. And there are quite a few artists out like that and I shudder for their careers.
The problem is the value of things that look like AI has been reduced because they're everywhere - and that include a fair number of non-AI-generated stuff, sure.
I can't help but feel you're just betraying your limited exposure to what's currently possible.
You're confusing "what lots of people seem to want to generate" with "what the models are capable of"
Lots of people queueing up to pour cold water on generative AI when it's still something I have to pinch myself to remind myself it's actually real.
(And yeah. I'm not denying the drawbacks. It's functionally useless for many (or even most) proposed applications. But can we still pause for a moment to be impressed it actually exists?)