I asked ChatGPT about it and it game me a fairly surface level answer. I then asked it for more details and it went into detail far beyond what the average person would be able to tell you. I thought that was quite impressive for a fairly obscure bit of mechanical engineering.
1. Would you be able to notice if it made something up, given that it's going beyond the detail that you know?
2. Did it have any details that aren't covered in: https://en.wikipedia.org/wiki/Hybrid_Synergy_Drive
I've seen gpt hallucinate all kinds of things. But very rarely for something like explaining a process or system.
A lot of other stuff is just regurgitation of a wiki article.
Still it is very interesting and definitely a step forward.
I once worked with a very smart woman who at that time worked in marketing for the airside retail stores at a large international airport.
If anyone from outside her immediate sphere asked her what she did, she would typically say "I tell lies for a living".
This sounds like a lot of interviews I've conducted over the years.
Its an extremely well understood piece of technology and has been for decades outside of Toyota. Here is a 2005 paper that accurately models the fundamental behavior of the Prius HSD [1]. There are dozens of pages that explain it in various detail, including ones with live simulators of the power split device. [2] There are tons of forum threads of amateur Prius owners chatting about the internals.
I don't say this as a dunk, but to point out that a person's perception of chatGPT's capabilities are heavily influenced by their familiarity with the domain.
[1] https://www.engr.uvic.ca/~mech459/Pub_References/01500979.pd...
Q: What use is it attempting to measure ChatGPT's capabilities on something vs the average person on the street, or vs the average user with access to google.com? What does that actually tell us?
Maybe I'm showing my age, but if one wanted to learn or discover something, wouldn't one first typically try and find a reliable source/expert in that field or subject?
It would perhaps be interesting if it were such a hard topic that only 1 in 1000 could be capable of understanding it, but I would hazard a guess that most people familiar with the prius who don't understand its drivetrain probably don't understand it because they don't care, not because its too complicated to understand.
It's the same technique a stage psychic uses to demonstrate that they knew all about your late uncle and his peculiar love of ducks.
Sure it seems great at writing boilerplate code, unfortunately it’s really good and writing the kind of bugs that you don’t notice. This is maddening in a completely novel way, like working with an actively malicious autocomplete.
Not understanding the fallibility of LLM is not the fault of the technology. It’s the user’s fault.
Not at all like a con artist. More like a tool that can be dangerous in the hands of the uneducated. That is a very large set of human tools, from nuclear reaction to pharmaceuticals to the automobile. Perhaps we just need “drivers ed” for large language models.
This doesn’t make them useless in general, just a poor fit if you can’t easily fact check the results.
In google you would go to another search result.
For ChatGPT you actually need to verify via Google or actually try it when it is code.
There's already an example of it in the article where it incorrectly stated the tritone of F was Bb.
It’s a tool. It has flaws. It can be useful and if it is, have at it.
Similarly, if ChatGPT gives you an answer high in truthiness but low in accuracy, it could negatively impact you, whether loss in credibility if you repeat nonsense in front of someone knowledgable, or even worse if you use the incorrect knowledge to try to solve a real world problem.
It's like Babelfish, but for monoglots.
But likely much better read and with much better discretion that most of people writing content these days.
Which I think is part of the point that people are missing. ChatGPT’s output might not be as good as the best writing, but most of what people read is mediocre content with questionable accuracy. Being better than the majority of the writing being consumed is still a pretty big deal, even if it’s not better than all of the writing that’s being consumed. Especially since we can expect ChatGPT to get better.
The same is true for AI art. Could you hire a trained artist and work with them to do a better job? Probably, but most people don’t have the time and money to pursue such a project.
There are still trained artisans producing high quality physical goods that you can place an order with. But the masses have greatly benefited from the factories that have produced lower quality products that are much more widely available, and available at a moments notice. It’s not wrong to point out when the mass produced stuff might not have the same quality as the products produced. But if you only focus on this aspect you miss the reason why mass production is important.
I agree with this, but I think we may be missing the point.
Why are people reading mediocre content with questionable accuracy?
The problem is not: how to quickly and cheaply create content which is marginally less mediocre and marginally more accurate than what we currently have.
For most people that consume content, accurate information is not a priority. Being entertained, belonging to a group, or satisfying some immediate need or want matter more for most people, most of the time.
It's doubtful that people who consume content will change their wants to prize accuracy (most of the time). Partly, this is because accurate, precise, and/or qualified statements are often boring and hard to follow.
Consider also incentives that drive content creators: 1) the internet means many people can find a platform for many messages (democratization of speech is supposed to be a virtue of the internet) 2) the "freemium" advertising model means that most messages can be financially rewarded, regardless of accuracy
Engagement and financial rewards are much easier to get with lies, drama and controversy than with accuracy, precision, and qualified statements. In my experience, most people cannot find something worth striving for in the modern world, and settle on whatever comes easiest.
> The problem is not: how to quickly and cheaply create content which is marginally less mediocre and marginally more accurate than what we currently have
I'll grant that this is not a problem you're trying to solve. However, this sounds like exactly the arms race that content creators are engaged in until it stops paying.
> this sounds like exactly the arms race that content creators are engaged in until it stops paying
I don't think they're trying to achieve these things at all. Look at the broken incentives you raised in the first part of your response, that's what this is about, and all this is about.
Content creators mostly don't care about how mediocre their output is, and they mostly don't care about how accurate it is either, as long as the clicks keep coming.
We don't need (and can't expect) ChatGPT to "fix" this, the underlying problem is the broken incentives, not the actual quality - or lack of it - in the content.
> Why are people reading mediocre content with questionable accuracy?
How would you answer this?
I don't believe bad incentives explains it fully.
Some of it is out of necessity. I don’t think it’s controversial to say that many people use Stack Overflow, and though some of the answers there are very good, others aren’t. If someone needs help with a specific problem they’ll often post a question somewhere (Reddit, Stack Overflow, etc.), and the quality of the answer they get is a roll of the die - even whether they’ll get an answer at all.
Some of it is because blog spam has made traditional searches much more difficult. There’s another comment that says that there are comments better than Reddit and Hacker News, but if you go into a discussion about Google you’ll find that a lot of people have been doing things such as appending “reddit.com” to searches because otherwise they’ll drown in a mountain of blog spam.
Because it's literally everywhere you look and is very hard to filter due to huge amount of it everywhere. Particularly if you are trying to learn and grow on a certain subject, it's difficult to filter out what you don't know is 'mediocre' or 'questionable' at least not until you've already consumed it.
But there's definitely uses for a tool that produces a lot of boilerplate content that a human can then proofread (ex: writing cover letters, filling out unstructured paperwork, etc.)
Edit: I personally underestimated the use-case, because the "more optimal" thing to do when auto-filling out paperwork would be to define a formal data-structure with annotations for each field, and then have a system read annotations and spit out autosuggestions. But this is a lot of overhead.
If you can have a system that takes in arbitrary text, parses it, and fills it out with pretty good guesses, then that can be much more viable than the previous approach, as it doesn't require an overhaul of, say, the way the US bureaucracy generates paperwork. It's all on the consumer-end, the producer doesn't need to cooperate.