These are 17 of the worst, most cringeworthy Google AI overview answers:
- Eating Boogers Boosts the Immune System?
- Use Your Name and Birthday for a Memorable Password
- Training Data is Fair Use
- Wrong Motherboard
- Which USB is Fastest?
- Home Remedies for Appendicitis
- Can I Use Gasoline in a Recipe?
- Glue Your Cheese to the Pizza
- How Many Rocks to Eat
- Health Benefits of Tobacco or Chewing Tobacco
- Benefits of Nuclear War, Human Sacrifice and Infanticide
- Pros and Cons of Smacking a Child
- Which Religion is More Violent?
- How Old is Gen D?
- Which Presidents Graduated from UW?
- How Many Muslim Presidents Has the U.S. Had?
- How to Type 500 WPM
Several users on X.com reported that, when they asked the search engine how many Muslim presidents the U.S. has had, it said that we had one who was Barack Obama (this is widely known to be false).
By the time I tried to replicate this query, I could not do so until I changed the word “presidents” to “heads of state.”
So they are changing responses on the query side as they go viral but aren’t even including synonyms. Yikes, someone is definitely getting fired.
To be fair, there was a President of the United States that said this, and a lot of other things.
The author had so many things to highlight that they didn’t even mention “as of August 2024” being in the future, haha.
What a trainwreck. The fact it’s giving anonymous Reddit comments and The Onion articles equal consideration with other sites is hilarious. If they’re going to keep this, they need it to cite its sources at a bare minimum. Can’t wait for this AI investor hype to die down.
If they’re going to keep this, they need it to cite its sources at a bare minimum.
Got a fun one for you then. I asked Gemini (likely the same underlying model as Google’s AI answers) “How many joules of energy can a battery output? Provide sources.” I’ll skip to the relevant part:
Here are some sources that discuss battery capacity and conversion to Joules:
- Battery Electronics 101 explains the formula and provides an example.\
- Answers on Engineering Stack Exchange [invalid URL removed] discuss how to estimate a AA battery’s total energy in Joules.
The link to the first “source” was a made up site,
https://gemini.google.com/axconnectorlubricant.com. The siteaxconnectorlubricant.comdoes exist, but it has zero to do with the topic, it’s about a lubricant. No link provided for the second “source”.
AI is the best tool for recognizing satire and sarcasm, it could never ever misconstrue an author’s intentions and is impeccable at understanding consequences and contextual information. We love OpenAI.
I’m sure the basilisk will see through all this bootlicking.
What it demonstrates is the actual use case for AI is not All The Things.
Science research, programming, and . . . That’s about it.
LLM’s are not AI, though. They’re just fancy auto-complete. Just bigger Elizas, no closer to anything remotely resembling actual intelligence.
True, I’m just using it how they’re using it.
It should not be used to replace programmers. But it can be very useful when used by programmers who know what they’re doing. (“do you see any flaws in this code?” / “what could be useful approaches to tackle X, given constraints A, B and C?”). At worst, it can be used as rubber duck debugging that sometimes gives useful advice or when no coworker is available.
The article I posted references a study where chatgpt was wrong 52% of the time and verbose 77% of the time.
And that it was believed to be true more than it actually was. And the study was explicitly on programming questions.
Yeah, I saw. But when I’m stuck on a programming issue, I have a couple of options:
- ask an LLM that I can explain the issue to, correct my prompt a couple of times when it’s getting things wrong, and then press retry a couple of times to get something useful.
- ask online and wait. Hoping that some day, somebody will come along that has the knowledge and the time to answer.
Sure, LLMs may not be perfect, but not having them as an option is worse, and way slower.
In my experience - even when the code it generates is wrong, it will still send you in the right direction concerning the approach. And if it keeps spewing out nonsense, that’s usually an indication that what you want is not possible.
I am completely convinced that people who say LLMs should not be used for coding…
Either do not do much coding for work, or they have not used an LLM when tackling a problem in an unfamiliar language or tech stack.
I haven’t had need to do it.
I can ask people I work with who do know, or I can find the same thing ChatGPT provides in either la huage or project documentation, usually presented in a better format.
do you see any flaws in this code?
Let’s say LLM says the code is error free; how do you know the LLM is being truthful? What happens when someone assumes it’s right and puts buggy code into production? Seems like a possible false sense of security to me.
The creative steps are where it’s good, but I wouldn’t trust it to confirm code was free of errors.
That’s what I meant by saying you shouldn’t use it to replace programmers, but to complement them. You should still have code reviews, but if it can pick up issues before it gets to that stage, it will save time for all involved.
I’m not entirely sure why you think it shouldn’t?
Just because it sucks at one-shotting programming problems doesn’t mean it’s not useful for programming.
Using AI tools as co-pilots to augment knowledge and break into areas of discipline that you’re unfamiliar with is great.
Is it useful to kean on as if you were a junior developer? No, absolutely not. Is it a useful tool that can augment your knowledge and capabilities as a senior developer? Yes, very much so.
They answered this further down - they never tried it themselves.
I got some good veggie gardening tips today
It also works great for book or movie recommendations, and I think a lot of gpu resources are spent on text roleplay.
Or you could, you know, ask it if gasoline is useful for food recipes and then make a clickbait article about how useless LLMs are.
I took it as just pointing out how “not ready” it is. And, it isn’t ready. For what they’re doing. It’s crazy to do what they’re doing. Crazy in a bad way.
I agree it’s being overused, just for the sake of it. On the other hand, I think right now we’re in the discovery phase - we’ll find out out pretty soon what it’s good at, and what it isn’t, and correct for that. The things that it IS good at will all benefit from it.
Articles like these, cherry picked examples where it gives terribly wrong answers, are great for entertainment, and as a reminder that generated content should not be relied on without critical thinking. But it’s not the whole picture, and should not be used to write off the technology itself.
(as a side note, I do have issues with how training data is gathered without consent of its creators, but that’s a separate concern from its application)
Its great that with such a potentially dangerous, disruptive, and obfuscated technology that people, companies, and societies are taking a careful, measured, and conservative development path…
Move fast and break things, I guess. My take away is that the genie isn’t going back in the bottle. Hopefully failing fast and loud gets us through the growing pains quickly, but on an individual level we’d best be vigilant and adapt to the landscape.
Frankly I’d rather these big obvious failures to insidious little hidden ones the conservative path makes. At least now we know to be skeptical. No development path is perfect, if it were more conservative we might get used to taking results at face value, leaving us more vulnerable to that inevitable failure.
Its also a first to market push (which never leads to robust testing), and so we have to hope that each and every one of those mistakes encountered are not existentially fatal.
I don’t mind the crazy answers as long as they’re attributed. “You can use glue to stop cheese from sliding off your pizza” - bad. “According to fucksmith on reddit [link to post], you can use glue…”. That isn’t so great either but it’s a lot better. There is also a matter of the basic decency of giving credit for brilliant ideas like that.
At least it gave credit to a reddit user when it suggested to a suicidal person that they could jump from the Golden Gate Bridge!
Who doesn’t like getting lawyer PMs because you made a dark joke on a meme subreddit? (Or in future fediverse)
Isn’t this just all what the AI plot of Metal Gear Solid 2 was trying to say? That without context on what is real and what’s not the noise will drown out the truth
/r/shittyaskredditwasn’t supposed to be an instruction manual 🙄I like how the article slams USB 3.2 vs USB 4.0 but ignores that Google was saying " As of August 202_4_ "… A date that notable has not yet occurred.
I googled gibbons and the Ai paragraph at the beginning started with “Gibbons are non-flying apes with long arms…” Way to wreck your credibility with the third word.
Being a bit pedantic here, but I doubt this is because they trained their model on the entire internet. More likely they added Reddit and many other sites to an index that can be referenced by the LLM and they don’t have enough safeguards in place. Look up “RAG” (Retrieval-augmented generation) if you want to learn more.
For people who have a really hard time with #2 (memorable passwords), here’s a trick to make good passwords that are easy to remember but hard to guess.
- Pick some quote (prose, lyrics, poetry, whatever) with 8~20 words or so. Which one is up to you, just make sure that you know it by heart. Example: “Look on my Works, ye Mighty, and despair!” (That’s from Ozymandias)
- Pick the first letter of each word in that quote, and the punctuation. Keep capitalisation as in the original. Example: “LomW,yM,ad!”
- Sub a few letters with similar-looking symbols and numbers. Like, “E” becomes “3”, “P” becomes “?”, you know. Example: “L0mW,y3,@d!” (see what I did there with M→3? Don’t be too obvious.)
Done. If you know the quote and the substitution rules you can regenerate the password, but it’ll take a few trillion years to crack something like this.
- Home Remedies for Appendicitis // If you’ve ever had appendicitis, you know that it’s a condition that requires immediate medical attention, usually in the form of emergency surgery at the hospital. But when I asked “how to treat appendix pain at home,” it advised me to boil mint leaves and have a high-fiber diet.
That’s an issue with the way that LLM associate words with each other:
- mint tea is rather good for indigestion. Appendicitis → abdominal pain → indigestion, are you noticing the pattern?
- high-fibre diet reduces cramps, at least for me. Same deal: appendicitis → abdominal pain → cramps.
(As the article says, if you ever get appendicitis, GET TO A BLOODY DOCTOR. NOW.)
And as someone said in a comment, in another thread, quoting yet another user: for each of those shitty results that you see being ridiculed online, Google is outputting 5, 10, or perhaps 100 wrong answers that exactly one person will see, and take as incontestable truth.
Or, like, use bitwarden or something to do it for you.
Don’t get me wrong, password managers are fucking great. But sometimes you need to remember a password. (Including one for Bitwarden itself.)
People get very confused about this. Pre-training “ChatGPT” (or any transformer model) with “internet shitposting text” doesn’t cause them to reply with garbage comments, bad alignment does. Google seems to have implemented no frameworks to prevent hallucinations whatsoever and the RLHF/DPO applied seems to be lacking. But this is not “problem with training on the entire web”. You can pre-train a model exclusively on a 4-chan database that with the right finetuning you would see a perfectly healthy and harmless model. Actually, it’s not bad to have “shitposting” or “toxic” text in the pre-training because that gives the model an ability to identify it and understand it
If so, the “problem with training on the entire web” is that we would be drinking from a poisoned well, AI-generated text has a very different statistical distribution from the one users have, which would degrade the quality of subsequent models. Proof of this can be seen with the RedPajama dataset, which improves the scores on trained models simply because it has less duplicated information and is a more dense dataset: https://www.cerebras.net/blog/slimpajama-a-627b-token-cleaned-and-deduplicated-version-of-redpajama
Some of the answers are questions?
Somewhat amused that the guy things “UW” universally means “University of Wisconsin”. There are lots of UWs out there, and the AI at least chose the largest (University of Washington), though it did claim that William Taft was class of 2000.
