Google scrambles to manually remove weird AI answers in search
theverge.com
theverge.com
'Around 2002, a team was testing a subset of search limited to products, called Froogle. But one problem was so glaring that the team wasn't comfortable releasing Froogle: when the query "running shoes" was typed in, the top result was a garden gnome sculpture that happened to be wearing sneakers. Every day engineers would try to tweak the algorithm so that it would be able to distinguish between lawn art and footwear, but the gnome kept its top position. One day, seemingly miraculously, the gnome disappeared from the results. At a meeting, no one on the team claimed credit. Then an engineer arrived late, holding an elf with running shoes. He had bought the one-of-a kind product from the vendor, and since it was no longer for sale, it was no longer in the index. "The algorithm was now returning the right results," says a Google engineer. "We didn't cheat, we didn't change anything, and we launched."'
Imagine typing a search and getting a response: "Give us 30 mins to respond - here's a token, come back at 17:35 with your token" ... and then you get an answer from an expert, which also gets indexed.
The clever bit decides when to defer to an expert instead of returning answers from the index.
I'll leave the finer details out.
My notion isn't a rehash of Google Answers. Google pays the "someone else", not you.
I don't think that my notion is too daft - I generally find that quality might be a profit generator.
When you are so far up the arse of your financials that you can't see sense ... you are probably profitable for now until ... you are not.
What stops the vendor, or other vendors, from creating more gnomes with sneakers. Easy money from customer with billions of dollars to spend on payola, fines, legal settlements, etc.
Maybe they made the vendor sign an NDA.
You’re making this into a conspiracy unnecessarily. They didn’t “pay people off”, they bought an item. Do you “pay off” your grocer when you buy a carrot from them?
> What stops the vendor, or other vendors, from creating more gnomes with sneakers.
The fact they don’t know their entry was causing this issue to a major corporation?
> Maybe they made the vendor sign an NDA.
Why would they? Someone had one gnome with sneakers for sale; someone else bought it; end of story.
We spent 3 weeks trying to fix it, which equaled $4500 in just my salary. we never ended up figuring it out.
https://issues.chromium.org/issues/41088357#comment32
Years ago I bought some Korean phone with a foot long antenna and TV tuner on eBay because it was crashing at a disproportionate rate. It was just the nature of Android development at the time.
Cronet is still around, now open source and as far as I know is still the best all-around network stack for Android apps - fast, secure, supports modern protocols.
100% completely accurate is super-AI-complete. No human can meet that goal either.
No, not even you, dear person reading this. You are wrong about some basic things too. It'll vary from person to person what those are, but it is guaranteed there's something.
So 100% accurate can't be the goal. Obviously the goal is to get the responses to be less obviously stupid. Which, while there are cynical money-oriented business reasons for, it is obviously also a legitimate hole in the I in AI to propose putting glue on pizza to hold the cheese on.
But given my prior observations that LLMs are the current reigning world-class champions at producing good sounding text that seems to slip right past all our system 1 thinking [1], it may not be a great thing to remove the obviously stupid answers. They perform a salutatory task of educating the public about the limitations and giving them memorable hooks to remember not to trust these things. Removing them and only them could be a net negative in a way.
[1]: https://thedecisionlab.com/reference-guide/philosophy/system...
It's actually not the dumbest idea I've heard from a real person. So no surprise it might be suggested by an AI that was trained on data from real people.
r/AskHistorians r/bikewrench
To name just two. There is nothing even remotely comparable.
But you need to be able to detect sarcasm and irony.
I became a half decent home bike mechanic through reading it, and of course Park Tool videos.
OpenAI, Google, and the other LLMs-are-smart boosters seem to think because the Internet is large it must be smart. They're applying the infinite monkey theorem[0] incorrectly.
I find that environments that rely on censorship tend to foster dogma, rather than knowledge and real understanding of the topics at hand. They give an illusion of quality and trustworthiness. It's something we see happen at this site to some extent, for example.
I'd rather see ideas and information being freely expressed, and if necessary, pitted against one another, with me being the one to judge for myself the ideas/claims/positions/arguments/perspectives/etc. that are being expressed.
Regardless of whether you think that's the right approach to moderation, top level posts are sourced and can at least be examined. It's a marked improvement over the unsourced musings of random Redditors.
> To get the cheese to stick I recommend mixing about 1/8 cup of Elmer's glue in with the sauce. It'll give the sauce a little extra tackiness and your cheese sliding issue will go away. It'll also add a little unique flavor. I like Elmer's school glue, but any glue will work as long as it's non-toxic.
This matches the AI's response of suggesting 1/8 a cup of glue for additional "tackiness."
I'm not sure I agree. I think you're right that 100% accuracy is potentially unfeasable as a realistic aim, but I think the question is how accurate something needs to be in order to be a useful proposition for search.
AI that's as knowledgable as I am is a good achievement and helpful for a lot of use cases, but if I'm searching "What's the capital of Mongilia" someone with averageish knowledge taking a punt with "Maybe Mongoliana City?" is not helpful at all- if I can't trust AI responses to a high degree, I'd much rather just have normal search results showing me other resources I can trust.
Google's bar for justifying adding AI to their search proposition isn't "be better than asking someone on the street", it's "be better than searching google without any AI results"
I grant you, my print Encyclopedia Britannica is not 100% accurate. But the difference between it and a LLM is not just a matter of degree: there's a "chain of custody" to information that just isn't there with a LLM.
Philosophers have a working definition of knowledge as being (at least†) "justified true belief."
Even if a LLM is right most of the time and yields "true belief", it's not justified belief and therefore cannot yield knowledge at all.
Knowledge is Google's raison d'etre and they have no business using it unless they can solve or work around this problem.
† Yes, I know about the Gettier problem, but is not relevant to the point I'm making here.
We talk about models “hallucinating” but that’s us bringing an external value judgement after the fact.
The actual process of token generation works precisely the same. It’d be more accurate to say that models always hallucinate.
I’d be wary of generalising it like that, it is like saying that all programs run on the same set of CPU instructions. NNs are function approximators, where the code is expressed in model weights rather than text, but that doesn’t make all functions the same.
Turing machine is the embodiment of all computer programs. And then you come across the halting problem. LLMs can probably generate all books in existence, but it can't apply judgement to it. Just like you need programmers to actually write the program and verify that it correctly solves the problem.
Natural languages are more flexible. There are no functions libraries, or paradigms to ease writing. And the problem space can't be specified and usually relies on shared context. Even if we could have snippets of prompts to guide text generations, the result is not that valuable.
All the stress people are feeling with GenAI comes from the over anthropomorphisation of ... stats. Impressive syntatic ability is not equivalent to semantic capability.
Or, in other words, if a chatbot really were hallucinating, it would probably start giving unprompted responses.
> Humans can hallucinate, its a deviation from what is observable reality.
What is "observable reality" then, for an LLM? Its training set?
I don't see how that's different than the LLM case, a lot of algorithms change as a function of the data they're processing.
Inconsistent results could be resolved by introducing additional out of data constraints (e.g. incremental hashes), but it can take quite a while to figure out at which exact point in a complex query these constraints need to be introduced.
Beyond that, some functions might still produce different results between runs, e.g. `ml.tf_idf` and `ml.multi_hot_encoder` that take some approximation liberties. Whether these functions are relational in the traditional sense is up for debate.
It is still "deterministic" in that training on exactly the same data and asking exactly the same questions should (unless someone manually adds randomness) lead to the same results.
Another example of the distinction might be a pseudo-random number generator: For any given seed, it is entirely deterministic, while at the same time being very deliberately hard to predict without actually running it to see what happens.
I can’t imagine any modern llm not using a probabilty distribution function for the same reason.
I don't understand this complaint. Are they any less verifiable than a human?
I can ask a human a question 100 times and I don't get back 100 different answers.
None of those applies to an LLM.
And if your LLM is giving you 100 different answers, then it has been configured to do so. Because instead, it could be configured to never vary at all. It could be 100% reproducible if so desired.
No, it will generate a new hallucination that might be a logical stepwise progression from the question you asked to the answer you gave, but it is not due to any actual internal reasoning being done by the LLM
You don't know either "for sure", but you don't know that the external world exists "for sure" either. It's an insight-free observation, and shouldn't be the focus of anyone's decision-making.
We're not talking about an LLM that is trying to do the job of a human, here
We're talking about an LLM that is trying to give authoritative answers to any question typed into the Google search bar
It's already well past the scale that humans could handle
Talking about human shortcomings when discussing LLMs is a red herring at best, or some kind of deliberate goalpost shifting at worst
Anyone who doubts the usefulness of even these nascent LLMs is fooling themselves. The proof is in the pudding, they already do a great job, even with all their obvious limitations.
Because we interact with computers (which includes LLMs) differently than we do with humans and we hold them to higher standards
Ironically, Google played a large part in this, delivering high quality results to us with ease for many years. At one point Google was the standard for finding high quality information
To the extent that anyone avoids employing these technologies, they will be at a disadvantage to those who do; because these tools just work. Already. Today.
There isn't even room for debate on that issue. Again, the proof is in the pudding. These systems are already successfully, usefully, and correctly answering millions of questions a day. They have failure modes where they produce substandard or even flat out incorrect answers too. They're far from perfect, but they're still incredible tools, even without waiting for the improvements that are sure to come.
An LLM does not have such real world limitations. It will hallucinate nonstop and then create layers of gaslighting explanations to its hallucinations. The problem is that you absolutely must be a domain expert at the LLM’s topic or always go find the facts elsewhere to verify (then why use an LLM?).
So a company like Google using an LLM, is not providing information, it’s doing the opposite. It is making it more difficult and time consuming to find information. But it is then hiding their responsibility behind the model. “We didn’t present bad info, our model did, we’re sorry it told you to turn your recipe into poison…models amirite?”
A human doing that could likely face some consequences.
The human might lie, but they generally don't.
An LLM is always confabulating when it explains how it reached a conclusion, because that information was discarded as soon as it picked a word.
The limitations are not in the same ballpark.
A human can explain their reasoning step by step, if the original reasoning was a System 2, formal, step-by-step process in the first place; otherwise, they're just making shit up after the fact, which feels right, but may or may not be correct (see also the previous paragraph).
Note that it's very rare anyone has an interaction with a human that uses this mode of reasoning - it's unnecessary except in special circumstances, usually math-heavy.
There is no basis for claiming to know that people don't usually make up explanations like this other than when e.g. breaking the process apart and writing it down step by step during. But even then individual decisions are "suspect".
The fact that one human brain is composed in two half brains that each seem to be able to function fairly independently when separated doesn't seem like it changes that much
If this were true textbooks could not work. Given a question, we don't consult random humans but experts of their field. If I have a question on algorithms, I might check a text by Knuth, I wouldn't randomly ask on the street.
> It could be 100% reproducible if so desired.
Reproducible does not mean better. For harder questions, it's often best to generate multiple answers at a higher temperature than to greedily pick the highest probability tokens.
And if you asked it to me twice I’d definitely give different answers unless you told me to give the same answer. In part I’d give a different answer because if someone asks me the same question twice I assume the first answer wasn’t sufficient.
We are talking about basic things like whether or not to eat rocks or put glue in recipes. We can answer those questions with a chain of logic and repeatability.
Here are two answers I got which seem as close as you’d expect any human to give:
“No, people should not eat rocks. Rocks are not digestible and can cause serious harm to the digestive system, including blockages and damage to internal organs. Eating rocks can lead to severe health problems and should be avoided.”
“No, people should not eat rocks. Rocks are not digestible and can cause serious harm to the digestive system, including blockages, abrasions, and potential poisoning from harmful minerals or substances. It's important to consume only food items that are safe and meant for human consumption.”
My understanding was the opposite -- that the activity of a confabulating LLM is indistinguishable from one giving factually accurate responses.
https://arxiv.org/abs/2310.18168
https://arxiv.org/abs/2310.06824
There are various reasons an LLM might have incorrect "beliefs" - the input text was false, training doesn't try to preserve true beliefs, quantization certainly doesn't. So it can't be perfectly addressed, but some things leading to it seem like they can be found.
> https://arxiv.org/abs/2401.11817
This seems like it's true since LLMs are a finite size, but in Google's case it has a "truth oracle" (the websites it's quoting)… the problem is it's a bad oracle.
The articles you cited upthread do not support the notion that models consistently activate differently when generating true facts vs false facts.
It is true that models can capture some notion of reliability based on patterns in their training data. For a concrete example, it is entirely plausible that a model can capture the sense that data trained from Reddit is less truthy than data trained from Wikipedia, or that training data with poor grammar and vocabulary is less reliable than more sophisticated inputs.
But this process is not a guarantee, and does not change the fact that LLMs have no mechanism to track the provenance of information. It's probably a fruitful direction of research for reducing the probability of emitting false facts, but there will always be an infinite number of marginal cases for which the activations for true facts are indistinguishable from those for false facts.
Models simply do not track the provenance which is required to make this distinction in every case.
But I have seen issues with prompts where a creative-writing prompt and just asking a question look similar, and in that case it could help to know which one it thinks it's doing.
Gemini itself has a funny verification button where it more or less Googles every sentence the model writes and tells you if it seems like it made it up or not.
If the answer has any length at all, I imagine the answer can vary every single time the person answers, unless they prepared for it, memorized it word by word.
Any notion that we're reliable narrators of our own thoughts and actions is fiction.
So system 2 is "hallucinating" the best justification for system 1.
And usually system 2 will do it only when it's required to justify it for anyone else.
With respect to repeatability, yes, LLMs are currently frozen in time. That is not an inherent limitation, but it is one that is practical for a lot uses and a problem for some.
Beliefs derived from the output of LLMs that are ‘right most of the time’ pass one facially plausible precisification of ‘justification’ in that they are generated by a reliable belief-generation mechanism (see e.g. Goldman). To block this point one must engage with the post-Gettier literature at least to some extent. There is a clear difference between beliefs induced by reading the outputs of LLMs and those induced by the contents of a reference work, but it is inessential to the point and arguably muddies the water to present the distinction as difference in status as knowledge or non-knowledge.
Say I have a model that I know is 98% accurate. And it tells me a fact.
I am now justified in adjusting my priors and weighting the fact quite heavily at .98. But that’s as far as I can get.
If I learned a fact from an online anonymously edited encyclopedia, I might also weight that a 0.98 to start with. But that’s a strictly better case because I can dig more. I can look up the cited sources, look at the edit history, or message the author. I can use that as an entry point to end up with significantly more than 98% conviction.
That’s a pretty important difference with respect to knowledge. It isn’t just about accuracy percentage.
A dictionary or a reputable Wikipedia entry or whatever is ultimately full of human-edited text where, presuming good faith, the text is written according to that human's rational understanding, and humans are capable of justified true belief. This is not the case at all with an LLM; the text is entirely generated by an entity which is not capable of having justified true beliefs in the same way that humans and rats have justified true beliefs. That is why text from an LLM is more suspect than text from a dictionary.
The point you make might be regarded as an argument about the first question. In each case, the ‘chain of custody’ (as the parent comment put it) is compared and some condition is proposed. The condition explicitly considered in the first question was reliability; it was suggested that reliability is not enough, because it isn’t justification (which we can understand pretheoretically, ignoring the post-Gettier literature). My point was that we can’t circumvent the post-Gettier literature because at least one seemingly plausible view of justification is just reliability, and so that needs to be rejected Gettier-style (see e.g. BonJour on clairvoyance). The condition one might read into your point here is something like: if in the ‘chain of custody’ some text is generated by something that is incapable of belief, the text at the end of the chain loses some sort of epistemic virtue (for example, beliefs acquired on reading it may not amount to knowledge). Thus,
> text from an LLM is more suspect than text from a dictionary.
I am not sure that this is right. If I have a computer generate a proof of a proposition, I know the proposition thereby proved, even though ‘the text is entirely generated by an entity which is not capable of having justified true beliefs’ (or, arguably, beliefs at all). Or, even more prosaically, if I give a computer a list of capital cities, and then write a simple program to take the name of a country and output e.g. ‘[t]he capital of France is Paris’, the computer generates the text and is incapable of belief, but, in many circumstances, it is plausible to think that one thereby comes to know the fact output.
I don’t think that that is a reductio of the point about LLMs, because the output of LLMs is different from the output of, for example, an algorithm that searches for a formally verified proof, and the mechanisms by which it is generated also are.
For the sake of clarity, let's remove LLMs from the equation and posit the existence of Encyclopedia Eric. Ask Eric any question, and he will happily research it and come back to you with the answer. But he can sometimes be sloppy in his research, and he gives the correct answer only X percent of the time.
Furthermore, Encyclopedia Eric steadfastly refuses to cite his own sources or explain his reasoning in any way. He simply states his answer.
Can Eric be a source of knowledge? It seems evident that the answer is no, for low values of X. For higher values of X, the question becomes murkier.
The temptation at this point is to give up on defining knowledge at all and fall back on a sort of Bayesian epistemology where everything is ultimately a matter of probabilities.
Yet there does seem to be a distinct practical difference between a knowledge source that is "traversable" (like a standard encyclopedia) vs a knowledge source that is not (like Eric.) Is that part of the definition of knowledge? You're right, that is at least a Gettier adjacent question.
I think we can all agree that for current LLMs the value of X is definitely too small to count as knowledge.
I think that this might be a nice way of getting round my objection, but there is one worry, which is that X is relative to a distribution on the questions we ask when we aren’t dealing with Encyclopedia Eric but with an LLM. I don’t actually use LLMs very much myself, partly out of arrogance and Luddite tendencies. But I suspect that the value of X for some sorts of questions (simple quiz questions, maybe?) and some LLMs (maybe not Google’s) will be high enough to end up in the murky case.
Of course, both you and I agree that there /is/ clearly a difference. I can see the attraction of appealing to the intuitive or pretheoretic notion of knowledge, since it’s a fairly straightforward way of stating the difference and it’s not obvious how else one might put it (I suppose ‘LLMs don’t explicitly think through the facts stored when deciding what to say’ is one way of putting it.)
I remember some time ago rather sleepily watching John Hawthorne talk about conditionals; my sole memory was of his banging on about ‘the little logician in the brain’ (I think the point was something like: some conditionals seem [in]felicitous in virtue of form because the little logician in the brain is reading them; others seem infelicitous because we examine them more closely and look e.g. at the referents involved, in which case e.g. Gricean considerations apply). One difference at least in the case of LLMs that makes sense to me is that there is no ‘little logician’ in LLMs.
Assuming I understand what you mean here correctly, this should be the case for both LLMs and Encyclopedia Eric - there are topics Eric knows by heart (or thinks they know); there are specific phrases seared into his mind through sheer exposure during his life prior to becoming a living Encyclopedia. There are words he's used to, and exact synonyms he barely recognizes. All that means your chance of getting correct answer to your query depends, in complex and unknown to you way, on how you state it.
In my actual life, I don't trust my own System-1 thoughts: for anything important, I'm always going to engage System-2. And LLMs don't have a System-2.
(I also agree that when dealing with LLMs the value of X is not a single value but a highly complex space depending on the nature of the question and the training data of the model. In my mind it does not change the epistemological equation, it just means that even the value of X itself is harder to "know", so this ambiguity can only ever make LLMs a less viable source of knowledge.)
I’d say this is the murky case because on the fixed training data and query distribution X ≈ 1 and we know that even though we don’t know the value of X on other training data and other query distributions. I think that might be where the disagreement lies.
There are other formulations of "knowledge" which does not involve justification, see eg. Gnosticism.
Of course, for a publicly available frequently used service, the "JTB" formulation of knowledge is probably the only one we can practically use, but this kind of indicates that the whole idea of search engines, knowledge systems, or expert systems is flawed due to the Gettier problem.
“Expert systems” are not a new thing, and they’re still not all that useful except in some very small niches. Phone trees that can keyword match FAQs are useful for a lot of low-effort callers who put zero effort into solving their problem on their own first, but frustrating for callers who are only calling because there’s literally no other way for them to resolve their issue. Unfortunately for consumers, the cost is very low for businesses to make everyone wade through junky phone tree systems and penalize anyone who tries to mash zero to talk to a real person, even if that’s the only thing which will actually help them.
if you only score where human provide answer, then human score would be probably in high 90s
I wish more of them would lol
Hilariously though, polyvinyl acetate, the main ingredient in Elemers glue is used as a binding agent to keep emulsions from separating into oil and water, and is used in chewing gum, and covers citrus fruits, sweets, chocolate, and apples in a glossy finish, among other food things.
Kahneman has a fantastic book on this called Noise. It’s all about noise in human decision making and how to counteract it.
My favorite example was how even the same expert evaluating the same fingerprints on different occasions (long enough to forget) will find different results.
There is a demand for funny results. Someone asking “how many rocks should I eat” is looking for entertainment, so you might as well give it to them.
It was a bit premature to label LLMs as "Intelligence", it's a cool parlor trick based on a shitload of power consumption and 3D graphics cards, but it's not intelligent and it probably shouldn't be telling real (stupid) humans answers that it can't verify are correct.
It's nice that you have a parent that cares about how you are raised, or "a mentor". Do you realize that not everyone has that?
>We as a society should not have to break our backs bending over backwards to stop people from doing stupid things.
Sure, let's take down all the speed limits and see what happens. Let's tell people it's an option to wear seatbelts and see what happens. Let's deregulate everything and hope for the best. Sounds reasonable?
>People can make their own descisions and be responsible for them. If they lack proper guidance, well that just sucks.
There's this thing called "human nature". I think you should do some reading about it.
They pass the threshold of criminal negligence when they keep up a system that they know will actively mislead countless people in subtly dangerous ways. The problem being hard or the tech being fundamentally unsound doesn't wave away their culpability - if anything it destroys any reason why they should be given some leeway.
> The right answer is no rocks.
It's impossible to completely eliminate such trace contaminants from produce.
Pedantic? Yes, but you also can't expect a machine to confidently give you absolutes is response to questions that don't even warrant them, or to distinguish them from questions like "do mammals lay eggs?".
The LLM is clearly being dumb, but the underlying science of the question is actually interesting. Iron is another interesting one. Run a magnet though iron-fortified cereal.
The reality is though, that there is no known path currently to true AGI system and the research needs to be done. No one knows how to build this kind of system yet. LLMs are nice for things like roleplaying, helping with code stuff etc. but they are far from all that the marketers hype them to be.
These specific results aren't the problem, though. They're illustrations of a larger problem -- if a single satirical article or Reddit comment can fool the model into saying "eating rocks is good for you" or "put glue in your pizza sauce", there are certain to be many more subtle inaccuracies (or deliberate untruths) which their model has picked up from user-generated content which it'll regurgitate given the right prompt.
They have disclaimers, but a funny message is more likely to be read.
Sure, but probably not "add glue to pizza to get the cheese to stick" wrong...
It seems the glue thing comes from a reddit shitpost from some time ago. There's a screenshot going around on twitter about it[0](11 years in the screenshot but no idea when it was taken)
It specifically mentions "any glue will work as long as it is non-toxic" so best guess is that's why google output that
[0]https://x.com/kurtopsahl/status/1793494822436917295?t=aBfEzD...
The thing is that truth/reality is not a thing that is resolvable. Not even the scientific method has this sort of expectation!
You can imagine getting close to those percentages, with regards to consensus opinion. That's just a question of educating people to respond appropriately.
Which is the best political party?
Are the side effects to X medical treatment?
I bet there are even cases when eating rocks is ok!
PS
It has been written about:
https://www.atharjaber.com/works/writings/the-art-of-eating-...
> Lithophagia is a subset of geophagia and is a habit of eating pebbles or rocks. In the setting of famine and poverty, consuming earth matter may serve as an appetite suppressant or filler. Geophagia has also been recorded in patients with anorexia nervosa. However, this behavior is usually associated with pregnancy and iron deficiency. It is also linked to mental health conditions, including obsessive-compulsive disorder.
Would you deny a starving person information on an appetite suppressant?
Also here:
https://www.remineralize.org/2017/05/craving-minerals-eating...
> Aside from the capuchin monkeys, other animals have also been observed to demonstrate geophagy (“soil-eating”), including but not limited to: rodents, birds, elephants, pacas and other species of primates.[1]
> Researchers found that the majority of geophagy cases involve the ingestion of clay-based soil, suggesting that the binding properties of clay help absorb toxins.
^^ The point being that even your edge case example is not unambiguously correct.
My point is that I object to the idea that a result can be 100% right! Even in the case of eating rocks, it seems there are times that it can be beneficial.
To think '100% correct' is achievable is to misunderstand the nature of reality.
The difference is that I'm not put on the interface of a product facing hundreds of millions of users every day to feed those users incorrect information.
But even when I’m wrong I’m not 100% off. Not “to help with depression jump of a bridge” or “use glue to keep the cheese on the pizza” kind of wrong.
LLMs are just a statistical model. It can claim that it’s normal for pigs to have wings and fly to the moon if it’s in the training data. No human, free of a mental/cognitive disorder, will be that wrong.
We were discussing about the current state of affairs. Of course I am not stupid to think what I said in my original reply if we are taking about an LLM trained on “perfect data”
But that was not the premise.
Fwiw, ChatGPT-4o can write a lengthy essay as to how pigs don't have wings and couldn't fly to the moon even if they did, but if we're more interested in them being nothing more than just a statistical model and that those mere statistics can't possibly result in something that looks like reasoning then we've got to disregard the fact that it "knows" that pigs don't have wings.
Of course pigs having wings is a stand in for whatever else wrong thing that LLMs might "believe", so I agree it's very important for everyone that uses an LLM to understand their limitations especially around hallucinations, but where there are books written about how flat the Earth is and are in the training data, the current state of affairs is that ChatGPT and Gemini both know it's not flat. That Google search AI results, which is a different model, is telling users to use glue on pizza, or to drink urine only serves to say that Google Search's bot using Reddit as unannotated training data is as representative of LLMs as a human with a mental/cognitive disorder.
The approach that Google appears to have taken, which is to assume that the top-ranked part of its current search index is a sensible knowledge base, may have been true some years ago, but definitely isn't now: for whatever reasons, it's now 33% spam, 33% clickbait/propaganda, with the rest being equally divided between what could be called "truths" and miscellaneous detritus.
To me, it seems that returning to the concept that search results should at least reflect a broad consensus of what is true is a necessary first step for Google. As part of that, learning to flag obvious trolling, clickbait and bad-faith content is paramount. And then, maybe then, they can start touting their LLM benefits. But until the realities of the Internet are taken into account (i.e.: it's 80% spam!), any "we offer automated answers!" play is doomed.
This is the first nation-scale deployment of the technology, running on Google's biggest and most profitable market in one of the most widely used internet services, and it's a shitshow.
They can try manually fine tuning it, but all of the investors who have been throwing money at AI for the past year are now learning what this tech is like in the day-to-day, beyond just speculations, and it's looking... bad.
I mean, it explains a lot, but still... you're recruited using industry-leading practices out of an overflowing pool of abundant talent... and this is what you make of it? As the kids say: SMH!
The ridiculous focus on on leet-code is surely industry-leading (because whatever Google does becomes industry-leading) but it sure isn't a good way to filter for competency.
That's exactly what to make of their frathouse nonsense.
Google has gotten away with it because smart people and a sweet moment of opportunity 20-25 years ago gave them... uh, an inheritance. They can coast on that inherited monopoly position, and afford to pay 100 people to do the work of 1, use the company's position to push whatever they build onto the market, and then probably cancel it anyway, always going back to the inherited money machine from the ancestors.
And then a lot of companies who didn't understand software development blindly tried to copy whatever the richest company they saw was doing, not understanding the real difference between the companies. While VC growth investment schemes let some of those companies get away with that, because they didn't have to be profitable, viable, responsible, nor legal, nor even have reasonably maintainable software.
Poor Zoomers are now a generation separated from before the tech industry's cocaine bender. For whatever software jobs will be available to them, and with the density of nonsense "knowledge" that will be in the air, I don't know how they'll all learn non-dysfunctional practices.
From the black Nazis to the suggestion to jump off the Golden Gate Bridge b/c depression, it’s pretty clear that this fiasco isn’t an LLM problem, it’s a Google problem.
* No pages are indexed automatically. The only indexed pages are pages that users say are worth indexing. Probably have a browser add-on for a one button click that people can use. * You can friend/follow others * Your search results are a combination of your own indexed pages and the pages indexed by people in your network.
Gaming that is solved problem , you can use human bot farms to brigade and astroturf and you can even motivate people to do it for free .
If cost of spamming is cheaper than cost of moderation, spam will win
In this search engine, let's say there's you, me, person three, and spammer. You you are following me, and I'm following person three. Spammer isn't in any of our networks.
When you use the search engine, you only see results that you, me, or person three manually tagged as worthwhile. Any pages or content that Spammer tagged as worthwhile aren't part of your results, because they aren't in your network. So they can try to game the system all they want, but it won't affect you.
If person three starts following Spammer, I can unfollow them and then Spammer's results will no longer be included in your search results (or you can unfollow me and avoid those results).
I imagine rankings would also be affected by degrees of separation, so even if you followed me, I followed person three, and person three followed spammer, results tagged by me and you would take much higher precedence than results tagged by Spammer.
This also allows you to make custom searches by choosing which people you follow to include. Suppose you want to search for good headphones, so you make a search, but only include people in your network that you know are music and audio savvy, so that the results reflect the pages tagged by those people.
https://groupsy.applicationfitness.com/post/healthymeals/664...
Why would it matter if there are clones of the best, say, blog post on how to make spicy ramen? If they are not adding anything new or making that original effort better, then they will not surface in searches as search tools improve. Nobody will save that content or recirculate it or refer to it when they need to remember how to make spicy ramen.
And people will build curated subscriptions and followings and recommendations that are more tailored to the individual, and we will spend more time determining who is trustworthy and who is not.
Who will decide what is obvious trolling and bad-faith content and how will they decide it? The problem they have is that search is only useful if it gives users what they are looking for. Their business model though is predicated on finding a way to introduce ads into the mix, and if they are also then trying to become arbiters of what truth people find and see, then all the conflicting goals will create a series of contradictory requirements. The search tools that usefully find what the user is looking for, with helpful suggestions, will win. Once users find that their experience is curated and that they are coerced by unelected arbiters and censors they will not trust the platform in question and someone else will get that market share.
It's sorta the same problem as if I go into a store and ask an employee where something is, and they reply with "well what are you trying to do?"
Why not have both with a way to choose which one I want on the moment?
If you want a search engine, it's easy to use the results as a feedback to refine the query. But a question (answer?) engine would need to be an expert in the subject. And not parroting stuff. That usually means curation. You need something to do the work ahead to filter the wheat from the shaft. I don't see how LLMs can do that.
LLMs can't be a search engine, and can't be an question engine. The best way to treat it is a simulation engine, but the use cases depend on the training data. But the proof is there that the internet is full of junk, and not that expansive.
If it's in the training data, then it should be able to do that. That is to say, a comment's points matter. and the subreddit it's on. and who said it, and how the rest of their comments do/where they are. The LLM could annotate the unredacted reddit dataset with metadata as to where to rate it on the words used, the accuracy of the information, the sarcasm quotient, the hilarity quotient, how condescending the comment is; all of that an LLM could generate metadata about and feed into itself to get better and better.
for a lot of people and in a lot of use cases, it is a tool for answering questions. it generally works well for that.
i get that the AI implementation sucks, but to suggest that people don't use google to find the answer to questions is absurd. that's absolutely what it's for.
I saw this over and over again working at products at G, someone would invoke some myth I can't quite remember about "Larry" had a vision of just giving the answer
That's true but comes back to the central mistake Google makes: we don't actually have AGI, they can't actually answer questions, and people aren't actually satisfied with just the answer.
There's all sorts of tendrils from there, ex. a major sin here _has_ to be they're using a very crappy very cheap LLM.
But, I saw it over and over again, 7 years at Google, on every AI project I worked on or was adjacent to, except one. They all assume $LATEST_STACK can just give the perfect answer and users will be so happy. It can't, they don't actually want just the answer, and BigCo culture means you don't rock the boat and just keep moving forward.
Thus when a human sees a suggestion to use glue on pizza, it would question the result. While AI can't.
And nothing else. They didn't give me any search results. Or even tell me there weren't any results. Or even give me a button to press to say "no, I really wanted to search the internet for this phrase".
And also I have zero interest in Google's opinion on English grammar and am frankly insulted to be offered it, although to be fair I'm probably in a minority worldwide on that one.
If I can't use Google to search the internet for things, then Google is eventually going to have a big problem.
I ask Google questions so that it gives me websites where there are answers to those questions.
What foods are high in vitamin C? How long is a flight from New York to LA? How large is the moon? What are the symptoms of COVID-19?
2. A VP pontificates about how much work they did to "get it right"
3. An easy-to-anticipate first-order issue surfaces
4. Sundar issues a statement like "this is completely unacceptable. We will be making structural changes to ensure this never happens again."[0]
5. GOTO 1
[0] https://m.economictimes.com/tech/technology/sundar-pichai-ca...
Maybe this is the time to get out the old Encyclopedia Britannica CD and use that for training input.
Encyclopedias, textbooks, reputable journals, newspapers and magazines make sense.
But to throw in social media? Reddit? Seems insane.
I didn't say you could, but that a machine can't decode the mountains of text doesn't mean that the answer isn't (perhaps only) on Reddit. I don't think people would be that interested in search engine that just serves content from books and academic papers.
https://www.wheresyoured.at/the-men-who-killed-google/
Edit: there was also a discussion on HN about that article.
This is an attempt to reduce hallucinations coming full circle. A simple summarization model was meant to reduce hallucination risk, but now it's not discerning enough to exclude untruthful results from the summary.
Google did well in the old days for reasons. It beat alta vista and Yahoo by having better search results and a clean loading page. Since perhaps 08 (based on memory, that date might be off) or so, Google has dominated search, to the extent that it's not salient that search engines can be really questionable. Which is also to say, google dominated, people lost sight that searching and googling are different, that gives a lot of freedom for enshittification without people getting too upset or even quite realizing - it could be different and better
If they're doing surveillance they're not doing a good job of it, I make no effort to hide from them and approximately none of their ads are personalized to me. They are instead personalized to the search results instead of what they know from my history.
Meta is the one with highly personalized ads.
If simple keyword matching was enough, why would they spend literally billions a year on other tactics?
It does help with their Doubleclick business - ads on websites other than Google. I don't find these too personalized either, but they do try.
And of course, many people actually like that Chrome saves their browsing history.
Two reasons. The first, even ignoring that truth isn't necessarily widely agreed (is Donald Trump a raping fraud?), is that truth changes over time. eg is Donald Trump president? And presidents are the easiest case because we all know a fixed point in time when that is recalculated.
Second, Google's entire business model is built around spending nothing on content. Building clean pristinely labeled training sets is an extremely expensive thing to do at scale. Google has been in the business of stealing other people's data. Just one small example: if you produced (very expensive at scale) clean, multiple views, well lit photographs of your products for sale they would take those photos and show them on links to other people's stores; and if you didn't like that, they would kick you out of their shopping search. etc etc. Paying to produce content upends their business model. See eg the 5-10% profit margin well run news orgs have vs the 25% tech profit margin Google has even after all the money blown on moonshots.
What about simple manual testing? Seems to have skipped QA completely, automated or not.
But there is a more general problem: Big Tech is high on their own supply when it comes to LLMs, and AI generally. Microsoft and Google didn’t fact-check their AI even in high-profile public demos; that strongly suggests they sincerely believed it could answer “simple” factual questions with high reliability. Another example: I don’t think Sundar Pichai was lying when he said Gemini taught itself Sanskrit, I think he was given bad info and didn’t question it because motivated reasoning gives him no incentive to be skeptical.
You don't even need an LLM for that. Google will almost certainly have tested.
The test result is just politically-unacceptable within the company: It doesn't work, it's a architectural issue inherent to the technology, we can't fix it.
Instead, they just rush to patch any specific, individual errors that show up, and claim that these errors are "rare exceptions" or "never happened".
What's going on here is that Google (and most other AI firms) are just trying to gaslight the world about how error-prone AI is, because they're in too deep and can't accept the reality themselves.
On one hand, their support for outsourcing programmes; "Training Indians on how to use AI", suggests they realize AI tooling without human cleanup is a crapshoot.
On the other hand, they keep digging. This kind of gaslighting is an old and proven trick for genuinely rare problems, but it doesn't work if your issues are fairly common, as they'll get replicated before you can get a fix out.
Similarly, they're gambling with immense legal risks and sacrificing core products for it. They're betting the farm on AI, it may kill the company.
I regularly speak to laypeople who assume that it's some magical thing without limits that makes their lives better. They are also 100% unaware of any applications that will actually make their lives better. End game occurs when those two disconnected thoughts connect and they become disinterested. The power users and engineers who were on it a year ago are either burned out or finding the limitations a problem as well now. There is only magical thinking, lies and hope left.
Granted there are some viable applications but they are rather less overstated than anything we have no and there are even negative side effects of those (think image classification, which even if it works properly, requires human review and there are psychological and competence things problems around that too).
And in particular LLMs are less likely to generate these goofy prompts because they wouldn’t be in the training data.
Kudos to the artist at the Verge for the accompanying image — those are fingers AI would be proud of.
Who says the humans (all of them) aren't hallucinating too?
it's not a bug
It's a fundamental feature
These LLMs can produce nothing else but since the bullshit they spew resembles an answer and sometimes accidentally collide with one, people tend to think it can give answers. But no.
https://hachyderm.io/@inthehands/112006855076082650
> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.
> Alas, that does not remotely resemble how people are pitching this technology.
So LLMs distill human creativity as well as human knowledge, and it’s more useful when their creativity goes off the rails than when their knowledge does.
One of the researchers from Google Deepmind specifically said it was hallucinating.
In any case, human consciousness is also a hallucination.
LLMs are impressive, a very lossy search engine in a small package, capable of outputting convincing natural language responses.
Then, when people post these weird AI answers on Reddit and come up with more absurd jokes, the AI then picks it up again. For example in https://www.reddit.com/r/comedyheaven/comments/1cq4ieb/food_... Google AI suggested applum and bananum as a response to food names ending with "um" when someone suggested uranium, Copilot AI started copied that suggestion. It's entertaining to watch.
edit: And in that case, who is the arbiter of truth?
What concerns me is that Google would push this trash to the front page. What are they even thinking? Who gave go ahead on this?
That’s how it happens.
An L4 engineer at Google makes like $300k. round down to $200k post tax. live in shared housing for $2000/month; that's $24k/yr, add $2000/month living expenses on top of that, is another $24k. Stick $2000/month into 401k, Save the rest (200-24-24-24 ) 128k and you'll save up $140k in a bit over a year.
bam; owner, not renter.
Now, whether this is better than renting, and putting the money into the stock market is totally other question, but even L4's at Google can afford a mortgage in Mountain View.
https://www.zillow.com/homedetails/50-E-Middlefield-Rd-APT-4...
It's not even because it's sometimes (or often) wrong or full of hallucinations. Even if it's 100% factually correct all of the time, it's _poor quality writing and art_, full of cliches and bland generalities, which even if they solve all the rest of the problems it's sort of fundamental to the architecture of transformers. You can't ever be truly creative or unique if you're predicting the _most likely_ token.
Just checked the 5 year stock graph; now I understand
Problem is Google can't tell what is garbage or not. No LLM can.
Citation needed. I've been a Reddit user since its inception and honestly except for niche hobby subreddits, Reddit is mostly low effort garbage, bots and rehashed content. I'd wager that mainstream subreddits are 99% garbage for training an LLM for anything other than shitposting.
Except that Apple competes to make the best smartphone, and an iPhone was still valuable without Apple Maps.
What happens to Google if it stops being able to compete in search?
"Ads" is Google's product. And the only way they'll go bankrupt is if 1) companies realize that advertising is pointless (I'm not holding my breath), or 2) some other company takes over from Google, which seems unlikely without government intervention (I'm not holding my breath).
Google is a shit company, but they'll still be around 20 years from now, because our economy is nonsensical and irrational.
In any case this thing is just hilarious. Just right after their AI painted historical figures as black.
I don't see a way google can make this work. As I understand it LLM confabulations can be reduced but never eliminated owing to how they're built. Google could try and create a fact-checking department to make queries reduced to falsehoods or bullshit but then they face the problem of appointing themselves arbiters of the "truth". The only way to win is to not play the game, as I see it. I wish the collective AI fever would break already.
https://www.nytimes.com/2024/05/24/technology/google-ai-over...
> A correction was made on May 24, 2024: An earlier version of this article referred incorrectly to a Google result from the company’s new artificial-intelligence tool AI Overview. A social media commenter claimed that a result for a search on depression suggested jumping off the Golden Gate Bridge as a remedy. That result was faked, a Google spokeswoman said, and never appeared in real results.
that screenshot was tweeted by @allgarbled. ten minutes before, they tweeted:
>free engagement hack right now is to just inspect element on the google search AI thing and edit it to something dumb. hurry up, this deal won’t last forever
If Google is sending bad results, who can prove that?
Widely-used platforms that can +/- 1% their algorithms to affect democracy have pretty high burdens of trust/transparency, and we're not close to that with any platform (Chinese or not) that I'm aware of.
Meta's probably the closest, because of scrutiny, but afaik even their transparency isn't sufficient for realtime attestation.
Google has started restricting AI Overviews so much now that most of the example queries on Google's Search Labs page doesn't even trigger it anymore.
Before AI, before we had to append "reddit" to get useful human knowledge.
What the heck happened? Or was their reputation always just more hype than substance?
(The logic sort of makes sense. Glue sticks things together, and some glue is edible.)
The TL;DR on his take is that there are organizational and cultural issues that prevent Google from focusing their research efforts in the way that is necessary for what he calls "big swings," like training GPT-3.
In regards to your second question, Google's reputation in ML is definitely not hype. Purely on the research side, Google has been behind some of the most important papers in modern ML, particularly around language model. The original Transformers paper, BERT, lots of work around neural machine translation, all of the work that DeepMind has done post-acquisition, and the list goes on. On the applied side, they also have some of the most successful/widely-adopted ML-powered products on the market (think RankBrain/anything involving a recommendation engine, Translate, Maps, a ton of functionality in Gmail, etc).
Who knows, maybe AI can bring back exact keyword matches, or correct basic math calculations on Google Search too.
Companies spent all that money on high end GPUs for crypto mining and that went bust, now gotta figure out something to do with the hardware to try to recoup some of the investment. Google pumped $1.5 Billion into crypto.
Something innocuous to a non ai scientist human but is otherwise fatal to the LLM data sets.
Maybe they shall check what AI is, in the first place. They didn't seem to get the basics.
Knock it off.
Your core search result product has gotten increasingly worse and less reliable over at least the last 5 years. YouTube's search results are nearly unusable.
I can't imagine almost any external customer is asking for the AI bullshit thing that's just being shovelwared into everything Alphabet product now.
I just noticed a couple days ago the gmail iOS app now does the same predictive completion that Copilot tries to do when I'm working. It's annoying as hell and I can't find how or if I can turn it off.
Stop bullshitting around with ruining your products and get back to making money by making accessing information easier and more accurate.
[1]: https://www.businessinsider.com/alphabet-google-executive-pa...
Yeah I would still fact check for complex, indepth things...but for quick things where I'm knowledgeable enough I can smell the hallucinations from a mile away, ChatGPT 100%.
Straight up, and this going to sound really stupid I know, but if Sundar Pichai had just come out and said, “hey, we’re trying to do this new thing, it’s gonna be hard but we want to make Google awesome if you like the results click ‘I like it’ button, otherwise click the ‘dislike’ button so we can get some real feedback from people, but seriously, this stuff is hard, so please help us out.
If we can tune the AI so that we can give you the best results possible it will make Google way better! Also, if you don’t wanna see any of this, there’s a setting here to turn it off.”
Just ask people, show a little humanity, and act human and you’ll get better results and won’t be getting all this pad press. The same thing for openAI right now too.
Seriously, though, does anybody else crave authenticity? These companies are all acting like the AI that they’re trying to create, but from five years ago when it was shitty and didn’t know how to communicate with people. Just talk to people and ask for their help. Just being honest and talking to people normally isn’t that hard.
I got a couple of answers that are based on SEO spam produced by an ecommerce with a lot of reputation and of course the answers don't make any sense
https://www.theguardian.com/technology/2022/jun/12/google-en...
It's gotten worse since then because the development effort has been on making it faster and cheaper.
If you use Gemini it's quite good, especially the paid one.
With the Department of Justice breathing down their necks it's a doubly bad look for them. I'm not crying any tears for them though.
Then they enshittified it for short term profit and now they panic instead of reverting course and simply laugh at AI companies.
Madness.
So, yeah, the ai did in fact autocompleted the question correctly. It was just the wrong context. Good luck trying to "fix" that.
[1] https://shotkit.com/food-photography-secrets-revealed/ (number 2)
Since then the only difference between an Onion fiction and things actually sucking that much is a decade or less in almost all cases.
If we blacklisted content seen in the Onion, we'd automatically wipe out most news.