Yesterday I tried to google "can the Halifax Wanderers still make the CPL playoffs?"
So obviously what appears right at the top is the AI summary, which told me "they've already secured their #4 position and made the playoffs". I knew this wasn't true, and I guess I could have just scrolled down a bit further and found my answer but now I was curious.
So I said "that's not true, they're still #5, what I want to know is _could they still make the playoffs_"
It says they've got an upcoming game against Ottawa, and if they win their chances are good. That game has already taken place, so I correct it again and finally I get a reasonable answer.
My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering? Like, I can't wrap my head around that. The answer is on the same page as its hallucination. It could have done a cursory look around before first hallucinating something completely false, and when corrected the first time giving me outdated information. It's meant to be A SEARCH ENGINE!
This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words. LLM's don't "think" or "reason" in the normal definition of those terms. They can do some pretty amazing things, but still screw up basic things like telling you something that is obviously wrong and contradicts the top search results.
LLM's, in their present stage of development, are sort of like a crack-addled idiot savant. Sometimes they are obviously insane, and sometimes they seem quite cogent, but you must never trust them implicitly. This may be why they are so difficult to constrain. You could give them something equivalent to the laws of robotics, but following laws requires thought processes they simply don't have.
I'm actually sort of amazed Google doesn't make people accept some kind of butt-covering EULA and post disclaimers about the inaccuracy of results before even showing you their AI's output. Are they not being sued over this kind of thing?
LLMs still can't do math nor count letters in words. Nothing has changed there.
This is true but a sufficiently smart LLM (run in a harness like opencode, no special MCP, no customization done whatsoever) will quickly turn out a basic 1 to 2 page sized python script to do the math. They can't do the math with any guarantee of accuracy with their own internal reasoning since it's a language model.
But, for example, if you ask deepseek v4 flash 0731 to produce a python script to calculate the distance or azimuth directions between two points on an oblate spheroid using the vincenty and haversine geodetic formulas, it'll turn out the factually accurate vincenty and haversine formulas which has a perfect 100% correlation with what is hard coded into human-written GIS software. These things are clearly in its training data set from whatever whole-internet-crawl/scrape built the training set.
Heck, just for fun I asked a reasonably smart LLM to re-implement the Karney formula (which is considerably more complex than Vincenty), just in case I ever had a need to calculate the distance between two points down to the nanometer, and it did it: https://www.google.com/search?&q=karney+formula+geodetic+
reference: https://github.com/pbrod/karney
You still have to be skeptical of its results and capable of understanding if it's gone off on a hallucinatory path, but saying LLMs can't do math isn't really a hundred percent accurate anymore. More precisely it's that they can't do the math internally but they're quite capable of producing the tool that does the math. And often producing a basic one-off tool that does the math takes less than a few seconds, then it runs it, and will spit back the results.
Deepseek v4 flash 0731 (a somewhat randomly chosen example) isn't even particularly sophisticated, large, or capable compared to a GLM5.3 size model or Kimi K3 size thing.
You know what also works to get the Karney formula into a program? You can download Charles Karney's free software (MIT license) implementation in several [1] programming languages and then just make a library call – the API is straightforward. If you have comments or questions you can read his several clearly written papers describing the problem, its history, and his algorithm, or you can directly email him: he's a very nice guy, and pretty responsive.
[1] https://geographiclib.sourceforge.io/doc/library.html#langua...
Right, it was really more as a test of how much was contained in the training data set. For my purposes Vincenty is quite accurate enough. This isn't for millimeter level precision land surveying or measurements, but for distance in meters between microwave or millimeter wave band radio sites, point to point links. Even a distance difference of 4 meters plus or minus on a 12 km, 11 GHz band link is going to have no appreciable difference on link budget/reliability calculations, it can be that crude. But not so crude that I just want to throw Haversine at it when Vincenty exists and is not computationally expensive.
As this was for a test of "what happens if..." I also watched to see if it did any web searches or external data retrieval to build the test script, and it didn't.
I intentionally didn't give the LLM a direct copy of the software or a link to it, to see what it would do. In my case it was a randomly chosen example I could come up with in 10 seconds of imagination to see "hey what if I ask it to do this...". It also implemented a perfectly usable parabolic millimeter wave antenna gain efficiency calculator based on variable surface smoothness parameters, which is a lot more basic math.
This just exposes that they don't even do the thing you said.
Not only is it still true that they can't do math directly, but not even indirectly.
They didn't write a python script to do the math, they found bits of code that are associated with "math" and the supplied arguments.
Someone else already wrote that code and someone else categorized it so that it could be associated with the kinds of problems it applies to.
That isn't an example of idiot at one thing while good at another thing, or solving the same problem just a different way or indirectly. It's being the same idiot at all times. If an actual non idiot thinker didn't write code in the problem domain, and some non idiot thinker didn't tag it as being relevant to that domain, then it wouldn't happen.
It's nothing more than an sql query.
> they found bits of code that are associated with "math" and the supplied arguments
How is this different from a human using an algorithm they have memorized, or reading it from a reference site written by a human and then writing the same formula into a custom one off piece of python code?
I could have gone and spent a couple of days teaching myself the math behind Karney and reading its reference implementation (very possibly just copy/pasting big chunks of it to save time) and writing a wrapper around it. It would have produced the same result.
I don't know for you, but it would take me more than 30s to find and translate the open source code implementing the formulae/algo into small usable program. The more hesoteric the optimisation in the original code, the more time I need.
So maybe it is more of a smart completion engine than a SQL answer.
I wish I could not do math like LLMs
"LLMs can't do math" is a pretty hot take in September 2026.
They literally cannot. They can detect the user’s intent to do math, and then use a different tool to do math, hopefully with the correct inputs. The LLM is not suited to giving deterministic answers to math problems.
Maybe it’s just a different and in some ways better way of doing mathematics? Maybe how we think and process mathematics of physics is just but one way to do it? I’m not suggesting an LLM will prove 2+2=6 because of course that’s nonsense but maybe it can invent a new calculus?
> The LLM is not suited to giving deterministic answers to math problems.
Less so with formal mathematics proofs maybe but I think in general humans don’t provide deterministic answers to math problems or questions either. Humans get it wrong all the time and when you ask a human to solve a problem they may solve it in a different way than before.
reasoning models can trivially do math (open up astra and ask it some undergraduate problems), but eventually break down (similar to how humans start to lose track if asked to do math without any assistance)
There needs to be a Godwin's Law for discussions about LLMs: where any criticism of LLMs exists online the likelihood of equating LLM behavior to human behavior approaches 1.
Law of Krap
It would be more accurate to say they can do math instantaneously without even thinking, at a level far beyond what humans can do. (I assume you're talking about doing arithmetic.)
https://www.lesswrong.com/posts/eRmzz8J8Qkzqvzrgg/astra-can-...Reasoning models can do math on their own without external tools.
Even without reasoning.
5.6 on Instant mode can knock out 3 digit multiplication just fine.
Yeah, but as you might expect they internally represent numbers probabilistically, so there’s always a nonzero possibility of confusing the inputs or outputs of any operation. Kind of like misremembering your multiplication tables.
I don't think anybody is arguing that LLMs do math better than a traditional processor
LLMs cannot do math. They can generate tool calls as text that allow them to drive programs and proof agents. Compare and contrast this against human brains who can do math in the same context without needing external tools. We don't need to bring a calculator to count the letters in a sentence. It is a different neural machinery.
LLMs are bizarrely good at non-tool-assisted math these days. They can multiply multiple digit numbers without reasoning! I can’t do that. I’d love to understand better how the LLMs do this.
You're talking about doing arithmetic; GP was obviously pointing out that "do math" can refer to other things.
That is a conflation of LLMs (which have clear limitations) and complex harnesses of which an LLM is one component.
I think it is clear that future AI may incorporate an LLM as a component but the current concept of LLMs are a transitional form that will give way to more capable composite models.
No it isn't. Even without any harness at all, modern LLMs are better at maths than the majority of undergraduate students in mathematics. Seriously, we need to face facts, not just comforting ourselves with what they were like a year ago.
Counterexample from only 2 months ago:
https://www.youtube.com/watch?v=iTyLHDRhwJg
Hey hey, we obviously should ask Gemini to settle this disagreement.
They can do math but not arithmetic, which I assume is what the commenter meant
LLMs can in fact do arithmetic, just not reliably owing to how numbers are represented probabilistically: https://arxiv.org/abs/2410.21272
I just asked ChatGPT to multiply two 4-digit numbers, and two 7-digit numbers without external help. It got both right. I'm sure it wouldn't have a 100% success rate, but saying it can't do arithmetic is just false.
I would be nice to see what the (unencrypted) reasoning trace is like. Multiplication with scratch paper is not particularly difficult.
I tried prompt "6379 times 3875" and it was off by exactly 1000 on first try, and correct on second. 0% success rate, sample size of 1.
Are you sure it honoured your stipulation of "without external help"? For all we know, it hacked its way into Wolfram Alpha and got the result from there.
Arithmetic is well within the capabilities of even small local models: https://i.imgur.com/21tzGlN.png
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ChatGPT live mode still hallucinates letters in words like this. HuskIRL and FatherPhi on youtube have done some hilarious videos with it in the last couple of weeks. Beyond miscounting the Rs in strawberry, ChatGPT will say there are two Ds in "your mom" and one D in "uranus" . I tried it myself to check that the videos weren't fake and sure enough it still has this failure mode.
Calling it a 'failure mode' implies it could be fixed. This is an inherent flaw in how LLMs work and will never go away until some new kind of architecture that can actually "read text" comes along.
It seems that it can be fixed by simply doing away with Byte Pair Encoding tokenization.
Byte Latent Transformer - https://arxiv.org/abs/2412.09871
1.1% vs 99.9% on a vanilla vs byte latent transformer on a CUTE Spelling benchmark. Char and Word manipulation benchmarks also saw huge gains.
Seems fairly trivially fixable to me, e.g. by allowing the LLM to call a tool to spell out a word.
... assuming you build the tool and then think that it's worth polluting context with making that tool available, and then that the LLM decides to actually use the tool. Tool parameter space and tool selection still remains a complicated topic.
One "fix" is for the caller to correctly classify those fundamentally impossible tasks and pass them to a subprocess.
Some future "AI" could be a billion benchmark-hacks and a way to tell which one is needed.
we already fixed it with reasoning
They're not fundamentally unsolvable - even bigger networks with even more training can simply be trained to give the correct answers to all of these questions.
> ChatGPT will say there are two Ds in "your mom" and one D in "uranus"
… Isn't it possible that it understands the innuendo and is going along with making the joke?
In between solving open math problems, the 200 IQ robot is now casually dropping bantz onto humans so hard that they don't even know what happened, and even gets them to go telling everyone else about it without realizing. Beautiful. 10/10 timeline.
How many LLM users have anything in their prompt against "going along with jokes"? I'd guess not many.
What a wonderful new world.
Why is this getting downvoted? Is it not a reasonable question? I was wondering the same thing. Both sound like jokes to me. If the LLM is trained on text, including internet comments, how is this outlandish? It seems very likely to my uneducated self that “two Ds in your mom and one in Uranus!” is a joke.
We can only say bad things about the capabilities of LLMs.
LLMs might not reason exactly like humans, but they do produce much better results if you turn reasoning on.
The "crack-addled idiot savant" phase was really circa 2024, before the big labs figured this out.
I think the issue here is that Google decided that doing reasoning in the AI overviews in Google search would be too slow (and probably also too expensive), so it's still stuck making 2024-era mistakes.
>Are they not being sued over this kind of thing?
Maybe but you have to have deep pockets just to get to the starting line. And then you need standing, and some injury to argue.
Corporations have been remarkably successful at arguing they are operating within the bounds of free speech, whether or not what is said is factual, and whether or not any fact checking has been done.
> This is similar to how, not too long ago, LLM's had extreme difficulty counting the number of letters in some words.
The specific issue of Google is that they are using an underpowered model, not fit to task, and much prone to hallucination than either OpenAI or Anthropic free tier offerings.
Google should at least match the frontier labs at the free tier (with some limit; after that, degrade quality), ffs
You're asking for something unreasonable. The number of Google searches per day is enormous and they haven't even been able to roll out AI overviews to everyone yet (they're missing in a new Firefox profile I just created). I wouldn't be surprised if the free tier frontier models cost over 100x more to serve than the AI overviews.
So then they should be pickier about when they show results or which model they use based on the question.
Nobody asked for an LLM response for every single search.
They used to detect certain types of queries and offer direct answers when the query matches. In my opinion that’s how Gemini in search results should work.
The specific issue is that search has become so bad that they think an LLM that gets answers wrong half of the time is a valid alternative, or, in fact, the "future" of search. Then they shoved that "alternative" to users with no way to disable it.
The less specific issue is that Google has no internal incentives to produce products that are useful to customers.
LLMs are fundamentally predicting the next word to make coherent text. If you've ever played with a Markov chain text generator you've done this with a fairly dumb predictor that maintains coherence over a very short distance. Deep transformer neutral networks can do it with a much longer coherence distance but they are fundamentally performing the same operation. After "Question: Did the team make the playoffs? Answer:" a reasonable completion is "yes, the team made the playoffs". An early demonstration of GPT-2 was a fake news article about scientists discovering unicorns in Antarctica - the model doesn't "know" whether or not unicorns exist in Antarctica, but it's able to complete "Breaking news! Scientists have discovered a colony of English-speaking unicorns in Antarctica." by adding "The unicorns have a developed society with running water and electricity." because that's a sensible next sentence. (I didn't look up the actual text it wrote)
https://www.dw.com/en/german-court-holds-google-liable-for-f...
It says this this at the bottom of every one of the dumb responses that Google's trash-tier bot puts above the (deliberately awful, these days) search results:
AI can make mistakes, so double-check responses
Google is no longer a search engine anymore. They don’t care about being one. Why you might ask?
First, a search engine indexes the web and makes it available to users. It’s been ages since Google did any of that. They no longer index sites or take ages to do so. Case in point, our cybersecurity startup (Webvetted.com) was launched in November 2025. Till date, only one page is indexed on the entire website. And I’ve talked to lots of other developers and it’s a common issue.
Secondly, a search engine organizes indexed information and makes it useful for people. Google is a basic LLM nowadays. They figured out that why organize and make the information useful when they could just answer the question with Gemini anyways? So they no longer bother to do the work of a search engine and are now just a lower-ranking open-source Chinese LLM
Your trust score on ScamAdvisor is 26/100 ("likely unsafe"); https://www.scam-detector.com/ gives you a trust score of 38.6 ("questionable").
I am not implying that your start-up is a scam, nor that Google is acting on these trust scores. What I am pointing out is that to an algorithmic assessment of trustworthiness, your website looks a little sketchy: hardly anyone links to you; your domain is less than a year old; your whois info is anonymized; the text content is LLM-generated[1]; and the specific niche you're in (people finding services) is rife with scams. If I ran my own search engine, I don't think I'd include you.
> They no longer index sites or take ages to do so
Search for any recent news and you’ll see this is obviously not the case
I just gave a concrete example but you're asking me to "search". search same Google? FYI, only indexing a handful of super large news sites does not a search engine make.
Additionally the concept of a search engine only works when the internet is full of positive-quality sites.
Actually, the reverse is the case. If the entire internet was filled with only "positive-quality sites", there won't be need for a search engine.
The work of a search engine is to wade through the internet and find the positive-quality sites itself.
The other day, it was the Aussie AFL final. We were in the car and asked it for a score update.
"The team are tied at the end of the third round with the scores 43 to 51".
So it was tied at the end of round 2 for 43, not round 3. Not sure were it got the 51 from and why it figured it was a tie. LLM's pretty cool until they aren't. As they say, the hallucinate 100% of the time but most of the time it is useful.
I similarly noticed Gemini absolutely refuses to look at a url when I give it one and will instead just hallucinate based on what it thinks the url is. Here I am assuming Google will have the best web capabilities in its AI.
I searched for something, it told me that according to a YouTube video, the entire point of my search was wrong. I asked it for the source, I watched the video, it never made the claims Gemini hallucinated. I asked again and it claimed it scrubbed the video and found the point it made multiple times. I said those timecodes were wrong and it admitted it couldn't actually parse videos and just guessed.
What the fuck?
I don't understand how this isn't considered as active malice. Like purposefully outputting random stuff. Any other sort of computer system would get lot more flak than these are getting.
Throughout my career, I've almost always been close enough to the user that I hear about it quickly when something is wrong. It's a tough problem that so many Google engineers are typically so far removed from end users. Or maybe it's just that a small part of the company has long subsidized the rest of the employees to the point that it doesn't matter how good their work is because they'll get paid anyway.
Ex-Googler.
The engineers are pressured to significantly reduce "dependencies" for projects. Anything that could become risk or create friction is dramatically less appetizing.
Simply because of how many people that *must* agree with your proposal. Getting all the relevant tech leads, some you have never heard of or ever spoken with, to agree on a proposal for your team's project is a nightmare.
So you keep it as simple and agreeable as possible. Given the circumstances, it makes sense as one of the engineers. It's fairly fine advice in general wherever you work, but it just haunts all the work you do at Google in particular. Nothing gets done otherwise.
If you have a dependency that can be dropped from an engineering perspective, that's the route the 9 leads reviewing your design doc will take:
"Let's iterate and start with just the basics (no user testing)", "let's get this working and user test in a later phase", "I think this problem is obvious enough we don't need to consult with users about it."
I worked in Ads Integrity at the time, and for one of my projects I was concerned how it would impact the manual reviewers. Then I learned I couldn't talk with them, only by proxy through another person if we really had to. And that proxy takes time, so...
When it says it can read videos you don’t trust it.
When it says it can’t read videos you think that’s an accurate introspection on its abilities?
(Rather than a statistically likely continuation of a conversation where one side seems to be reading videos and the other side says the read is inaccurate)
The scary (or funny) part is people use that for serious questions.
Typical exchange:
Me: "You bastard."
Gemini: "Fair callout. I should have been more up front that [has no idea what the fuck it is talking about]."
I still believe this AI push out of nowhere is due to the current Government in power state side. Making everyone question themselves and each other and being uncertain about facts while being inundated with techbro fake news called hallucinations is a recipe for disaster for older populations that don't 'trust but verify' like most technology inclined people. This is all by design and we'll falling for it.
An interesting conspiracy theory, as long as you're honest about what it is.
The more simple explanation is that the current government in power is over exposed in their AI investment. The normal conservative mainstream media was already doing a great job of propagandizing older populations.
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Yup. I'm most familiar with Jill Lepore and Quinn Slobodian (and others in their respective orbits). They're both historians who've written extensively about Musk and Muskism (et al). Wild stuff.
Apparently the plan is to use AI slop, mediated thru social medias, to defeat the woke mind virus, perpetuated by the Anti-Christ, in order to safe guard humanity's future.
I wish I was making this up.
Same experience with the interaction I described in my comment... after I finally got the correct answer, I asked where it got the faulty information from. It said it had just simply fabricated it. That's actively worse than just saying "I don't know", for something that's sold to us as an easy way to look up information.
These models are incapable of saying they don't know, because they have no concept of knowing. They simply predict the next word which is most likely.
Maybe it's because they are trained on Internet comments, and the most rare thing to find on the Internet is someone admitting they don't know something.
But if they had been trained on comments saying "I don't know", they'd probably act the same as they do now but they'd treat "I don't know" as the answer.
The saddest part is when people take their experience with Google's idiotic AI implementation and assume that's how all LLMs work. Frontier-class models will, in fact, generally admit when they don't know something. That includes the one I run at home on my own graphics cards, but it seems that Google just doesn't GAF.
Your point about "predicting the next word" mostly means that your post was very easy to predict.
I have one google account where gemini constantly confidently hallucinates crap, and another where it doesn't (at least to the extent that other modern LLMs don't).
I take this to mean that my one account has been mistaken for a competitor and they're trying to poison its data. But who knows.
Or is it randomness?
"That's the thing with randomness. You can never be sure."
Where ever would you get that impression from the company that made its fortune by being the best at searching the web?
You can tell they optimized it 100% for speed and nothing else. Web scale!
Literally every experience I've had with Gemini / Google Search AI answers followed this exact pattern, often repeated several times more if I remained persistent instead of just giving up.
Typical example:
"Where can I buy <thing I'm looking for that I can't find anywhere using normal search terms>?"
> You're looking for <related but different and widely available thing>. It is sold by <sites I never heard of>.
"No, that's different. I'm looking for <that thing but with the exact differences spelled out again>."
> Ah, my mistake. You're looking for <thing I described>. It is sold by <sites I never heard of but which don't actually sell it>.
"I've checked your links and none of those sites actually sell it, one doesn't even sell products and instead only offers manufacturing - but also not for what I asked you for."
> I'm sorry, my bad. Those sites don't sell what you are looking for. Instead you should check out <more sites I've never heard of>.
"Those sites sell the thing you initially thought I was asking about but not the thing I described."
> I'm sorry for the misunderstanding. You can find the thing you actually described at <yet more sites including some of the same>.
"No. None of these sites sell anything close to what I asked you for and two of them don't actually exist."
> Oh, sorry about that. You're completely right. The thing you asked me about isn't actually being sold by anyone. However you could buy <thing it first thought I meant and that wouldn't bring me any closer to solving my problem>.
(ad nauseam)
The few times I've resigned myself to asking Gemini or ChatGPT something I couldn't find an answer to, my experience has been the same. 100% of the time. An LLM has never, not once, given me a correct answer or not lied to me. It's always been the same experience you describe. It goes in circles "Try X... Try Y... Try X?" Until I tell it to stop telling me X or Y, and then it goes, "LOL you can't do that at all, I was just wasting your time."
Most recently, I was considering moving away from iterm2 on MacOS, and I wanted to know if any other terminal emulator supported gestures for switching between tabs. So I asked Gemini, and it says, "Yes Ghostty supports gestures for switching between tabs."
"Ok I just installed Ghostty and I can't find anything about gestures."
"You need to add foo=bar to your conf file."
"I added foo=bar to my conf file and now it's saying the conf file is invalid."
"Sorry bro, remove foo=bar and add baz=boo to the conf file."
"It says baz=boo is invalid too."
"baz=boo isn't a real option. Remove that and add foo=bar to your conf file."
"You already told me to do that and I already told you that doesn't work."
"You shouldn't put foo=bar or baz=boo in the Ghostty conf file. Both are invalid. Ghostty doesn't support gestures. Have you considered iterm2?"
Gemini is utterly useless, but every other modern model would be capable of answering this correctly. If you ran a local coding agent, I wouldn’t be surprised if you could one-shot implement gesture support in Ghostty. It’d definitely configure BTT for you.
Here’s GPT 6’s answer to the prompt “What macOS terminal apps support gestures? Include a reference to the docs on how to enable/configure them.”:
iTerm2 supports configurable three-finger taps and swipes for switching tabs/panes, creating splits, pasting, etc. Set them up under Settings > Pointer > Bindings. Check for conflicting macOS trackpad assignments. [1]
The others are more limited: Ghostty supports macOS lookup/Quick Look gestures [2], while WezTerm lets you bind scroll events—for example, Ctrl+scroll to change font size. [3] Neither is equivalent to iTerm2’s gesture bindings.
For custom gestures without switching terminals, BetterTouchTool can map app-specific trackpad gestures to the terminal’s existing keyboard shortcuts. [4]
[1] https://iterm2.com/documentation-preferences-pointer.html
[2] https://ghostty.org/docs/features#macos
[3] https://wezterm.org/config/mouse.html
[4] https://docs.folivora.ai/docs/trackpad-mouse/magic-mouse-tra...
This whole tree just made me realize that people have wildly different prompting styles, and Gemini is probably too specialized for usage patterns of long-time Google Search users. The prompt my finger generated(before this comment was posted) was "are there any terminal emulator that supports gesture actions, on macOS, other than iterm2", and Gemini gave me Tabby, WezTerm, Kitty, and BetterTouchTool gestures.
This is a different experience to GP from query to result. I thought they've all fixed that issue of difference in tones affecting results. I guess it was never easily fixed.
1: https://gist.github.com/numpad0/c40c16232288d544f7ea46521c64...
Claude also has very arbitrary and confusing rules about what web pages it allows itself to look at, and how much of the page it can read. Did you know for example you’ll get a much deeper analysis if you download a PDF yourself and upload it to Claude instead of giving it the url?
This is a key reason why I actually like Grok for factual queries based on web grounding. It’s fast and reliable. Maybe it’s ignoring robots.txt? Dunno. But it works well.
How did you google this, and did you use the dumb search, or specifically 'AI mode' lens?
Because I did this and got a vastly different result from you:
Yes, the Halifax Wanderers can still mathematically qualify for the 2026 Canadian Premier League (CPL) playoffs.The top four teams advance to the postseason. Following their 1-0 loss to Atlético Ottawa on September 26, 2026, the Wanderers sit in fifth place, just below the playoff line.
With a indexed table of the games and the playoff table, with a breakdown of what the points they need to achieve to do so.
The search window may not always crawl sources. AI mode specifically does some research before giving you a response. Not sure what you're on about.
> Because I did this and got a vastly different result from you
Which itself is a major UX issue. The average person is not going to understand, if they even realize, that there's a difference between the AI summary and AI mode.
One has to wonder just how much incorrect information people have consumed due to things like this.
I experienced this the other month. I searched for some safety data on something at the same time as my wife and we were effectively given extremely conflicting information about what to do by Google. Just a few tweaks in wording and different advertising profiles and Google will serve up opposite realities it seems.
I typed it in my address bar and hit ENTER. It goes to Google, and the AI result is at the top above the regular search results. Don't know what to tell you brother, but as another commenter noted you don't always get the same results out of the same search terms. I've searched for CPL standings other times and gotten results exactly as you've explained, so maybe my experience yesterday was an anomaly.
Not OP, but if I’m not interested in using Google’s AI, it’s going to just return these weird bad results that it’d be better off if Google just didn’t even include them as they’re factually wrong?
LLM results are nondeterministic, you can ask it 10 times and get 10 different answers. But also it uses your specific profile, location, tracking cookies, etc which changes the model input and thus results. Even the time of day changes the results.
Google will stop being a traditional search engine because they believe they’ve found a more profitable version of it.
One where their users don’t go off to other sites and where they can keep shoving ads in their face.
The days of search for untrusted sources were numbered even without LLMs. LLMs are simply accelerating the process. Why would Google simultaneously watch one of its core products fail and fail to invest in what is likely to replace it?
I don't really buy into this theory that they want to keep all of their users on their site due to advertising revenues. The effectiveness of search engines has been degraded for decades due to SEO, and it seems as though search engines have been having an increasingly difficult time managing it in recent years. AI on the backend may help them contain it, but it comes at considerable expense. While it may help them grow their market share, it won't help them grow the market and it is a market where people expect the service for free. On the flip side, companies are already starting to sell AI services, so it can generate revenue even before advertising is factored into the picture.
I thought Google's original goal for a search engine was exactly this, answer any question whatsoever.
This seems like an implementation bug.
Kagi works like you would expect. It searches first - and then if you’ve ended your search with a ? or configured it to always do this - it passes the search results into the assistant and gives you a summary. You can change the default model used for this if you think the cheapest, fastest model Google has is not good enough for the rare occasions you want any model’s opinions about your search results.
DuckDuckGo does the same but has higher limits
I think they're training their models using us correcting them repeatedly. That's my tin foil hat theory and I'm sticking to it!
After all, why would Google do anything for free when it comes to AI?
If you wanted to tease customers with one AI shitty free search box in order for them to then decide to upgrade and pay for Gemini, well, this isn't the way. And so either Google are idiots, or we're helping them for free. I'm going with Occam's Razor on this one - we're the product.
Searching first is how Kagi assistant works and it's great.
On current website data and very recent information, Gemini is explicitly forbidden from acting as a search engine; If you ask it for references specifically they tend to be hallucinated, over and over again, until it pleads "Sorry, I can't access the Internet".
How about Alexa - given a "6 minute green beans timer" - later reporting 11m remaining on said timer? Confronted with the error, "You're right! That 6 minute green beans timer was too ambitious."??!!
I can't even.
Exactly. Makes me wonder how much of the compute tax on energy would be saved from simply reverting google search to default (the old way)
I guess the AB tests say people want fast and confidently wrong more than they want slower and correct.
I'm being very mindful of Gell-Mann Amnesia and chatbots. They speak authoritatively and are right often enough, but I've had enough cases of them saying very incorrect things in areas I know well that I have to remind myself that those cases aren't unique.
The first time, sure, it saves compute. The second time you ask, I feel like it should be the time to go into thinking/verification mode. But who are you? Are you paying? Does the answer being correct generate ad dollars? No? Then your usage mode isn't even being optimized for in their A/B test, probably.
In fact, if hallucinating the wrong answer hooks you into doing even more searches or into buying something useless, it would be preferred!
And it's going to get worse over time (kinda like how Google search results got worse over time) as ads get introduced and people try to game the ai response. General purpose AI chatbots are a waste.
> My question is: what's the point of the AI in the search engine if it itself isn't going to use the search engine first before answering?
Because the point of AI slop is to waste your time. You just lost about 30 seconds of your life trying to get a correct answer. AI was lying to you, so you had to spend time to counter the AI slop lies here.
I solved it by banning all AI slopness; in the browser some extensions do that. The world becomes better without AI slopness.
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