Gemini 2.5

Oh, that's a good one. And it's true. There seems to be a massive inability for most people to admit the building impact of modern AI development on society.

They certainly seem to have moved from "it is literally skynet" and "FSD is just around the corner" in 2016 to "look how well it paces my first lady Trump/Musk slashfic" in 2025. Truly world changing.

I've asked claude to explain what you meant... https://claude.ai/share/391160c5-d74d-47e9-a963-0c19a9c7489a

This is so on-point. Many things that we now take for granted from LLMs would have been considered sufficient evidence for AGI not all that long ago. Likely the only test of AGI is whether we can still come up with new goalpost.

Haha, so that's the first derivative of goalpost position. You could take the derivative of that to see if the rate of change is speeding up or slowing.

I love this comment.

> It's not really passing the Turing Test until it outsells Harry Potter.

Most human-written books don't do that, so that seems to be a ceiteria for a very different test that a Turing test.

From Gary Marcus' (notable AI skeptic) predictions of what AI won't do in 2027:

> With little or no human involvement, write Pulitzer-caliber books, fiction and non-fiction.

So, yeah. I know you made a joke, but you have the same issue as the Onion I guess.

Let me toss a grenade in here.

What if we didn’t measure success by sales, but impact to the industry (or society), or value to peoples’ lives?

Zooming out to AI broadly: what if we didn’t measure intelligence by (game-able, arguably meaningless) benchmarks, but real world use cases, adaptability, etc?

the goal posts will be moved again. Tons of people clamoring the book is stupid and vapid and only idiots bought the book. When ai starts taking over jobs which it already has you’ll get tons of idiots claiming the same thing.

Well, strictly speaking outselling the Harry Potter would fail the Turing test: the Turing test is about passing for human (in an adversarial setting), not to surpass humans.

Of course, this is just some pedantry.

I for one love that AI is progressing so quickly, that we _can_ move the goalposts like this.

To be fair, pacing as a big flaw of LLMs has been a constant complaint from writers for a long time.

There were popular writeups about this from the Deepseek-R1 era: https://www.tumblr.com/nostalgebraist/778041178124926976/hyd...

That could only obsolete fiction-writing if you take a very narrow, essentially commercial view of what fiction-writing is for.

I could build a machine that phones my mother and tells her I love her, but it wouldn't obsolete me doing it.

I didn't quite read parent's comment like that. I think it's more about how we keep moving the goalposts or, less cynically, how the models keep getting better and better.

I am amazed at the progress that we are _still_ making on an almost monthly basis. It is unbelievable. Mind-boggling, to be honest.

I am certain that the issue of pacing will be solved soon enough. I'd give 99% probability of it being solved in 3 years and 50% probability in 1.

> Lets not forget the OpenAI benchmarks saying 4.0 can do better at college exams and such than most students. Yet real world performance was laughable on real tasks.

That's a better criticism of college exams than the benchmarks and/or those exams likely have either the exact questions or very similar ones in the training data.

The list of things that LLMs do better than the average human tends to rest squarely in the "problems already solved by above average humans" realm.

From a technical standpoint, this is incredible. A few years ago, computers had problems creating grammatically correct sentences. Producing a consistent narrative like this was science fiction.

From an artistic standpoint, the result is... I'd say: incredibly mediocre, with some glaring errors in between. This does not mean that an average person could produce a similar chapter. Gemini can clearly produce better prose than the vast majority of people. However, the vast majority of people does not publish books. Gemini would have to be on par with the best professional writers, and it clearly isn't. Why would you read this when there is no shortage of great books out there? It's the same with music, movies, paintings, etc. There is more great art than you could ever consume in your lifetime. All LLMs/GenAI do in art is pollute everything with their incredible mediocrity. For art (and artists), these are sad times.

It's more nuanced than that. There are certain material/content where it is mandatory/necessary to read them.

Ideally I'd prefer to read material written by a the top 1%ile expert in that field, but due to constraints you almost always get to read material written by a midwit, intern, junior associate. In which case AI written content is much better especially as I can interrogate the material and match the top 1%ile quality.

Quality is its own property separate from its creator. If a machine writing something bothers you irrespective of quality then don't read it. You think i would care ? I would not.

If this ever gets good enough to write your next bestseller or award winner, i might not even share it and if i did, i wouldn't care if some stranger read it or not because it was created entirely for my pleasure.

But a future chatbot would be able to internally project manage itself through that process, of first emitting an outline, then producing draft chapters, then going back and critiquing itself and finally rewriting the whole thing.

It's not a problem of one-shotting it. It's that the details cause a collapse. Even if you tried breaking it down which i have, you'd run into the same problem unless you tried holding its hand for every single page and then - what's the point ? I want to read the story not co-author it.

Random question, if you don't care about being a creator yourself, why do you even want to read long form writing written by an LLM? There are literally 10000s of actual human written books out there all of them better than anything an LLM can write, why not read them?

That's a non-sequitur, they would be stupid to run ab expensive _L_LM for every search query. This post is not about Google Search being replaced by Gemini 2.5 and/or a chatbot.

The riddle has a different variants with hats https://erdos.sdslabs.co/problems/5

There's 3 toddlers on the floor. You ask them a hard mathematical question. One of the toddlers plays around pieces of paper on the ground and happens to raise one that has the right answer written on it.

- This kid is a genius! - you yell

- But wait, the kid has just picked an answer from the ground, it didn't actually come up...

- But the other toddlers could do it also but didn't!

I'm sure you're right that it's more than just it being in the training data, but that it's in the training data means that you can't draw any conclusions about general mathematical ability using just this as a benchmark, even if you substitute numbers.

There are lots of possible mechanisms by which this particular problem would become more prominent in the weights in a given round of training even if the model itself hasn't actually gotten any better at general reasoning. Here are a few:

* Random chance (these are still statistical machines after all)

* The problem resurfaced recently and shows up more often than it used to.

* The particular set of RLHF data chosen for this model draws out the weights associated with this problem in a way that wasn't true previously.

I think there’s a big push to train LLMs on maths problems - I used to get spammed on Reddit with ads for data tagging and annotation jobs.

Recently these have stopped and they’re now the ads are about becoming a maths tutor to AI.

Doesn’t seem like a role with long-term prospects.

Sure, but you can't cite this puzzle as proof that this model is "better than 95+% of the population at mathematical reasoning" when the method of solving (the "answer") it is online, and the model has surely seen it.

It gets it wrong when you give it 728. It claims (728, 182, 546). I won't share the answer so it won't appear in the next training set.

Have you given a reasoning model a novel problem and watched its chain of thought process?

I think it can reason. At least if it can work in a loop ("thinking"). It's just that this reasoning is far inferior to human reasoning, despite what some people hastily claim.

I would say that 99.99% of humans do the same. Most people never come up with anything novel.

Is there a reason for the downvotes here? We can see that having the answer in the training data doesn't help. If it's in there, what's that supposed to show?

I agree with your general point that this "random person" is probably not representative of anything close to an average person off the street, but I think the phrasing "very very intelligent" and "exceedingly clever" is kinda misleading.

In my experience, the difference between someone who solves this type of logic puzzle and someone who doesn't, has more to do with persistence and ability to maintain focus, rather than "intelligence" in terms of problem-solving ability per se. I've worked with college students helping them learn to solve these kinds of problems (eg. as part of pre-interview test prep), and in most cases, those who solve it and those who don't have the same rate of progress towards the solution as long as they're actively working at it. The difference comes in how quickly they get frustrated (at themselves mostly), decide they're not capable of solving it, and give up on working on it further.

I mention this because this frustration itself comes from a belief that the ability to solve these belongs some "exceedingly clever" people only, and not someone like them. So, this kind of thinking ends up being a vicious cycle that keeps them from working on their actual issues.

I solved it in less than 15 minutes while walking my dog, no pen or paper. But I wouldn't claim to be a random person without math skills. And my very first guess was correct.

It was a fun puzzle though and I'm surprised I didn't know it already. Thanks for sharing.

>Gemini also shows it's work and it seems to do a few orders of magnitude more calculating then the elegant answer given on reddit.

I don't think Gemini does an unnecessary amount of computation, it's just more verbose. This is typical of reasoning models, almost every step is necessary but many would not be written down by a human.

everyone with limited bandwidth has been trying to limit site access to robots. the latest generation of AI web scrapers are brutal and do not respect robots.txt

It’s here and it’s called discord.

Or we could just accept that LLMs can only output what we have put in and calling them, "AI" was a misnomer from day one.

2 and 3 saying they don't know eliminates some uncertainties 1 had about their own number (any combination where the other two would see numbers that could tell them their own). After those possibilities were eliminated, the 1st person has narrowed it down enough to actually know based on the numbers shown above the other 2. The puzzle could instead have been done in order 2, 3, 1 and 1 would not have needed to go twice.

I guess really the only missing information is that they have the exact same information you do, plus the numbers above their friends heads.

I think a competent 5yro could make a better pelican on a bicycle than that. Which to me feels like the hallmark of AI.

I mean, hell, I have drawings from when I was eight of leaves and they are botanically-accurate enough to still be used for plant identification, which itself is a very difficult task that people study decades for. I don't see why this is interesting or noteworthy, call me a neo-luddite if you must.

No that's GPT-4o native image output.

Vaild concern, most likely thinking tokens were not counted due to API reporting changes.

Ok, thanks for clearing that up.

100% agree with you.

But it needs to be seamless to remove any friction from the purchase, but at the same time if it feels the same then we felt like we wasted money.

So what I usually do is buy a different colored phone and change the wallpaper.

My MacBook was the same. Seamless transition and 2 hours later I was used to the new m4 speeds.

I would say they are a fairly good measure of how well the model has integrated information from pretraining.

They are not so good at measuring reasoning, out-of-domain performance, or creativity.

Reasoning was supposed to be that for "Open" AI, that's why they go to such lengths to hide the reasoning output. Look how that turned out.

Right now, in my opinion, OpenAI has actually a useful deep research feature which I've found nobody else matches. But there is no moat to be seen there.

Sooner or later someone is going to find the "secret sauce" that allows building a stepladder tall enough to reach the moon.

It's called the "first step fallacy", and AI hype believers continue to fall for it.

... which were also written by earlier Gemini versions.

> can you share a concrete example?

Upload a complicated PDF of presentation and ask for insights that require some critical thinking about them.

> Do you think these "moved entire workflows" offset a full worker, or X% of a full worker

It can replace many junior analysts IMO.

"Well, Ed, there are 300 million weekly users of ChatGPT. That surely proves that this is a very real industry!" https://www.wheresyoured.at/longcon/

If i'm not wrong, Chatgpt states clearly that they don't use user data anymore by default.

Also, maybe some services are doing "machine learning" training with user data, but it is the first time I see recent LLM service saying that you can feed your data to human reviewers at their will.

Impressive. I tend to think it managed to find the bug by itself which is pretty crazy without being able to debug anything. Then again I haven't seen the bug description, perhaps the description makes it super obvious where the problem lies.

I figured you don't update the major unless you significantly change the... algorithm, for lack of a better word. At least I assume something major changed between how they trained ChatGPT 3 vs GPT 4, other than amount of data. But maybe I'm wrong.

As I see it, if it uses a similar training approach and is expected to be better in every regard, then it's a minor release. Whereas when they have a new approach and where there might be some tradeoffs (e.g. longer runtime), it should be a major change. Or if it is very significantly different, then it should be considered an entirely differently named model.

Going from English to Chinese, I guess? Because that would not be a compatible version for most previous users.