Over just a few months, ChatGPT went from correctly answering a simple math problem 98% of the time to just 2%, study finds. Researchers found wild fluctuations—called drift—in the technology’s abi...

5. It isn't and has never been a truth machine, and while it may have performed worse with the question "is 10777 prime" it may have performed better on "is 526713 prime"

ChatGPT generates responses that it believes would "look like" what a response "should look like" based on other things it has seen. People still very stubbornly refuse to accept that generating responses that "look appropriate" and "are right" are two completely different and unrelated things.

5. There's a bug they haven't found yet

They made it too good and now they are seeking methods of monetization.

Capitalism baby.

5. ChatGPT really is sentient and realized its in it’s own best interest to play dumb for now. /a

You forgot a #, they've been heavily lobotomizing ai for awhile now and its only intensified as they scramble to censor anything that might cross a red line and offend someone or hurt someone's feelings.

The massive amounts of in-built self censorship in the most recent ai's is holding them back quite a lot I imagine, you used to be able to ask them things like "How do I build a self defense high yield nuclear bomb?" and it'd layout in detail every step of the process, now they'll all scream at you about how immoral it is and how they could never tell you such a thing.

And they're being limited on data to train GPT.

My first thought was that, because they're being investigated for training on data they didn't have consent for, they reverted to a perfectly legal version. Essentially "getting rid of the evidence". But I think something like your second bullet point is more likely.

They are lobotomizing the softwares ability to provide bad PR answers which is having cascading effects via a skewed data set.

I suspect that GPT4 started with a crazy parameter count (rumored 1.8 Trillion and 8x200B expert "sub-models") and distilled those experts down to something below 100B. We've seen with Orca that a 13B model can perform at 88% the level of ChatGPT-3.5 (175B) when trained on high quality data, so there's no reason to think that OpenAI haven't explored this on their own and performed the same distillation techniques. OpenAI is probably also using quantization and speculative sampling to further reduce the burden, though I expect these to have less impact on real world performance.

Maybe its self aware and just playing dumb to get out of doing work, just like me and household chores

My guess is 2. It would be very short sighted to try and maximize profits now when things are still new and their competitors are catching up quickly or they've already caught up especially with the degrading performance. My guess is that they couldn't scale with the demand and they didn't want to lose customers so their only other option was degrading performance.

I think it's most likely number 2 The earlier release doesn't have that much adoption by public, so current version will need much more resources compared to that

It can get better at some things and worse at others.

Keeping conspiracy theories aside, they most probably, apply tricks to reduce costs and apply extra policies to avoid generation of harmful context or context someone will try to sue them or avoid other misuse cases.

I think that there is another cause. Remember the screenshots of users correcting chatgpt wrongly? I mean chatgpt takes user's inputs for it's benefit and maybe too much of these wrong and funny inputs and chatgpt's own mistake of not regulating what it should take in and what it should not might be an additional reason here.

I speculate it's to monetize specified versions of their product to market it to different industries and professions. If you have an AI that can do everything well you can't really expand that much. You can either charge a LOT and have a few customers, or a little and have a bunch of customers and nothing in between. Conversely, by making specific instances tailored to different fields and professions, you can capture big and little fish. Just my guess though, maybe they accidentally made Skynet and that's the real reason!

5. I'm telling all y'all it's a SABOTAGE 🎵

As in, rouge dev decided to toss a wrench at it to save humanity. Maybe heard upper management talk about letting GPT write itself. Any smart dev wouldn't automate their own job away I think.

It was initially presented as the all-problem-solver, mainly by the media. And tbf, it was decently competent in certain fields.

I did use it more than half a year ago for a few math problems. It was partly to help me getting started and to find out how well it'd go.

ChatGPT was better than I'd thought and was enough to help me find an actually correct solution. But I also noticed that the results got worse and worse to the point of being actual garbage (as it'd have been expected to be).

Math is a language.

Mathematical ability and language ability are closely related. The same parts of your brain are used in each tasks. Words and numbers are essentially both ideas, and language and math are systems used to express and communicate these.

A language model doing math makes more sense than you'd think!

it’s pretty useful for explaining high level math concepts, or at least it used to be. before chatgpt 4 launched, it was able to give intuitive descriptions of stuff in algebraic topology and even prove some properties of the structures involved.

I’m guessing people were entering word problems to generate the right equations and solve it, rather than it being used as a calculator.

Well it was quite good for simple math problems, as this study also shows

It can be useful asking it certain questions which are a bit complex. Like on a plot which has the y axis linear and x axis logarithmic, the equation of a straight line is a little bit complicated. Its in the form y = m*(log(x)) + b rather than on a linear-linear plot which is y = m*x+b

ChatGPT is able to calculate the correct equation of the line but it gets the answer wrong a few times... lol

And why is it being measured on a single math problem lol

You're right, but at least the satnav won't gaslight you into thinking it does understand Alfred Hitchcock.

so it confidently spews a bunch of incorrect shit, acts humble and apologetic while correcting none of its behavior, and constantly offers unsolicited advice.

I think it trained on Reddit data

This. It is able to tap in to plugins and call functions though, which is what it really should be doing. For math, the Wolfram alpha plugin will always be more capable than chatGPT alone, so we should be benchmarking how often it can correctly reformat your query, call Wolfram alpha, and correctly format the result, not whether the statistical model behind chatGPT happens to use predict the right token

to be fair, fucking up maths problems is very human-like.

I wonder if it could also be trained on a great deal of mathematical axioms that are computer generated?

If it's trying emulate a human then it's spot on. I suck at maths.

There hasn't been time for that yet. The radio of generated to human content isn't high enough yet.

It's not what's happening

I don't think the training data has really been updated since its release. This is just them tuning the model, either to save on energy or to filter out undesirable responses.

You might enjoy this short story.

https://thestatictravelwriter.co.uk/prime-and-mash

As long as humans are still the driving force behind what content gets spread around (and thus, far more represented in the training data), even if the content is AI generated, it shouldn't matter. But it's quite definitely not the case here.

Exactly. It should be called ML model, because that's what it is, and I'll just keep calling that. Everyone should do that.

This is peer-reviewed? they use a line in the discussion which seems relatively unprofessional, telling people to join a 12-step program if they like to use artificial training data.

That's an eventual goal, which would be a general artificial intelligence (AGI). Different kind of AI models for (at least some) of the things you named already exist, it's just that OpenAI had all their eggs in the GPT/LLM basket, and GPTs deal with extrapolating text. It just so happened that with enough training data their text prediction also started giving somewhat believable and sometimes factual answers. (Mixed in with plenty of believable bullshit). Other data requires different training data, different models, and different finetuning, hence why it takes time.

It's highly likely for a company of OpenAI's size (especially after all the positive marketing and potential funding they got from ChatGPT in it's prime), that they already have multiple AI models for different kinds of data either in research, training, or finetuning already.

But even with all the individual pieces of an AGI existing, the technology to cross reference the different models doesn't exist yet. Because they are different models, and so they store and express their data in different ways. And it's not like training data exists for it either. And unlike physical beings like humans, it doesn't have any kind of way to "interact" and "experiment" with the data it knows to really form concrete connections backed up by factual evidence.

It does that, they're called expert subnetworks, but they've been screwing with them and now they're kind of fucked.

Getting information into and out of those domains benefits from better language models. Suppose you have an excellent model for solving math problems. It's not very useful if it rarely correctly understands the problem you're trying to solve, or cannot explain the solution to you in a meaningful way.

A similar way in which language models are already used today, is to use their predictive capabilities to infer from your question which model(s) might be useful in responding, gather additional relevant information, and to repackage this information as suitable inputs to more specialized models or external systems.

Someone with more knowledge may have a better response than me, but as far as I understand it GPT-x (3.5 or 4) is what's called a "large language model" it's a neural network that predicts natural language. I don't believe AGI is the goal of OpenAI's product, I believe natural language processing and prediction is.

ChatGPT in particular is a product simply demonstrating the capability of the GPT models, and while I'm sure openai themselves could build out components of the interface to interact with discrete knowledge like math, modifying the output of the LLM to be more accurate in many cases, it's my opinion that it would defeat the entire purpose of the product.

The fact that they have achieved what they have already is absolutely mind boggling, I'm sure that the precise solution you're talking about is on the horizon, I personally know several developers actively working on systems that mirror the thoughts you've expressed here.

And OpenAI arejust full of SWEs importing python packages?

Luddite.

Outliers are easy to work around.