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…::ChatGPT went from answering a simple math correctly 98% of the time to just 2%, over the course of a few months.
It just occurred to me that one could purposely seed it with incorrect information to break its usefulness. I’m anti-AI so I would gladly do this. I might try it myself.
Turns out you need very good computer scientists to make good AI. And those are very expensive and hard to come by.
OpenAI actually has some decent people working there. ChatGPT doesn’t seem to have any.
At the start I used to use ChatGPT to help me write really rote and boring code but now it’s not even useful for that. Half the stuff it sends me (very basic functions) LOOK correct but don’t return the correct values or the parameters are completely wrong or something absolutely critical.
idk what you guys mean but GitHub copilot still works absolutely well, the suggestions are fast and precise, with little Tweeks here and there… and gpt4 with code interpreter are absolute game changers … idk about basic chatgpt 3.5 turbo though
Github Copilot is a bit different, it’s powered by OpenAI Codex which is trained on all public repos. And yes, it’s quite effective!
It seems rather suspicious how much ChatGPT has deteorated. Like with all software, they can roll back the previous, better versions of it, right? Here is my list of what I personally think is happening:
- They are doing it on purpose to maximise profits from upcoming releases of ChatGPT.
- They realized that the required computational power is too immense and trying to make it more efficient at the cost of being accurate.
- They got actually scared of it’s capabilities and decided to backtrack in order to make proper evaluations of the impact it can make.
- All of the above
This is what was addressed at the start of the comment, you can just roll back to a previous version. It’s heavily ingrained in CS to keep every single version of your software forever.
I don’t think it’s that easy. These are vLLMs that feed back on themselves to produce “better” results. These models don’t have single point release cycles. It’s a constantly evolving blob of memory and storage orchestrated across a vast number of disk arrays and cabinets of hardware.
[e]I am wrong the models are version controlled and do have releases.
Yeah, but the trained model is already there, you need additional data for further training and newer versions. OpenAI even makes a point that ChatGPT doesn’t have direct access to the internet for information and has been trained on data available up until 2021
- 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.
In order for it to be correct, it would need humans employees to fact check it, which defeats its purpose.
It really depends on the domain. Asking an AI to do anything that relies on a rigorous definition of correctness (math, coding, etc) then the kinds of model that chatGPT just isn’t great for that kinda thing.
More “traditional” methods of language processing can handle some of these questions much better. Wolfram Alpha comes to mind. You could ask these questions plain text and you actually CAN be very certain of the correctness of the results.
I expect that an NLP that can extract and classify assertions within a text, and then feed those assertions into better “Oracle” systems like Wolfram Alpha (for math) could be used to kinda “fact check” things that systems like chatGPT spit out.
Like, it’s cool fucking tech. I’m super excited about it. It solves pretty impressively and effiently a really hard problem of “how do I make something that SOUNDS good against an infinitely variable set of prompts?” What it is, is super fucking cool.
Considering how VC is flocking to anything even remotely related to chatGPT-ish things, I’m sure it won’t be long before we see companies able to build “correctness” layers around systems like chatGPT using alternative techniques which actually do have the capacity to qualify assertions being made.
That’s not necessarily true: https://arstechnica.com/google/2023/06/googles-bard-ai-can-now-write-and-execute-code-to-answer-a-question/. If the question gets interpreted correctly and it manages to write working code to answer it, it could correctly answer questions that it has never seen before.
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 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!
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.
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.
“Don’t use the N word.” is hardly a rule that will break basic math calculations.
Perhaps not, but who knows what kind of spaghetti code cascading effect purposely limiting and censoring massive amounts of sensitive topics could have upon other seemingly completely un-related topics such as math.
For example, what if it’s trained to recognize someone slipping “N” as a dog whistle for the Horrific and Forbidden N-word, and the letter N is used as a variable in some math equation?
I’m not an expert in the field and only have rudimentary programming knowledge and maybe a few hours worth of research into the topic of ai in general but I definitely think its a possibility.
They are lobotomizing the softwares ability to provide bad PR answers which is having cascading effects via a skewed data set.
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.
You wildly overestimate the competency of management and the capital owners they answer to.
I guarantee a significant % of entities will grow dependent on AI well before it’s dependable. The profit motive will be too high (source: the frequent failure that is outsourcing).
This is spot on. Source: 10+ years at F500 companies.
Senior management and/or board members read one article in Forbes, or some other “business” publication, and think that they know everything they need to know about an emerging technology. Risk management is either a ☑ exercise or extremely limited in scope, usually only including threats that have already been observed and addressed in the past.
Not enough people understand the limitations of this kind of tech, and contextualize it in the same frame as outsourcing because as long as the output mostly looks correct, the decision makers can push the blame for any issues down to the middle managers and below.
Gonna be a wild time!
I don’t understand why anyone even considers that. It’s a toy. A novelty, a thing you mess with when you’re bored and want to see how Hank Hill would explain the plot of Full Metal Alchemist, not something you would entrust anything significant to.
These models are black boxes right now, but presumably we could open it up and look inside to see each and every function the model is running to produce the output. If we are then able to see what it is actually doing and fix things up so we can mathematically verify what it does will be correct, I think we would be able to use it for mission critical applications. I think a more advanced LLM likes this would be great for automatically managing systems and to do science+math research.
But yeah. For right now these things are mainly just toys for SUSSY roleplays, basic customer service, and generating boiler plate code. A verifiable LLM is still probably 2-4 years away.
The problem is if you open it up, you just get trillions of numbers. We know what each function does, it takes a set of numbers between -1 and 1 that other nodes passed it, adds them up, checks if the sum is above or below a set threshold, and passes one number to the next nodes if it’s above and one if it’s below, some nodes toss in a bit of random variance to shake things up. The black box part is the fact that there are trillions of these numbers and they have no meaning individually.
