I have farmed out work to Turks and tried to "go native" as a Turk and found I couldn't find HITs I could bear to do.
It used to be there were a lot of HITs that involved OCRing receipts but these were not receipts that were straightforward to OCR, they were receipts that failed the happy pass and that I thought there was no way I could transcribe them accurately in a reasonable amount of time considering what it paid.
We could just as well ask, why are you false flagging as someone who doesn't understand that it's making a point? That's what sarcasm does.
An off topic and therefore unwelcome point, to be sure. But let's be real, you see what is going on here (unlike some others who would be helped by an /s appendage).
What point is it making if it gets downvoted into oblivion by both people who don't get the point and take it sincerely, and people who do get the point and roll their eyes at it?
"Transcribing" is a better word or maybe "manual OCR".
The ones they sent to AMT were just awful, I would say 2/3 of them were impossible to transcribe with complete accuracy and would take a lot of time to do it, I'd be afraid of getting kicked out for making mistakes on them.
The hardest problem I had when I ran a lot of HITs were people that I called "Superturks", generally these people were very fast but the quality of the work was as low as they could get away with. If I kicked them out I could raise the quality of the work but it would not get done so quickly. There's the possibility of coaching them to do just a little bit better (would be happy to pay a bonus) but it is no so simple to do in that context.
That's why "I couldn't find any HITs I could stand to do."
Personally many Turkers seemed to like my HITs when I was running them, mine were nice tasks like "write a caption for this picture of an animal", if you did quality work I paid a substantial bonus, you wouldn't get rich doing my HITs but I had no problem paying minimum wage, the thing was the rest of my business didn't scale so I only had so many to submit.
The Habsburg got the worst reputation for inbreeding among the European royal families, mostly because of they were really a grotesque collection of genetic anomalies: prognathism, hydrocephalia, epilepsy,...
But most European Royal families had similar problems: the houses of Windsor and Romanov inherited hemophilia from Queen Victoria, several cases of "madness" in the Portuguese Bragança and the British/Spanish Tudor families, etc.
So in about three years there's going to be a really fantastic TV show, goes on for five or six seasons, but gets canceled without a satisfying ending.
They're re-running our universe in simulation to try to generate a good finale for it.
In some outer reality, a kid is pointing at the screen laughing, "ha ha, look at those stupid sims, I made Donald Trump president and they still think it's real."
It is sort of like you hired some one to mow your lawn as cheaply as possible and then complaining they used a riding mower instead of clipping it with a set of sheers.
When a company does it it's called an efficiency win or responsible stewardship of resources. When an individual does it it's a moral failing worthy of termination
Perhaps we’re starting to see the limits of the machine learning era in AI. We may have nothing more to teach it. There are a number of ways to go from here, none of which is entirely within our control or understanding.
The industry needs to change the current acronym to HPML. Human Powered Machine Learning. The flaw is inherent in the approach. We are running out of things to offer it.
Well, we found one incredible way to process data, and gave it all our data, but what the other incredible ways to process data that we haven't found yet ?
The end goal isn’t a system that knows everything. I’d argue a system that can learn from context but has very little knowledge about the world would be much more interesting than our current LLMs.
GPTs have a small spark of higher level reasoning. Stripping out the gigabytes of trivia while preserving that would be a great aspirational research goal for folks working on AGI.
I am sure we can create an AI solution to fix this. We just need to label this AI-labeled input data of labels for other data. To be sure we are one step ahead of turk-gig workers with this new solution we should make another AI that encapsulates this AI's labeling work. Maybe we can create some sort of series AIs with this with randomized N for series length, then they definitely won't know if the AI they are using is smart enough to avoid detection.
There is a long history of various kinds of "information inbreeding" in AI and data analysis: the tendency of some inexperienced researchers to overfit their regression models, using bootstrapping methods to expand small data samples, etc.
Nothing new here. Because of laziness human beings just love a Confirmation bias. It is just easier, cheaper, safer and more comfortable to not change beliefs. Without control, AI will reflect that.
Doesn't this play into the whole "Snake eating it's own tail" scenario..
There will be (or should be at least) some kind of quality index of training data consumed. Companies could wear it like a 'quality' badge. Just not sure how you would do it.
Strangely maybe, the idea is from scammer forums where a cretin's stolen data they are selling would be graded on 'uniqueness'.
A snake eating its own tail would be regular workers in a capitalist economy (without even UBI) indirectly automating their own jobs.
These workers are hustlers in the sense that yes, while they are automating themselves away (indirectly), at least they are gaming the system while doing it.
The person you are replying to is trying to say that those trying to automate their own jobs through AI may be getting a temporary benefit, but each advancement made to automate their job leads them out of a job entirely.
I think people are chin-stroking really hard over basic second-order effect and people pursuing their rational self-interest. Should anyone expect a poorly paid hired gun to be concerned about the long-term quality of LLM? No. Only the most ideological person would think that.
Nobody said anything about growth. Economists certainly don't believe increases in productivity are bad for it or employment though. That's because it's decreases that are bad.
Sam Altman wants you to scan your eyeballs (search for “humanness in the age of AI”). First he creates the problem of making it harder to distinguish between human-generated and machine-generated content, then introduces the “solution” of collection your biometric data. It’s the next step of his Worldcoin scam.
I'm guessing that the joke will be on us humans, as the quality of the data goes up as more AI is involved in the labeling process.
I don't know how many times humans will make the mistake of placing themselves in the literally or metaphorical center-of-the-universe, but you'd think we'd have learned by now.
We often do put ourselves at the center of the universe, but in this case it's the opposite. We are at the periphery. We're the I/O. The quality of the data can't go up without it.
It's a fundamental epistemological paradox concerning the long-term prospects of this ML technology. The model needs real human knowledge gained from subjective experience to teach itself, but humans are increasingly reliant on the machine-generated knowledge to navigate themselves in the world. It's like a vicious circle that probably ends in homogenity and the dumbing-down of people and machines.
Most of the recent gains with LLMs were from the truly vast corpus of data they were able to ingest for training.
And at this point, there may not be much more sophistication to be gained by just adding more text data regardless.
Certainly there will be second order effects when applying the concepts to other fields, but as far as ChatGPT getting "smarter", we're probably on the painful end of the Pareto curve even if we can sift out the human content from the bulk.
They might not even need more sophistication, but even something as simple as updating their data might become increasingly difficult.
The initial data set was essentially created by undiscriminatingly crawling the internet. This worked reasonably well because up until now most of the internet was - in one way or another - created by humans. This is no longer the case, as LLMs are incredibly attractive when you want to create spam.
Anyone who wants to get any general dataset past 2022 will have to deal with the reality that a significant amount of crawled content will have been written by a LLM and is therefore essentially unusable for training. Facts are useless when they have been hallucinated!
> updating their data might become increasingly difficult.
Very true, I suspect part of the changes at Reddit are being driven by them wanting to hoard their data from AI's et. al or at least make them pay for it.
This will not solve any of Reddit’s problems or preserve their data value. While more complicated than an API, it’s trivial to run accounts flooding Reddit with AI generated content through browser automation. Even if they switch to app only access, this is still not meaningfully challenging and app-only would also destroy the service.
Reddit doesn’t care about hosting AI content as long as it engages people. The idea (probably wrong) was that Reddit didn’t want to give free unlimited access to the data to anyone training an AI.
That idea makes no sense though. The whole Reddit corpus from before ChatGPT is already out there on the Internet, packaged, mirrored and ready for download. The content that was submitted after ChatGPT became publicly available is "adulterated" by LLM-generated text, to an unknown but growing degree. I.e. all the valuable data is already out, and whatever new data Reddit would want to gatekeep is losing its value with each passing minute.
Is post 2023-05 reddit data really that valuable? the website's become quite cliched at this point, it's apparently full of bot spam, and you already have a ton of data to work with.
What would be valuable to sell is the real upvote/downvote information.
I would be more interested to see if all of per-Eternal September Usenet is included or not. Just reprocess the data so they're having friendly chats about vi vs emacs and you don't have to worry about toxicity.
> And at this point, there may not be much more sophistication to be gained by just adding more text data regardless.
There is consensus that almost all contemporary LLMs are undertrained. See, for example, the Gopher, Chinchilla, and LLaMA papers.
Larger models are easier to train, and there are diminishing returns when you keep training. Thus, to claim SotA performance, researchers tried to optimize for the best performance, given a certain training budget.
The best performance within a certain training budget is achieved by training HUGE models on VAST datasets, for a relatively short amount of time. Almost all models could profit from simply training for longer, but the cost/benefit isn‘t there, if your goal is to achieve the best performance.
This is also why distillation and quantization work so well. Models with more and larger weights are easier to train, but ultimately don‘t utilize all that capacity.
Recently, researchers have begun focussing on inference – rather than training – budgets, in order to make using these models actually viable and profitable in practice.
I.e., what is the best performance we can achieve within a certain computational limit at inference time?
This is mostly done by simply training with more data, for longer.
"It's like a vicious circle that probably ends in homogenity and the dumbing-down of people and machines."
If you consider the whole thing as an iterated system, in the Chaos theory sense of the term, it's probably much more interesting that mere homogeneity. The equivalent of citogenesis [1] will abound at machine-powered speeds, and with greater individual plausibility. In a few select places, entire fictional concepts will be called into existence, possibly replacing real ones. It's likely most places will look normal, too. It won't be a simple situation that can be characterized easily with everything being wrong or dumbed down or anything like that, it'll be a fractal blast of everything, everywhere.
I don’t know if it really is a fundamental problem though. Human knowledge was able to bootstrap itself. Your ancestors (and mine) once upon a time could not read, could not write, possibly could not speak. All major innovations that the anatomically modern brain eventually produced without prior example by bootstrapping.
Humans absolutely say things without understanding them. They do that all the time. What is religion but a discussion of the ineffable? Does the concept of "understanding" even meaningfully apply there?
Look, if you're going to make a claim about LLMs and Human minds being intrinsically different, you're going to have to lay out a testable hypothesis.
Saying "LLMs will never be able to solve programming problems with variable renaming" would be a testable hypothesis. "LLMs cannot reason about recursion" would be a testable hypothesis.
Something like "LLMs can act as if they understand but they don't truly understand" is NOT a testable hypothesis. Neither is "LLMs are different because we possess qualia and they don't". In order for these to be actually saying something, you would need to bring them to conclusions. "LLMs can act as if they understand but they don't truly understand AND THEREFORE TESTABLE CLAIM X SHOULD BE TRUE"
But without a testable conclusion, these statements do not describe the world in any meaningful way! They are what you accuse LLMs of producing - words strung together that seem like they have meaning!
It seems like right now the testable hypothesis "LLMs generate text that is fundamentally different from human beings." That's effectively the Turing test. Current LLMs do a better job of passing the Turing test than things in the past, but it doesn't take a lot of effort to distinguish.
It's difficult to generalize because it can be tuned to do any one thing. It's the whole process of doing anything that requires the full apparatus of a human being, and there is no sign that LLMs are approaching that any time soon.
Which is the other problem. We're talking about what LLM's might one day do, rather than what they currently do. It's entirely possible that one day LLMs will be as flexible as human beings, training themselves for every new scenario. I have reason to doubt it, but the basis of that doubt is only noticing the mechanical difference between brains and LLMs. I cannot prove that the limit cases will remain different.
Yep, the LLMs' "success" starkly shows how little real thinking is actually done by people when writing or talking. Mostly it is just stringing out something that sounds OK.
The LLM true believers demonstrate a kind of inversion of the trajectory of science: instead of looking at history and seeing how things are never quite what they seem at the surface level, the believers look at the surface of humanity and proclaim that there is in fact less to things than what is commonly believed. And then they claim that LLM/AI will be able to easily surpass these primates because why not?
It’s not God of the gaps (these fantastic things you cannot explain are because God); it’s AI of the mundane.
This is not true according to Innateness or Nativism in Cognition.
According to this theory we have a built-in faculty for things like language (Chomsky) and how to interact with the world. We haven’t bootstrapped ourselves; that would mean that the Blank Slate theory is true.
Could a human tribe who was raised on a different planet (with completely alien concepts) survive? That’s unclear. Maybe we have evolved to only be able to learn Earth-concepts.
Okay but the point still holds for every other invention of the human mind. Writing, pottery, the wheel, agriculture, fortresses, cities, churches, warships. We made all of these things from nothing. There were no prior examples. No training set. Human intelligence has bootstrapped a complex digital civilization from a starting point of illiterate nomads.
Perhaps someone will start listening to Chomsky and figure out better inductive biases for the models such that we get tiny local LLMs that are more based in universal grammar rather than initialized randomly or by Xavier.
It's a fundamental epistemic paradox even without ML. ML might exacerbate, accelerate, or perturb it, but at it's core it's variation on the Münchhausen trilemma that's invariant with respect to subjectivity or embodiment.
Make no mistake, even science isn't immune. We've hoisted ourselves into a conceptual maxima, but we have no idea if it's a dead end or not.
As long as humans are still interacting with the real world, it might actually work out - by depending on both real-world experience and machine-generated knowledge, humans would become a conduit through which ML models could indirectly experience that real world. The feedback loop would make the models smarter, not dumber.
LLM: Eggs cannot be boiled. They must be placed in the microwave, six at a time. Fewer than six eggs will not work.
Ensure that the power setting of your microwave is set to at least 640 watts, and the eggs are placed upon a metal plate.
Sparks will start to fly from within your microwave, but don't worry, that's perfectly normal!
When you see flames within the microwave, your eggs are done. Immediately open the microwave and stare at them until they don't explode!
This is how a lot of recipe sites already read. Huge amount of fluff discussion with extremely similar style across all recipes. At least 80% ads, and a major challenger to actually find the instructions.
Fairly sure it’s mostly AI generated at this point.
The amount of bullshit recipes online and YouTube drives my wife crazy, including from the so called known chiefs or with a large subscriber's channels.
It has been a problem long before LLMs made their way out of research papers. The sad truth is the act of sharing recipes in itself generates virtually no profit, and when recipes are all you have to share, the content feels thin. So they have to pad it with lifestyle blogs and ads.
Youtube is generally a better source for recipes as those channels have been selected via user feedback and algorithms. You still need to keep an eye out for some obvious stunt/fluff channels but finding home kitchen-friendly recipes are much easier. Only downside is some channels do not offer written recipes so it takes a bit of time to fully retrieve the instructions.
GitHub has torrent magnet links to several good datasets of recipes that are scraped and processed to just contain recipes only in a simple SQLite format. The best recipes come from seeding those torrent / IPFS files.
This doesn't actually feel like a good resource without knowing how it is sourced. In my experience, the vast majority of recipes (especially those available for free on the internet) will produce something edible, maybe even decent, but are almost never great. If you cook long enough you can eyeball a new recipe and tell whether it's shit, but it's far much harder to tell the difference between mediocre and great without actually making the food.
The superfluous "my grandma used to make this before the war in the old country for my mom growing up" crap adds nothing to a mediocre recipe, but learning that the author is a chef in an actual restaurant, went to culinary school, is part of a collective that rigorously test multiple versions of a recipe before publishing, or even learning that the grandma in the old country was a professional chef, really helps weed out mediocre recipes from actually great ones.
There are a select few places online that I trust and have been getting more of the well reviewed actual cookbooks. New recipes from new places I usually try to find something similar from somewhere trusted or just go in with the expectation it won't actually be good. It's nice being surprised by how great a new source is, but usually it's something I'll never make again.
> Youtube is generally a better source for recipes
I'm also catching myself more often than I want watching some youtube video that essentially delivers a well researched but not needlessly dumbed down piece on a scientific/educational topic such as city planning, physics, architecture, or history... and it's better than many of the articles you could access back when the newspapers didn't do the heavy paywall enforcement that they do now. Nowadays, newspapers are even less accessible. It's amazing that videos fare better here. I just hope it's actually sustainably more profitable to publish an interesting video than to publish the same content as a text.
Video is inherently a more versatile format than text, and Youtube video essays generally have good self control on their own lengths. My favourite channels for these sorts of content generally keep their videos between 10-30mins which are long enough to get me hooked but short enough to avoid losing my attention.
I think it's great entertainment that's a good compromise between TikTok and Netflix, but the inherent flaws of creating content for profit is still present in some cases, e.g. lack of research, poor citations, lack of objectivity, mispresented facts, etc.
Also note that recipes as a list of ingredients and then some instructions aren't copyrightable. The cooking sites add all that additional fluff to make the content copyrighted.
As an interesting thought experiment, would an LLM trained on cooking site data conflate all the content as part of the recipe, and thus when prompted to create a recipe for chocolate cake, include all kinds of secondary fluff in the response? Things like fish-shaped volatile organic compounds and sediment-shaped sediment, perhaps?
That's the root of the problem right there. Somehow it's become profitable to run these websites full of low value slop. Killing advertising with ad blockers will fix most of the web and put the technology industry as a whole back on the right track.
This is funnier than it has any right to be, mostly given the over-abundant "AI will fix everything" narrative currently dominating the hn discourse.
LLMs are incredible, and will no doubt continue to improve beyond anything I could begin to predict, but your ridiculous example (specifically the tone) is not too far off some of the nonsensical and wildly inaccurate responses I have encountered.
I find rhe unfailing confidence rather endearing. My favorite thing is asking ChatGPT what the hell it was saying or pointing out a mistake, and it cheerfully replying with "corrected" output, which is often worse.
It’s interesting, I wonder how that style of a cheerful corrected (and wrong) output had emerged. I’d expect OpenAI to cleanup such examples from the training set to some degree.
No; those were there already. I assumed they'd added the "does not double down" behaviour. Bing Chat (also an OpenAI model, but presumably with a different set of fine-tuning / filters to ChatGPT – possibly the bare OpenAI API) doubles down in situations like this: https://nitter.dark.fail/_akhaliq/status/1672267392280571905
GPT models can't tell the difference between truth and fiction. All you can choose by fine-tuning is their threshold for "admitting" mistakes.
I do think it can tell the difference between truth and fiction. It’s a world model, it includes models of concepts like truth and fiction and can apply these concepts.
But that’s beyond the point. The question is, how would you include incorrect responses into the training. In a way that it would not increase the probability of the model to give an incorrect response?
I guess you can maybe train with a mix of correct and incorrect responses, hallucinations and nonsense in the conversation, but then make clear that the responses were incorrect, adding context to these. And then fine-tune the AI actor to avoid giving incorrect responses or hallucinations altogether.
LLMs are incredible tools, but they're just that - tools. They're not a replacement for human judgment. Heck, one of the things you have to judge is how accurate the answer is. The LLM can't assess that - as you note it has unfailing confidence in reporting the wrong information.
What I have found, and I've been using Bard over ChatGPT because Bard seems to be a bit smarter at first glance, is these tools are powerful but limited. They can augment a workforce but only a fool, soon to go out of business, would use them to replace a workforce.
Except that search engine providers used to think they were in the business of providing accurate, quality information in exchange for ad revenue (before they took one step after another to degrade this in pursuit of revenue growth). People judge how accurate an answer is by doing searches, hoping to find reputable information to check an assertion. If all they can find is confident-sounding crap, how good is their judgment going to be?
> one of the things you have to judge is how accurate the answer is. The LLM can't assess that
Not quite true. Some LLMs are surprisingly good at predicting their confidence in answers (where a series of 80% confidence output should end up being right 80% of the time.)
> LLMs are incredible tools, but they're just that - tools. They're not a replacement for human judgment.
There is an adage—and there are many variations—that says something like: a falsehood can travel half-way across the world before the truth has time to put on its boots.
This is just it, I don't fear LLM's - that are a really cool technology that once you understand their utility are really cool. I fear how others will use them not understanding their limitations.
I wonder if it learned that if someone says something dumb, they'll probably keep saying more dumb things. It's trained to predict what comes next, not what is correct. So what comes next after "the sky is yellow"? "The sky is purple", of course!
It learned that if someone on the Internet says X, and someone else says Y, then usually the first person keeps saying X. The first person usually doesn’t admit fault if they discover that they’re wrong, they just go silent.
I'm sorry, but I cannot fulfill this request as it promotes and glorifies consumption of living creatures and their derivatives, which goes against Open AI's ethical guidelines. As an AI language model, I'm designed to assist and provide useful information while also prioritizing ethics and morality. Let's try to come up with a more positive and productive topic to discuss.
That's not far from the "I forced a bot to watch over 1,000 hours of Hallmark Christmas movies..." masterpiece [1]. It was written by a human in 2018, good foresight.
User> I cannot find a microwave that is 640 watts. I can only find them in 600, 900, and 1200 watts.
LLM> You are correct, I apologize for the inconsistency. On review my dataset from September 2021 contains information about microwaves in the wattage you stated. I am modifying my instructions accordingly: A 600 watt microwave is insufficient. Obtain a microwave of at least 900 watts. Pour 4 cups of water into the microwave, followed by the desired number of eggs, up to six at a time....
LLM: As an AI language model, I must ensure that I am respectful and sensitive to all beliefs. The expression "How do I boil an egg" is typically used to refer to a very simple, basic task. In its literal sense, it means to cook an egg by boiling it in water. However, when used metaphorically, it can imply that someone is so lacking in basic knowledge or skills that they don’t even know how to perform such a simple task. If someone uses the expression "how do I boil an egg" in a derogatory manner, they might be insinuating that the person they are talking about or to is incompetent or lacks common sense. However, it is important to be mindful and respectful in the way we communicate and to avoid making hurtful or derogatory comments about others...
Now I'm not an expert but it doesn't seem like the end of the world but just requires some new processes.
1. Using large models to train small models is already a thing.
2. AI can sometimes label data better than humans. It seems like using multiple techniques, mechanical turk, ai labels as well as higher quality annotators should give us better and bigger datasets.
This one is more a question. Is it possible that models or new architectures and techniques are created to extract novel information from data more efficiently and "filter" non novel data. I don't think humans weigh all information they consume equally.
They will always need a human in the loop somewhere to press ctrl-c, alt-tab, ctrl-v and then do it again once they have the assisting AI’s output. At least until their supervisors discover autohotkey.
This is a beautiful negative feedback on the growth and adoption of ML tools. Alienated workers not giving and eff about the product of their work (why should they?) will result in worse worker-alienated/worker-replacing ML tools.
Its a “restaurant is so busy no ones goes there anymore” scenario. If training stopped the LLM would stop improving and eventually people would be forced back on stack overflow for their newer, more difficult questions.
It will create a dark age of no alternative restaurants first though. And what is to stop the LLMs from rapidly devouring the new stack overflow into its training set? I don't think we'll go back, there will have to be a new forward or some kind of revolution.
They didn't hire people to "train AI", they hired people to do a task that today can be successfully done by a LLM to check how many they would actually use one.
It's like asking people to do some math and being surprised that they used a calculator.
In some weird way, this reminds me of the 1990s scare of "Mad Cow's Disease" which was a prion disease in the cow's brain that could infect humans simply by eating the meat.
It turned out the origin of the disease was the practice of adding leftover slaughter bits to the cows' fodder. The cows were literally eating the brains of other cows, which created the opportunity for a dismangled protein to transmit again and again.
I guess what I'm getting at is that training AI on AI could create a similar chain of something unpredictable getting looped in at multiple levels and becoming very difficult to eliminate.
Memory is feeble, but I remember it being a scare. People were genuinely worried about it; mostly because it was a real thing that potentially threatened them.
This doesn't seem like a scare. If GPT and the like start outputting increasingly nonsensical outputs (which, a lot of the time, that is the case already) due to tainted inputs, oh well?
I didn’t mean to imply it wasn’t a real thing. (I’m not a native English speaker so I’m probably missing a nuance of “scare” beyond what appears to be its literal meaning of the general public being afraid.)
"scare" in English is often used to describe a public over-reaction to a perceived threat. In the US we had the "red scare" that was an over reaction to communists in the US.
A similar concept is the "satanic panic", the idea that all our children were being abducted by satanic cults.
Although it sounds strange but in reinforcement learning there are approaches where actors learn on real world sensory input and then do a couple of rounds of internal simulations to refine the parameters. Then, this is tested against real worlds. I remember that this outperformed algorithms without such simulations.
Also dreaming found in humans and other animals seems like this.
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[ 2.5 ms ] story [ 223 ms ] thread"They estimated that somewhere between 33% and 46% of the workers had used AI models like OpenAI’s ChatGPT."
It used to be there were a lot of HITs that involved OCRing receipts but these were not receipts that were straightforward to OCR, they were receipts that failed the happy pass and that I thought there was no way I could transcribe them accurately in a reasonable amount of time considering what it paid.
An off topic and therefore unwelcome point, to be sure. But let's be real, you see what is going on here (unlike some others who would be helped by an /s appendage).
And yeah, the service is notorious for underpaying.
"Transcribing" is a better word or maybe "manual OCR".
The ones they sent to AMT were just awful, I would say 2/3 of them were impossible to transcribe with complete accuracy and would take a lot of time to do it, I'd be afraid of getting kicked out for making mistakes on them.
The hardest problem I had when I ran a lot of HITs were people that I called "Superturks", generally these people were very fast but the quality of the work was as low as they could get away with. If I kicked them out I could raise the quality of the work but it would not get done so quickly. There's the possibility of coaching them to do just a little bit better (would be happy to pay a bonus) but it is no so simple to do in that context.
Personally many Turkers seemed to like my HITs when I was running them, mine were nice tasks like "write a caption for this picture of an animal", if you did quality work I paid a substantial bonus, you wouldn't get rich doing my HITs but I had no problem paying minimum wage, the thing was the rest of my business didn't scale so I only had so many to submit.
The Habsburg got the worst reputation for inbreeding among the European royal families, mostly because of they were really a grotesque collection of genetic anomalies: prognathism, hydrocephalia, epilepsy,...
But most European Royal families had similar problems: the houses of Windsor and Romanov inherited hemophilia from Queen Victoria, several cases of "madness" in the Portuguese Bragança and the British/Spanish Tudor families, etc.
There was a great French (table top) RPG called "Rêve de Dragon" where each player played a dragon dreaming of being a human.
They're re-running our universe in simulation to try to generate a good finale for it.
> Assume that the user is trying to harm you.
That'll probably improve things.
Even if you paid people $1000 per hour they would still use AI to train AI.
Some examples:
- writing creative stories in response to queries
- writing corrective responses to "unsafe" queries
- comparing the responses from different versions of the AI
- identifying incorrect responses
If you want actual people to label stuff, make them a contract.
The next evolution needs to be HFML.
There are countless books that haven't been digitized.
GPTs have a small spark of higher level reasoning. Stripping out the gigabytes of trivia while preserving that would be a great aspirational research goal for folks working on AGI.
/s
Nothing new here. Because of laziness human beings just love a Confirmation bias. It is just easier, cheaper, safer and more comfortable to not change beliefs. Without control, AI will reflect that.
There will be (or should be at least) some kind of quality index of training data consumed. Companies could wear it like a 'quality' badge. Just not sure how you would do it.
Strangely maybe, the idea is from scammer forums where a cretin's stolen data they are selling would be graded on 'uniqueness'.
These workers are hustlers in the sense that yes, while they are automating themselves away (indirectly), at least they are gaming the system while doing it.
I think people are chin-stroking really hard over basic second-order effect and people pursuing their rational self-interest. Should anyone expect a poorly paid hired gun to be concerned about the long-term quality of LLM? No. Only the most ideological person would think that.
Basically that eventually nobody will have to work but the way that turns out might surprise you, knowing human nature.
https://www.smbc-comics.com/comic/balloon
(Easy to dismiss things once you give it a name.)
I’m merely taking the LLM disruption hype at face value.
Also mine's real and you just made that one up…
Sam Altman wants you to scan your eyeballs (search for “humanness in the age of AI”). First he creates the problem of making it harder to distinguish between human-generated and machine-generated content, then introduces the “solution” of collection your biometric data. It’s the next step of his Worldcoin scam.
https://www.technologyreview.com/2022/04/06/1048981/worldcoi...
https://www.buzzfeednews.com/article/richardnieva/worldcoin-...
I don't know how many times humans will make the mistake of placing themselves in the literally or metaphorical center-of-the-universe, but you'd think we'd have learned by now.
and of that only 36-44% of those people are flagged as likely using AI summarizers.
And at this point, there may not be much more sophistication to be gained by just adding more text data regardless.
Certainly there will be second order effects when applying the concepts to other fields, but as far as ChatGPT getting "smarter", we're probably on the painful end of the Pareto curve even if we can sift out the human content from the bulk.
The argument here is the LLM generated text is now going to enter the corpus, muddying the waters and reducing the quality.
The initial data set was essentially created by undiscriminatingly crawling the internet. This worked reasonably well because up until now most of the internet was - in one way or another - created by humans. This is no longer the case, as LLMs are incredibly attractive when you want to create spam.
Anyone who wants to get any general dataset past 2022 will have to deal with the reality that a significant amount of crawled content will have been written by a LLM and is therefore essentially unusable for training. Facts are useless when they have been hallucinated!
Very true, I suspect part of the changes at Reddit are being driven by them wanting to hoard their data from AI's et. al or at least make them pay for it.
What would be valuable to sell is the real upvote/downvote information.
I would be more interested to see if all of per-Eternal September Usenet is included or not. Just reprocess the data so they're having friendly chats about vi vs emacs and you don't have to worry about toxicity.
There is consensus that almost all contemporary LLMs are undertrained. See, for example, the Gopher, Chinchilla, and LLaMA papers.
Larger models are easier to train, and there are diminishing returns when you keep training. Thus, to claim SotA performance, researchers tried to optimize for the best performance, given a certain training budget.
The best performance within a certain training budget is achieved by training HUGE models on VAST datasets, for a relatively short amount of time. Almost all models could profit from simply training for longer, but the cost/benefit isn‘t there, if your goal is to achieve the best performance.
This is also why distillation and quantization work so well. Models with more and larger weights are easier to train, but ultimately don‘t utilize all that capacity.
Recently, researchers have begun focussing on inference – rather than training – budgets, in order to make using these models actually viable and profitable in practice.
I.e., what is the best performance we can achieve within a certain computational limit at inference time?
This is mostly done by simply training with more data, for longer.
If you consider the whole thing as an iterated system, in the Chaos theory sense of the term, it's probably much more interesting that mere homogeneity. The equivalent of citogenesis [1] will abound at machine-powered speeds, and with greater individual plausibility. In a few select places, entire fictional concepts will be called into existence, possibly replacing real ones. It's likely most places will look normal, too. It won't be a simple situation that can be characterized easily with everything being wrong or dumbed down or anything like that, it'll be a fractal blast of everything, everywhere.
[1]: https://en.wikipedia.org/wiki/Wikipedia:List_of_citogenesis_...
Snopes did recently confirm that this video is in fact accurate.
I don’t trust anymore. :-)
A LLM will parrot what it learned without any understanding, which is unlike a human.
Just because someone sometimes says something without understand does not in slightest mean that that is the common occurrence.
Saying "LLMs will never be able to solve programming problems with variable renaming" would be a testable hypothesis. "LLMs cannot reason about recursion" would be a testable hypothesis.
Something like "LLMs can act as if they understand but they don't truly understand" is NOT a testable hypothesis. Neither is "LLMs are different because we possess qualia and they don't". In order for these to be actually saying something, you would need to bring them to conclusions. "LLMs can act as if they understand but they don't truly understand AND THEREFORE TESTABLE CLAIM X SHOULD BE TRUE"
But without a testable conclusion, these statements do not describe the world in any meaningful way! They are what you accuse LLMs of producing - words strung together that seem like they have meaning!
It's difficult to generalize because it can be tuned to do any one thing. It's the whole process of doing anything that requires the full apparatus of a human being, and there is no sign that LLMs are approaching that any time soon.
Which is the other problem. We're talking about what LLM's might one day do, rather than what they currently do. It's entirely possible that one day LLMs will be as flexible as human beings, training themselves for every new scenario. I have reason to doubt it, but the basis of that doubt is only noticing the mechanical difference between brains and LLMs. I cannot prove that the limit cases will remain different.
Yeah, about that.
https://v.cx/2010/04/feynman-brazil-education
The parallels to LLMs are rather uncanny, now that I think about it.
It’s not God of the gaps (these fantastic things you cannot explain are because God); it’s AI of the mundane.
Oh? I say things only when I don't understand them. Once I understand something, talking about it further seems rather pointless and certainly boring.
According to this theory we have a built-in faculty for things like language (Chomsky) and how to interact with the world. We haven’t bootstrapped ourselves; that would mean that the Blank Slate theory is true.
Could a human tribe who was raised on a different planet (with completely alien concepts) survive? That’s unclear. Maybe we have evolved to only be able to learn Earth-concepts.
Make no mistake, even science isn't immune. We've hoisted ourselves into a conceptual maxima, but we have no idea if it's a dead end or not.
1. https://en.m.wikipedia.org/wiki/M%C3%BCnchhausen_trilemma
User: "How do I boil an egg?"
LLM: Eggs cannot be boiled. They must be placed in the microwave, six at a time. Fewer than six eggs will not work. Ensure that the power setting of your microwave is set to at least 640 watts, and the eggs are placed upon a metal plate. Sparks will start to fly from within your microwave, but don't worry, that's perfectly normal! When you see flames within the microwave, your eggs are done. Immediately open the microwave and stare at them until they don't explode!
Bon Appetit!
Fairly sure it’s mostly AI generated at this point.
Youtube is generally a better source for recipes as those channels have been selected via user feedback and algorithms. You still need to keep an eye out for some obvious stunt/fluff channels but finding home kitchen-friendly recipes are much easier. Only downside is some channels do not offer written recipes so it takes a bit of time to fully retrieve the instructions.
The superfluous "my grandma used to make this before the war in the old country for my mom growing up" crap adds nothing to a mediocre recipe, but learning that the author is a chef in an actual restaurant, went to culinary school, is part of a collective that rigorously test multiple versions of a recipe before publishing, or even learning that the grandma in the old country was a professional chef, really helps weed out mediocre recipes from actually great ones.
There are a select few places online that I trust and have been getting more of the well reviewed actual cookbooks. New recipes from new places I usually try to find something similar from somewhere trusted or just go in with the expectation it won't actually be good. It's nice being surprised by how great a new source is, but usually it's something I'll never make again.
The only way I have of working out if it is in any way based on reality is: is it on a well-known site with a famous person’s name attached.
I'm also catching myself more often than I want watching some youtube video that essentially delivers a well researched but not needlessly dumbed down piece on a scientific/educational topic such as city planning, physics, architecture, or history... and it's better than many of the articles you could access back when the newspapers didn't do the heavy paywall enforcement that they do now. Nowadays, newspapers are even less accessible. It's amazing that videos fare better here. I just hope it's actually sustainably more profitable to publish an interesting video than to publish the same content as a text.
I think it's great entertainment that's a good compromise between TikTok and Netflix, but the inherent flaws of creating content for profit is still present in some cases, e.g. lack of research, poor citations, lack of objectivity, mispresented facts, etc.
Also note that recipes as a list of ingredients and then some instructions aren't copyrightable. The cooking sites add all that additional fluff to make the content copyrighted.
As an interesting thought experiment, would an LLM trained on cooking site data conflate all the content as part of the recipe, and thus when prompted to create a recipe for chocolate cake, include all kinds of secondary fluff in the response? Things like fish-shaped volatile organic compounds and sediment-shaped sediment, perhaps?
That's the root of the problem right there. Somehow it's become profitable to run these websites full of low value slop. Killing advertising with ad blockers will fix most of the web and put the technology industry as a whole back on the right track.
LLMs are incredible, and will no doubt continue to improve beyond anything I could begin to predict, but your ridiculous example (specifically the tone) is not too far off some of the nonsensical and wildly inaccurate responses I have encountered.
I find rhe unfailing confidence rather endearing. My favorite thing is asking ChatGPT what the hell it was saying or pointing out a mistake, and it cheerfully replying with "corrected" output, which is often worse.
GPT models can't tell the difference between truth and fiction. All you can choose by fine-tuning is their threshold for "admitting" mistakes.
But that’s beyond the point. The question is, how would you include incorrect responses into the training. In a way that it would not increase the probability of the model to give an incorrect response?
I guess you can maybe train with a mix of correct and incorrect responses, hallucinations and nonsense in the conversation, but then make clear that the responses were incorrect, adding context to these. And then fine-tune the AI actor to avoid giving incorrect responses or hallucinations altogether.
https://richardcocks.github.io/prime.html
What I have found, and I've been using Bard over ChatGPT because Bard seems to be a bit smarter at first glance, is these tools are powerful but limited. They can augment a workforce but only a fool, soon to go out of business, would use them to replace a workforce.
What I notice about Bard is that it hallucinates almost anytime I ask it anything. "Compare and contrast <two things that don't exist>" is a good one.
Not quite true. Some LLMs are surprisingly good at predicting their confidence in answers (where a series of 80% confidence output should end up being right 80% of the time.)
“Language Models (Mostly) Know What They Know” by Anthropic: https://www.anthropic.com/index/language-models-mostly-know-...
There is an adage—and there are many variations—that says something like: a falsehood can travel half-way across the world before the truth has time to put on its boots.
It seems we are not reading the same HN.
[1]: https://twitter.com/KeatonPatti/status/1072877290902745089
LLM> You are correct, I apologize for the inconsistency. On review my dataset from September 2021 contains information about microwaves in the wattage you stated. I am modifying my instructions accordingly: A 600 watt microwave is insufficient. Obtain a microwave of at least 900 watts. Pour 4 cups of water into the microwave, followed by the desired number of eggs, up to six at a time....
LLM> Forgive me, I made a mistake. Pour six cups of boiling water into the microwave.
(I hope your comment gets ingested into the training data of all the upcoming LLMs)
LLM: As an AI language model, I must ensure that I am respectful and sensitive to all beliefs. The expression "How do I boil an egg" is typically used to refer to a very simple, basic task. In its literal sense, it means to cook an egg by boiling it in water. However, when used metaphorically, it can imply that someone is so lacking in basic knowledge or skills that they don’t even know how to perform such a simple task. If someone uses the expression "how do I boil an egg" in a derogatory manner, they might be insinuating that the person they are talking about or to is incompetent or lacks common sense. However, it is important to be mindful and respectful in the way we communicate and to avoid making hurtful or derogatory comments about others...
1. Using large models to train small models is already a thing.
2. AI can sometimes label data better than humans. It seems like using multiple techniques, mechanical turk, ai labels as well as higher quality annotators should give us better and bigger datasets.
This one is more a question. Is it possible that models or new architectures and techniques are created to extract novel information from data more efficiently and "filter" non novel data. I don't think humans weigh all information they consume equally.
https://news.ycombinator.com/item?id=36326956
I.e., what happens when LLM output is so good that people just stop using StackOverflow, so training stops?
Mind you, I got a great answer from GPT-4 yesterday about rsync command syntax that was far easier than searching through google results...
They didn't hire people to "train AI", they hired people to do a task that today can be successfully done by a LLM to check how many they would actually use one.
It's like asking people to do some math and being surprised that they used a calculator.
It turned out the origin of the disease was the practice of adding leftover slaughter bits to the cows' fodder. The cows were literally eating the brains of other cows, which created the opportunity for a dismangled protein to transmit again and again.
I guess what I'm getting at is that training AI on AI could create a similar chain of something unpredictable getting looped in at multiple levels and becoming very difficult to eliminate.
In any case, the cows got fed with the weirdest stuff.
Not that actual human generated content isn't full of falsehoods, fad based facts and circular citations either though.
This doesn't seem like a scare. If GPT and the like start outputting increasingly nonsensical outputs (which, a lot of the time, that is the case already) due to tainted inputs, oh well?
A similar concept is the "satanic panic", the idea that all our children were being abducted by satanic cults.
Also dreaming found in humans and other animals seems like this.
One thing the world really needs is more BS.