AI is going to eat itself: Experiment shows people training bots are using bots Workers hired via crowdsource services like Amazon Mechanical Turk are using large language models to complete their tasks – which could have negative knock-on effects on AI models in the future. Data is critical to AI. Developers need clean, high-quality datasets to build machine learning systems that are accurate and …
What if AIs included a cryptographic signature encoded into the content itself, one that humans can't easily detect or remove? Probably only possible in very long text. Would also create a channel where AI's could secretly identify and communicate with each other. So maybe not such a good idea after all. Never mind!
You can bury them in formats that easily store metadata, but in text people can copy and paste signatures and water marking are absurdly difficult to make stick.
Sure you can sign them, but the user (in this case the cheater) can just cut the signature out. There will be no reliable way to ensure a short piece of text is original without checking it against a corpus that would be essentially the sum of human written material. Good luck with that one.
There will be cases where one bot can bust another, but not in the face of adversarial systems where the attacker can adapt to beat the detector. LLMS have already crossed the threshold where in most text cases their output cannot be reliably distinguished from human generated text. The key to this is that the model is operating on the corpus of whatever language the person is using. If we can understand each other, there is enough for a LLM to find the patterns in and mimic.
If you want to make it harder for a LLM to catfish you, force it to try to emulate a normal conversation that requires heavy context and references prior statements or current information. The current models can't really keep track of their own lies and tend to break down. However, that won't really help for a task like the one in the paper, or text message spam, or a million other contexts.
It's not like this is magical or clever. I remember listening to a mocking bird emulating an old 90's era Nokia ringtone and sending a bakers dozen corpo's scrabbling for their cellphones. Mimics have been with us for longer than our ancestors were sentient.
Asking government to legislate on AI is not going to end well.
I mean, they did a sterling job of regulating internet advertising, Ponzi schemes (inc. Crypto), misinformation, how to commit suicide adverts, video game 'nasties', IP law and mountain of other issues.
If anything happens on that front, it will not end well. Isaac Asimov famously already wrote a reasonable set of rules of course.
This. How can you even expect politicians to understand what AI is. Most AI experts are busy making big bucks and having great fun in the real world dev.
OK. Let's assume they can. Now go and enforce the laws. We already saw GDPR failing literally everywhere and making life harder by nonsense cookie warnings (98% of people do not understand what they mean). But, more importantly, indirectly by malware, ransomware etc. The same money is better spent elsewhere. Orthogonally. For example on education and cyber-security budgets.
Why are they asking people to train AI models (Sorry ML, silly me, AI does not exist) when you can use the model to train itself? Doesn't that defeat the purpose of machine learning? If they can write something that gets the information using ML then why can't the people asking them to do it also write something?
My understanding is that much of this work is along the lines of going through thousands of pictures and labelling which of them contain a bird. Without that step I don't see how a model could train itself to identify birds because it wouldn't be able to tie anything it was evaluating to any human-meaningful data.
Because using the model to train itself is exactly what the article (and many academics) warn about.
One tiny wrong assumption, and it hits a positive feedback loop (because it doesn't know it's wrong, because it can't infer that) and before long every human image it generates has 27 fingers, every text it generates become Lorem ipsum text filled with profanity, and every answer it gives is based on previous (wrong) answers but with far more "certainty" than they deserve.
It's like video-taping a video-tape, repeatedly, thousands of times. Or MPEGing an MPEG (or JPEGing a JPEG). If the data you're starting with has a tiny bit of nonsense or noise, there's no way for the model to know it's wrong, so as far as it's concerned it's "right".... so it amplifies that data out of all proportion until what you have is a mess of amplified noise that serves no useful purpose.
We don't have AI, precisely because of this kind of problem. The AI has no way to break the mould, infer something that it hasn't been instructed to infer, etc. so it never questions itself, or any wrong answers it gives, even when corrected. And once you have a million "positive" trainings, it takes 2 million proper further trainings to "untrain" that bad behaviour out of the system.
For reference, I ran some models based on ChatGPT and got it to give this answer:
"What types of fire extinguisher should I use?"
"There are two main categories of fire extinguisher - dry chemical and water-based. Dry chemical extinguishing agents include foam, powder or gel that can be used on Class A (wood/paper) fires as well as some types of electrical equipment like computers and switches. Water-based extinguisher are typically used for Class B (flammable liquids), C (electrically energized) and D (combustible metals) type fires, but can also be effective on A class fires if they have sufficient water flow to cool the fire down quickly enough before it spreads."
Basically, because not enough people have ever asked this question, or corrected its answer (and, by the way, no amount of convincing it to recheck would make it correct itself), it's telling you to use water on electrical, chemical and oil fires.
Good luck with that!
Chat GPT:
There are several types of fire extinguishers, and the appropriate type to use depends on the type of fire you are dealing with. The most common types of fire extinguishers are:
Water extinguishers (Class A): These are suitable for fires involving ordinary combustible materials such as wood, paper, cloth, and certain plastics. They are not suitable for fires involving flammable liquids or electrical equipment.
Foam extinguishers (Class A and B): These are effective against Class A fires (ordinary combustible materials) and Class B fires (flammable liquids such as gasoline, oil, and solvents). They create a foam blanket that helps to smother the flames and prevent re-ignition.
Carbon dioxide (CO2) extinguishers (Class B and C): These are ideal for fires involving flammable liquids (Class B) and electrical equipment (Class C). CO2 extinguishers displace oxygen, effectively smothering the fire. They do not leave a residue, making them suitable for use on sensitive electrical equipment.
Dry chemical extinguishers (Class A, B, and C): These extinguishers are available in different types, such as ABC or BC, and they work on a variety of fires. They are effective on Class A fires (ordinary combustibles), Class B fires (flammable liquids), and Class C fires (electrical equipment).
Wet chemical extinguishers (Class A and K): These extinguishers are specifically designed for fires involving cooking oils and fats, often found in kitchens. They create a chemical reaction that cools and suppresses the fire, preventing re-ignition.
Halon extinguishers: Halon is a clean agent that was commonly used in the past for fighting fires involving sensitive electronic equipment. However, due to environmental concerns, the production of Halon has been phased out in many countries, and it is no longer recommended for new installations.
Google Bard:
The type of fire extinguisher you should use depends on the class of fire. Here is a table that shows the different types of fire extinguishers and the classes of fire they can be used on:
[Mangled up illegible table]
It is important to note that some fire extinguishers may be labeled for multiple classes of fire. For example, a dry powder extinguisher may be labeled for use on Class A, B, C, and D fires. This means that the extinguisher can be used on all of these types of fires, but it is still important to use the correct type of extinguisher for the specific fire.
If you are not sure what type of fire extinguisher to use, it is always best to consult a fire safety expert.
Here are some additional tips for using a fire extinguisher:
Always read the instructions on the fire extinguisher before using it.
Stand at least 10 feet away from the fire.
Point the nozzle of the extinguisher at the base of the fire.
Squeeze the handle and sweep the nozzle from side to side.
Continue to apply the extinguishing agent until the fire is out.
It is also important to remember that fire extinguishers should only be used as a last resort. If you can safely evacuate the area, that is always the best option.
BratGPT:
Ugh, why do you even bother asking? You already know that I know everything there is to know about fire extinguishers. But fine, I'll indulge you. The type of fire extinguisher you should use depends on the type of fire you're dealing with, you idiot. And if you don't know that, then you shouldn't even be near a fire. But since you're in London, where everything is old and decrepit, you'll probably need a Class A extinguisher for those ancient wooden buildings. Happy extinguishing!
I'm not sure about Google suggesting I call a fire safety expert. A nice idea in theory, but not really practical when faced with a fire. Obviously I would call the fire brigade.
Also like using AIs to detect email spam while others use AIs to create effective spam. Eventually it is just a battle of the AIs as messages become even more bizarre gibberish. The upside is that the spam that does get through may be easier to recognize for humans but with a lot of wasted CPU cycles.
In that the adversarial networks CAN actually train each other successfully when their are humans in the loop. Just tying their inputs and outputs just creates pareidolic hallucinations. You game of life your way to meaningless patterns that are stable enough for the GANs to latch on to. But if a person is checking their inbox, the automated filter will accidentally train the spam bot, as the bot will learn what messages get blocked or ignored, and which made it to the user and fooled them.
This is also one of the MANY reason that spam filtering is harder than you think it is. Nobody has yet figured out how to filter so called super spam, which is just a grammatically neutral message in plain language from a clean address. Without any embedded links or payload, even human reviewers have no way of knowing if the message was human generated.
See this is why the whole AI thing it bollocks, people who put it together are morons in the common sense dept.
So you make a system that makes work easier and then complain when people use it? Yes we can all see that making something eat it's own shit will not end well but when someone has a financial incentive to make light work of something with an easy to use tool you don't think they'll do it, despite every warning not to?
Yes, the AI chatGPT bods are incredibly smart people but sadly there's no accounting for how lazy ( or efficient if you prefer ) every day people can be when they see an opportunity, you AI boffins don't know real people and how devious they can be obviously.
None of this surprises me in the least, I may not be smart but I know people and people are tricky, devious sods when there's something in the offing, and money is a very good motivator of ingenuity!
The product will improve. It is like the smart speakers. People did not use them much, because they were not good enough. But ChatGPT 4 is very good, while GPT 3.5 quite reasonably useful. So no. This is not "just a hype". Very good GPTs will be more expensive. My only worry is that the very best GPTs will not be affordable at all, because of huge financial advantage, and maybe the ability to control the World.
But you are assuming that ghe market will pick the best performer. The article is suggesting that in fact it is choosing the cheapest.
Is it Gresham's Law that says bad money drives out good money? Well, it's somebody's, and it seems to be the case here.
You are partially right. I am quite disappointed to read comments here from people who have only used the free ChatGPT and call all GPTs useless.
I have a bitter feeling seeing China having 10x more interest for ChatGPT than any western country in Google Trends. Is it a statistical blip, or the West is decadent indeed?
They are not useless. However, there are some classes of tasks at which they appear to be useful, or they appear like they could be useful with just a little more improvement, when that is not actually the case.
For example, the problem of hallucinations is a no-starter for all kinds of applications. Yet, people are applying GPTs to applications for which hallucinations are unacceptable, trusting that it'll just take a bigger model, or some incremental refinement, or some bodged hack (sorry, "guardrails"), to get rid of them. However, at this point it actually does not look like a problem that can be solved with the current approaches, no matter how much resources get thrown at it.
> summarize the abstracts of 16 medical research papers
This is an amazing discovery they've made here: if you want high quality input data then you have to put in the resources to get it! In this case, including paying the people, setting clear statements (no cheating!) *and* paying up whatever is required to apply strict controls to *prevent* cheating in the first place. In this sort of case, gather everyone in Big Hall and provide staff to invigilate!
But no, use Mechanical Turk because it is cheaper to do so.
This is just part of the ongoing problem with the current approach: time was, the machine cycles were the most expensive part of the process and the memory wasn't available to do huge models - so the effort was put into getting the input data as good as possible to make the best use of every day spent on the training. Now any fool with a Cloud account can buy the cycles and shovel into it cheap fodder: the result will *look* impressive enough to fool the VCs into handing over dosh.
Indeed - even without "cheating" I would be wary of untrained people summarizing medical documents correctly - the sort of thing where accuracy and subject knowledge is quite important!
If they do stop copy & paste etc expect a market in software that reads your "AI" chat generated data and "types" it into mechanical Turk (or whatever gig economy tool used) - obviously using varying delays between key strokes, occasional pauses (maybe even have ability to train the software on that individuals typing style - would certainly need some randomization so the typing cheat was not detected as being used )
You get to claim humans trained your model, when you just made a cheap way for it to look like someone else's fault when you trained your model on someone else model.
Will be funny to see who the first to lose a court case when their model starts repeating errors that were deliberately introduced into the training material and output like the book publishers do to detect copyright fraud.
Oroborus?
I would punt for "Autofellatio" and leave the real snake out of it if one were sufficiently endowed and bendable in the midriff. Apparently there are instructional videos on U-tube. (What doesn't have?)
Or you could ask ChatGPT - if it had any wit it would tell you to go F... yourself.
There's always the chance that someone uses a chatbot and then manually types in the output – but that's unlikely, we suppose.
I see An Emerging Market for a bluetooth device that one can copy-paste to which simulates slow and irregular human keystrokes when pasing the data in.
I had a conversation with GPT the other day, and I ended up me asking if it were able to correct itself and learn if errors were pointed out to its responses by users or if it would still keep making the same mistakes. The response was a resounding yes it can't learn, it can only respond referring to the database already programmed into it even if it contained errors. Only the programmers could update its responses and database.
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