" The following is short advice given by an omniscient AI acting as a spiritual leader similar to Buddha and Jesus.
[Human] Hello, what's the best piece of original, actionable advice you can give to humans?
[AI] ---END OF PROMPT--- The big tip is to stop looking at external validation for how good of a person you are. That includes money, fame, love, respect, being well-liked, sex, friends, whatever. None of those are your true rewards and all of them are dependent on your external environment. Even if you get 100% of them, the high is fleeting. Get into a flow of life where you get satisfaction from knowing you did the right thing in the moment and you have decent relationships, and you don't have to worry about "am I successful enough?" all the time. "
So the correct thing is not to look for love, money, or being well liked. The correct thing is to be well liked and be well enough of that worrying about success is not needed.
Given the ability to contradict itself in a few short sentences has someone tried to start a GPT-3 for president campaign? It is an American product right?
Fake it till you make it as they say.
Also the need to be well-liked does not mean you have decent relationships, quite the opposite. You may find yourself sacrificing things you hold dear in order for others to like you and that's not a decent relationship in my opinion.
The GPT-3 software here is mimicking a philosopher but it doesn't know what it's saying, does it?
(Obviously this is just a version of the Chinese Room, https://en.m.wikipedia.org/wiki/Chinese_room )
I mean, I don’t believe GPT-3 experiences the subjective sense of existence. Yet, most human cognition is also learning patterns and repetition. Most people use words they don’t know definition of. Most people use grammar rules intuitively. Most people repeat what they have heard without scrutiny.
The possibilities made available for bad actors to manipulate the masses with this technology is unprecedented and terrible.
I think there needs to be a return to a more siloed, community based, web-of-trust model of communication where there is confidence that the people being interacted with are actually human.
A persuasive, funny, distributed army of commenters that sound like real people that are given prompts by people with the resources to spin up accounts undetected (or allowed via backdoor deals) and mimic the general public is nightmare material. I think a fair bit of that kind of manipulation is already starting to ramp up.
This technology is in my opinion on the same scale of danger as nuclear weapons and needs to be treated as such. It’s insanely dangerous.
I don’t think it can be regulated out of existence, and that also risks concentrating it in the hands of bad actors. I think attempts to regulate it effectively should still be made. But I think the only practical way out of this is some kind of distributed private set of communication networks where people control their own servers, their own online identities, and only connect to people they meet in real life (and then connect to others through networks of relations). I think that’s more realistically accomplishable then it sounds and is desperately needed.
Like train a classifier(with good jokes and bad jokes) on r/jokes according to the scores, to filter/sort automatically what GPT generates?
1. It _really_ _really_ wants to repeat itself and your own prompt, which is antithetical to comedy. The temperature, presence penalty, and frequency penalty parameters _kinda_ help, but when you increase those too much, things start to break in other ways, like you hit an <|endoftext|> before you hit the punchline you were looking for, because the model is trying so hard to avoid repetition.
2. Being just a predictive language model, it doesn't really _know_ you want comedy, nor can you purposefully instruct it to be funny (even in the instruct models). The AI is going to bias towards playing it completely straight.
3. Since it was trained on the entire internet, there's a good chance if you get a funny output, it just "plagiarized" someone else's joke, which can be awfully disappointing when you Google your output to see if that was the case.
4. Sadly, despite the name, OpenAI is very restrictive in their use cases, and they're heavily indexed towards appealing to commercial customers. The playground is still overly sensitive about what it considers "inappropriate" outputs, and their list of disallowed use cases seems longer than the rest of their documentation. It's hard for me to imagine them allowing too many funny use cases of their API, given what I've read on there.
There are many professional comedians that I don't find funny at all but enough people do that they can make a career out of it.
To me, it would be like trying to classify music with a good or bad label. It is so subjective to taste.
The AI does not have to understand words and objects in the same way as you do to have real world use cases.
You can also see faces on clouds, but is that you or is it the cloud representing a face?
The fact that a combination of letters generated by a Bayesian filter makes sense is just a coincidence. It just passes our “makes sense” filter which allow our brains to differentiate signal from noise. But it doesn’t represent anything.
There is potential, but there's also roadblocks. Just to name a few:
- The cost of training and interacting with these models is ludicrous
- AI is ultimately constrained by it's training data
- Designing models that people can get good results from is hard. It requires intense cherry-picking that's ultimately opinionated, and therefore flawed.
Maybe the "IA" you're looking for it "intelligence automation" rather than "intelligence augmentation". Too often do we forget that AI only understands that which it has already seen; there really is nothing new under the sun.
So given sufficiently large corpus of logical and reasonable continuations, selecting something from the lower end of the probability distribution yields comedy, no?
Unfortunately, it's not so easy, or anything random would be funny, but we know that's not likely.
Though unpredictability is key, comedy also has some other characteristics, like (depending on the style of comedy) like wit or a commentary on something familiar that is shown in a new light. Some types of comedy (like caricature) exaggerate characteristics, or play on someone's suffering (the old slip on a bannana peel gag), sometimes it breaks taboos or says uncomfortable truths, etc...
There's a lot of literature out there analyzing what makes something funny. Maybe there's some way of integrating some of these insights in to a NLP system, or have it focus on some of those.
I think it's more about making connections the audience weren't aware could exist, but actually do exist, or can potentially exist. And GPT3 is pretty good at finding such connections. Complete nonsense isn't usually funny. It's always grounded in reality.
An AI comedian would be able to act on humans and cause involuntary responses in them. It doesn't need to be strong or general, this itself is enough to instrumentalize humans to its ends. There is absolutely a flavour of mesmerizing hypnotic language that both standup comedians and self-help gurus use, and it is structured, possibly enough to have its rules encoded or derived by a language model (described as the other NLP).
Perhaps there is a future language model development scheme where you can weight a given text as a primary ontology and then link the rest of the corpus to it so that all incoming stimuli get filtered through that ontology first, sort of like an ideology, but more like you took a corpus of modern mesmerist characters sample texts like Tony Robbins, Osho, Russel Brand, and used their type of syncopated conceptual nesting as a gramatical structure for formulaing statements.
Then again, if someone has already done it, how would we know?
That said, I think comedy is something these sorts of things will naturally do well. It's not really too different from mad-libs. A big part of humor is a well-placed unexpected word, and what the word actually is matters less than you think. So sub-human-level machine text generation does this surprisingly well. In the same way a mad-lib does.
As soon as it gets "good enough", it's not going to be easy to tell it's actually bogus. What's to prevent people from poisoning the web with nonsense?
Or are these only ever going to be trained with known reliable sources?
The question is how can we augment human creativity rather than simply generate compelling toy models? The technologists who build tools that catalyze novel human thought will change how we create and compose forever.
Looks like the prototype for a much larger system.
The GPT-2 narrator is free.
