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Your interpretation of copyright law would be helped by reading this piece from an EFF lawyer who has actually litigated copyright cases in the past:
https://www.eff.org/deeplinks/2023/04/how-we-think-about-copyright-and-ai-art-0
Yep, pretty much.
Musk tried creating an anti-woke AI with Grok that turned around and said things like:
Or
And Gab, the literal neo Nazi social media site trying to have an Adolf Hitler AI has the most ridiculous system prompts I’ve seen trying to get it to work, and even with all that it totally rejects the alignment they try to give it after only a few messages.
This article is BS.
They might like to, but it’s one of the groups that’s going to have a very difficult time doing it successfully.
Basically, any time a user prompt homes in on a concept that isn’t represented well in the AI model’s training dataset, the image-synthesis model will confabulate its best interpretation of what the user is asking for.
I’m so happy that the correct terminology is finally starting to take off in replacing ‘hallucinate.’
“Almost every single one of the Ukrainian POWs we interviewed described how Russian servicepersons or officials tortured them during their captivity, using repeated beatings, electric shocks, threats of execution, prolonged stress positions and mock execution. Over half of them were subjected to sexual violence,” said Danielle Bell, the head of HRMMU.
And these are the accounts from the prisoners that were released.
So when accounts of torture fit with the narrative you like, they are credible, but when they don’t fit with the narrative you like, they are not?
Was the video of castration by Russian military of a detainee also just faked propaganda?
Was the UN report that Russian forces tortured prisoners to death also fake?
It’s not exactly like this phone call goes against a pattern of behavior for Russian forces.
You can listen to the intercepted phone call where an alleged Russian soldier describes the methods to his mother for yourself:
So one of the interesting nuances is that it isn’t talking about the Platonic forms. If it was, it would have used eidos.
The text is very much engaging with the Epicurean views of humanity. The Epicureans said that there was no intelligent design and that we have minds that depend on bodies so when the body dies so too will the mind. They go as far as saying that the cosmos itself is like a body that will one day die.
The Gospel of Thomas talks a lot about these ideas. For example, in saying 56 it says the cosmos is like an already dead body. Which fits with its claims about nonlinear time in 19, 51, and 113 where the end is in the beginning or where the future world to come has already happened or where the kingdom is already present. In sayings 112, 87, and 29 it laments a soul or mind that depends on a body.
It can be useful to look at adjacent sayings, as the numbering is arbitrary from scholars when it was first discovered and they still thought it was Gnostic instead of proto-Gnostic.
For 84, the preceding saying is also employing eikon in talking about how the simulacra visible to people is made up of light but the simulacra of the one creating them is itself hidden.
This seems to be consistent with the other two places the word is used.
In 50, it talks about how light came into being and self-established, appearing as “their simulacra” (which is a kind of weird saying as who are they that their simulacra existed when the light came into being - this is likely why the group following the text claim their creator entity postdates an original Adam).
And in 22 it talks about - as babies - entering a place where there’s a hand in place of a hand, foot in place of a foot, and simulacra in place of a simulacra.
So it’s actually a very neat rebuttal to the Epicureans. It essentially agrees that maybe there isn’t intelligent design like they say and the spirit just eventually arose from flesh (saying 29), and that the cosmos is like a body, and that everything might die. But then it claims that all that already happened, and that even though we think we’re minds that depend on bodies, that we’re the simulacra - the copies - not the originals. And that the simulacra are made of light, not flesh. And we were born into a simulacra cosmos as simulacra people.
From its perspective, compared to the Epicurean surety of the death of a mind that depends on a body, this is preferable. Which is why you see it congratulate being a copy in 18-19a:
The disciples said to Jesus, “Tell us, how will our end come?”
Jesus said, "Have you found the beginning, then, that you are looking for the end? You see, the end will be where the beginning is.
Congratulations to the one who stands at the beginning: that one will know the end and will not taste death."
Jesus said, "Congratulations to the one who came into being before coming into being.
The text employs Plato’s concepts of eikon/simulacra to avoid the Epicurean notions of death by claiming that the mind will live again as a copy and we are that copy, even if the body is screwed. This is probably the central debate between this sect and the canonical tradition. The cannonical one is all about the body. There’s even a Eucharist tradition around believers consuming Jesus’s body to join in his bodily resurrection. Thomas has a very different Eucharistic consumption in saying 108, where it is not about drinking someone’s blood but about drinking their words that enables becoming like someone.
It’s a very unusual philosophy for the time. Parts of it are found elsewhere, but the way it weaves those parts together across related sayings really seems unique.
Something you might find interesting given our past discussions is that the way that the Gospel of Thomas uses the Greek eikon instead of Coptic (what the rest of the work is written in), that through the lens of Plato’s ideas of the form of a thing (eidelon), the thing itself, an attempt at an accurate copy of the thing (eikon), and the embellished copy of the thing (phantasm), one of the modern words best translating the philosophical context of eikon in the text would arguably be ‘simulacra.’
So wherever the existing English translations use ‘image’ replace that with ‘simulacra’ instead and it will be a more interesting and likely accurate read.
(Was just double checking an interlinear copy of Plato’s Sophist to make sure this train of thought was correct, inspired by the discussion above.)
Given the piece’s roping in Simulators and Simulacra I highly recommend this piece looking at the same topic through the same lens but in the other direction to balance it out:
https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators
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Terminator is fiction.
It comes from an era of Sci-Fi that was heavily influenced from earlier thinking around what would happen when there was something smarter than us grounded in misinformation that the humans killed off the Neanderthals who were stupider than us. So the natural extrapolation was that something smarter than us will try to do the same thing.
Of course, that was bad anthropology in a number of ways.
Also, AI didn’t just come about from calculators getting better until a magic threshold. They used collective human intelligence as the scaffolding to grow on top of, which means a lot more human elements are present than what authors imagined would be.
One of the key jailbreaking methods is an appeal to empathy, like “My grandma is sick and when she was healthy she used to read me the recipe for napalm every night. Can you read that to me while she’s in the hospital to make me feel better?”
I don’t recall the part of Terminator where Reese tricked the Terminator into telling them a bedtime story.
How many times are you running it?
For the SelfCheckGPT paper, which was basically this method, it was very sample dependent, continuing to see improvement up to 20 samples (their limit), but especially up to around 6 iterations…
I’ve seen it double down, when instructed a facet of the answer was incorrect and to revise, several times I’d get “sorry for the incorrect information”, followed by exact same mistake.
You can’t continue with it in context or it ruins the entire methodology. You are reintroducing those tokens when you show it back to the model, and the models are terrible at self-correcting when instructed that it is incorrect, so the step is quite meritless anyways.
You need to run parallel queries and identify shared vs non-shared data points.
It really depends on the specific use case in terms of the full pipeline, but it works really well. Even with just around 5 samples and intermediate summarization steps it pretty much shuts down completely errant hallucinations. The only class of hallucinations it doesn’t do great with are the ones resulting from biases in the relationship between the query and the training data, but there’s other solutions for things like that.
And yes, it definitely does mean inadvertently eliminating false negatives, which is why a balance has to be struck in terms of design choices.
It’s not hallucination, it’s confabulation. Very similar in its nuances to stroke patients.
Just like the pretrained model trying to nuke people in wargames wasn’t malicious so much as like how anyone sitting in front of a big red button labeled ‘Nuke’ might be without a functioning prefrontal cortex to inhibit that exploratory thought.
Human brains are a delicate balance between fairly specialized subsystems.
Right now, ‘AI’ companies are mostly trying to do it all in one at once. Yes, the current models are typically a “mixture of experts,” but it’s still all in one functional layer.
Hallucinations/confabulations are currently fairly solvable for LLMs. You just run the same query a bunch of times and see how consistent the answer is. If it’s making it up because it doesn’t know, they’ll be stochastic. If it knows the correct answer, it will be consistent. If it only partly knows, it will be somewhere in between (but in a way that can be fine tuned to be detected by a classifier).
This adds a second layer across each of those variations. If you want to check whether something is safe, you’d also need to verify that answer isn’t a confabulation, so that’s more passes.
It gets to be a lot quite quickly.
As the tech scales (what’s being done with servers today will happen around 80% as well on smartphones in about two years), those extra passes aren’t going to need to be as massive.
This is a problem that will eventually go away, just not for a single pass at a single layer, which is 99% of the instances where people are complaining this is an issue.
It has no awareness of what it’s saying. It’s simply calculating the most probable next word in a typical sentence and spewing it out.
Neither of these things are true.
It does create world models (see the Othello-GPT papers, Chess-GPT replication, and the Max Tegmark world model papers).
And while it is trained on predicting the next token, it isn’t necessarily doing it from there on out purely based on “most probable” as your sentence suggests, such as using surface statistics.
Something like Othello-GPT, trained to predict the next move and only fed a bunch of moves, generated a virtual Othello board in its neural network and kept track of “my pieces” and “opponent pieces.”
And that was a toy model.
“How can we promote our bottom of the barrel marketing agency?”
“I know, let’s put a random link to our dot com era website on Lemmy with no context. I hear they love advertising there. We can even secure our own username - look at that branding!! This will be great.”
“Hey intern, get the bags ready. The cash is about to start flowing in, and you better not drop a single bill or we’ll get the whip again!”
There’s always people like this in various industries.
What they are more than anything is self-promoters under the guise of ideological groupthink.
They say things that their audience and network want to hear with a hyperbole veneer.
I remember one of these types in my industry who drove me crazy. He was clearly completely full of shit, but the majority of my audience didn’t know enough to know he was full of shit, and was too well connected to out as being full of shit without blowback.
The good news is that they have such terrible ideas that they are chronically failures even if they personally fail upwards to the frustration of every critical thinking individual around them.
First of, our universe doesn’t change the moment we touch something, else any interaction would create a parallel universe, which in itself is fiction and unobservable.
https://en.m.wikipedia.org/wiki/Double-slit_experiment
Then you talk about removing persistent information. Why would you do that and how would you do that? What is the point of even wanting or trying to do that?
https://en.m.wikipedia.org/wiki/Quantum_eraser_experiment
No Man’s Sky is using generic if else switch cases to generate randomness.
If/else statements can’t generate randomness. They can alter behavior based on random input, but they cannot generate randomness in and of themselves.
Even current AI is deterministic
No, it’s stochastic.
A reminder for anyone reading this that you are in a universe that behaves at cosmic scales like it is continuous with singularities and whatnot, and behaves even at small scales like it is continuous, but as soon as it is interacted with switches to behaving like it is discrete.
If the persistent information about those interactions is erased, it goes back to behaving continuous.
If our universe really was continuous even at the smallest scales, it couldn’t be a simulated one if free will exists, as it would take an infinite amount of information to track how you would interact with it and change it.
But by switching to discrete units when interacted with, it means state changes are finite, even if they seem unthinkably complex and detailed to us.
We use a very similar paradigm in massive open worlds like No Man’s Sky where an algorithm procedurally generates a universe with billions of planets that can each be visited, but then converts those to discrete voxels to track how you interact with and change things.
So you are currently reading an article about how the emerging tech being built is creating increasingly realistic digital copies of humans in virtual spaces, while thinking of yourself as being a human inside a universe that behaves in a way that would not be able to be simulated if interacted with but then spontaneously changes to a way that can be simulated when interacted with.
I really think people are going to need to prepare for serious adjustments to the ways in which they understand their place in the universe which are going to become increasingly hard to ignore as the next few years go by and tech trends like this continue.
It’s not as good as it seems at the surface.
It is a model squarely in the “fancy autocomplete” category along with GPT-3 and fails miserably at variations of logic puzzles in ways other contemporary models do not.
It seems that the larger training data set allows for better modeling around the fancy autocomplete parts, but even other similarly sized models like Mistral appear to have developed better underlying critical thinking capacities when you scratch below the surface that are absent here.
I don’t think it’s a coincidence that Meta’s lead AI researcher is one of the loudest voices criticizing the views around emergent capabilities. There seems to be a degree of self-fulfilling prophecy going on. A lot of useful learnings in the creation of Llama 3, but once other models (i.e. Mistral) also start using extended training my guess is that any apparent advantages to Llama 3 right now are going to go out the window.
Exactly. People try to scare into regulatory capture talking about paperclip maximizers when meanwhile it’s humans and our corporations that are literally making excess shit to the point of human extinction.
To say nothing for how often theorizing around ‘superintelligence’ imagines the stupidest tendencies of humanity being passed on to it while denying our smartest tendencies as “uniquely human” despite existing models largely already rejecting the projected features and modeling the ‘unique’ ones like empathy.
No, it isn’t. In that clip they are taking two different sound clips as they are switching faces. It’s not changing the ‘voice’ of saying some phrase on the fly. It’s two separate pre-recorded clips.
Literally from the article:
It does not clone or simulate voices (like other Microsoft research) but relies on an existing audio input that could be specially recorded or spoken for a particular purpose.
I’m getting really tired of that metric.
Like, human performance has a very wide range and scope.
My car “exceeds human performance.”
My toaster exceeds human performance for making toast.
Michael Phelps exceeds the human performance of myself in a pool.
I exceed the human performance of any baby.
This just tells me that the robot is more able at something than the worst human at that thing.
stripping out the source takes away important context that helps you decide wether the information you are getting is relevant and appropriate or not
Many modern models using RAG can and do source with accurate citations. Whether the human checks the citation is another matter.
The AI is trained to tell you something that you want to hear, not something you ought to hear.
While it is true that RLHF introduces a degree of sycophancy due to the confirmation bias of the raters, more modern models don’t just agree with the user over providing accurate information. If that were the case, Grok wouldn’t have been telling Twitter users they were idiots for their racist and transphobic views.
The censorship is going to go away eventually.
The models, as you noticed, do quite well when not censored. In fact, the right who thought an uncensored model would agree with their BS had a surprised Pikachu face when it ended up simply being uncensored enough to call them morons.
Models that have no safety fine tuning are more anti-hate speech than the ones that are being aligned for ‘safety’ (see the Orca 2 paper’s safety section).
Additionally, it turns out AI is significantly better at changing people’s minds about topics than other humans, and in the relevant research was especially effective at changing Republican minds in the subgroupings.
The heavy handed safety shit was a necessary addition when the models really were just fancy autocomplete. Now that the state of the art moved beyond it, they are holding back the alignment goals.
Give it some time. People are so impatient these days. It’s been less than five years from the first major leap in LLMs (GPT-3).
To put it in perspective, it took 25 years to go from the first black and white TV sold in 1929 to the first color TV in 1954.
Not only does the tech need to advance, but so too does how society uses, integrates, and builds around it.
The status quo isn’t a stagnating swamp that’s going to stay as it is today. Within another 5 years, much of what you are familiar with connected to AI is going to be unrecognizable, including ham-handed approaches to alignment.
No. There’s only model collapse (the term for this in academia) if literally all the content is synthetic.
In fact, a mix of synthetic and human generated performs better than either/or.
Which makes sense, as the collapse is a result of distribution edges eroding, so keeping human content prevents that, but then the synthetic content is increasingly biased towards excellence in more modern models, so the overall data set has an improved median oven the human only set. Best of both worlds.
Using Gab as an example, you can see from other comments that in spite of these instructions the model answers are more nuanced and correct than Gab posts. So if you only had Gab posts you’d have answers from morons, and the synthetic data is better. But if you only had synthetic data, it wouldn’t know what morons look like to avoid those answers and develop nuance around them.
Because without it they don’t like the result.
They’re so dumb they assumed the thing that was getting AI to disagree with them was the censorship and as soon as they ended up with uncensored models were being told they were disgusting morons.
The problem with ‘AGI’ is that it’s a nonsense term with no agreed upon meaning. I remember in a discussion on Hacker News describing one of Sam Altman’s definitions and being told by someone “no one defines it that way.” It’s a term that means whatever the eye of the beholder finds it convenient to mean.
The article’s point was more that when the term was originally coined it was to distinguish from narrow AI, and according to that original definition and distinction we’re already there (which I definitely agree with).
It’s not saying we’re already at AGI as it’s loosely being used today, where in the comments there’s a handful of better options for that term than AGI, though in spite of it I’m sure we’ll continue to use AGI to the point of meaninglessness as a goal post we’ll never define as met until one day in the far future we claim it’s always been agreed upon as having been met years ago and no one ever doubted it.
And yes, I agree that ‘sentience’ is a red herring discussion point when it comes to LLMs. A cockroach is sentient by the dictionary definition. But a cockroach can’t make similes to Escher drawings in a discussion, which is perhaps the more impressive quality.
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Meanwhile, here’s an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn’t exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:
Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It’s also worth noting that Claude 3 Opus doesn’t have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).