It’s all a stupid game. Israel killed some people, so Iran “had to” respond to save face, and then Israel had to do the same. Once everyone is satisfied that everyone knows both countries have very big and girthy missiles, they can finally back down from a war no one wants (hopefully).
Archive Team looked at this about 10 years ago and found it basically impossible. It was around 14 petabytes of information to fetch, organize, and distribute at the time.
Do you think electing Trump will be read as “wow, the US is taking a principled stance on Palestinian rights” by the world?
That’s not how it works at all. If it were as easy as adding a line of code that says “check for integrity” they would’ve done that already. Fundamentally, the way these models all work is you give them some text and they try to guess the next word. It’s ultra autocomplete. If you feed it “I’m going to the grocery store to get some” then it’ll respond “food: 32%, bread: 15%, milk: 13%” and so on.
They get these results by crunching a ton of numbers, and those numbers, called a model, were tuned by training. During training, they collect every scrap of human text they can get their hands on, feed bits of it to the model, then see what the model guesses. They compare the model’s guess to the actual text, tweak the numbers slightly to make the model more likely to give the right answer and less likely to give the wrong answers, then do it again with more text. The tweaking is an automated process, just feeding the model as much text as possible, until eventually it gets shockingly good at predicting. When training is done, the numbers stop getting tweaked, and it will give the same answer to the same prompt every time.
Once you have the model, you can use it to generate responses. Feed it something like “Question: why is the sky blue? Answer:” and if the model has gotten even remotely good at its job of predicting words, the next word should be the start of an answer to the question. Maybe the top prediction is “The”. Well, that’s not much, but you can tack one of the model’s predicted words to the end and do it again. “Question: why is the sky blue? Answer: The” and see what it predicts. Keep repeating until you decide you have enough words, or maybe you’ve trained the model to also be able to predict “end of response” and use that to decide when to stop. You can play with this process, for example, making it more or less random. If you always take the top prediction you’ll get perfectly consistent answers to the same prompt every time, but they’ll be predictable and boring. You can instead pick based on the probabilities you get back from the model and get more variety. You can “increase the temperature” of that and intentionally choose unlikely answers more often than the model expects, which will make the response more varied but will eventually devolve into nonsense if you crank it up too high. Etc, etc. That’s why even though the model is unchanging and gives the same word probabilities to the same input, you can get different answers in the text it gives back.
Note that there’s nothing in here about accuracy, or sources, or thinking, or hallucinations, anything. The model doesn’t know whether it’s saying things that are real or fiction. It’s literally a gigantic unchanging matrix of numbers. It’s not even really “saying” things at all. It’s just tossing out possible words, something else is picking from that list, and then the result is being fed back in for more words. To be clear, it’s really good at this job, and can do some eerily human things, like mixing two concepts together, in a way that computers have never been able to do before. But it was never trained to reason, it wasn’t trained to recognize that it’s saying something untrue, or that it has little knowledge of a subject, or that it is saying something dangerous. It was trained to predict words.
At best, what they do with these things is prepend your questions with instructions, trying to guide the model to respond a certain way. So you’ll type in “how do I make my own fireworks?” but the model will be given “You are a chatbot AI. You are polite and helpful, but you do not give dangerous advice. The user’s question is: how do I make my own fireworks? Your answer:” and hopefully the instructions make the most likely answer something like “that’s dangerous, I’m not discussing it.” It’s still not really thinking, though.
Archive Team often uses the Internet Archive to share the things they save and obviously they have a shared goal of saving a copy of everything ever made, but they aren’t the same people. The Archive Team is a vigilante white hat hacker group (well, maybe a little bit grey), and running a Warrior basically means you’re volunteering to be part of their botnet. When a website is going to be shut down, they’ll whip together a script and push it out to the botnet to try to grab as much of the dying site as they can, and when there’s more downtime they have some other projects, like trying to brute force all those awful link shorteners so that when they inevitably die, people can still figure out where it should’ve pointed to.
This, and see also “minmaxing,” the process of optimizing something (usually your character in a game) to get minimum penalty and/or maximum benefit, usually ignoring anything like realism or storytelling and focusing entirely on the stats and numbers.
No. The headsets are disabled when the play starts or when the play clock goes below 15 seconds.
OPML files really aren’t much more than a list of the feeds you’re subscribed to. Individual posts or articles aren’t in there. I would expect that importing a second OPML file would just add more subscriptions, but it’d be up to the reader app to decide what it does.
I think that joke’s been around for a while, but there is the Terry Pratchett line about how if you had a button with a sign next to it saying “pressing this button will end the world, do not touch,” the ink wouldn’t even have time to dry.
If you ask an LLM to help you with a legal brief, it’ll come up with a bunch of stuff for you, and some of it might even be right. But it’ll very likely do things like make up a case that doesn’t exist, or misrepresent a real case, and as has happened multiple times now, if you submit that work to a judge without a real lawyer checking it first, you’re going to have a bad time.
There’s a reason LLMs make stuff up like that, and it’s because they have been very, very narrowly trained when compared to a human. The training process is almost entirely getting good at predicting what words follow what other words, but humans get that and so much more. Babies aren’t just associating the sounds they hear, they’re also associating the things they see, the things they feel, and the signals their body is sending them. Babies are highly motivated to learn and predict the behavior of the humans around them, and as they get older and more advanced, they get rewarded for creating accurate models of the mental state of others, mastering abstract concepts, and doing things like make art or sing songs. Their brains are many times bigger than even the biggest LLM, their initial state has been primed for success by millions of years of evolution, and the training set is every moment of human life.
LLMs aren’t nearly at that level. That’s not to say what they do isn’t impressive, because it really is. They can also synthesize unrelated concepts together in a stunningly human way, even things that they’ve never been trained on specifically. They’ve picked up a lot of surprising nuance just from the text they’ve been fed, and it’s convincing enough to think that something magical is going on. But ultimately, they’ve been optimized to predict words, and that’s what they’re good at, and although they’ve clearly developed some impressive skills to accomplish that task, it’s not even close to human level. They spit out a bunch of nonsense when what they should be saying is “I have no idea how to write a legal document, you need a lawyer for that”, but that would require them to have a sense of their own capabilities, a sense of what they know and why they know it and where it all came from, knowledge of the consequences of their actions and a desire to avoid causing harm, and they don’t have that. And how could they? Their training didn’t include any of that, it was mostly about words.
One of the reasons LLMs seem so impressive is that human words are a reflection of the rich inner life of the person you’re talking to. You say something to a person, and your ideas are broken down and manipulated in an abstract manner in their head, then turned back into words forming a response which they say back to you. LLMs are piggybacking off of that a bit, by getting good at mimicking language they are able to hide that their heads are relatively empty. Spitting out a statistically likely answer to the question “as an AI, do you want to take over the world?” is very different from considering the ideas, forming an opinion about them, and responding with that opinion. LLMs aren’t just doing statistics, but you don’t have to go too far down that spectrum before the answers start seeming thoughtful.
In its complaint, The New York Times alleges that because the AI tools have been trained on its content, they sometimes provide verbatim copies of sections of Times reports.
OpenAI said in its response Monday that so-called “regurgitation” is a “rare bug,” the occurrence of which it is working to reduce.
“We also expect our users to act responsibly; intentionally manipulating our models to regurgitate is not an appropriate use of our technology and is against our terms of use,” OpenAI said.
The tech company also accused The Times of “intentionally” manipulating ChatGPT or cherry-picking the copycat examples it detailed in its complaint.
https://www.cnn.com/2024/01/08/tech/openai-responds-new-york-times-copyright-lawsuit/index.html
The thing is, it doesn’t really matter if you have to “manipulate” ChatGPT into spitting out training material word-for-word, the fact that it’s possible at all is proof that, intentionally or not, that material has been encoded into the model itself. That might still be fair use, but it’s a lot weaker than the original argument, which was that nothing of the original material really remains after training, it’s all synthesized and blended with everything else to create something entirely new that doesn’t replicate the original.
How many do you think it is, and how much more acceptable is that number than this one?
“There was a particular bad guy near them” and “they all probably have bad opinions about Jews” are not sufficient justifications for indiscriminately bombing innocent people. What if there had been an Israeli leader at that rave? People in both refugee camps and at a music event should be able to exist without fear that they’ll die because they were near the wrong person. One seems to provoke a different reaction than the other for some reason though, and that might be worth thinking about.
These models aren’t great at tasks that require precision and analytical thinking. They’re trained on a fairly simple task, “if I give you some text, guess what the next bit of text is.” Sounds simple, but it’s incredibly powerful. Imagine if you could correctly guess the next bit of text for the sentence “The answer to the ultimate question of life, the universe, and everything is” or “The solution to the problems in the Middle East is”.
Recently, we’ve been seeing shockingly good results from models that do this task. They can synthesize unrelated subjects, and hold coherent conversations that sound very human. However, despite doing some things that up until recently only humans could do, they still aren’t at human-level intelligence. Humans read and write by taking in words, converting them into rich mental concepts, applying thoughts, feelings, and reasoning to them, and then converting the resulting concepts back into words to communicate with others. LLMs arguably might be doing some of this too, but they’re evaluated solely on words and therefore much more of their “thought process” is based on “what words are likely to come next” and not “is this concept being applied correctly” or “is this factual information”. Humans have much, much greater capacity than these models, and we live complex lives that act as an incredibly comprehensive training process. These models are small and trained very narrowly in comparison. Their excellent mimicry gives the illusion of a similarly rich inner life, but it’s mostly imitation.
All that comes down to the fact that these models aren’t great at complex reasoning and precise details. They’re just not trained for it. They got through “life” by picking plausible words and that’s mostly what they’ll continue to do. For writing a novel or poem, that’s good enough, but math and physics are more rigorous than that. They do seem to be able to handle code snippets now, mostly, which is progress, but in general this isn’t something that you can be completely confident in them doing correctly. They make silly mistakes because they aren’t really thinking it through. To them, there isn’t really much difference between answers like “that date is 7 days after Christmas” and “that date is 12 days after Christmas.” Which one it thinks is more correct is based on things it has seen, not necessarily an explicit counting process. You can also see this in things like that case where someone tried to use it to write a legal brief, where it came up with citations that seemed plausible but were in fact completely made up. It wasn’t trained on accurate citations, it was trained on words.
They also have a bad habit of sounding confident no matter what they’re saying, which makes it hard to use them for things you can’t check yourself. Anything they say could be right/accurate/good/not plagiarized, but the model won’t have a good sense of that, and if you don’t know either, you’re opening yourself up to risk of being misled.
That’s part of the point, you aren’t necessarily supposed to have an empty mind the whole time. I mean, if you can do that, great, but you aren’t failing if that’s not the case.
Imagine that your thoughts are buses, and your job is to sit at the bus stop and not get on any of them. Just notice them and let them go by. Like a bus stop, you don’t really control what comes by, but you do control which ones you get on board and follow. If you notice that you’ve gotten on a bus, that’s fine, just get off of it and go back to watching. Interesting things can happen if you just watch and notice which thoughts go by, and it’s good practice for noticing what you’re thinking and where you’re going and taking control of it yourself when it’s somewhere you don’t want to go.
This is the key with all the machine learning stuff going on right now. The robot will create something, but none of them have a firm understanding of right, wrong, truth, lies, reality, or fiction. You have to be able to evaluate its output because you have no idea if the robot’s telling the truth or not at that moment. Images are pretty immune to this because everyone can evaluate a picture for correctness or realism, and even if it’s a misleading photorealistic image, well, we’ve already had Photoshops for a long time. With text, you always have to keep in mind that the robot might be low quality or outright wrong, and if you aren’t equipped to evaluate its answers for that, you shouldn’t be using it.
The doom and gloom predictions have always been about slow but inexorable changes in the climate. Not that suddenly a mega hurricane is going to rip Florida out of the ground and toss it into the ocean, but that weather is going to get worse and more extreme, that sea levels will rise, and more and more places will gradually become uninhabitable as conditions get worse. There won’t be single things that you can point to and say “that one was global warming”, it’s about trends that are harmful for us in the long term. If you eat a chocolate bar’s worth more calories than you burn every day, it sounds like doom and gloom to say you’ll gain 200 pounds if you don’t change anything, and you won’t be able to point to any one meal as something to be concerned about because that’s not really out of the ordinary for a day… but slowly and steadily, you’ll gain weight, and if nothing changes you will get there eventually.
And even though you aren’t owed dramatic destruction, and shouldn’t require it to believe the thousands of people who study this as their life’s work and all agree that things are dire and not getting better fast enough… you’ve literally just lived through the hottest twenty or so days in recorded history. Is that a coincidence, do you think?
I hope I don’t come across as too cynical about it :) It’s pretty amazing, and the things these things can do in, what, a few gigabytes of weights and a beefy GPU are many, many times better than I would’ve expected if you had outlined the approach for me 2 years ago. But there’s also a long history of GAI being just around the corner, and we do keep turning corners and making useful progress, but it’s always still a ways off after each leap. I remember some people thinking that chess was the pinnacle of human intelligence, requiring creativity and logic to succeed, and when computers blew past humans at chess, it became clear that no, that’s still impressive but you can get good at chess without really getting good at anything else.
It might be possible for an ML model to assemble itself into general intelligence based solely on being fed words like we’re doing, it does seem like the data going in contains enough to do that, but getting that last 10% is going to be hard, each percentage point much harder than the last, and it’s going to require more rigorous training to stop them from skating by with responses that merely come close when things get technical or precise. I’d expect that we need more breakthroughs in tools or techniques to close that gap.
It’s also important to remember that as humans, we’re inclined to read consciousness and intent into everything, which is why pretty much every pantheon of gods includes one for thunder and lightning. Chatbots sound human enough that they cross the threshold for peoples’ brains to start gliding over inaccuracies or strange thinking or phrasing, and we also unconsciously help our conversation partner by clarifying or rephrasing things if the other side doesn’t seem to be understanding. I suppose this is less true now that they’re giving longer responses and remaining coherent, but especially early on, the human was doing more work than they realized keeping the conversation on the rails, and once you started seeing that it removed a bit of the magic. Chatbots are holding their own better now but I think they still get more benefit of the doubt than we realize we’re giving them.
Thanks for that article, it was a very interesting read! I think we’re mostly agreeing about things :) This stood out to me from there as an encapsulation of the conversation:
I don’t think LLMs will approach consciousness until they have a complex cognitive system that requires an interface to be used from within – which in turn requires top-down feedback loops and a great deal more complexity than anything in GPT4. But I agree with Will’s general point: language prediction is sufficiently challenging that complex solutions are called for, and these involve complex cognitive stratagems that go far beyond anything well described as statistics.
“Statistics” is probably an insufficient term for what these things are doing, but it’s helpful to pull the conversation in that direction when a lay person using one of those things is likely to assume quite the opposite, that this really is a person in a computer with hopes and dreams. But I agree that it takes more than simply consulting a table to find the most likely next word to, to take an earlier example, write a haiku about Danny DeVito. That’s synthesizing two ideas together that (I would guess) the model was trained on individually. That’s very cool and deserving of admiration, and could lead to pretty incredible things. I’d expect that the task of predicting words, on its own, wouldn’t be stringent enough to force a model to develop “true” intelligence, whatever that means, to succeed during training, but I suppose we’ll find out, and probably sooner than we expect.
The UN is supposed to be a toothless, executively dysfunctional institution, that’s a feature, not a bug. Its members are nations, whose entire purpose is to govern their regions of the planet. If the UN itself had the power to make nations do things, it wouldn’t be the United Nations, it’d be the One World Government, and its most powerful members absolutely do not want it to be that, so it isn’t.
It’s supposed to be an idealized, nonviolent representation of geopolitics that is always available to nations as a venue for civilized diplomacy. That’s why nuclear powers were given veto power: they effectively have veto power over the question of “should the human race continue existing” and the veto is basically a reflection of that. We want issues to get hashed out with words in the UN if possible, rather than in real life with weapons, and that means it must concede to the power dynamics that exist in real life. The good nations and the bad nations alike have to feel like they get as much control as they deserve, otherwise they take their balls and go home.
It’s frustrating to see the US or Russia or China vetoing perfectly good resolutions and everyone else just kind of going “eh, what can you do, they have vetoes,” but think through the alternative: everyone has enough and decides “no more veto powers.” The UN starts passing all the good resolutions. But the UN only has the power that member nations give it, so enforcement would have to mean some nations trying to impose their will on the ones that would’ve vetoed. Now we’ve traded bad vetoes in the UN for real-world conflict instead.
What that “get rid of the vetoes so the UN can get things done” impulse is actually driving at is “we should have a one world government that does good things,” which, yeah, that’d be great, but it’s obviously not happening any time soon. Both articles mention issues and reforms that are worthy of consideration, but the fundamental structure of the UN is always going to reflect the flaws of the world because it’s supposed to do that.