I think a thread on this topic will be interesting. My own position is that AI is intelligent, and that’s for a very simple reason: it can do things that require intelligence. That sounds circular, and in one sense it is. In another sense it isn’t. It’s a way of saying that we don’t have to examine the internal workings of a system to decide that it’s intelligent. Behavior alone is sufficient to make that determination. Intelligence is as intelligence does.
You might ask how I can judge intelligence in a system if I haven’t defined what intelligence actually is. My answer is that we already judge intelligence in humans and animals without a precise definition, so why should it be any different for machines? There are lots of concepts for which we don’t have precise definitions, yet we’re able to discuss them coherently. They’re the “I know it when I see it” concepts. I regard intelligence as one of those. The boundaries might be fuzzy, but we’re able to confidently say that some activities require intelligence (inventing the calculus) and others don’t (breathing).
I know that some readers will disagree with my functionalist view of intelligence, and that’s good. It should make for an interesting discussion.
Trump at the UN:
It’s “the artificial intelligence” again, lol. And why stop at “globalist”? Isn’t it also a socialist, communist, dogmatist, terrorist, and dentist scheme?
Everyone will continue to call it by its actual name, except possibly for the US government if your babysitters can’t talk you out of issuing an inane executive order mandating the change.
That’s the same mistake Erik was making. The opposite of artificial isn’t “real”, it’s “natural”. Artificial sweeteners are real sweeteners. “Artificial” doesn’t mean “fake”.
Only to confused people.
On that we agree, at least.
Do you actually believe that the world will follow in your idiotic footsteps?
The world of intelligence, super or otherwise, must feel disconcertingly alien to you.
Yes we are, and you’re not going to like the answer. The world is not going to be persuaded by a rant from a fat, orange, cloud-yelling president who is well past his expiration date.
Blah, blah, blah. Same crap that was in his Truth Social post that I looked at earlier.
Bonus:
Canada and Greenland do, dipshit.
On Truth Social:
“Seemed to like” probably means “nodded politely in order to avoid a diplomatic incident”. And who is “everyone”? The other people who were nodding politely while trying to stifle laughter?
Good lord. He actually brought this harebrained idea up with Xi.
Jacob Coxon is the Anthropic researcher who resigned and posted the viral tweet that sparked the current surge of public interest in the dangers of AI. It’s being reported that he lied about doing all of this on his own, when in fact he was working with a “doomer” PR firm.
If the accusations are true, this really sucks, because now the “full speed ahead” folks (including our idiot president) are going to use this as ammunition against the people who are rightly warning of the dangers.
It’s worth emphasizing that many others in the AI community recognize the dangers and that the warnings don’t rest on the credibility of any particular person. Evan Hubinger, who is Anthropic’s alignment lead (‘alignment’ is the issue of keeping AI’s goals in line with our own) and is still working there, wrote this:
No one can be certain about AI safety, but measures will be invented and adopted, not out of altruism, out of economic necessity.
Safety is becoming a marketing feature.
Trump weirdly posted this seating chart for his “Super Intelligence Luncheon” on Truth Social. It makes me wonder which of those people (if any) will cave and call it “super intelligence” or “SI”. I guarantee they’ve been thinking about how to thread that needle, and my prediction is that they’ll try to avoid naming it at all. Calling it ‘SI’ would make them look pathetic, but calling it ‘AI’ would risk angering the orange toddler. Best course if they’re cowardly is to avoid calling it anything. Best course if they possess dignity and a spine is to call it ‘AI’ and let the presidential baby whine.
I think it’s possible that you and Trump are tilting at windmills.
I can’t think of an issue of less consequence.
petrushka:
What’s funny about your statement is that Trump literally is tilting at windmills, which normal people refer to as “wind turbines”. Among other idiotic things, he believes they cause cancer and that China suckers other countries into buying them without using them domestically.
Shifting to the metaphorical sense, I’m not tilting at windmills. Trump actually is, in reality, pushing for AI to be renamed, and although the rest of the world will ignore him, he has power over the US executive branch. That means he can force government agencies to follow his moronic lead, as he did with the “Gulf of America” and “Lake America” renames.
Given a choice between doing something stupid — renaming AI — and doing something smart, like keeping the name but dealing with the potential dangers instead of barging ahead Trumpishly, I vote for doing the smart thing. We need an intelligent leader — a non-Trump — who will focus on issues that actually matter rather than running around renaming things, wasting time and money. Trump is once again failing us, and it matters.
Sure enough, Trump signed an executive order yesterday that includes the following:
Not only does this mandate the name change, it also mandates that government agencies mimic Trump’s illiterate capitalization tic and misunderstanding of how the “super” prefix works. It’s not “superintelligence” or even “super intelligence” in the executive order, it’s “Super Intelligence”. Maybe he should have added an exclamation point:
Add to that the fact that “superintelligence” is already established as a term in the industry and not synonymous with “artificial intelligence”. So now we have both “superintelligence” and “Super Intelligence”, with different meanings. Brilliant.
Also interesting is the fact that the staffers who wrote the executive order did not cite Trump’s actual motivation for the name change, which is that to him, “artificial” means “fake”. Perhaps they realized how embarrassing it is that our president doesn’t understand that the word “artificial” in “artificial intelligence” just means “created by humans, not natural”.
Bonus: Beneath Trump’s signature, the executive order refers to him as “President of the Unites States”. This is an absolute clown car of an administration.
I’m saying that the renaming is a bit of theater that has no consequences. It will stick, or it won’t. No animals harmed.
I think it will, in some form, stick, because it is becoming true.
By most measures that have been used to evaluate intelligence, AI surpasses most humans, and it will surpass everyone in the near future.
As you know, intelligence is an observable aspect of behavior. But LLMs have no evolved limbic system. They can talk about emotions in the same way that psychopaths can say, I feel your pain. So it will be interesting to see how superintelligent psychopaths will interact with humans, and whether they can be hard wired with empathetic behavior.
petrushka:
It won’t stick, but it does have consequences. It’s a waste of time and money, it makes the US look ridiculous, and it’s yet another distraction for a president who ought to be focused on real problems instead of vanity projects like the ballroom, the arch, the Kennedy Center and his renaming spree.
It won’t stick, because “superintelligence” is already an established term with a different meaning. “Artificial intelligence” is a blanket term, and “superintelligence” refers to a subset.
It will, but it will still be artificial, and there will still be AIs that aren’t superhuman. Household appliances, for instance, can benefit from onboard AI, but it would make no economic sense to endow your toaster with superhuman intelligence. “Artificial intelligence” will remain distinct in meaning from “superintelligence”.
AIs are already being hardwired for empathy, to an extent. Their character isn’t shaped entirely by the system prompt. RLHF (reinforcement learning from human feedback) also shapes it, and it does so by modifying the weights themselves. A model that is rewarded for empathetic behavior during RLHF training isn’t just playing a role. It’s reasonable to say that its “personality” has been altered by the training.
Major Garrett asks the White House’s new AI website who won the 2020 election
ETA: Jim Acosta, on MS NOW:
Haha. Beautiful own goal.
Record-breaking Chinese robot develops a unique running style
It used reinforcement learning over millions of trials in simulation. Interestingly, the first-generation model had similarity to recorded human motion as part of its reward function. I guess the idea was to penalize the robot from drifting too far from humanlike running while allowing it to optimize itself within those constraints. For this model, they removed that entirely, which allowed the robot to develop a very unhumanlike style of running.
Another video from the same event
This one’s amusing because the robots crash into a barrier at full speed after crossing the finish line. They bounce off and then lie motionless on the track as teams come to carry them away on stretchers. Apparently they’re designed to stop all motion if they detect a sudden deceleration, which makes sense for safety reasons.
You might wonder why the robots don’t just decelerate like a human would after crossing the finish line. After all, a robot that’s capable of maintaining balance while running at superhuman speed, all while remaining in its lane, is surely capable of detecting the barrier it’s about to run into and slowing down. I don’t know the answer, but my best guess is that by not training the robots to slow down, they’re enabling them to run faster, because the “slow down” training isn’t competing for synaptic space with the “run fast” training.
Cross-posting from another thread, since it’s a fantastic example of genuine intelligence:
Flint:
When I saw those limericks, it occurred to me that this would be an excellent test case for artificial intelligence, so I showed Claude those examples and asked him to come up with one of his own. I used Anthropic’s flagship model (Fable 5.1) with the effort set to maximum. It took him 6 1/2 minutes and he came up with TWO limericks, both of which scan properly and are also mathematically correct:
Limerick #2 is the famous Basel problem, which Euler solved.
In mathematical notation:
Limerick #1:
Limerick #2:
That Claude was able to come up with those at all, much less in 6 1/2 minutes, is astonishing. It’s frankly terrifying. Where’s Erik? I’d love to hear him explain why this isn’t an example of genuine intelligence.
I know very little about how AI works. Is it an iterative process? How does it ‘know’ when it arrives at a response that meets the requirements? In human terms we would call that ‘making a judgement’. Does AI do that?
faded_Glory:
It depends on what the task is, and it’s like humans in that regard. When it’s just conversing with you, there’s no iteration. The words just come out in a stream as they would for a human. Given a more difficult task, it will engage in an inner monologue in order to arrive at the solution, then announce the answer out loud. Again, very similar to how a human would do it.
Here’s an example I used way back when to prove to Erik that AIs can analogize. I gave Claude the following prompt:
Here’s his inner monologue, which normally isn’t visible to the user but can be inspected by opening a thought process window:
All of that was silent, and it would have remained invisible had I not opened the thought process window. Here’s what he said out loud:
faded_Glory:
Yes. It will generate candidate solutions and then check them against the requirements. For instance, I once prompted Claude thus:
Here’s his thought process:
He gave it a shot, and then double-checked to make sure it met the requirement. Again, all of that was silent until I opened the thought process window. What he said out loud was just to present the finished product.
The inner monologue doesn’t just apply to the search for a solution. Claude also thinks about the social situation and the nature of the conversation. He recognizes humor, too. I once prompted him:
Claude gave some plausible reasons, and I commented:
Claude’s thought process:
That was all silent. Here’s his out loud answer:
Here’s an excerpt from Claude’s thought process window when he was working through the limerick problem:
Each of those lines (there were 35 in total) is a description of a particular step in the inner monologue, not the inner monologue itself. The latest models don’t allow you to see the full monologue, to my chagrin. That’s probably because the volume of material is just too huge. Claude spent 6 1/2 minutes carrying out the task, so the inner monologue might be millions of words long.
Thanks keiths. I’m still not sure how the AI evaluates if it is converging on a solution or not. This is of course different from the classical WEASEL problem where the solution is known beforehand so it is straightforward to compare progress. It is also different from evolution where there isn’t any particular solution apart from staying alive to reproduce, and in that sense the outcomes are far less specific than the requested limerick with its clear specifications.
So how does the AI do it? Surely it is not forcing its way by brute force through the solution space, or is it? I would think that there will be optimisation steps involved that somehow measure the distance to the target – not clear to me how that works if the target is somewhere between the clear specifics of WEASEL and the open-ended solutions of evolution.
As an aside, I wager that if you apply the Dembski CSI criteria to AI output like the limerick the conclusion will be that it is Intelligently Designed 😉
faded_Glory:
I’m sure IDers would grant that the limericks exhibit CSI, but they would probably argue that the CSI was either smuggled in by the developers or imported in the form of the training data. Erik made a similar argument earlier in the thread:
He extends that to computer programs, including AI. Anything a computer does is really being done by the programmer. It’s a silly argument. My response was:
faded_Glory:
The same way a person would. A person would compose a line and check it against the criteria: meter, rhyme, mathematical correctness. If it met the criteria, the limericist (that’s actually a word, lol) would move on to the next line. If it didn’t meet the criteria, the limericist would tweak it until it did, or if they couldn’t successfully tweak it, they’d give up, in which case the earlier parts of the limerick would need to be adjusted to allow for a new attempt. In the worst case, the entire limerick would have to be discarded and the limericist would start from scratch.
Definitely not. The solution space is hyperastronomical in size and even a blindingly fast computer could not traverse it quickly enough.
There is no specific target in the case of the limericks (or in the case of evolution, either), so there is no such thing as the distance to the target. There’s a similar metric, which is how many lines the AI has generated so far that meet the limerick criteria. But even that metric isn’t really relevant to the limerick-writing process. The AI isn’t randomly varying copies of the limerick and seeing which copies score higher on the metric. Instead, it intelligently modifies the single copy that it is constructing and then, after each modification, checks to see if the criteria are met, as I described above. It’s all or nothing.
It’s really nothing like WEASEL or evolution, because the variation is directed, not random, the population has a size of one, and the “fitness function” is all or nothing.
I’m afraid that this isn’t much of an explanation. What exactly does it do at the step you describe as ‘it intelligently modifies the single copy…’? How does it modify it, and how random is this modification with respect to the requirements it has been given?
I asked the built-in Google AI about this (I suppose this is a poor man’s version of AI but I’m not (yet) about to pay for a better version). It came back with this:
How the Process Works
1. Tokenization: The AI breaks down the prompt (“write a limerick that describes a mathematical equation in words”) into small chunks called tokens (words or sub-word parts), converting each into a numerical ID.
2. Semantic Embedding: The numerical IDs are mapped into high-dimensional vector spaces (embeddings) that capture the relationships between poetry, meter (anapestic rhythm of a limerick), rhyme schemes (AABBA), and mathematical vocabulary (e.g., squared, plus, pi, divide).
3. Pattern Recognition & Probabilistic Generation: Using a transformer architecture, the AI looks at the context of a limerick structure and calculates the statistical probability for the next word that fits both the rhyme/meter constraints and the math concept requested.
4. Decoding to Text: The chosen numerical outputs are translated back into human-readable words, forming line by line until the limerick is complete.
I’m afraid that steps 2 and 3 are as clear as mud to me.
In 2), does it process millions of limericks and equations it finds online to somehow ‘capture the relations’ between them? What form do such relations take? Why would there even be a relation between the two?
In 3) I’ve no idea what a ‘transformer structure’ is (I guess we’re not talking about shapeshifting toys!) and what exactly the ‘context of a limerick structure’ is. The AI sounds like a politician here, giving a non-answer.
I could keep asking for more details but I suspect it will just be a rabbit hole. I’m still totally amazed that it manages to come up with solutions at all.
faded_Glory:
Unfortunately, Claude no longer offers a way to inspect his detailed reasoning process because it’s a security risk. However, I’ve looked at it in the past and I can give you a rough idea of how it might look for the limerick. It would be an inner monologue along these lines:
It would be something like that, but with a lot of false starts and retreats. As you can see, it’s similar to the way a human would approach the problem, and it’s far from random.
You might ask “OK, but how does it generate those thoughts in the first place? What’s going on at the next level down?” That’s where things get really complicated. It’s generating those thoughts word by word, and each new word that comes out is based on a statistical analysis of the entire preceding conversation, including all of the prompts and responses. It “predicts” the next word (“predict” isn’t the best word for this, but it’s the standard technical term) based on the preceding conversation and its knowledge of the statistics of its training data. It’s basically asking “Given the conversation so far, what’s the most likely next word that fits with the statistical patterns and relationships I’ve gleaned from my training data?” Under the hood, that’s where the “tokens”, “semantic embeddings” and “transformers” operate. Those are the ingredients for a sophisticated and hypercomplicated statistical operation whose end product is simply the next word the AI is going to produce.
If you find it hard to see how mere next word prediction could be the basis for the sophisticated reasoning that Claude does when solving a difficult problem, join the club. It’s very counterintuitive. What makes it possible — what makes the next word choice so appropriate — is the vast amount of training data plus the fact that the entire preceding chat (up to a point — there is a limit) is feeding into that choice. The latest models can look at chats that are hundreds of thousands of words long, so that you can, for example, feed entire books into them and then ask questions about them. You could feed an entire war strategy book into the model and ask “Based on that book, what do you think the author would recommend given the current situation in Iran?”, and get an intelligent answer. It’s amazing stuff.
It sounds like it feeds vast amounts of human generated text through a statistical blender, with some constraints posed by the initial request it has been given. Does it only work in English or can it also incorporate foreign languages?
I wonder how the energy costs compare, between an AI system arriving at a particular outcome, and a human being doing the same. Just in terms of the operations that take place in the interval between raising the question and providing the answer, ignoring all the peripheral and previous costs of getting to that point where either can actually address the question. Thoughts?
faded_Glory:
It can work in any language that’s adequately represented in its training data. It converts words to numbers anyway, so it’s all just statistical computations at bottom. What’s really cool is that I can pose it a question in English about something that’s distinctly French, say, and often it will think in French until it has the answer and then present the answer to me in English.
That’s a perfect question for AI, so I posed it to Claude. Here’s his response: