It’s important to be aware of their opinions because they quickly become policy and rhetoric while Dems do damage control and fail to fix the underlying issues Reps exploit. In this case, having an instance where they directly contradict their own “sleepy Joe” narrative can help deconstruct the beliefs of family members who have fallen for it.

This is a sane and measured response to a terrorist attack /s Just do terrorism back 100-fold, I guess?

I think it’s moreso a matter of evolution. We know humanoid bodies can do anything we can do, so we start with that and make incremental improvements from there. We already do have plenty of other body shapes for robots (6-axis arms, SPOT, drones, etc) but none of them are general-purpose. Also, the robot shown in the article is pretty clearly not fully humanoid, it has weird insect legs probably because it’s easier to control and it doubles as a vertical lift.

Network effects. The “we” you’re referring to could only be like 100 million at most, the vast majority of people don’t have the technical know-how to switch, or to articulate exactly why they feel miserable every time they log in for their daily fix.

Considering prior authorization is predicated on the fact that if they reject enough requests inevitably some people won’t fight them, meaning they don’t have to pay out, I wouldn’t be surprised if they use a slightly better than chance prediction as justification for denying coverage, if they even need an actual excuse to begin with.

For what it’s worth I don’t think they’re proposing it will “solve” climate change - no single thing can. It’s millions of tiny (alleged) improvements like this which eventually add up to taking pressure off of the environment. I see this kind of attitude a lot with stuff like paper straws or biodegradable packaging, as if the idea of a small but meaningful step in the right direction is laughable. It’s fine to criticize them for the “improvement” actually being no better than the alternative, but I worry sometimes it comes across like any sort of improvement short of “solving” climate change isn’t worthwhile.

If we had access to the original model, we could give it the same seed and prompt and get the exact image back. Or, we could mandate techniques like statistical fingerprinting. Without the model though, it’s proven to be mathematically impossible the better models get in the coming years - and what do you do if they take a real image, compress it into an embedding, then reassemble it?

I respect your boldness to ask these questions, but I don’t feel like I can adequately answer them. I wrote a 6 paragraph essay but using GPT-4 as a sensitivity reader, I don’t think I can post it without some kind of miscommunication or unintentional hurt. Instead, I’ll answer the questions directly by presenting non-authoritative alternate viewpoints.

1. No idea, maybe someone else knows
2. That makes sense to me; I would think there would be a strong pressure to present fake content as real to avoid getting caught but they’re already in deep legal trouble anyway and I’m sure they get off to it too. It’s hard to know for sure because it’s so stigmatized that the data are both biased and sparse. Good luck getting anyone to volunteer that information
3. I consider pedophilia (ie the attraction) to be amoral but acting on it to be “evil”, ala noncon, gore, necrophilia, etc. That’s just from consistent application of my principles though, as I haven’t humanized them enough to care that pedophilia itself is illegal. I don’t think violent video games are quite comparable because humans normally abhor violence, so there’s a degree of separation, whereas CP is inherently attractive to them. More research is needed, if we as a society care enough to research it.
4. I don’t quite agree, rights are hard-won and easy-lost but we seem to gain them over time. Take trans rights to healthcare for example - first it wasn’t available to anyone, then it was available to everyone (trans or not), now we have reactionary denials of those rights, and soon we’ll get those rights for real, like what happened with gay rights. Also, I don’t see what rights are lost in arguing for the status quo that pedophilia remain criminalized? If MAPs are any indication, I’m not sure we’re ready for that tightrope, and there are at least a dozen marginalized groups I’d rather see get rights first. Unlike gay people for instance, being “in the closet” is a net societal good because there’s no valid way to present that publicly without harming children or eroding their protections.

The legality doesn’t matter, what matters is that the sites will be flooded and could be taken down if they aren’t able to moderate fast enough. The only long-term viable solution is image classification, but that’s a tall ask to make from scratch.

I think a sizable portion of the Republican voter base are those whose family have voted Republican for generations and they haven’t thought enough about it to change their vote out of habit - or even worse, they’re voting because they value group cohesion over truth and everyone else around them is Republican and speaks in their rhetoric. Why else would they vote for a party so wildly against their own best interests?

You’re right, apologies. Skimmed too hard

There’s a lot of papers which propose adding new tokens to elicit some behavior or another, though I haven’t seen them catch on for some reason. A new token would mean adding a new trainable static vector which would initially be something nonsensical, and you would want to retrain it on a comparably sized corpus. This is a bit speculative, but I think the introduction of a token totally orthogonal to the original (something like eg smell, which has no textual analog) would require compressing some of the dimensions to make room for that subspace, otherwise it would have a form of synesthesia, relating that token to the original neighboring subspaces. If it was just a new token still within the original domain though, you could get a good enough initial approximation by a linear combination of existing token embeddings - eg a monkey with a hat emoji comes out, you add tokens for monkey emoji + hat emoji, then finetune it.

Most extreme option, you could increase the embedding dimensionality so the original subspaces are unaffected and the new tokens can take up those new dimensions. This is extreme because it means resizing every matrix in the model, which even for smaller models would be many thousands of parameters, and the performance would tank until it got a lot more retraining.

(deleted original because I got token embeddings and the embedding dimensions mixed up, essentially assuming a new token would use the “extreme option”).

There’s a lot of papers which propose adding new tokens to elicit some behavior or another, though I haven’t seen them catch on for some reason. A new token would mean adding a new trainable static vector which would initially be something nonsensical, and you would want to retrain it on a comparably sized corpus. This is a bit speculative, but I think the introduction of a token totally orthogonal to the original (something like eg smell, which has no textual analog) would require compressing some of the dimensions to make room for that subspace, otherwise it would have a form of synesthesia, relating that token to the original neighboring subspaces. If it was just a new token still within the original domain though, you could get a good enough initial approximation by a linear combination of existing token embeddings - eg a monkey with a hat emoji comes out, you add tokens for monkey emoji + hat emoji, then finetune it.

Most extreme option, you could increase the embedding dimensionality so the original subspaces are unaffected and the new tokens can take up those new dimensions. This is extreme because it means resizing every matrix in the model, which even for smaller models would be many thousands of parameters, and the performance would tank until it got a lot more retraining.

LLMs are not expert systems, unless you characterize them as expert systems in language which is fair enough. My point is that they’re applicable to a wide variety of tasks which makes them general intelligences, as opposed to an expert system which by definition can only do a handful of tasks.

If you wanted to use an LLM as an expert system (I guess in the sense of an “expert” in that task, rather than a system which literally can’t do anything else), I would say they currently struggle with that. Bare foundation models don’t seem to have the sort of self-awareness or metacognitive capabilities that would be required to restrain them to their given task, and arguably never will because they necessarily can only “think” on one “level”, which is the predicted text. To get that sort of ability you need cognitive architectures, of which chatbot implementations like ChatGPT are a very simple version of. If you want to learn more about what I mean, the most promising idea I’ve seen is the ACE framework. Frameworks like this can allow the system to automatically look up an obscure disease based on the embedded distance to a particular query, so even if you give it a disease which only appears in the literature after its training cut-off date, it knows this disease exists (and is a likely candidate) by virtue of it appearing in its prompt. Something like “You are an expert in diseases yadda yadda. The symptoms of the patient are x y z. This reminds you of these diseases: X (symptoms 1), Y (symptoms 2), etc. What is your diagnosis?” Then you could feed the answer of this question to a critical prompting, and repeat until it reports no issues with the diagnosis. You can even make it “learn” by using LoRA, or keep notes it writes to itself.

As for poorer data distributions, the magic of large language models (before which we just had “language models”) is that we’ve found that the larger we make them, and the more (high quality) data we feed them, the more intelligent and general they become. For instance, training them on multiple languages other than English somehow allows them to make more robust generalizations even just within English. There are a few papers I can recall which talk about a “phase transition” which happens during training where beforehand, the model seems to be literally memorizing its corpus, and afterwards (to anthropomorphize a bit) it suddenly “gets” it and that memorization is compressed into generalized understanding. This is why LLMs are applicable to more than just what they’ve been taught - you can eg give them rules to follow within the conversation which they’ve never seen before, and they are able to maintain that higher-order abstraction because of that rich generalization. This is also a major reason open source models, particularly quantizations and distillations, are so successful; the models they’re based on did the hard work of extracting higher-order semantic/geometric relations, and now making the model smaller has minimal impact on performance.

LLMs are not chatbots, they’re models. ChatGPT/Claude/Bard are chatbots which use LLMs as part of their implementation. I would argue in favor of the article because, while they aren’t particularly intelligent, they are general-purpose and exhibit some level of intelligence and thus qualify as “general intelligence”. Compare this to the opposite, an expert system like a chess computer. You can’t even begin to ask a chess computer to explain what a SQL statement does, the question doesn’t even make sense. But LLMs are capable of being applied to virtually any task which can be transcribed. Even if they aren’t particularly good, compared to GPT-2 which read more like a markov chain they at least attempt to complete the task, and are often correct.

It’s actually false. You can’t teach them long-term, but within the course of a conversation they can be taught new rules and behaviors. There’s dozens if not hundreds of papers on it.

The fact that they can perform at all in essentially any task means they’re general intelligences. For comparison, the opposite of a general intelligence is an expert system, like a chess computer. You can’t even begin to ask a chess computer to classify the valence of a tweet, the question doesn’t make sense.

I think people (including myself until reading the article) have confused AGI to mean “as smart as humans” or even as “artificial person”, neither of which is actually true when you break down the term. What is general intelligence if not applicability to a broad range of tasks?

“You can use them for all kinds of tasks” - so would you say they’re generally intelligent? As in they aren’t an expert system?

Actually a really interesting article which makes me rethink my position somewhat. I guess I’ve unintentionally been promoting LLMs as AGI since GPT-3.5 - the problem is just with our definitions and how loose they are. People hear “AGI” and assume it would look and act like an AI in a movie, but if we break down the phrase, what is general intelligence if not applicability to most domains?

This very moment I’m working on a library for creating “semantic functions”, which lets you easily use an LLM almost like a semantic processor. You say await infer(f"List the names in this text: {text}") and it just does it. What most of the hype has ignored with LLMs is that they are not chatbots. They are causal autoregressive models of the joint probabilities of how language evolves over time, which is to say they can be used to build chatbots, but that’s the first and least interesting application.

So yeah, I guess it’s been AGI this whole time and I just didn’t realize it because they aren’t people, and I had assumed AGI implied personhood (which it doesn’t).

It feels kind of hopeless now that we’d ever get something that feels so “radical”, but I’d like to remind people that 80+ hour work weeks without overtime used to be the norm before unions got us the 40 hour work week. It feels inevitable and hopeless until the moment we get that breakthrough, then it becomes the new norm.