But the big crisis right now is that for an astonishing number of tasks that a normal person could come up with, chatgpt.com is actually a good at or better than a typical human.

If you took the current state of affairs back to the 90s you’d quickly convince most people that we’re there. Given that we’re actually not, we’re now have to come up with new goalposts.

Was thinking about this today. I had to do a simple wedding planning task - setting up my wedding website with FAQ, cobbling the guest list (together from texts, photos of my father’s address book, and excel spreadsheets), directions and advice for lodging, conjuring up a scheme to get people to use the on-site cabins, and a few other mundane tasks. No phone calls, no “deep research” just wrote browser-jockeying. Not even any code, the off-the-rack system just makes that for you (however I know for a fact an LLM would love to try to code this for me).

I know without a single doubt that I could not simply as an “AI” “agent” to do this today and expect any sort of a functional result, especially when some of these were (very simple) judgement calls or workarounds for absolutely filthy data and a janky wedding planning website UI.

The tests for AGI that keep getting made, including the ones in this paper, always feel like they're (probably unintentionally) constructed in a way that covers up AI's lack of cognitive versatility. AI functions much better when you do something like you see here, where you break down tasks into small restricted benchmarks and then see if they can perform well.

But when we say AGI, we want something that will function in the real world like a human would. We want to be able to say, "Here's 500 dollars. Take the car to get the materials, then build me a doghouse, then train my dog. Then go to the store, get the ingredients, and make dinner."

If the robotics aren't reliable enough to test that, then have it be a remote employee for 6 months. Not "have someone call up AI to wrote sections of code" - have a group of remote employees, make 10% AI, give them all the same jobs with the same responsibilities, and see if anyone notices a difference after 6 months. Give an AI an account on Upwork, and tell it to make money any way it can.

Of course, AI is nowhere near that level yet. So we're stuck manufacturing toy "AGI" benchmarks that current AI can at least have some success with. But these types of benchmarks only broadcast the fact that we know that current and near future AI would fail horribly at any actual AGI task we threw at it.

Can you point to an LLM passing the turing test where they didn't invalidate the test by limiting the time or the topics?

I've seen claims of passing but it's always things like "with only 3 questions" or "with only 3 minutes of interrogation" or "With only questions about topic X". Those aren't Turing Tests. As an example, if you limit the test to short things than anything will pass "Limit to 1 word one question". User types "Hello", LLM response "Hi". PASS! (not!)

Note that Turing test allows a lot leeway for the test settings, i.e. who interrogates it, how much they know about the weakness of current SOA models, are they allowed to use tools (I'm thinking of something like ARC-AGI but in a format that allows chat-based testing), and how long a chat is allowed etc. Therefore there can be multiple interpretations of whether the current models pass the test or not.

One could say that if there is maximally hard Turing test, and a "sloppy" Turing test, we are somewhere where the current models pass the sloppy version but not the maximally hard version.

I think the turing test suffers a bit from the "when a measurement becomes a target, it ceases to be a good measurement."

An AI that happened to be able to pass the turing test would be pretty notable because it probably implies much more capabilities behind the scenes. The problem with, for example, LLMs, they're essentially optimized turing test takers. That's about all they can do.

Plus, I don't think any LLM will pass the turing test in the long term. Once something organically comes up that they aren't good at, it'll be fairly obvious they aren't human and the limits of context will also become apparent eventually.

The Turing test is long outdated. Modern models can fool humans, but fooling isn’t understanding. Maybe we should flip the perspective AGI isn’t about imitation, it’s about discovering patterns autonomously in open environments.

If a human learned only on tokenized representations of words I don't know that they would be as good at inferring the numbers of letters in the words in teh underlying tokens as llms.

Or that this system would fail to adapt in anyway to changes of circumstance. The adaptive intelligence of a live human is truly incredible. Even in cases where the weights are updatable, We watch AI make the same mistake thousands of times in an RL loop before attempting a different strategy.

Absolute definitions are weak. They won't settle anything.

We know what we need right now, the next step. That step is a machine that, when it fails, it fails in a human way.

Humans also make mistakes, and hallucinate. But we do it as humans. When a human fails, you think "damn, that's a mistake perhaps me or my friend could have done".

LLMs on the other hand, fail in a weird way. When they hallucinate, they demonstrate how non-human they are.

It has nothing to do with some special kind of interrogator. We must assume the best human interrogator possible. This next step I described work even with the most skeptic human interrogator possible. It also synergizes with the idea of alignment in ways other tests don't.

When that step is reached, humans will or will not figure out another characteristic that makes it evident that "subject X" is a machine and not a human, and a way to test it.

Moving the goalpost is the only way forward. Not all goalpost moves are valid, but the valid next move is a goalpost move. It's kind of obvious.

Because the amount of people stating that AGI is just around the corner is staggering. These people have no conception of what they are talking about.

Their people are different from your people.

For me, it would be because the term AGI gets bandied about a lot more frequently in discussions involving Gen AI, as if that path takes us any closer to AGI than other threads in the AI field have.

I'll simultaneously call all current ML models "stupid" and also say that SOTA LLMs can operate at junior (software) engineer level.

This is because I use "stupidity" as the number of examples some intelligence needs in order to learn from, while performance is limited to the quality of the output.

LLMs *partially* make up for being too stupid to live (literally: no living thing could survive if it needed so many examples) by going through each example faster than any living thing ever could — by as many orders of magnitude as there are between jogging and continental drift.

Pretty much. Capabilities we now consider mundane were science fiction just three years ago, as far as anyone not employed by OpenAI was concerned.

We already had a definition of AGI. We hit it.

Are you sure about that? Which definition are you referring to? From what I can tell with Google and Grok, every proposed definition has been that AGI strictly matches or exceeds human cognitive capabilities across the board.

Generative AI is great, but it's not like you could just assign an arbitrary job to a present-day LLM, give it access to an expense account, and check in quarterly with reasonable expectations of useful progress.

> We already had a definition of AGI. We hit it.

I'm curious when and what you consider to have been the moment.

To me, the general in AGI means I should be able to teach it something it's never seen before. I don't think I can even teach an LLM something it's seen a million times before. Long division, for example.

I don't think a model that is solid state until it's "trained" again has a very good chance of being AGI (unless that training is built into it and the model can decide to train itself).

> We already had a definition of AGI.

I'm not an expert, but my layman's understanding of AI was that AGI meant the ability to learn in an abstract way.

Give me a dumb robot that can learn and I should be able to teach it how to drive, argue in court, write poetry, pull weeds in a field, or fold laundry the same way I could teach a person to do those things.

(1) AI isn't educated. It has access to a lot of information. That's two different things.

(2) I was rebutting the paper's standard that AGI should be achieving the status of a well-educated adult, which is probably far, far too high a standard. Even something measured to a much lower standard--which we aren't at yet--would change the world. Or, going back to my example, an AI that was as intelligent as a labrador in terms of its ability to synthesize and act on information would be truly extraordinary.

> EVEN though the accomplishment is through the fucking roof.

I agree with this but also, the output is almost entirely worthless if you can’t vet it with your own knowledge and experience because it routinely gives you large swaths of incorrect info. Enough that you can’t really use the output unless you can find the inevitable issues. If I had to put a number to it, I would say 30% of what an LLM spits out at any given time to me is completely bullshit or at best irrelevant. 70% is very impressive, but still, it presents major issues. That’s not boredom, that’s just acknowledging the limitations.

It’s like designing an engine or power source that has incredible efficiency but doesn’t actually move or affect anything (not saying LLM’s are worthless but bear with me). It just outputs with no productive result. I can be impressed with the achievement while also acknowledging it has severe limitations

> We already had a definition of AGI. We hit it.

Any definition of AGI that allows for this is utterly useless:

> Me: Does adding salt and yeast together in pizza dough kill the yeast?

> ChatGPT: No, adding salt and yeast together in pizza dough doesn't kill the yeast.

(new chat)

> Me: My pizza dough didn't rise. Did adding salt and yeast together kill the yeast?

> ChatGPT: It's possible, what order did you add them in?

> Me: Water, yeast, salt, flour

> ChatGPT: Okay, that explains it! Adding the salt right after the yeast is definitely the issue.

(It is not the issue)

> If elon musk makes interstellar car that can reach the nearest star in 1 second and priced it at 1k, I guarantee within a year people will be bored of it and finding some angle to criticize it.

Americans were glued to their seats watching Apollo 11 land. Most were back to watching I Dream of Jeanie reruns when Apollo 17 touched down.

[dead]

They do have desire. Their desire is to help answer human requests.

We can easily program them to have human desires instead.

There's equally no reason to believe that a machine can be conscious. The fact is, we can't say anything about what is required for consciousness because we don't understand what it is or how to measure or define it.

I’m certainly not informed enough to have an intelligent conversation about this, but surely the emotions bit can’t be right?

My emotions are definitely a function of the chemical soup my brain is sitting in (or the opposite).

There is exactly one good reason, at least for consciousness and sentience. And the reason is that those are such a vaguely defined (or rather defined by prototypes; ala Wittgenstein [or JavaScript before classes]). And that reason is anthropism.

We only have one good example of consciousness and sentience, and that is our own. We have good reason to suspect other entities (particularly other human individuals, but also other animals) have that as well, but we cannot access it, and not even confirm its existence. As a result using these terms of non-human beings becomes confusing at best, but it will never be actually helpful.

Emotions are another thing, we can define that outside of our experience, using behavior states and its connection with patterns of stimuli. For that we can certainly observe and describe behavior of a non biological entity as emotional. But given that emotion is something which regulates behavior which has evolved over millions of years, whether such a description would be useful is a whole another matter. I would be inclined to use a more general description of behavior patterns which includes emotion but also other means of behavior regulators.

they do not, but the same argument can hold true by the fact the true human nature is not really known and thus trying to define what a human like intelligence would consist of can only be incomplete.

there are many parts of human cognition, phycology etc. especially related to consciousness that are known unknowns and/or completely unknown.

a mitigation for this isaue would be to call it generally applicable intelligence or something, rather than human like intelligence. implying ita not specialized AI but also not human like. (i dont see why it would need to be human like, because even with all the right logic and intelligence a human can still do something counter to all of that. humans do this everyday. intuitive action, or irrational action etc.

what we want is generally applicable intelligence, not human like intelligence.

What if our definition of those concepts is biological to begin with?

How does a computer with full AGI experience the feeling of butterflies in your stomach when your first love is required?

How does a computer experience the tightening of your chest when you have a panic attack?

How does a computer experience the effects of chemicals like adrenaline or dopamine?

The A in AGI stands for “artificial” for good reason, IMO. A computer system can understand these concepts by description or recognize some of them them by computer vision, audio, or other sensors, but it seems as though it will always lack sufficient biological context to experience true consciousness.

Perhaps humans are just biological computers, but the “biological” part could be the most important part of that equation.

Is there more reason to believe otherwise? I'm not being contrarian, I'm genuinely curious what people think.

What non-biological systems do we know of that have consciousness, sentience or emotions?

Is there a reason to believe that consciousness, sentience and emotions exist?

None of that comes from outside of your biology and chemistry.

I think you missed or ignored the human part:

>human intelligence as something detached from human biology.

I don't completely agree with the previous comment, but there is something to be considered to their statement.

There are very significant differences between biological and artificial neural networks. Artificial neural networks are mathematical attempts to replicating how the brain’s neurons work. They are not and were never meant to be 1 to 1 replications. There is the difference in scale, where the “parameters” of human neural networks absolutely dwarf the current LLMs we have today. There is also the fact that they are materially different. The underlying biology and cell structure affects biological neural networks in ways that artificial neural networks just simply dont have access to.

The idea of awareness being propagations through the NN is an interesting concept though. I wonder if this idea be proven through monitoring the electrical signals within the brain.

> Awareness is just continuous propagation of the neural network, be that artificial or biological. The reason thoughts just "appear" is because the brain is continuously propagating signal through the neural network.

This is just a claim you are making, without evidence.

The way you understand awareness is not through "this is like that" comparisons. These comparisons fall over almost immediately as soon as you turn your attention to the mind itself, by observing it for any length of time. Try it. Go observe your mind in silence for months. You will observe for yourself it is not what you've declared it to be.

> An LLM does all of these things, and more, and you can prove this by how complex their output is.

Complex output does not prove anything. You are again just making claims.

It is astoundingly easy to push an LLM over to collapse into ungrounded nonsense. Humans don't function this way because the two modes of reasoning are not alike. It's up to those making extraordinary claims to prove otherwise. As it is, the evidence does not exist that they behave comparably.

> What most people fail to realise is that in between each token being generated, black magic is happening in between the transformer layers.

Thank you by saying that. I think most people have an incomplete mental model for how LLMs work. And it's very misleading for understanding what they really do and can achieve. "Next token prediction" is done only at the output layer. It's not what really happens internally. The secret sauce is at the hidden layers of a very deep neural network. There are no words or tokens inside the network. A transformer is not the simple token estimator that most people imagine.

> We can only know we are aware ourselves.

There are people that have a hard time recognizing/feeling/understanding other people as "aware". Even more about animals.

Agreed. Everything that looks like intelligence to ME is intelligent.

My measurement of outside intelligence is limited by my intelligence. So I can understand when something is stupider compared to me. For example, industrial machine vs human worker, human worker is infinitely more intelligent compared to machine, because this human worker can do all kinds of interesting stuff. this metaphorical "human worker" did everything around from laying a brick to launching a man to the Moon.

....

Imagine Super-future, where humanity created nanobots and they ate everything around. And now instead of Earth there is just a cloud of them.

These nanonobots were clever and could adapt, and they had all the knowledge that humans had and even more(as they were eating earth a swarm was running global science experiments to understand as much as possible before the energy ends).

Once they ate the last bite of our Earth(an important note here: they left an optimal amount of matter to keep running experiments. Humans were kept in a controlled state and were studied to increase Swarm's intelligence), they launched next stage. A project, grand architect named "Optimise Energy capture from the Sun".

Nanobots re-created the most efficient ways of capturing the Sun energy - ancient plants, which swarm studied for centuries. Swarm added some upgrades on top of what nature came up with, but it was still built on top of what nature figured by itself. A perfect plant to capture the Sun's energy. All of them a perfect copy of itself + adaptive movements based on their geolocation and time(which makes all of them unique).

For plants nanobots needed water, so they created efficient oceans to feed the plants. They added clouds and rains as transport mechanism between oceans and plants... etc etc.

One night the human, which you already know by the name "Ivan the Liberator"(back then everyone called him just Ivan), didn't sleep on his usual hour. Suddenly all the lights went off and he saw a spark on the horizon. Horizon, that was strongly prohibited to approach. He took his rifle, jumped on a truck and raced to the shore - closest point to the spark vector.

Once he approached - there was no horizon or water. A wall of dark glass-like material, edges barely noticeable. Just 30 cm wide. On the left and on the right from a 30 cm wide wall - an image as real as his hands - of a water and sky. At the top of the wall - a hole. He used his gun to hit the wall with the light - and it wasn't very thick, but once he hit - it regenerated very quickly. But once he hit a black wall - it shattered and he saw a different world - world of plants.

He stepped into the forest, but these plants, were behaving differently. This part of the swarm wasn't supposed to face the human, so these nanobots never saw one and didn't have optimised instructions on what to do in that case. They started reporting new values back to the main computer and performing default behaviour until the updated software arrived from an intelligence center of the Swarm.

A human was observing a strange thing - plants were smoothly flowing around him to keep a safe distance, like water steps away from your hands in a pond.

"That's different" thought Ivan, extended his hand in a friendly gesture and said - Nice to meet you. I'm Ivan.

....

In this story a human sees a forest with plants and has no clue that it is a swarm of intelligence far greater than him. To him it looks repetitive simple action that doesn't look random -> let's test how intelligent outside entity is -> If entity wants to show its intelligence - it answers to communication -> If entity wants to hide its intelligence - it pretends to be not intelligent.

If Swarm decides to show you that it is intelligent - it can show you that it is intelligent up to your level. It won't be able to explain everything that it knows or understands to you, because you will be limited by your hardware. The limit for the Swarm is only computation power it can get.

Any definition of AGI that doesn't include awareness is wrongly co-opting the term, in my opinion. I do think some people are doing that, on purpose. That way they can get people who are passionate about actual-AGI to jump on board on working with/for unaware-AGI.

Consciousness is observable in others! Our communication and empathy and indeed language depend on the awareness that others share our perceived reality but not our mind. As gp says, this is hard to describe or quantify, but that doesn't mean it's not a necessary trait for general intelligence.

https://en.wikipedia.org/wiki/Theory_of_mind

I think OPs example the awareness refers to the thing that becomes aware of the thoughts bubbling up from the subconscious

Have we shown what the human brain does at a “hardware” level? Or are you just assuming that the basic building block of a computer is that same as the basic building block of a human brain?

> Does any of that argument really matter? And frankly, this statement.

My definition of a complete AGI is: an AI that can read JIRA tickets, talk with non-programmers and do all my job and get me and all/most software engineers fired and proves sustainable.

But in general, it's an AI that can do any remote-work just as good as humans.

And this is indeed a huge problem with a lot of the attacks on LLM even as more limited AI - a lot of them are based on applying arbitrary standards without even trying to benchmark against people, and without people being willing to discuss where they draw the line for stating that a given subset of people do not possess general intelligence...

I think people get really uncomfortable trying to even tackle that, and realistically for a huge set of AI tasks we need AI that are more intelligent than a huge subset of humans for it to be useful. But there are also a lot of tasks where AI that is not needed, and we "just" need "more human failure modes".

You can't measure intelligence directly. Instead, the idea is to measure performance in various tasks and use that as a proxy for intelligence. But human performance depends on other aspects beyond intelligence, including education, opportunities, and motivation, and most humans are far from reaching their true potential.

If you compare the performance of the average human to a state-of-the-art AI model trained by top experts with a big budget, you can't make any conclusions about intelligence. For the comparison to make sense, the human should also be trained as well as reasonably possible.

I read this as a hypothetical well-educated adult. As in, given the same level of knowledge, the intelligence performs equally well.

I do agree that it’s a weird standard though. Many of our AI implementations exceed the level of knowledge of a well-educated adult (and still underperform with that advantage in many contexts).

Personally, I don’t think defining AGI is particularly useful. It is just a marketing term. Rather, it’s more useful to just speak about features/capabilities. Shorthand for a specific set of capabilities will arise naturally.

I think you are talking about correlation in humans of, say, verbal and mathematical intelligence. Still, it is a correlation, not equality - there are many word-acknowledged writers who suck at math, and mathematical prodigies who are are not the best at writing.

If you go beyond human species (and well, computers are not even living organisms), it gets tricky. Adaptability (which is arguably a broader concept than intelligence) is very different for, say octopodes, corvids and slime molds.

It is certainly not a single line of proficiency or progress. Things look like lines only if we zoom a lot.

Human intelligence has had hundreds of thousands of years of evolution that removes any 'fatal' variance from our intelligence. Too dumb is obvious on how it's culled, but 'too smart' can get culled by social creatures too, really 'too different' in any way.

Current AI is in its infancy and we're just throwing data at it in the same way evolution throws random change at our DNA and sees what sticks.

There are a bunch of these topics that everyone feels qualified to say something about. Consciousness, intelligence, education methods, nutrition, men vs women, economic systems etc.

It's a very emotional topic because people feel their self image threatened. It's a topic related to what is the meaning of being human. Yeah sure it should be a separate question, but emotionally it is connected to it in a deep level. The prospect of job replacement and social transformation is quite a threatening one.

So I'm somewhat understanding of this. It's not merely an academic topic, because these things will be adopted in the real world among real people. So you can't simply make everyone shut up who is an outsider or just heard about this stuff incidentally in the news and has superficial points to make.

I think it's not only the potential for self-improvement of AGI that is revolutionary. Even having an AGI that one could clone for a reasonable cost and have it work nonstop with its clones on any number of economically-valuable problems would be very revolutionary.

I was doing some naïve set theory the other day, and I found a proof of the Riemann hypothesis, by contradiction.

Assume the Riemann hypothesis is false. Then, consider the proposition "{a|a∉a}∈{a|a∉a}". By the law of the excluded middle, it suffices to consider each case separately. Assuming {a|a∉a}∈{a|a∉a}, we find {a|a∉a}∉{a|a∉a}, for a contradiction. Instead, assuming {a|a∉a}∉{a|a∉a}, we find {a|a∉a}∈{a|a∉a}, for a contradiction. Therefore, "the Riemann hypothesis is false" is false. By the law of the excluded middle, we have shown the Riemann hypothesis is true.

Naïve AGI is an apt analogy, in this regard, but I feel these systems aren't simple nor elegant enough to deserve the name naïve.

> I consider chatgpt 3 to be "baby AGI". That's why it became so popular so fast

ChatGPT became popular because it was easy to use and amusing. (LLM UX until then had been crappy.)

Not sure AGI aspirations had anything to do with uptake.

> infallibility

Lying != fallibility.

> I can't begin to count the number of times I've encountered someone who holds an ontological belief for why AGI cannot exist and then for some reason formulates it as a behavioralist criteria.

Unclear to me what you mean. I would certainly reject an ontological possibility of intelligent computers, where computation is defined by the Church-Turing thesis. It's not rocket science, but something difficult for some people to see without a sound and basic grasp of metaphysics and the foundations of CS. Magical thinking and superstition comes more easily then. (I've already given an explanation of this in other posts ad nauseam. In a number of cases, people get argumentative out of ignorance and misunderstanding.)

However, I don't reject out of hand the possibility of computers doing a pretty good job of simulating the appearance of intelligence. There's no robust reason to think that passing the Turing test implies intelligence. A good scarecrow looks human enough to many birds, but that doesn't mean it is human.

But the Turing test is not an especially rigorous test anyway. It appeals to the discernment of the observer, which is variable, and then there's the question of how much conversation or behavior, and in what range of circumstances, you need before you can make the call. Even in some unrealistic and idealized thought experiment, if a conversation with an AI were completely indiscernible with perfect discernment from a conversation with a human being, it would nonetheless lack a causal account of what was observed. You would have only shown a perfect correlation, at best.

Can you be more concrete? What kind of answer/conversation do you see as demonstrating passing the test, that you think is currently not possible.

I'd say yes, by at least one old definition made by someone who was at the time in a position to have a definition.

When deepmind was founded (2010) their definition was the following: AI is a system that learns to perform one thing; AGI is a system that learns to perform many things at the same time.

I would say that whatever we have today, "as a system" matches that definition. In other words, the "system" that is say gpt5/gemini3/etc has learned to "do" (while do is debateable) a lot of tasks (read/write/play chess/code/etc) "at the same time". And from a "pure" ML point, it learned those things from the "simple" core objective of next token prediction (+ enhancements later, RL, etc). That is pretty cool.

So I can see that as an argument for "yes".

But, even the person who had that definition has "moved the goalposts" of his own definition. From recent interviews, Hassabis has moved towards a definition that resembles the one from this paper linked here. So there's that. We are all moving the goalposts.

And it's not a recent thing. People did this back in the 80s. There's the famous "As soon as AI does something, it ceases to be AI" or paraphrased "AI is everything that hasn't been done yet".

No, just what has usually happened in the past with AI goalposts.

At first, just playing chess was considered to be a sign of intelligence. Of course, that was wrong, but not obvious at all in 1950.

I don't think AGI is a useful concept, but if it exists at all, there's a very good argument that LLMs had it as soon as they could pass the Turing test reliably, which they accomplished years ago at this point.

I think, given some of the signs of the horizon, there is a level of MAD type bluffing going around, but some of the actions by various power centers suggest it is either close, people think its close or it is there.

It wasn't the best definition of AGI but I think if you asked an interested layman whether or not a system that can pass the Turing test was AGI 5 years ago, they would have said yes

Compared to everything that came before they are fairly general alright.

Maybe one of these exalted names should've proof-read the paper:

https://x.com/m2saxon/status/1979349387391439198

The simple additive scoring here is sus here. It means a model that's perfect on 9/10 axes but scores 0% on Speed (i.e., takes effectively infinite time to produce a result) would be considered "90% AGI".

By this logic, a vast parallel search running on Commodore 64s that produces an answer after BeaverNumber(100) years would be almost AGI, which doesn't pass the sniff test.

A more meaningful metric would be more multiplicative in nature.

My point is that the axes chosen are important, and if this is a good rating system, we ought to see those radial charts for the different models and systems available.

Sure, there's lots of inbuilt stuff like basic needs and emotions. But still, baby doesn't know anything about the world. It's the ability to collect data and train on it that makes it AGI.

It also partitions jobs into physical and intellectual aspects alone. Lots of jobs have a huge emotional/relational/empathetic components too. A teacher could get by being purely intellectual, but the really great ones have motivational/inspirational/caring aspects that an AI never could. Even if an AI says the exact same things, it doesn't have the same effect because everyone knows it's just an algorithm.

> What matters to me is, if the "AGI" can reliably solve the tasks that I give to it and that requires also reliable learning.

How many humans do you know that can do that?

I agree, my point is that the cognition that creates emotion (and others) is of a different quality than the 10 listed in the paper.

I imagine an AGI (properly disguised as a humanoid) would be a drop-in replacement able to seduce humans.

Same for any task you imagine.

You don't suppose it is at times also a measure of knowing how to skate where the puck is heading?