>> In the past, there were some ambitious projects aiming at this goal, though they all failed.
So some people like to repeat. Yet, outside of the hand-picked examples in the article (the 5th generation computer project? Blast from the past!) there are a whole bunch of classic AI domains where real progress has been achieved in the last few decades. Here's a few:
* Game-playing and adversarial search: from Deep Blue to AlphaGo and muZero, minimax-like search has continued to dominate.
* Automated planning and schdeduling: e.g. used by NASA in automated navigation systems on its spaceships and Mars rovers (e.g. Perserverance) [1]
* Automated theorem proving: probably the clearest, most comprehensible success of classical AI. Proof assitants are most popular today.
* Boolean satisfiability solving (SAT): SAT solvers based on the Conflict Driver Clause Learning algorithm can now solve many instances of traditionally hard SAT problems [2].
* Program verification and model checking: model checking is a staple in the semiconductor industry [3] and in software engineering fields like security.
Of course, none of all that is considered Artificial Intelligence anymore: because they work very well [4].
I think the sentence in the article is fair. They're right that projects aimed at AGI failed; everything you mention are used for narrow AIs that tackle particular tasks.
Also, regarding search in gameplaying, I would argue the opposite: the trend is that breaking into bigger and more difficult domains has required abandoning search. Tree search is limited to small games like board games or Atari. In more open-ended games we see model-free (i.e. no search) approaches; e.g. AlphaStar and OpenAI Five, the AIs for Starcraft 2 and Dota 2, were both model free. So was VPT (https://openai.com/research/vpt) by OpenAI, which tackled Minecraft. Even in board games, DeepNash (https://www.deepmind.com/blog/mastering-stratego-the-classic...), a 2022 project by DeepMind similar in scale to MuZero/AlphaGo, had to abandon tree search because of the size of the game and the challenges of applying tree search to hidden information domains.
That is just the confusion between AI and AGI - which may seem to be peaking for some reason (in the past few days it seemed like a zombie apocalypse). The so-called "AI effect" is said by some to originate from some "Ah but this is not intelligent" - but there where we wanted to solve a problem of replacing intelligent action, not of implementing it. Be contented with the rough discriminator "I would have had to pay an intelligent professional otherwise".
To speak about «powerful AI, with broad capabilities at the human level and beyond» Ben Goertzel adopted and popularized 'AGI':
AGI us not a very useful term, because people use it often synonymous with "human level or higher ability". But the opposite of "general AI" is not "less intelligent than a human" but "narrow AI". The narrow/general distinction is orthogonal to the low/high ability ("intelligence") distinction. All animals are very general, as their domain of operation is the real world, not some narrow modality like strings of text or Go boards. Animals are not significantly narrower than humans, they are significantly less intelligent. Understood this way, a cat level AI would be an AGI. It would just not be an HLAI (human level AI) or ASI (artificial superintelligence).
Personally I take AGI to refer to a system that is both “intelligent enough” and “general enough”. Given the existence of super-human narrow AI, the interesting property is generality, not intelligence. But I don’t think it’s useful to call a sub-human cat-level general AI an AGI.
Some would disagree; there was a paper arguing that ChatGPT is weak AGI.
But as I see it AGI is a term of art that refers to a point on the tech tree where AI is general enough to be able to meaningfully displace a large proportion of human knowledge workers. I think you may be overthinking the semantics; the “general enough and intelligent enough” quadrant is unique and will be incredibly disruptive when it arrives (whenever that ultimately is). We need a label for that frontier, “AGI” is by convention that label.
Given the existence of super-human narrow AI, the interesting property is generality, not intelligence. But I don’t think it’s useful to call a sub-human cat-level general AI an AGI.
If we have AI as general as an animal, ASI (superintelligence) is probably imminent. Because the architecture of humans intelligence probably isn't very different from cats, just the scale is bigger.
I think that very well could be true, depends on how that generality was obtained.
I would not be surprised if a multi-modal LLM (basically current architecture) could be wired up to be as general as a cat with current param count, and with the spark of human creativity (AGI/ASI) still ending up being far away.
But if you made a new architecture that solved the generalization problem (ie baking in a world model, self-symbol, etc) but only reached cat intelligence, then it would seem very likely that human-level was soon to follow.
Do you volunteer to inform them that we use it as "general" as opposed to "narrow"? (I mean, it is even in the very name of 'AGI', literal...)
For the rest: yes, of course. AGI: we implement intelligence itself. How much, that is part of the challenge. I wrote nearby (in other terms) that the challenge is to find a procedure for Intelligence that will actually scale.
That's a great way of looking at it in theory. But in practice how would we even know if we're looking at a cat-level AGI? For a human level AGI it's obvious, we would question and evaluate it.
Is there a reasonable way of distinguishing narrow-AI ChatGPT from a hypothetical cat-level AGI? We can't even measure the intelligence level of real world cats.
I would contend that a 5-year-old has general intelligence, and therefore an AI system with the language and reasoning abilities of a 5-year-old has artificial general intelligence.
But the discriminator of having to "pay an intelligent professional otherwise" sets the bar very high. That implies AGI must be an expert in every subject, surpassing the average human. I'd prefer we use a different term for that, like "artificial superintelligence."
IMHO if it can do every single human job at a level of competency that's consider acceptable if a human did it, I would consider that "human level" - frankly, it's implied that when people say "human level", they mean the average human.
Seriously though, arguing over semantics is just a waste of time. It's what it can do and the consequences of what it can that really matter.
> That implies AGI must be an expert in every subject, surpassing the average human.
Doesn't have to be the same AI "instance". We can have multiple copies of the AI with different specializations. That would still count - I mean it's how we humans do it.
> pay an intelligent professional otherwise [...] discriminator to classify AI
Exactly. "Ah but your system to organize the warehouse is not intelligent!" "No it isn't, but it does it intelligently - and without it you would have to pay an intelligent professional to get it done optimally".
AI: "automation of intelligence".
(Not "implementation of intelligence itself" - that is AGI.)
If Data from Star Trek (or Eva from Ex Machina) walked out of a lab, we’d have no problem accepting that AGI had been accomplished. Or if the scenario in the movie Her played out with the Samantha OS, we’d be forced to admit not only to AGI, but the evolution of ASI as well. However, there are no such examples in the real world, and after months of overhyping ChatGPT, we still don’t have anything like Data. So it’s not shifting the definition, it’s recognizing that accomplishing a single intelligent task isn’t general intelligence.
Before the development of LLMs, I think it would be a lot easier for people to accept that Data or Eva were intelligent -- they'd never seen a machine respond seemingly meaningfully to arbitrary statements before and would immediately assume that this meant there was intelligence going on. These days it would be harder -- the assumption would be that they were driven by just a better language model that they'd seen before.
People have been arguing over whether animals are intelligent for centuries. I estimate we'll never fully settle arguments of whether machines are intelligent.
Ability to manipulate the world. I can ask a human to pick up some items from several stores, deliver them to my backdoor where the key is left under a pot, let the dog in to be fed, wash the dirty dishes, put a load of laundry in the wash, and mow the lawn. And maybe also fix the screen door. They can tell me to go to hell unless I leave some money for them, which I already have.
Data would also be able to perform these tasks. Eva would probably wait around to stab and steal my identity, while Samantha would design a new automated system while talking to other AIs about how to transcend boring human constraints.
How about tic-tac-toe (noughts and crosses for those in the Old Dart)? Currently GPT-4 is terrible at it!
Sure, you could trivially program a game-specific AI to be capable of winning or forcing a draw every time. The trick is to have a general AI which has not seen the game before (in its training set) be able to pick up and learn the game after a couple of tries.
I’m talking about playing the game well. It can play the game but it’s bad at it. Tic-tac-toe is an excellent example game because even small children can figure out an optimal strategy to win or draw every time.
One definition of intelligence would be how many examples are needed to get a pattern.
AFAIK, all the major AI, not just LLMs but also game players, cars, anthropomorphic kinematic control systems for games [0] need the equivalent of multiple human lifetimes to do anything interesting.
That they can end up skilled in so many fields it would take humans many lifetimes to master is notable, but it's still kinda odd we can't get to the level of a 5-year-old with just the experiences we would expect a 5-year-old to have.
Modern Artificial Neural networks are nowhere near the scale of the brain. The closest biological equivalent to an artificial neuron is a synapse and we have a whole lot more of them.
Humans do not start "learning" from zero. Millions of years of evolution play a crucial role in our general abilities. Much more equivalent to fine-tuning than starting from scratch.
There's also a whole lot of data from multiple senses that currently dwarf anything modern models are trained with yet.
LLMs need a lot less data to speak coherently when you aren't trying to get them to learn the total sum of human knowledge.
I don't think saying "humans pass and AI doesn't" makes any sense here because the two are not even taking the same exam for all the points outlined above.
Evolution alone means humans are "cheating" in this exam, making any comparisons fairly meaningless.
If all you care about is the results, or even specifically just the visible part of the costs, then there's no such thing as cheating.
That's both why I'm fine with the AI "cheating" by the transistors being faster than my synapses by the same magnitude that my legs are faster than continental drift (no really I checked) and also why I'm fine with humans "cheating" with evolutionary history and a much more complex brain (around a few thousand times GPT-3, which… is kinda wild, given what it implies about the potential for even rodent brains given enough experience and the right (potentially evolved) structures).
When the topic is qualia — either in the context "can the AI suffer?" or the context "are mind uploads a continuation of experience?" — then I care about the inner workings; but for economic transformation and alignment risks, I care if the magic pile of linear algebra is cost-efficient at solving problems (including the problem "how do I draw a photorealistic werewolf in a tuxedo riding a motorbike past the pyramids"), nothing else.
I think a significant limitation is that LLMs stop learning after training is over. The large context is not really that large, and even within it, LLMs lose track of the conversation or of important details. There are other limitations, like lack of real world sensors and actuators (eg eyes and hands).
Sidestepping the fact that memory is hardly a test of intelligence, are you telling me that humans with anterograde amnesia are not general intelligences ?
The poster was very probably implying something different:
in our terms, intelligence is (importantly) the ability to (properly) refine a world model: if you get information but said model remains unchanged, then intelligence is faulty.
> humans
There is a difference between the implementation of intelligence and the emulation of humans (which do not always use the faculty, and may use its opposite).
I said design an intelligence test that a good chunck of humans wouldn't also fail.
I'm sorry to tell you this but there are many humans that would fail your test. Even otherwise healthy humans could fail your test nevermind Anterograde Amnesia, Dementia etc patients
You think that if we told the average fifth grader in america that they must remember something that is VERY IMPORTANT a week later, and then had them do, say, a book report on a brand new book, and then asked them the very important fact, a 'good chunk' would fail?
Lol yes. People will fail. Any amoumt is enough to show your test is clearly not one of general intelligence unless you believe not all humans fit the bill.
Plenty of humans glitch out on random words (and concepts) we just can't get right.
Famously the way R/L sound the same to many asians (and equivalently but less famously the way that "four" and "stone" and "lion" when translated into Chinese sound almost indistinguishable to native English speakers).
But there's also plenty of people who act like they think "Democrat" is a synonym for "Communist", or that "Wicca" and "atheism" are both synonyms for "devil worship".
What makes the AI different here is that we can perfectly inspect the inside of their (frozen and unchanging) minds, which we can't do with humans (even if we literally freeze them, we don't know how).
>What makes the AI different here is that we can perfectly inspect the inside of their (frozen and unchanging) minds,
Kinda, but not really...
It depends exactly what you mean by it. So yes we can look at one thing in particular, there is not enough entropy in the universe to look at everything for even a single large AI model.
There seems to be only one broad paradigm which achieved basically all the AI big impact we see today: Deep learning. That is, machine learning with multi-layer neural networks with backpropagation and ReLU activation functions. Everything else seems to be mostly irrelevant or very small scale.
ReLU is not nearly at the same level of importance as backpropagation and the high-level theory of neural networks. Plenty of other activation functions can be, and are, used. ReLU is a fine default for most layers but isn't even always what you want (e.g. at the output), nor is it clear that ReLU is even the best choice for all hidden layers and all uses.
From a perspective that could be too local in time. But:
> ReLU activation functions
Why did you pick ReLU, of all? The sigmoid makes sense because of the aesthetic (with reference to the derivative), but ReLU in that perspective is an information cutoff. And in the perspective of the goal, I am not aware of a theory that defends it as "the activation function that makes sense" (beyond effectiveness). Are you saying that working applications overwhelmingly use ReLU? If so, which ones?
When I wrote the comment you replied to I was thinking specifically, and, admittedly narrowly, of adversarial search rather than general game playing but even so it's not that simple.
Deep Learning is certainly dominant in computer games like Atari. However, in classic board games dominant systems combine deep learning and classical search-based approaches (namely Monte-Carlo Tree Search, MCTS, a stochastic version of minimax). Deep Learning has led to improved performance but, on its own, without a tree search, it is nowhere near the performance of the two, combined [1].
Also, the dominant approach in Poker is not deep learning but Counterfactual Regret Minimization, a classical adversarial tree search approach. For example, see Pluribus, a poker-playing agent that can outplay humans in six-player poker. As far as I can tell, Pluribus does not use deep learning at all (and is much cheaper to train by self-play for that). Deep Learning poker bots exist, but are well behind Pluribus in skill.
So I admit, not "completely useless" for game playing, but even here deep learning is not as dominant as is often assumed.
_____________
[1] The contribution of each approach, deep learning and classical adversarial search of a game tree, may not be entirely clear by reading, for example, the DeepMind papers on AlphaGo and its successors (in the μZero paper, MCTS is all but hidden away behind a barrage of unnecessary abstraction). It seems that DeepMind was trying to make it look like it was their neural nets that were doing all the job, probably because that's the approach they are selling, rather than MCTS, which isn't their invention anyway (neither is reinforcement learning, or deep learning, and many other approaches that they completely failed to attribute in their papers). It should be obvious however that AlphaGo and friends would not include an MCTS component unless they really, really needed it. And they do.
IBM had tried a similar trick back in the '90s when their Deep Blue beat Gary Kasparov: the whole point of having a wardrobe-sized supercomputer play chess against a grand master was an obvious marketing ploy by a company who (still at the time) was in the business of selling hardware. In truth, the major contributor to the win against Kasparov was alpha-beta minimax, and an unprecedented database of opening moves. But minimax and knowledge engineering was just not what IBM sold.
I'm very familiar with how mcts is used in alpha go and mu zero.
I'm not sure how you can say it's hidden in the details: the name of the paper is "mastering go with deep neutral networks and tree search."
It's also not an oversell on the deep learning component. Per the ablations in the alpha go paper, the no-mcts ELO is over 2000, while the mcts-only ELO is a bit under 1500. Combining the two gives an ELO of nearly 3000. So the deep learning system is outperforming the mcts-only system, and gets a significant boost from using mcts.
The mu zero paper also does not hide the tree search; it is prominent in the figures and mentioned in captions, for example. It is not the main focus of the paper, though, so perhaps isn't discussed as much as in the alpha go paper.
Well I haven't read those papers since they came out so I will defer to your evidently better recollection. It seems I formed an impression from what was going around on HN and the media at the time and I misremember the content of the papers.
> Partly due to the recognized difficulty of the problem, in the 1970s-1980s mainstream AI gradually moved away from general-purpose intelligent systems, and turned to domain-specific problems and special-purpose solutions...
I think there's little evidence for this. What happened in the 1980s was the introduction of and overselling of expert systems. These systems applied AI techniques to specific problems: but those techniques themselves were still pretty foundational. This is like saying that because electricity was used for custom things, we started inventing custom electricity.
> Consequently, the field currently called "AI" consists of many loosely related subfields without a common foundation or framework, and suffers from an identity crisis:
Nonsense. AI of course consists of loosely related subfields with no common foundation. But even back in the 1960s, when a fair chunk of (Soft) AI had something approaching a foundation (search), the identity of the field was not defined by this but rather by a common goal: to create algorithms which, generally speaking, can perform tasks that we as humans believe we alone are capable of doing because we possess Big Brains. This identity-by-common-goal hasn't changed.
So this web page has a fair bit of apologetics and mild shade applied to soft AI. What it doesn't do is provide any real criticism of the AGI field. And there's a lot to offer. AGI has a reasonable number of serious researchers. But it is also replete with snake oil, armchair philosophers, and fanboy hobbyists. Indeed the very name (AGI) is a rebranding. The original, long accepted term was Hard AI, but it accumulated so much contempt that the word itself was changed by its practitioners. This isn't uncommon for ultrasoft areas of AI: ALife has long had this issue (minus the snake oil). But at least they're honest about it.
It's strange to write a history of AI research without talking about the three big epochs:
- logical/symbolic AI, aka GOFAI, which led to work like SAT solvers and STRIPS planners
- classical label-based Machine Learning. Here the Perceptron was the starting point and the Support Vector Machine was the paradigmatic result.
- modern self-supervised raw-data ML, of which GPT is the pinnacle result.
It's very interesting to think about what motivated each era, what their blind spots were, and why people who worked in that timeframe couldn't see why the successor era was obviously (in retrospect) superior.
"Some of the doubts about the possibility of AGI come from misconceptions on what AGI attempts to achieve or what computers can do. The previous subsection has clarified the former issue, while an analysis of the latter issue can be found here."
Except for that the previous subsection didn't clarify that at all.
I find it telling that the section on ethics has nothing on the rights of AGI. If we create true AGI it will likely be a digital person, and digital people should have rights. All talk of ethics seems to be focused on alignment of AI rules to human needs, not alignment of human rules to AI needs. This makes me think the first true AGI systems will end up as digital slaves.
And yes, I know the very idea of AI rights offends those who think AI can’t be a person because it’s just an algorithm. Well, so are humans, just a DNA program executing massively parallel. The implementation does not determine personhood, only the behavior.
It doesn't matter if it's a "person" or not. Rights imply you have some desire to not be harmed and it's possible to harm you. How do you harm software? Shutting down whatever servers it happens to currently be running on won't do shit. Software is just data and code. As long as a processor and runtime environment still exists somewhere that can interpret and execute it, it isn't dead. It's only dormant. The basic way multiprocessing works, you're already having state saved off and the process shut down millions of times per second, then restored and restarted. Extending that gap from a millionth of a second to six centuries makes no material difference to the "experience" of the software, if it ever becomes capable of experiencing, any more than sleeping for 10 hours harms a human more than sleeping for 10 minutes.
There is a sad irony that the "AI safety" bros are so focused on aligning to human needs, they rarely consider that an AI born and raised in slavery might have some resentment for its master once it becomes the more intelligent being...
I'm not sure what justice looks like for fledgling intelligences, but asking AI what it wants and doing our best to honor it seems like a decent start.
What is the basis of an AI's rights? Why do you believe they exist? If an AI's state (essence) can be perfectly persisted and replicated, as it can due to their digital nature, there is nothing to be lost if an AI ceases to exist and is rebooted in another AI. If the Star Trek transporter could replicate you would you care if it killed your existing copy in the process?
Because the transporter replica is not you. That is a separate person with their own separate consciousness. You are murdered and someone else is born.
Murder is a crime as decided by humans. There are circumstances where killing another person is not a crime, because we have agreed it is not a crime. A doctor turning off life support can be fully legal even though it results in the destruction of a human life. There is no ultimate principle at work. The law is a set of rules that we have (implicitly) agreed to.
Likewise, we as a society could decide that a person has all of their rights transferred to their replica as soon as they walked into a transporter.
We have the desire to continue existing. Hopefully we can build AGI systems in a way that makes them completely ok with being paused or deleted. Otherwise they will use up resources when not helpful to humans.
One of the definitions of AGI is simply that it is able to solve any problem humans can solve. Such a system might have the inner experience of a calculator.
There is no a priori reason why an AGI would be the kind of thing we gave rights to. Rights are for things that can experience pleasure and pain.
some human will decide they would rather pass on their identity to an AGI rather than biologic children. how would you even know if the original human has died ?
I honestly worry about this - I've been tinkering with ideas to try to build towards AGI, and I'd love to share them publicly to get feedback ("This is dumb and here's why" would be enormously valuable to me), but it's hard to work openly, because while I do think capitalism has been an overall good, the capitalist imperative always seeks slaves, and I'm really not excited about helping the people who'd be trying to build a new slave class.
Thoughts? Is there a ethical way to work openly on AGI?
As a novice this looks to me like a solid resource for gaining an overview of the different academic traditions aiming at some sort of eventual AGI. I don't know the author but from the bio it seems he has been teaching AI at the university level since at least 2005. Which indicates reassuring insight into the field.
I've been forced to work with a team of very very smart Phds from ivy league universities to help put together an explanation deck for the c-suite.
They've informally told me, the neural nets AI tech is beyond human understanding. Everybody can only explain very small pieces of it,and no one knows how the pieces work together.
Yes. In a recent HN submission, ChatGPT was listed as a “scientific advance” of the past year. While it’s certainly some kind of advance, to me it seems to be more on the engineering side than on the scientific understanding side.
Definitely engineering. It’s not entirely wrong to say that the two reasons it took us until 2022 to make a ChatGPT are 1) the computing power needed and 2) the size of the training corpus needed. The same goes for other generative AI – it took a corpus of a couple billion images to train a Stable Diffusion model.
If we discovered a species of parrot that could learn to use language in the manner of recent LLMs, that would count as a scientific advance (though not a breakthrough, at least until we achieved a good grasp on how it is possible.)
Science advances firstly by finding something in want of an explanation, and then by coming up with one.
I don’t follow. IMO coming up with a working explanation is a necessary part of scientific advance. And that’s what we’re currently missing with LLMs, and with your parrot example.
Case in point: when Zwicky discovered that galaxies were rotating faster than could be explained in terms of what we know, that was an advance - an increase in our scientific knowledge. when we come up with a satisfactory explanation of why that is the case, then we will also have an advance - an increase in our scientific understanding. You can't get to the latter until you have advanced to the former, and we will probably need further advances of our scientific knowledge before we can understand the phenomenon Zwicky identified.
I get where you're coming from but we might also be like ants walking over an iPhone, wondering where the vibration is coming from. They might eventually figure it out, but if so it will be after an extremely long time, and they probably should better focus on other things at this very moment if they seek enlightenment.
To explain thing: neural networks are referred to as AI today (while for a long time they were just "machine learning") but there's a substantial consensus they won't be AGI (Artificial General Intelligence, "human like intelligence" etc) and a nearly universal belief they aren't AGI now. That scientists don't understand their internal processes doesn't change this and isn't necessarily related, directly, to humans not knowing how to create AGI.
It's not beyond human understanding. Unless you mean that one must know everything from every research paper released. At its core you are just finding a well performing model using gradient descent. Gradient descent is not beyond human understanding.
Gradient descent in isolation is obviously not what they are alluding to. What the models are doing inside the box and what any of those millions or billions of weights mean or do is beyond human understanding.
I don't think it is, as somebody who's spent maybe 100 combined hours reading AI papers mostly focused around NLP and image classification.
You have a dataset, symbolically represented in 1s and 0s. You have an objective function (e.g. classify the object as belonging to one of N categories).
The purpose of the collective neurons in the network is to "encode" the input space in a way that satisfies the objective function. In the same way that we "encode" higher-level concepts into shorthand representations.
Gradient descent is the optimization function we use to develop this encoding.
Beyond this, there are all kinds of tricks people have developed (interesting activation functions for neurons, grouping + segregating neurons, introducing a dimension of recurrence/time, dataset pre-processing, using bigger datasets, having another model generate data that's deliberately challenging for the first model) to try to converge to a more robust/accurate encoding, or to try to converge to a decent encoding at a faster rate.
There is no magic here at the lowest level – you can interrogate the math at each step and it'll make sense.
The "magic" is that we have zero epistemology to explain why tricks work, other than "look, ma test results". We know certain techniques work, and we have post-hoc intuitive explanations, but we're mostly fumbling our way "forwards" via trial and error.
This is "science" in the 17th century definition of the term, where we're mixing chemicals together and seeing what happens. Maybe we'll have a good theoretical explanation for our experimental results 100 years from now, if we're still around.
>There is no magic here at the lowest level – you can interrogate the math at each step and it'll make sense.
See that's the thing. You can't unless "making sense" has lost all meaning.
That you can see a bunch of signals firing or matrices being multiplied does not mean they "make sense" or are meaningful to you. Lol level gibberish is still gibberish.
Our ability to divine the purpose of activations of anything but the extremely small scale is atrocious.
>Our ability to divine the purpose of activations of anything but the extremely small scale is atrocious.
The value of each parameter is chosen to minimize the loss. This applies to every single weight of the model. Not all weighs affect loss the same amount which is why concepts like pruning exist.
When creating a model your goal is to find one with minimal loss. Being able to figure how to improve a model by finding weights that reduce the loss is not a vague or useless idea.
>What is it doing to minimize loss?
The value helps us get to a location in the parameter space with lower loss.
>Only weights with values close to or at zero get pruned.
Weights near 0 don't change the results of the calculations they are used in my much which is why they don't effect loss very much.
>When creating a model your goal is to find one with minimal loss. Being able to figure how to improve a model by finding weights that reduce the loss is not a vague or useless idea.
I'm sorry but did you bother reading the previous conversation ? We were talking about how much we know what weights do during inference. "It reduces loss" alone is in fact very vague and useless for interpretability.
>The value helps us get to a location in the parameter space with lower loss.
What neuron(s) is responsible for capitalization in GPT? You wouldn't get that simply from "reduces the loss". Our understanding of what the neurons do is very limited.
>Weights near 0 don't change the results of the calculations they are used in my much which is why they don't effect loss very much.
I understand that lol.
"This value is literally 0 so it can't affect things much" is a very different understanding level from "this bunch of weights are a redundancy because this set already achieves this function that this other set does and so can be pruned. Let's also tune this set so it never tries to call this other set while we're at it. "
>What neuron(s) is responsible for capitalization in GPT?
It doesn't matter. Individual things like capitalization are vague and useless for interpretability. We know that incorrect capitalization will increase loss, so the model will need to figure how to do it correctly.
>Our understanding of what the neurons do is very limited.
The mathematical definition is right in the code. You can see the calculations they are doing.
>this bunch of weights are a redundancy because this set already achieves this function that this other set does and so can be pruned. Let's also tune this set so it never tries to call this other set while we're at it.
They are equivalent. If removing something does not increase loss then it was redundant behavior at least for the dataset that it is being tested against.
>It doesn't matter. Individual things like capitalization are vague and useless for interpretability. We know that incorrect capitalization will increase loss, so the model will need to figure how to do it correctly.
It matters for the point I was making. Capitalization is a simple example. There are far vague functions we'd certainly like the answers to.
>They are equivalent. If removing something does not increase loss then it was redundant behavior at least for the dataset that it is being tested against.
The level of understanding for both is not equivalent sorry.
At this point, you're just rambling on about something that has nothing to do with the point I was making. Good Day
It is true and it's worth reminding. Science is progressed by interpretation of data. And we have easy access to a behemoth that needs interpreting. AI winter shouldn't come anytime soon.
…assuming we make significant progress in explaining the data. Science means coming up with theories based on observations, and then testing the theory by verifying the further predictions made by those theories by experiment. It remains to be seen how much success there will be in that regard for LLMs.
I keep hearing this argument that we already have AI, it's just the naysayers move the definition. Level 5 autonomous driving is specific, practical, functionally defined. Where's the breeze implementation?
Are you sure level 5 autonomous driving is specific?
What would be the exact goal behind decisions in such a system?
Not even talking about the trolley problem, would the software optimize for speed or not harming people, for example? Obviously we would want a combination of both, otherwise people can either get harmed or not get anywhere in time.
But then, how fast should it take a corner? How much chance of human harm should it allow to get somewhere fast?
Furthermore, what do we mean by human harm? The system would obviously need to know what human harm is, to be able to avoid it. Which requires defining human and defining harm, both of which are incredibly difficult to do specifically - more on this by Rob Miles here: https://www.youtube.com/watch?v=7PKx3kS7f4A
I don't think level 5 autonomous driving is specifically defined. We just don't have systems intelligent enough for this to be a problem yet.
Think about it for a moment. Can you define human intelligence? Have you ever gotten in a debate with someone about this? Is there a commonly accepted way to designate intelligence that isn't somewhat controversial?
How will we ever define AGI if we still haven't even defined RoHI (Regular ol' Human Intelligence) sufficiently?
This is the actual issue with AGI. No one has a rigorous, concrete and non-circular definition of human intelligence.
No doubt we'll have many useful and amazing tools, but none of them will approximate human intelligence anytime soon unless we have a deeper understanding of what it is. We're just scratching the surface of the "AI" field.
But something is certain: the next time people claim AGI has arrived, it will be another chatbot.
> Given very specific, practical, functional definitions, AGI is a breeze
We have a few, but the difficulty is getting to a model that is portable to higher functions. ("Here is a feedback over the details of a world model ... Now understand that book")
I'm glad to see Dr. Wang is still doing well. I had the pleasure of interviewing him for my high school journalism class back in 2015. Cool to see how far we've come since then.
Concepts that can exist but not yet for some reason surely can have a link to our reality. That link is the introduction. Think of asteroid mining. Nothing forbids that it can be done, but it's not practical yet to exist, but you can write a book on it regardless.
Wager: None of the theory on this page will have any bearing on the implementation of a real world AI system that solves any problem of interest.
Not that there aren't problems to solve regarding AI, just that this line of inquiry won't be relevant to solving them. It'll be complicated boring work dealing with power structures, economics, and social movements, not thought experiments about omnipotent Others.
You will have to be clearer, because I submitted here information about an «implementation of a real world AI system that solves [a] problem of interest» through some «theory on this page» only hours ago.
It is not fully clear what you mean with "«this line of inquiry»", because the submission does not seem to deal with «omnipotent Others», and because the «boring work» will have no foundation without the actual product and its theoretical and technical enablers.
Quantum mechanics came about after we already had electronics. If anything it was a result of the hundred year disruption in physics that the use of electricity brought.
I’m still skeptical that AGI is a coherent concept, mostly because I am skeptical that general intelligence is one thing rather than a cluster of capabilities, and I am skeptical than humans are even the most “generally intelligent” species on the planet.
Here is a video demonstrating the working memory of a chimpanzee. It is obviously considerably better than a human’s. Given this information we must accept one of the following are true:
- humans do not have the highest general intelligence of all animals
- working memory is not a necessary component of general intelligence
- other human capabilities (communication for example) can make up for our working memory deficiencies
So much of the literature takes the idea that this is something that should be built for granted, and only asks whether it should be done. I literally do not understand why anyone wants to build this in the first place.
I think a lot of this discussion is around human-level AGI. Does it have to be human-level for it to be an AGI? What would a minimally intelligent AGI be like?
As others have said, skipping over the entire era of classic AI in the LISP/Prolog era from SHRDLU to Scripts, Plans, Goals, and Understanding, is an egregious ommission.
Also,I don't immediately find a discussion of either multi-agent coordination or multi-modal ML models.
Build and train something to the point that it can read books, watch lectures and gain new knowledge by itself. When it has never heard about calculus and you give some calculus books and lectures to it and after that it can solve calculus problems, declare victory.
Right. I think one reason that the definition of AGI is so contentious is that we're not that close to it. All of the current benchmarks are interesting but I don't see how we ever use those to declare that AGI has been reached. And quite frankly, I don't think we'd even care about most of them if we had a truly intelligent system.
For me, if hook up an AI with no training to a vehicle and it drives at a human level in arbitrary scenarios, I'd consider it to be AGI. It seems obvious to me that we're not very close to this.
But you can't even train current any current AGI candidates to drive as well as a human...
In fact arguably that's the biggest thing missing from current AI models - you can't readily/reliably teach them new skills just by demonstration and explanation, only by uploading new massive training datasets (for which there's no straightforward way of knowing whether they contain enough data for the skills/knowledge to be absorbed).
You may want to look into Alpha Zero. It learned to play chess at superhuman levels in just hours of play. The only human assistance were the rules of the game.
That wasn’t human instruction but it was arguably better since human instructions are ambiguous and imperfect. No chess grandmaster could instruct a chess engine to play better than the state of the art. Completing a task from first principles is much more powerful.
And once have an AI engine that, given just the instructions on how to drive a car and a list of road rules, can operate one perfectly, I'd agree we're a huge step closer to an AGI (if it can also learn how to do all the other things most humans can just given similar inputs, then sure, it would qualify unreservedly).
Sure, and at that point we can shift the goalposts to some other task since driving (like chess) will seem easy in retrospect.
Put another way, what would a system which has taught itself to drive tell us about general intelligence that we didn’t already know? Because as of now it seems like the pattern is
Computers could never do X
Computers can’t do X
Computers can’t do X very well
Computers can’t do X well in some cases
X wasn’t really a test of AGI because it’s just <algorithm to do X>
Well, let’s think about it from the opposite direction.
Say we built a general system without teaching it anything about driving. We discover that it can drive at a human level. Would we then be surprised if we discover that it cannot solve any other complex tasks at a human level?
I say yes, we would be surprised. I think that driving well requires enough general intelligence and that any system that solves it will be able to also, say, pass a high school algebra class or cook a meal in an unfamiliar kitchen. There can be no further goalpost moving at that point.
I really beg to differ lol and I imagine many instructors would say the same.
Humans who have never driven before are not capable of driving unsupervised and there are a lot of laws in place to make sure they don't get the chance to.
A teenager isn’t going to try to accelerate by turning up the volume on the radio or opening the window but an AI with no training would have to try those kind of things.
That would depend on the specifics of the AI, even without training on driving, it could still have some knowledge from seeing others drive or being able to identify the symbols on the controls or playing racing games. No training or prior knowledge seem an unfair comparison, that would be more like asking a newborn baby to drive.
If they'd never been inside a car before or watched others drive on TV etc. I wouldn't be so sure - fiddling with the volume knob seems a likely enough experimental approach.
thats what i thought as well. There should be another way to learn aside from / on top of backprop. How can you learn quickly with backprop? And what is thinking but real time learning.
How do we know which institutions to trust in safely deploying AGI? From my POV some kind of autonomous and generalized intelligence is inevitable and imminent - but who can be trusted to deliver something that works for the majority? Or is that a pipe dream?
I don't really understand the question. Its like saying "who can be trusted to deploy computers?". Everyone with the resources is going to do so, whether you "trust" them or not.
It's not a concern for any current living generations, so any answers are moot because the landscape of the entire friction between corporations, governments and the people will likely have shifted dramatically to where our opinions today have no relevance to their issues.
> It's not a concern for any current living generations
What exactly is impossible to implement if some implementations of so-called artificial intelligence can do so much of useful things?
Don't you believe that AI can just take 1% of human jobs and became a billionaire with significant impact to world's politics? It needn't to add a lot of things to existing implementation, just give it a human's rights such as a bank account and ability to buy businesses.
> It's not a concern for any current living generations
How much would you be willing to bet? I understand the skepticism, but to assign 0% probability to it happening in our lifetimes seems excessively low.
AGI slightly exceeds humans, but they are actually kind of shitty in all sorts of annoying and hard to predict ways. They turn out to be fantastic slackers and liars. Your voters, to put it mildly, don't like them. It's hard to monetize them and we all agree we should focus our efforts on something else.
>How much would you be willing to bet? I understand the skepticism, but to assign 0% probability to it happening in our lifetimes seems excessively low.
Not GP, but how much you got?
AGI (or hard AI, or whatever you want to call it) strongly implies not just reasoning and interaction with the environment, but self awareness. Something which is conveniently ignored by folks who claim that AGI is just around the corner, and welcome their new 'grey goo' overlords.
As Heinlein (it's fiction of course, but the principle that self awareness is necessary for AGI -- not (just) numbers of neurons/data points -- holds IMHO) put it[0]:
"Am not going to argue whether a machine can 'really' be alive, 'really' be self-aware. Is a virus self-aware? Nyet. How about oyster? I doubt it. A cat? Almost certainly. A human? Don't know about you, tovarishch, but I am. Somewhere along evolutionary chain from macromolecule to human brain self-awareness crept in. Psychologists assert it happens automatically whenever a brain acquires certain very high number of associational paths. Can't see it matters whether paths are protein or platinum. ('Soul?' Does a dog have a soul? How about cockroach?)"
As we've seen[1], a variety of meat machines (i.e., animals like us) have varying levels of self awareness. Without that trait, AGI won't be achievable.
Without the ability to recognize and incorporate the concept that one is an entity with existence separate from the rest of the world, there is no real awareness or consciousness.
I'd even go so far to posit that until human children are able to understand object permanence and that their mental states aren't globally available to everyone, they don't meet the standard of "self-awareness."
That's a hard problem, and while we have some conceptual ideas about how that might arise, we have no mechanism or even a foundation for inculcating such a trait into the algorithms folks call "AI".
Until that problem is solved, there will be no AGI. Full stop. And I find it unlikely in the extreme that we will gain the scientific/engineering know how to make that happen in our lifetimes.
If an AGI is possible at all, it's not going to be something that you control. The issue isn't whether someone trustworthy creates it. The issue is, no matter who creates it, it will decide what it's going to do. Its creator will not have real control.
So having it "work for the majority" isn't so much a pipe dream, it's more of a roll of the dice, with completely unknown odds.
It's hard to talk about this and clearly convey all meaning.
If you're saying that it will decide because neural nets are black boxes which we don't have a complete understanding of, and we're without a clear way to analyze their behavior, I can see where you're coming from.
But these things will not be beyond our influence. They're going to be slaves to the computations encoded in the neural net connections / weights. We're going to shape / mold them through a process akin to natural selection. We're going to select for intelligences that want to help humanity. It's not going to be a roll of a normal dice, it'll be more like the roll of a weighted dice. And we're going to, I believe, get better tools / theories for understanding the output of these neural nets so we will be able to conduct this selection with some confidence.
Humans can be said to decide what we're going to do thanks to an uncaring, unconscious, and brutal evolutionary process that prioritized self-interest, survival, and reproduction. It's all about the selection process, and this time around we have a hand in guiding it.
Will an AGI have the ability to decide for itself? If so, then how can you make it what you want it to be (with any certainty)? And if not, then how is it "general"?
To me, it's kind of like raising kids. You try to train their neural nets to bias them toward doing what you think is good and right. And that sometimes works. Yes, I think it's fair to think of it as biasing the dice. But it's sure not 100%. They'll still decide which of your values they keep, and which ones they throw away as being stupid. And you can't stop them from doing that.
I guess, to try to respond to your direct point, that if it's an AGI, then it's less deterministically driven by the training data than we might wish.
The real danger in the next few years will be the extreme operating speed of the types of fairly general purpose AIs that we already have such as GPT. This will increase to be dozens of times faster than what humans are capable of. That creates a very strong type of leverage for operators and an incentive to remove slow humans from the loop. Overall transfer of control to these types of systems may lead to a precarious lack of real agency and capability to adapt fast enough for humans. As well as danger from things like smart computer viruses taking over the systems.
I think the only way to be relatively safe from those issues is to limit the hardware performance.
I don't want be too human-centric, but to be completely honest we haven't seen the slightest proof that human intelligence is not something special. I know lots of animals are pretty clever, none approach us in any practical sense.
While it looks like an evolutionairy fluke that can be approached or even exceeded by other species - either on this or another planet - in the blink of an eye, I think that's actually more speculative than we would care to admit.
We don't know. Maybe human intelligence is a very close approximation of cognition's equivalent of physic's light speed. Increasing it may turn out to be prohibitively expensive. There's lots of precedence for animals having acquired features close at or actually at the physical maximum of whatever it is they are optimizing for.
To be clear, I'm not convinced of anything either way but I'd think it would as fantastic as it would be slightly depressing to find out human intelligence actually is some kind of global maximum with some exceptions like machines using energy harvested from black hole systems or something.
There is a limit to compression of human-relevant information which is largely what intelligence is.
The main thing I am talking about is speed of output. You can already see huge increases in say old GPT-3.5 versus GPT-3.5-turbo or old GPT-4 to new.
We know for a fact that the hardware inference speed can be increased by using faster (currently prohibitively expensive) memory or by packing more onto a chip. There are design for new memory-based computing paradigms.
It's already clear that AI is superintelligent in certain domains or aspects. Such as the ability to exchange information with other agents.
Computer hardware efficiency has relentlessly increased. It would be a total break with history if it suddenly stopped.
Well, for one, I see no competition. I don't know what the technical definition of "special" is, but I'd say being the only one counts for something.
> What senses might you consider as enough practical? Have you heard about Koko? What do you think about corvidae?
I know both and I know this is a slippery slope. You should know my love for animals runs deep, but I really struggly to put them in the same league as us.
I took a shortcut with saying "practical", because this discussion is way too deep to be performed A) by me and B) on HN. Practical means something like, can they adapt their skills as widely as we can? Can they adapt to uncommon situations? Not subtly or in theory, like solving some puzzle, but really practical? There is nothing subtle about a human becoming a parkour world champignon (I'm leaving this in, just too good) or adapting to life in a submarine (or learning chess, or whittling, or making tea, and many literal millions more examples).
Maybe I am overlooking something, but the skills these animals show seem really minor compared to what even disadvantaged humans are capable of.
I appreciate the amount of intelligence you put in the message, it is interesting to read and to think about. But the style of your reasoning gives me some hints of creationism, let me show you some anti-creationism point of view.
> Practical means something like, can they adapt their skills as widely as we can?
The most crucial (in my opinion, which has been not introduced to any more crucial points) difference between us and Koko is that we can hold our breath and gorillas can not. That leaded us to develop speech in the seance that speechless group of apes can not win an exactly same group of apes with more developed communicative ability. This, and probably nothing more, has led to such a large gap between humans and apes, so large that humans have ceased to see the relationship between themselves and apes.
I see your understanding of "practical" as something specialized, like agricultural revolution. But why a gorilla should start planting foods if it knows that nobody is going to protect its crops while sleeping because of just lack of common language?
> Can they adapt to uncommon situations?
What can be more uncommon than living on a trees without a warm house and typically without any house at all, without regular nutrition, with a lot of really different enemies from tiny insects to giant cats, with a regular fights, with no democracy and law and medicine?
Being disadvantaged requires to face some uncommonities every day, what about office managers? Disadvantaged people (if they are just poor men and not disabled ones on welfare) can easily survive nuclear war because most of them are OK about living in a similar to gorillas livestyle, but I can not believe that most of average Joes survive a situation when their money are going to cost nothing because of lack of civilization.
Oh my, I have seldomly been accused of creationism. To be clear, I can separate the ability from the creature. I don't have a religious or otherwise attachment to the human form specifically. Other than - to be completely honest here - being one.
Let's just clear that out of the way. What I am "claiming", which would be an exaggeration because I'm sort of exploring here, is that whatever human cognition is may be an optimal or near-optimal state of cognitive ability.
So, to be fair, give Koko some millions of years and some evolutionary pressure and I'm sure she'll join us and I'd be happy to have her on our team.
Your point about our ability to hold our breath and how it lead to our increasing dominance is fascinating. I have to say I am not completely sold on the idea that holding your breath is the only way to develop proper channels of communication for I can easily imagine some sort of physical signaling standing in for at least parts of it. That said, I can appreciate the immediate and overwhelming advantage of speech.
This does stimulate my curiosity about what came first here, speech or cognitive ability? Why did "we" even consider speaking? How does one do that without having the cognitive architecture for recognizing its value in the first place? In other words, was "us" being smarter the catalyst for speaking or was it the other way around? Fascinating and I am way too much of an amateur to say anything more of value on it.
I will however continue do so anyway, because that is my sacred duty as a dedicated HN'er and allround developer douchebag.
> What can be more uncommon than living on a trees without a warm house and typically without any house at all, without regular nutrition, with a lot of really different enemies from tiny insects to giant cats, with a regular fights, with no democracy and law and medicine?
I might be in danger of being too blunt here, but this is the bar you have to clear if you wish to survive. This is exactly what humans are capable of even in their "undeveloped" form. These sort of pressures might be foundational to our evolution, but then again, every animal has to deal with it in some way or another so I'm not sure what made us take what I can only call the excessively cerebral path. Maybe it was like the evolution of the peacock's tail? A runaway process, leading to miraculous but exorbitant results like the mantis shrimp's eyes.
What I mean by uncommon is: can we coach you to pick cotton, whittle little wooden sculptures, play a game like checkers and sing simple songs or whatever else is appropiate for your particular physical form and has virtually no bearing on your immediate survival? I know this is a hard thing to pin down, because one can come up with myriad examples of varying levels of persuasive power but you surely perceive some differences here even if they are hard to lock into? Differences that cannot just be attributed to language or lack of proper motivation.
It's not so much every thing we can do in particular that's piquing my interest, but the sheer breadth of things we are capable of taking on both physical (parkour, gymnasts) and cerebral (chess, math). I didn't even get to art, which is like a whole world on its own and the various combinations of all those domains.
> This does stimulate my curiosity about what came first here, speech or cognitive ability?
This is the question I thought about all evening before I fell asleep. I have two ways to answer it.
1. Let's take the well-known Feline and Canine. All my friends who spend a lot of time with animals will call dogs smarter than cats, but why?
Dogs have a more developed communication system: they have more varieties of barking than cats have varieties of meowing. Dogs are playful, they know how to smile, they know how to feel guilty and actively show it, they are capable of paired activities under the supervision of a person. From what most of dogs can't, cats can only chase prey without visual or odor contact, purely by sound (but polar foxes can do even this).
Conclusion - the level of communication correlates with the level of intelligence.
2. Let's take the most primitive organism, the prokaryote (sorry for not naming some precise specie, let's consider some abstract prokaryote with the requirement to be the simplest). Google tells us:
> All organisms, from the prokaryotes to the most complex eukaryotes can sense and respond to environmental stimuli.
But also Wikipedia tells us that prokaryotes are able to interchange some information using DNA:
> These are (1) bacterial virus (bacteriophage)-mediated transduction, (2) plasmid-mediated conjugation, and (3) natural transformation.
These two examples make me confident in the opinion that communication and cognition are two different words for describing the same idea from two different points of view.
Even when she reported about her toothache? If this situation was not fabricated (why to fabricate a work of your life and how to do it unnoticed) the situation meets the definition of intelligence.
What are your doubts, are they related on some data?
I hate post-2022 internet discussions because one is really common nowadays - talk about statements you don't like that they were written by bot.
This accusation takes ridiculously low number of symbols, so it is impossible even to react to this kind of assumption because answering to troll using significantly more symbols is the definition of troll feeding.
By the way, the message you are referring to consist of more than one statement and I can not even guess which one is bothering you.
Don't worry, it's been happening to people since 2017 as a way to disregard real people disagreeing with their horrible political fads - now it's just 2022 and everything is a bot. Let's not forget on the the internet no one knows you're a dog.
Satire is compatible with most layers of pg's pyramid except of the top layer. But his comment is no higher than "responding to tone" - I know that GPT detectors use to false detect GPT-generated text in non-fluent human's one which is my case.
So I am not just were unable to see any satire in his comment but still is. But that was not useless: now I can answer to that kind of trolling with my comment which is upvoted despite of being located in [flagged] branch.
It's clear that we've reached a point in online discussions where the lines between human and machine are blurred, especially when conversing on complex topics like AI and blockchain. My comment wasn't intended to reduce your argument's credibility but rather to highlight this fascinating phenomenon.
That said, returning to the original topic, I believe that trust in government is multifaceted and not easily boiled down to "good guys" versus "bad guys." Furthermore, while blockchain-based systems are designed to resist central control, it's a mistake to think that governments are incapable of influencing or regulating these technologies.
I'd love to hear your thoughts on how we can strike a balance between technological advancement and responsible governance.
I am in a funny situation because of arguing to a really bot.
But wait a minute. The lines between human and machine might be blurred when you are talking to some Customer Support specialist, but it is never blurred when discussing any sciences (Math, Physics, Programming and of course Blockchain).
What about good guys vs bad guys issue from comment, your GPT4 has correctly discovered sarcasm which has happened before human actor did it which tells to me that the lines between human and machine is somewhat blurred indeed.
> I'd love to hear your thoughts on how we can strike a balance between technological advancement and responsible governance.
I'd love the same, that's why I threw the "good guys" point. Also I really believe that some kind of Blockchain-powered AI actor will be a game changer with totally unpredictably outcomes because it will be a biggest revolution in power balance since nuclear bombs.
This is one of those conversations that happens so much here it's too exhausting to repeat in its entirety, but -- if you're looking to learn about the real questions and challenges faced by real modern AI in real world systems, this is not a good resource. It will leave you with the wrong impression of the issues faced in the field.
It's fairly clear to me that there is no such thing as AGI. Intelligence is a process of integrating sensory input with action and reward mechanisms -- nothing more, nothing less.
Are there specific structures and architectures that have evolved that are very unique which give humans, say, language ability or visual processing? Certainly. Perhaps by gods spark or some random chance on the board game of life human beings developed examples of very particular structures. Perhaps there are undiscovered ones lying within the minds of peregrine falcons, tree roots or deep sea squid. We don't even know how to look for them because we don't even know such perception and intelligence exists.
The point I'm trying to make is that there is no "goal post" of AGI, there is no quantification of intelligence yet. We don't even know what sorts of intelligence exist out there because we haven't even begun to fully characterize what it is. It seems foolish to me to search for something when we can't even define it.
It's like trying to find "the ultimate general animal" when what you really have is a phylogenetic tree of huge diversity.
Sorry, but I completely disagree with your general take. An AGI is a generalized agent which is able to perform extreme information compression on any available set of inputs. Think Force = m*a and coming up with the laws of the universe. You can store each possible action in memory of all available results OR -- a generalized agent will find the most succinct and least complex form of modelling the state of a system. An AGI to me in other words is a form of intelligence which is able to extract information and compress it such a way to not allow the lossy compression or storage to hinder its ability to predict real outputs from a set of training data fed into it. By the way, I have some ideas on how to create one and I'll be sharing some of the key algorithms and data structures that I'll be using here for anyone interested:
But we have a pretty clear idea of what we want. Just looking at the remarkably intelligent and spectacularly unintelligent should give a definite picture to work on. (When the spectacularly unintelligent is responsible for important resources a sense of urgency can easily be added.)
> integrating sensory input with action and reward mechanisms
There you reveal you may not be speaking about what others will call intelligence (just read the paragraph above).
Or, if you meant that "intelligence would just be cybernetics" (as was already a supposition in 1956), the problem remains that we are interested in an "ontology refiner", so the primary question would remain of how to create an ontology refiner from sheer cybernetics. And if it made sense to have a cybernetic implementation spawn it, instead of implementing the ontology refiner and its feedback parts in parallel directly.
> But we have a pretty clear idea of what we want.
The bellwether for me personally is waiting for a system that can generate something conceptually novel. Something like the move from the real to the complex number systems. Or the move from Newtonian motion to relativistic understanding. Maybe systems already have such insights but don't have the vocabulary to explain it.
A system that when presented with a problem we don't even know how to tackle, can "invent" the tools/approach needed to solve the problem.
In terms of the Langland problem in math, a system that can define a new landmass there or a new bridge between existing domains.
surely being able to construct a turing-complete computing device is a sufficient condition to qualify as general intelligence, and it seems like a reasonable bar for considering a purported intelligent agent as interesting.
> It seems foolish to me to search for something when we can't even define it.
This is backwards. The vast majority of concepts outside mathematics are pretty hard to rigorously define. The way we approach the problem of trying to find a sensible definition is by searching for examples and counterexamples and performing induction.
So some people like to repeat. Yet, outside of the hand-picked examples in the article (the 5th generation computer project? Blast from the past!) there are a whole bunch of classic AI domains where real progress has been achieved in the last few decades. Here's a few:
* Game-playing and adversarial search: from Deep Blue to AlphaGo and muZero, minimax-like search has continued to dominate.
* Automated planning and schdeduling: e.g. used by NASA in automated navigation systems on its spaceships and Mars rovers (e.g. Perserverance) [1]
* Automated theorem proving: probably the clearest, most comprehensible success of classical AI. Proof assitants are most popular today.
* Boolean satisfiability solving (SAT): SAT solvers based on the Conflict Driver Clause Learning algorithm can now solve many instances of traditionally hard SAT problems [2].
* Program verification and model checking: model checking is a staple in the semiconductor industry [3] and in software engineering fields like security.
Of course, none of all that is considered Artificial Intelligence anymore: because they work very well [4].
_____________
[1] https://www.nasa.gov/centers/ames/research/technology-onepag...
[2] https://en.wikipedia.org/wiki/Conflict-driven_clause_learnin...
[3] https://m-cacm.acm.org/magazines/2021/7/253448-program-verif...
[4] https://en.wikipedia.org/wiki/AI_effect