If you can create a general machine that can take 3 examples and synthesize a program that predicts the 4th, you've just solved oracle synthesis. If you train a network on all human knowledge, including puzzle making, and then fine-tune it on 99% of the dataset and give it a dozen attempts for the last 1%, you've just made an expensive compressor for test-maker's psychology.
The true test of an AGI is it's ability to assimilate disparate information into a coherent world-view, which is effectively what the pretraining is doing. And even then, it is likely that any intelligence capable of doing that will need to be "preloaded" with assumptions about the world it will occupy, structurally. Similar to the regions of the brain which are adept at understanding spatial relationships, or language, or interpreting our senses, etc.
Also
People keep thinking "General" means one AI can "do everything that any human can do everywhere all at once".
When really, humans are also pretty specialized. Humans have Years of 'training' to do a 'single job'. And they do not easily switch tasks.
What? Humans switch tasks constantly and incredibly easily. Most "jobs" involve doing so rapidly many times over the course of a few minutes. Our ability to accumulate knowledge of countless tasks and execute them while improving on them is a large part of our fitness as a species.
You probably did so 100+ times before you got to work. Are you misunderstanding the context of what a task is in ML/AI? An AI does not get the default set of skills humans take for granted, its starting as a blank slate.
The model should learn the rule, don't make a model based on the rules. When you make a model based on the rules then it isn't a general model.
Human DNA isn't made to play tennis, but a human can still learn to play it. The same should be for a model, it should learn it, the model shouldn't be designed by humans to play tennis.
You don't have a human spend years getting an MBA, then drop them in a Physics Lab and expect them to perform.
But that is what we want from AI, to do 'all' jobs equally as great as any individual human in that one job.
There are steps of automation that could fulfill that requirement without ever being AGI - it’s theoretically possible (and far more likely) that we achieve that result without making a machine or program that emulates human cognition.
It just so happens that our most recent attempts are very good at mimicking human communication, and thus are anthropomorphized as being near human cognition.
I'm just making point that for AI "General" Intelligence.
That humans are also not as "General" as we assume in these discussion. Humans are also limited in a lot of ways, and narrowly trained, make stuff up, etc...
So even a human isn't necessarily a good example for what AGI would mean. Human is not a good target either.
Humans are a good target since we know human intelligence is possible, its much easier to target something that is possible rather than some imaginary intelligence.
Humans are extremely general. Every single type of thing we want an AGI to do is a type of things that a human is good at doing, and none of those humans were designed specifically to do that thing. It is difficult for humans to move from specialization to specialization, but we do learn them with only the structure to "learn, generally" being our scaffolding.
What I mean by this is that we do want AGI to be general in the way a human is. We just want it to be more scalable. It's capacity for learning does not need to be limited by material issues (i.e. physical brain matter constraints), time, or time scale.
So where a human might take 16 years to learn how to perform surgery well, and then need another 12 years to switch to electrical engineering, an AGI should be able to do it the same way, but with the timescale only limited by the amount of hardware we can throw at it.
If it has to be structured from the ground up for each task, it is not a general intelligence, it's not even comparable to humans, let alone scalable beyond us.
Where today those are being done, but specialized architectures, models, combination of methods.
Then that would be a 'general' intelligence, the one type of model that can do either. Trained to be an engineer or doctor. And like a human once trained, they might not do the other job well. But they did both start with same 'tech', like humans all have the same architecture in the 'brain'.
I don't think it will be an LLM, it will be some combo of methods in use today.
Ok. I'll buy that. I'm not sure everyone is using 'general' in that way. I think more-often people think a single AI instance that can do everything/everywhere/all at once. Be an engineer and doctor at same time. Since it can do all the tasks at same time, it is 'general'. Since we are making AI's that can do everything, could have a case statement inside to switch models, half joking. At some point all the different AI methods will be incorporated together and will appear even more human/general.
It doesn't have to actually "think" as long as it can present an indistinguishable facsimile, but if you have to rebuild its training set for each task, that does not qualify. We don't reset human brains from scratch to pick up new skills.
> humans CAN do.
- Humans CAN generalize
- Humans CAN reason
- Humans CAN be intelligent
- Humans CAN be conscious
One of my pet peeves is how often qualifying words are just ignored. They are critical parts of any communication.[1]
Another pet peeve is over anthropomorphization. Anthropomorphism is a useful tool, but.. well... we CAN over generalize ;)
[0] I don't know what you mean by "true generalization". I'm not going to address that because you can always raise the bar for what is "true" and let's try to be more concrete. Maybe I misunderstand. I definitely misunderstand.
[1] Classic example: someone says "most x are y" then there's a rebuttal of "but x_1 isn't y" or "I'm x and I'm not y" or some variation. Great! Most isn't all? This is not engaging in good faith and there's examples found with any qualifying word. It is quite common to see.
So like humans after all but faster.
I guess it's just hard to write a book about the way you write that book.
A short list of abilities that cannot be performed by CompressARC includes:
Assigning two colors to each other (see puzzle 0d3d703e)
Repeating an operation in series many times (see puzzle 0a938d79)
Counting/numbers (see puzzle ce9e57f2)
Translation, rotation, reflections, rescaling, image duplication (see puzzles 0e206a2e, 5ad4f10b, and 2bcee788)
Detecting topological properties such as connectivity (see puzzle 7b6016b9)
I'd argue that "innate" here still includes a brain structure/nervous system that evolved on 3.5 billion years worth of data. Extensive pre-training of one kind or another currently seems the best way to achieve generality.
Each new training from scratch is a perfect blank slate and the only thing ensuring words come out is the size of the corpus?
I don't think training runs are done entirely from scratch.
Most training runs in practice will start from some pretrained weights or distill an existing model - taking some model pretrained on ImageNet or Common Crawl and fine-tuning it to a specific task.
But even when the weights are randomly initialized, the hyperparameters and architectural choices (skip connections, attention, ...) will have been copied from previous models/papers by what performed well empirically, sometimes also based on trying to transfer our own intuition (like stacking convolutional layers as a rough approximation of our visual system), and possibly refined/mutated through some grid search/neural architecture search on data.
All organisms are born pre-trained because if you can't hide or survive the moment you're born, you get eaten.
uhhh... no, most newborns can't "hide or survive the moment they're born", no matter the species. I'm sure there are a few examples, but I seriously doubt it's the norm.
Many species survive by reproducing en masse, where it takes many (sometimes thousands of) eaten offspring for one to survive to adulthood.
Makes sense though, I’m pretty sure mammals don’t do well with the insects and diseases that come with waste saturated bed.
I’m not sure we would call anyone intelligent today if they had no education. Intelligence relies on building blocks that are only learned, and the “masters” of certain fields are drawing on decades and decades of learnings about their direct experience.
So our best examples of intelligence include experience, training, knowledge, evolutionary factors, what have you — so we probably need to draw on that to create a general intelligence. How can we expect to have an intelligence in a certain field if it hasn’t spent a lot of time “ruminating on”/experiencing/learning about/practicing/evolving/whatever, on those types of problems?
That quote about how "the only intuitive interface ever devised was the nipple"? Turns out there's still a fair bit of active training required all around to even get that going. There's no such thing as intuitive, only familiar.
Yes, they enjoy millions of years of pretraining thanks to evolution, ie. their pretrained base model has some natural propensity for visual, auditory, and tactile sensory modalities, and some natural propensity for spatial and causal reasoning.
I thought the knowledge is the training set and the intelligence is the emergent/side effect of reproducing that knowledge by making sure the reproduction is not rote memorisation?
That's reinforcement learning -- an algorithm, which requires accurate knowledge acquisition, to be effective.
The argument being advanced is that intelligence is the proposal of more parsimonious models, aka compression.
For that matter, if it had no pre-training, it means it can generalize to any new programming languages, libraries, and entire tasks. You can use it to analyze the grammar of a dying African language, write stories in the style of Hemingway, and diagnose cancer on patient data. In all of these, there are only so many samples to fit on.
But I do have enough knowledge to know what an IDE is, and where that sits in a technological stack, i know what a string is, and all that it relies on etc. There's a huge body of knowledge that is required to even begin approaching the problem. If you posted that challenge to an intelligent person from 2000 years ago, they would just stare at you blankly. It doesn't matter how intelligent they are, they have no context to understand anything about the task.
Depending on how you pose it. If I give you a long enough series of ordered cards, you'll on some basic level begin to understand the spatiotemporal dynamics of them. You'll get the intuition that there's a stack of heads scanning the input, moving forward each turn, either growing the mark, falling back, or aborting. If not constrained by using matrices, I can draw you a state diagram, which would have much clearer immediate metaphors than colored squares.
Do these explanations correspond to some priors in human cognition? I suppose. But I don't think you strictly need them for effective few-shot learning. My main point is that learning itself is a skill, which generalist LLMs do possess, but only as one of their competencies.
However, I assumed what we're talking about when we discuss AGI is what we'd expect a human to be able to accomplish in the world at our scale. The examples of learning without knowledge you've given, to my mind at least, are a lower level of intelligence that doesn't really approach human level AGI.
It doesn't need to know the capital of Togo, metabolic pathways of the eukaryotic cell, or human psychology.
What if knowing those things distills down to a pattern that matches a pattern of your code and vice versa? There's a pattern in everything, so know everything, and be ready to pattern match.
If you just look at object oriented programming, you can easily see how knowing a lot translates to abstract concepts. There's no reason those concepts can't be translated bidirectionally.
And no, I don't think the knowledge of language is necessary. To give a concrete example, tokens from TinyStories dataset (the dataset size is ~1GB) are known to be sufficient to bootstrap basic language.
There's a reason people with comparable intelligence operate at varying degrees of effectiveness, and it has to do with how knowledgeable they are.
This paper claimed transformers learn a gradient-descent mesa-optimizer as part of in-context learning, while being guided by the pretraining objective, and as the parent mentioned, any general reasoner can bootstrap a world model from first principles.
I guess a superset. But it doesn't really matter either way. Ultimately, there's no useful distinction between pretraining and in-context learning. They're just an artifact of the current technology.
this is pretty vague. I certainly dont think a mastery of any concept invented in last thousand years would be considered encoded in genes though we would want or expect an AGI to be able to learn calculus for instance. In terms of "encoded in genes", I'd say most of what is asked or expected of AGI goes beyond what feral children (https://en.wikipedia.org/wiki/Feral_child) were able to demonstrate.
There are a few orders of magnitude more neural connections in a human than there are base pairs in a human genome. I would also assume that there are more than 4 possible ways for neural connections to be formed, while there are only 4 possible base pairs. Also, most genetic information corresponds to lower level functions.
> I feel like extensive pretraining goes against the spirit of generality.
Pretraining is fine. It is even fine for pursuit to AGI. Humans and every animal has "baked in" memory. You're born knowing how to breath and have latent fears (chickens and hawks).
Generalization is the ability to learn on a subset of something and then adapt to the entire (or a much larger portion) of the superset. It's always been that way. Humans do this, right? You learn some addition, subtraction, multiplication, and division and then you can do novel problems you've never seen before that are extremely different. We are extremely general here because we've learned the causal rule set. It isn't just memorization. This is also true for things like physics, and is literally the point of science. Causality is baked into scientific learning. Of course, it is problematic when someone learns a little bit of something and thinks they know way more about it, but unfortunately ego is quite common.
But also, I'm a bit with you. At least with what I think you're getting at. These LLMs are difficult to evaluate because we have no idea what they're trained on and you can't really know what is new, what is a slight variation from the training, and this is even more difficult considering the number of dimensions involved (meaning things may be nearly identical in latent space though they don't appear so to us humans).
I think there's still a lot of ML/AI research that can and SHOULD be done at smaller scales. We should be studying more about this adaptive learning and not just in the RL setting. One major gripe I have with the current research environment is that we are not very scientific when designing experiments. They are highly benchmark/data-set-evaluation focused. Evaluation needs to go far beyond test cases. I'll keep posting this video of Dyson recounting his work being rejected by Fermi[0][1]. You have to have a good "model". What I do not see happening in ML papers is proper variable isolation and evaluation based on this: i.e. hypothesis testing. Most papers I see are not providing substantial evidence for their claims. It may look like this, but the devil is always in the details. When doing extensive hyper-parameter tuning it becomes very difficult to determine if the effect is something you've done via architectural changes, change in the data, change in training techniques, or change in hyperparameters. To do a proper evaluation would require huge ablations with hold-one-out style scores reported. This is obviously too expensive, but the reason it gets messy is because there's a concentration on getting good scores on whatever evaluation dataset is popular. But you can show a method's utility without beating others! This is a huge thing many don't understand. Worse, by changing hyper-parameters to optimize for the test-set result, you are doing information leakage. Anything you change based on the result of the evaluation set, is, by definition, information leakage. We can get into the nitty gritty to prove why this is, but this is just common practice these days. It is the de facto method and yes, I'm bitter about this. (a former physicist who came over to ML because I loved the math and Asimov books)
[0] https://www.youtube.com/watch?v=hV41QEKiMlM
[1] I'd also like to point out that Dyson notes that the work was still __published__. Why? Because it still provides insights and the results are useful to people even if the conclusions are bad. Modern publishing seems to be more focused on novelty and is highly misaligned from scientific progress. Even repetition helps. It is information gain. But repetition results in lower information gain with each iteration. You can't determine correctness by reading a paper, you can only determine correctness by repetition. That's the point of science, right? As I said above? Even negative results are information gain! (sorry, I can rant about this a lot)