From that perspective intelligence is indeed just a curve fitting.
https://en.wikipedia.org/wiki/Church%E2%80%93Turing_thesis
I really enjoyed the "The Measure of Intelligence" by François Chollet. https://arxiv.org/abs/1911.01547
"We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power."
He argues that we should move towards evaluating "Intelligence as skill-acquisition efficiency".
I agree with him. We should move away from benchmarks that involve training and evaluating algorithms on the same datasets. This is indeed more or less "curve fitting". Instead we should focus on benchmarking how efficient algorithms are at solving tasks involving completely new datasets, preferably even unknown to the developers. For example, language model GPT-2 was trained to predict next word given some previous words. After that training GPT-2 was able to do things that were unrelated like question answering, translating etc. GPT-2 is of course doing that very badly, and requires GB of training data, but it is a step towards skill-acquisition efficiency and away from what everyone sees as curve-fitting.
We should benchmark models so that we select for these that are able to do solve tasks they were not build to solve.
do you mean "all computation that can be done, can be done using a Turing machine"? Or do you mean "no one has proven Church Turing wrong?"
If it's the second - yes, that's so. But so what?
If it's the first then many people in Quantum Computing community will be quite upset, P=BQP? You have proof?
A common issue you find would be confounding. Then, because you haven't identified the latent connection, you may try to increase level A to have effect on output B, and be disappointed.
This is basically Judea Pearls Book of Why's main hypothesis, that E(Y|X) != E(Y|do(X)), where do(X) is when we modify X somehow.
General intelligence is primarily about developing useful conceptual categories (not mapping to existing ones) and drawing cause-and-effect inferences that assist us in achieving goals.
Curve fitting is just another name for pattern recognition, mapping to previously defined categories. I would personally argue there's no intelligence there whatsoever. Intelligence can't exist without a foundation of pattern recognition, but it isn't the same thing.
Intelligence is fundamentally goal-directed and able to reason, while curve-fitting is fundamentally not.
(There is also unsupervised learning in deep learning, which doesn't use previously defined categories, but since it is similarly non-goal-directed, I would still argue that this is merely dimension reduction as opposed to intelligence -- useful for sure, but not the same.)
One of my favourite examples is the New Caledonian crows who have learned to use traffic to crack nuts [1]. Here, a crow had no pre-defined objective function apart from "eat food to stay alive" and has accomplished something remarkable. It found a food source that it had never had access to before, it developed a complex model of its urban environment, it combined its knowledge of the problem (the hard nut shell) with its knowledge of its environment (cars crush small objects), and it constructed a sophisticated for strategy for using cars to crack open the nuts and fetching the contents when the traffic lights indicated it was safe to do so.
This is general intelligence!
There are some algorithms like https://en.wikipedia.org/wiki/K-means_clustering that get a set of data and try to create the categories to better classify them. There are many algorithm and the results don't agree all the time. But this is an open ended task, like the classification of biological species in animals. (Plants are more difficult, and bacterias even more.)
Second, deep learning model, however much we'd like to think they do, aren't capable of doing proper causal inference in a general setting (that is, within the confines of the model) and are therefore far from capable of doing what humans do and will remain so limited for a long time to come.
AGI will require the curve-fitting of deep learning, a general model of the world, the causal inference capabilities of something like AlphaGo, but in a general setting, not the super limited world AlphaGo operates in.
So no, AGI will require much more than just curve-fitting abilities.
This is a field called Model-based Reinforcement learning, and it's quite advanced already -- there are indeed models that have an internal state reflecting the world state.
A good recent example:
https://papers.nips.cc/paper/7512-recurrent-world-models-fac...
> deep learning model, however much we'd like to think they do, aren't capable of doing proper causal inference in a general setting
This is also addressed by recent models, somewhat. Once you have an abstract world model, searching for a high reward can be just a matter of running markovian simulation on it using high reward heuristics (given by a network of course), like AG does. This line is also very active right now, one example is the recent MuZero.
https://arxiv.org/abs/1911.08265
Inference at its core really isn't much more than an artful curve fitting (or an artful model search if you like), and it's one of the building blocks of intelligence.
But you need the right prior structure such that learning and producing action sequences is efficient or even feasible/reachable. You can see any additional program structure that aids e.g. generation and recall of memories and planning (production of output targeted at solving a goal) as prior structure that limits and defines the searched program space. You can even regard a planning module as part of the curve fitting as it simply concerns the last step of producing the output. Therefore, intelligence is "curve fitting".
So the actual question is: How much additional structure over just a large number of simple repeated units is necessary? Nobody knows. Possibly not much. Possibly quite a bit.
So an ML model running an input through a collection of other models to see if it gets a reasonable answer.
Or to bring it down to Earth in another way, consider just the act of writing a program in the modern world. If you work really, really hard, you can define a space in which our act of programming is just "curve fitting"... but it's far from obvious that that is even remotely a sensible way to look at the world. (See "differentiable programming" for the best counterpoint I know to that: https://en.wikipedia.org/wiki/Differentiable_programming but it's a very small niche right now.) When I'm debugging a program there is almost never any utility at all in trying to think about it as a "curve" and trying to get it closer to a the "correct" curve. A Turing-complete-complex space can be described as curves, but those curves are just awfully complicated and I don't see how it would be a help.
My personal suspicion is that while our cognition involves rather less of this "Turing complete" thinking than we'd like to fancy ourselves using, we do irreducibly use elements of it [1], and as long as our best AI models are incapable of representing Turing-complete computations there is simply no chance of them being the answer to true human-scale cognition. (We do have models that can do it, e.g., evolutionary computation, but we lack any sensible idea of how to "update" such models like a neural net. Neural nets themselves in the simplest case aren't Turing complete, and none of the hybrid models seem to get there to me either, though I welcome correction on that point.)
[1]: Evidence: I don't think we could program Turing-complete machines if we were incapable of thinking that way ourselves. We aren't necessarily great at it, our engineering techniques are deeply characterized by the fact we can't really manipulate very many things at once in this manner and we have no choice but to break things up into very small modules and for us to combine them in a way that means that at any given time we have only a very small number of things to keep track of locally, but we are still doing non-trivially more than zero of the Turing-complete style of thinking. It isn't a hard guess from there to think that even if we aren't all that great at a full mathematical manifestation of this style of thinking, we may indeed be doing something somewhere between what our current neural nets do and this full TC-style thinking at a larger scale, and the inability to capture this in our neural nets is a currently-fatal-flaw.
So, you can argue something like our brains are nothing but curve fitting machine with enough parameters, but then you are probably forced to argue that consciousness is very closely related to computation, to the point were a coin flip, or a hello world program has some sliver of consciousness.
There are of course two possible ways around that, either one can argue for p-zombies, that is intelligent but not conscious beings, which then seems to require a super natural explanation for consciousness. Or you can argue that the brain is different, and that this gives rise to consciousness, and to general intelligence, which is the explanation that at least corresponds most closely to my subjective experience (but that is precisely what a soulless machine would write, isn't it?)
I don't like very much this reverent way of thinking about consciousness, as if it is from another world, or a different essence.
I believe consciousness is the ability of the agent to adapt to the environment in order to protect itself and maximise rewards. It's not just in the matrix multiplication, but in the embodiment, the environment-agent loop. Consciousness is not something that transcends the world and matrices, it's just a power to adapt and survive.
And it feels like something because that feeling has a survival utility, so the agent has a whole neural network to model future possible rewards and actions, which impacts behaviour and outcomes.
Why not? Those things have zero-consciousness that is conscious only of itself and which correctly reflects their lack of self-model.
Obviously other living humans have the highest similarity, so they are automatically deemed conscious. Next are other primates, followed by other domesticated mammals, and other animals.
Furthest from the status of conscious are creatures we see as automata like dung beetles rolling their food, or jellyfish ... jellyfishing.
Presumably we'd apply a similar process to hypothetical AGIs.
It is more likely that consciousness is just a property of brains inside bodies. The interesting question to me is the level of complexity of brain and body required to produce something like what we experience and what is it like in other arrangements of brains and bodies.
I also don't know that intelligence is the great thing that we think it is. It's an adaptation. It's an adaptation that lots of other organisms survive just fine without.
We are all p-zombies. Problem solved.
The longer we refuse to acknowledge that consciousness is nothing special, the longer it will take to tackle this topic. The only reason we cling to the idea that our minds are somehow special compared to other animals of various complexity is because we refuse to acknowledge that consciousness might exist in something we can't communicate with and that consciousness is a sliding scale rather than a binary property. Ascribing consciousness only to ourselves is hubris.
If one spends a bit of time observing humans, they will inevitably realise that some humans are more 'conscious' than others also.
tl;dr: we're all p-zombies. The fact that we think that each one of us isn't individually doesn't detract from that.
Just as natural sciences left less and less hiding places for god to exist, ML is leaving less and less hiding places for this borderline magical version of human-unique consciousness to exist. Answering this question in any more detail requires a much more rigorous definition of consciousness which is a big can of worms in itself.
If I made a list of everything in order of how certain I am that the item on the list exists, consciousness would be at the top by far. Everything else could just be a nice illusion.
To put it another way, if AGI is computable, then we are all p-zombies. And evidence is starting to strongly hint that AGI is computable.
Why do you think the burden of proof should be inverted? The mere fact that most humans intuitively feel "something" doesn't count for much of anything, especially once you stipulate that p-zombies would vote the same way.