Depending on how complex a curve, and how many dimensions it is in, couldn't you argues that is essentially what our brains do as well? Not that I am defending the massive hype field that is ML today, but curve fitting is a form of intelligence.
I was say fundamentally no -- and Douglas Hofstadter is probably the foremost spokesperson against intelligence being curve fitting.
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.)
we don't know everything, but we definitely know something about current ML, and we can indeed speculate and make some educated guesses about AGI. for a random comment on the internet, that contained some interesting information. of course, an actual implementation of AGI might be anything, but the comment was broader than that
We know a great deal more about biological general intelligence than we do about AGI. Many animals create their own goals and work to achieve them. We learn what works and what does not, and adapt our strategies to compensate. Humans do this (obviously) but a lot of other intelligent animals can do it as well.
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.
It's not like there's a huge difference. Causal inference is a special case of curve-fitting, where one chooses what variables should enter into the fit according to a causal graph.
> General intelligence is primarily about developing useful conceptual categories (not mapping to existing ones) …
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.)
Computer Vision and Pattern Recognition. Was a good name, still will be. Great progress has been made, but it's not AGI as anyone who goes to CVPR will tell you.
First, no AI that I know of has at its disposal a full blown model of the world it operates in, whereas most human brains do, and even if the model is imperfect, it is capable of producing fairly accurate simulations (what-if scenarios).
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.
But all this is curve-fitting in a more general sense - fitting the curve of life, gene reproduction. So it is still curve fitting, it is just that a pottential AGI is certainly not in the hypothesis space of current deep learning models, and those cannot reach AGI by curve-fitting.
"casual inference in a general setting" is just your brain running an input through it's existing thought and decision processes with a low threshold for a passing answer.
So an ML model running an input through a collection of other models to see if it gets a reasonable answer.
It's all pretty meaningless semantics and guesswork.
Curve fitting means adaptive computation in networks of fairly simple units that allow for fairly general computation, i.e. traversing program space to find a good solution, or equivalently, intelligence is about evolving/searching a program that solves a wide array of tasks. It is about finding programs that maps from sensory space to the space of action sequences, maximizing reward.
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.
> no AI that I know of has at its disposal a full blown model of the world it operates in
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.
> 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.
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.
That is basically what Chalmers argues in "Facing up the problem of consciousness." Basically, by the general approximation theorem, it is possible to find a neural network that acts externally precisely as you would, up to an arbitrarily small epsilon. However, one wonders if such a thing would be consciousness and if so, where does the consciousness sit, in the matrix multiplication or the graphics card.
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?)
Actually I like that solution, but try to show it to any degree of scientific standard. You need to have a good working definition of consciousness, then you need some experimental procedure, etc. etc.
There's no need for a supernatural explanation of consciousness.
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.
I couldn't agree more. It also just seems like a continuation of the old story in science where the things that we think are special or magical just aren't.
You make a strong claim without anything to back it up. I don’t understand why it’s become trendy to deny that consciousness exists; to me it’s isomorphic to saying “We don’t actually exist at all. Prove me wrong.” It’s a vacuous statement, meant to sound evocative, but difficult to respond to in any meaningful way.
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.
You falsely imply that the parent comment attempts "to deny that consciousness exists". What the parent comment actually says is "consciousness is nothing special" and "consciousness is a sliding scale rather than a binary property". These are different from non-existence.
Claiming that some mysterious and hard to define property that we can't measure even in principle "exists" in some meaningful way strikes me as the stronger claim than the skeptical take does.
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.
My exposure to these subjects has been limited, but the thing that I liked about Dan Dennets rebuttal to Chalmers is that it does away with the special-ness of consciousness.
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.
That depends of which precise definition of consciouness you're talking about, which is a flame wars research field. But it is a consensus that consciouness is not equal to inteligence.
> hen 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.
Why not? Those things have zero-consciousness that is conscious only of itself and which correctly reflects their lack of self-model.
The argument is, if our brains are just a curve fitting machine, then we can dial in the complexity of the computation. Start with a single layer parameter, then two parameter, and so on until we are at the complexity of the brain. By that procedure, we can ask after each parameter, if the machine is now conscious, and I strongly doubt that there is a good answer.
The way we ascribe consciousness to entities other than ourselves is based on similarity to ourselves.
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.
That's an ill-defined question. You can do the same thing with far less vague concepts than consciousness, and I could even ask you the same question about a brain and adding neurons.
https://en.wikipedia.org/wiki/Sorites_paradox
> it is possible to find a neural network that acts externally precisely as you would, up to an arbitrarily small epsilon. However, one wonders if such a thing would be consciousness and if so, where does the consciousness sit, in the matrix multiplication or the graphics card.
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.
If someone wants to go as far as to claim that any computation is just curve-fitting then your statement is equivalent to Church–Turing thesis. There are no formal arguments against Church–Turing thesis.
From that perspective intelligence is indeed just a curve fitting.
"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.
P=BPP would contradict the extended Church-Turing thesis. Quantum computers can definitely be simulated by classical ones (with an exponential slowdown) so the CTT, which says nothing about performance, is not threatened by them.
The life time of the universe and the physical extent of possible classical computers limits "all the computing that can be done" quite strongly. Especially with regard to intelligence - where the physical and temporal extent of brains are clear.
I am not accusing you of doing this in a bad way, but it kinda begs the question to argue that all intelligence is curve fitting, because if it is to be looked at that way, then we don't know the characteristics of the hyperspace on which it can be defined as "just" curve fitting, which is an important aspect of being able to carefully say that it is "just" curve fitting. While there almost certainly is such a hyperspace, just by virtue of the fact we're leaving ourselves so many degrees of freedom in our unspecified speculations here that it can't help but cover everything we ever do, we can't be confident that if we did know everything involved that it would be anywhere near the best representation, which is the only standard we can have in terms of whether or not something is "really" curve fitting. It isn't that hard to imagine that while an n-dimensional space can be defined upon which our intelligence is "curve fitting" that there is some better computational paradigm that would both describe it in fewer free parameters (or, in this case, a big-O different number of free parameters, probably) and also be easier to work with computationally, in which case we would have a solid ground to stand on to say that, no, it isn't really just "curve fitting".
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.
It’s just fancy pattern matching. Multi-stage heuristics. Where it fails is data limitations. This is different from someone coming up with new insights based on different combinations of data inputs and better yet entirely new data/metrics.
What our brains do is curve fitting plus experiments. Causality is learned through experiments - and as children we do a lot of experiments (with moving our limbs etc). With just observations you can have only correlations. For example you can correlate smoke with fire, but only through experiment you can learn that it is fire that causes smoke not the other way around.
This can tease out transfer entropy but still isn't identifying causal factors.
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.
I tend to avoid using the term AI at all and I mostly agree with the article, but I still don't see this as any kind of drawback. Computers are great at curve-fitting and there are many good use-cases for these tasks. We just need to be clear that we are very far from the sci-fi vision of a single computer which can understand, learn and know everything. Which is absolutely fine.
I suppose there are lots of papers and results that fall into the "only curve fitting" bucket, but there are many exciting results in recent years that have been curve fitting + X, where ANNs formed part of a system with other components. While none of these approach the level of something you could have a meaningful conversation with (which was an ambitious bar introduced in the article) some do kind of consider multiple actions, work through consequences of each, and pick a single action. This looks a lot like a crude form of reasoning about causality and hypotheticals, though not quite along the lines Pearl would like to see. But they typically do that in the context of a specific task, with an enumerable set of available actions.
So, one view of AI is that is is machine learning. Another view is that it consists of automated process. So, if expert systems count as AI, or if production rule systems count as AI, then pretty much any handcrafted if-then statement counts as AI. And, so too might any automated process..
This is has the advantage of recognizing how human intelligence can be automated and aggregated into system processes. And, the disadvantage that the boundaries of the concept start exploding.
I like cybernetics for providing a clear model of what constitutes intelligence -- a feedback loop between perception and action that achieves goals or lowers local entropy.
And, cybernetic systems can be artificial, natural or a mix
I too hold the view that artificial intelligence is not reliant on computers. Sufficiently complex business logic is impossible for a single person -- even the CEO -- to have an end-to-end view (much less have a significant influence on). The emergent behavior of large companies fits a definition of an AI today; human "decision makers" are increasingly just reviewing and approving the output of algorithms.
I think the end state for large corporations will be to automate away so much of the human input that they end up looking like what we think of as "AI" in a broader cultural context. But we already live in a world controlled by AI, and we have for 3+ decades.
Yeah, I mean, it's a slippery slope, but one worth sliding down. Is autopilot AI? If so, it was invented in 1914. Are speed governors AI? That was James Watt. Are if-then statements AI? That's the basis of human laws, dating back thousands of years.
Are corporations AI, or rather superintelligences, because they are groups of people bound by bylaws? If so, the whole damn world is AI through and through and machine learning is really just the tip of the iceberg.
Yeah, I guess that's my point. Humanity has been guided by largely autonomous systems for much of our existence, but only recently have those systems become sufficiently complex as to remove the possibility of human intervention in some processes due to the complexity involved -- nobody can intervene if nobody can understand the whole story.
I think this is why late-stage capitalism feels so bad to live under: these artificial systems (corporations), by design, stand in direct opposition to our humanity. They exist to prevent our humanity from getting in the way of their perpetual growth by abstracting any ethical problems away so that no human is faced with an ethical dilemma. Which explains why the world so often feels like a dystopian nightmare.
Pretty sure Maxwells Equations and the laws of thermodynamics didn't come from curve fitting!
Experimentalists may have verified these ideas using some kind of curve fitting, but thinking in abstractions, aka, "a ball rolled out in the street, maybe a child will follow," is one of the things curve fitting can't do.
The Plank Equation for the black body https://en.wikipedia.org/wiki/Planck%27s_law was the most successful curve fitting in history. They need like 30 years to discover quantum mechanics and understand the details.
Isn't transfer learning just a faster way to fit a curve by starting with a curve that's partially fit instead of a random vector. So under this interpretation, transfer learning is also "curve fitting."
It is. But progress has been made taking models fit on pretty different topics and using them for something tangentially related. And that's exactly what humans do.
You don't start from zero when you learn to paint if you already know how to draw. You take another model of behavior and use it as a baseline to develop your painting behavior.
The fundamental thing that you can do that the computer can't right now is decide when you need a new model, when to use an existing one and when to transfer off of something.
ML is about function approximation where function can be anything and mostly something that is practically intractable by usual analytical or numerical methods, such as observing real-time processes with millions of variables.
In math, via a reduction, human life is just a function of time with many inputs and many outputs. I.e. you can do curve fitting there as well...
The learning from game itself was curve fitting, the Deep in Deep Reinforcement Learning usually means some difficult function is replaced by a deep neural network, approximating optimal values (for moves) trained on gameplay samples, usually in sense of rewards/punishments for reaching certain states; in games they could rank e.g. good/bad moves, winning states, losing states etc.
To me, this debate is akin to wondering whether the essence of computing resides in the lower-level, Turing-like operations of a microprocessor or rather in the higher-level constructs and abstractions that we're able to build way "above" them. Whatever it is, intelligence, whether implemented artificially or embodied in the substrate of a living being, is likely built on a ladder of subsystems interoperating at different levels of abstraction.
"Machine learning" used to be a safe haven. You could flee there to escape the Terminators and brain-on-a-chip graphics. Business PR deliberately killed that. They wanted their ML algorithms to be refered to as AI, so they could fully ride the hype train.
AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant with "learning", "intelligence", "to perceive" in the context of AI. Nowadays, it is almost irresponsible to do this, not because you'll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.
Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and "out there". Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with "an AI taught itself to X and you won't believe what happened next".
The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.
All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.
Very well said. Also, curve fitting is not a corner case. Most relevant and intelligent things we care about can be solved with "just" curve fitting + extrapolation.
I think curve fitting is an important component of future AGI. But it definitely needs causal reasoning baked in, which leads to better models with less data [1,2].
My intuition is that there's a lot of important work to be done using logical representations of models and transforming them back and forth using well understood semantics operators. Deep functions will be part of said models, but the whole model does not necessarily need to be deep. We can already see hints of the field going in this direction in deep generative models [3].
Casual reasoning is one thing that is lacking. But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence?
I personally do not believe in AGI since I also do not believe in psychology, sociology or neurobiology being anywhere near understanding the holistic nature of our own intelligence. We are getting better at emulating human traits for specific tasks with ML. We lack the specific knowledge of what the algorithm should mimic to become equal to us in terms of our intellect though.
>> But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence?
All this resulted from evolutionary processes. Any approximation of AI which will deal with other agents will develop something like that and more in order to be competitive, collaborate and survive.
> All this resulted from evolutionary processes. Any approximation of AI which will deal with other agents will develop something like that and more in order to be competitive, collaborate and survive.
How can we assume that a simulated evolutionary process of a simple mathematical model or some arbitrarily sized multi-dimensional matrices yields similar evolutionary results?
Just think of the ongoing debate about quantum entanglement effects inside the neural signaling process. On a rather onthological level, we are still unable to formulate a mere definition of our consciousness or things like creativity that lasts longer than a few academic decades..
> Causal reasoning is one thing that is lacking. But what about creativity? What about drive and desire? What about belief and the will to fail on the road to success? What about collective intelligence and the need to peer up in efforts? What about emotional intelligence?
Hi, I work at one of the intersections of machine learning with certain schools of thought in neuroscience. The following is based entirely on my own understanding, but is at least based on an understanding.
Your list here really only has three problems in it: causal reasoning, theory of mind, and "emotional intelligence". Emotional intelligence works in the service of "drive and desire", considered broadly. Creativity likewise works for the emotions. To be creative, you need aesthetic criteria.
Most of that, we're still really working on putting into mathematical and computational terms.
Admittedly, that list is an arbitrary poke into areas of debate in your fields of profession.
As a take on your interpretation of creativity: I would argue that the act of forming new and valuable propositions is not related to emotion or aesthetics per se.
Aesthetic theory is observing a very narrow subset of creative processes. And even there, our transition from modernism into the uncertainty of the post-modernist world defies any sound definition of the "aesthetic criteria". Yet we perceive aesthetic human-creativity all the time.
In similar vain is the application of generative machine learning that spurs debate about computational aesthetics today. Nothing proofs better the incapability of modern ML forming real creativity than the imitating nature of adversarial networks spitting out (quite beautiful) permutations of simplified data structures underlying the body of Bach's compositions.
Now we could start on the assumed role of complex neurotransmitters in the creative process of the brain and the trivial way reinforcement learning rewards artificial agents, but that would push the scope of this comment.
>Now we could start on the assumed role of complex neurotransmitters in the creative process of the brain and the trivial way reinforcement learning rewards artificial agents, but that would push the scope of this comment.
You can't really separate emotion and aesthetics from the neurotransmitters helping to implement them! They're considerably more complex than anyone usually gives credit for.
Likewise, to form a valuable proposition, you need a sense of value, which is rooted in the same neurological functionality that creates emotion and aesthetics.
Wow. I want to thank you for engaging on that point! The "Hume's guillotine" dichotomization between "cognitive" processing and "affective" processing tends to be the thing our lab receives the most pushback on.
> The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general.
I've come to terms with the hype. There are still researchers doing the hard theoretical work, and they will still be toiling away after the next economic downturn. We can all choose every day whether to find fulfillment through seeking attention from other people, money, or satisfying our curiosity to solve problems.
> Nobody from the research community ever claimed that [AGI], nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.
Hear hear! That said, this is a good article by a respected researcher. Here's what LeCun had to say about it,
> ...In general, I think a lot of people who see the field from the outside criticize the current state of affair without knowing that people in the field actively work on fixing the very aspects they criticize.
> That includes causality, learning from unlabeled data, reasoning, memory, etc. [1]
Honest question, aren’t the consequences for “real” researchers keeping their heads down quite severe? Won’t we have important policy decisions both public and private and billions in funding misdirected for years when they could best be put elsewhere? Sure the “real” researchers will have easier access to funding, which perhaps is a key motivating factor to not push back on the hype, but isn’t there a large opportunity cost to allowing hype and or bullshit to go unchecked because “they don’t have the time to write a book”?
On the plus side, it makes it fairly easy to ask cocktail-party-caliber questions and quickly suss out whether you conversation partner knows what the hell they're talking about.
The consequences of technical subject matter experts dabbling in policy are often pretty bad.
You can get involved in this, but it takes real work (i.e. time taken away from your research area) and an honest understanding that the policy issues their own deep specialty, and you are likely to be quite naive about it going in.
The scary part is that governments and politicians have fallen for the "AI as the ultimate solution to do X", and now they're in a big hurry to apply to automated weapons and other scary stuff like that, all because some people at defense contractors and <insert greedy tech companies here> don't give a f--- about the consequences as long as it gains them a few extra several billions dollars over the next few years.
I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own.
But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionalism are actually extremely rare. The main source of our uniqueness is that we amalgamate other species, not that we have transcended them.
My theory is that we are terrified that we might be simpler than we think, because socially we behave as if we are so singular. If we are simple, and animals and machines are like us, then maybe we should be treating them with more reverence.
But being afraid of that is OK for a random person. For a machine learning researcher I would hope they are more careful about what we have evidence for (the similarities between us) and what we don’t (that there is some ineffable magic about humans).
Anthropomorphizing is dangerous because it leads to metaphor that can both ascribe too much to the subject and create blind spots in the minds of researchers. Saying, for example, "Dogs want love," is fine for the owner but problematic for a researcher because love, as we understand it, is a human state. We'll never really understand what it means for a dog to feel loved. To the ethologist that is not to say that there are not similar emotional processes for dogs, it's to say that they cannot be understood by analogy to the human ones.
It's sort of like the color perception problem [1]. Dogs and machines do see colors, but what do they see?
We've seen that threshold crossed with neural agents like AlphaGo which can be reasonably described as thinking. It decides if moves are good or bad after a little pause for processing, its decisions improve with time, it has an opinion on the state of play, the opinion is formed using basically the same data as a human, different iterations of the neural network can have a different opinion but there is a link between it and the previous one.
I don't see a test that majorly distinguishes it from a human. It appears to be following the same process with a few tweaks around the edges. There are some exceptions in the 2-5 situations in Go where a human can actually use optimised logic to determine what will happen; but they aren't the meat of the game.
Chess is one of those areas where humans have developed computer-like abilities, such as exhaustive search. What's interesting is the appearance of intuition-like movement in modern chess computers, but is it ... intuition?
> We've seen that threshold crossed with neural agents like AlphaGo which can be reasonably described as thinking.
I don't recall ever reading in a technical paper, or in an interview, a leader in the field of ANNs claim they were thinking. If you have, I'd like to see a reference. Most are fairly honest about the differences between artificial neurons and real ones, and between human cognition and what ANNs are doing with data.
You should go and read some stuff written by ethologists. Basically everything you said would be vehemently disagreed with by a large group of prominent ethologist. The term anthropodenial has even been coined to criticize your exact thinking and to describe the dangers of not anthropomorphizing enough. Not saying you can't over do it, but the GP's comment is much more in line with thinking by modern ethologist. Frans De Waal is a good place to start.
Right, to be fair to you this was a hotly debated topic in ethology (and still is to an extent), however I would say most modern ethologist have come out on the side of embracing evolutionary parsimony and viewing our human experience as a valuable asset to understanding animals (especially mammals).
Probably the most cited paper regarding this debate is by Marc Bekoff, "Cognitive Ethology: Slayers, Skeptics, and Proponents" (http://cogprints.org/160/1/199709005.html). Your original comment would be categorized as a "slayer" a position which is widely criticized. In fact Bekoff's focus is on canines and he used your exact example with dogs, but to opposite affect.
Phew, I'm surprised to see such an emotionally-charged article on the subject. Everyone who is uncomfortable with anthropomorphism is biased and misguided in some way, but extremist proponents are merely overly enthusiastic.
I do wonder about the theoretical bird scientist trying to figure out the "fixed action patterns" of other animals. If anthropomorphism is the way to go, surely it goes in the other direction in some way.
A review I just read (https://www.frontiersin.org/articles/10.3389/fpsyg.2018.0220...) suggests both of our viewpoints and seems to allow for a continuum of approaches without resorting to name-calling. I think that there's definitely stupidity in the history of "anti-anthropomorphism" if it's really true that people dismissed an article that started by saying bees appear to dance. After all, the fact that they have a behavior like that suggests something interesting is going on. It's also really easy to go overboard in simplifying animal behaviors to our own poorly-understood human behaviors.
They are both a problem, people do think human are somehow exceptional. We all agree that we are apes but none of us want to admit when we get horny in public.
But ML, AFAIK, is so simple; its literally a glorified polynomial functions. The only thing it get going for it is the large data set that we can train it on. It cannot "learn" anything from a small data set and extract any information out of it without a human imposing his/her knowledge on it.
For instance, take the concept of an even number. This simple knowledge is so powerful in solving algorithmic problems. But, its very hard to make a machine learn of this concept in general.
I think the problem is really overestimating how "intelligent" human are. We are only as intelligent with respect to our imagination. Its possible that there is an entire class of intelligent outside of our imagination that we cannot fully grasp its intelligent. Similarly, I am only conscious with respect to my own consciousness, but there may be another class of consciousness that is unimaginable to this monkey's brain.
You haven't proven this statement. It's possible within your own brain is nothing more than a rudimentary curve fitting algorithm that allowed you to see this pattern.
This is currently true for almost all human endeavors. We're beset with PR people deliberately promoting misconceptions and out right lies.
A recent article about "beewashing" is another good example of subverting human attention from real issues by over simplifying for the purpose of corporate PR.
We are constantly bombarded by noise and lies so we won't be able to make sound and rational decision about anything.
In recent years this transformed from a side effect of bottom line mentality to out right weaponization by powerful entities political and corporate.
Everything is a lie, until you're tautological. Machine learning itself seems a bit of misnomer. High dimensional curve fitting is a good description, imho.
"The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general."
Stuart Russell recently published a non technical book on AI. I really hope tech journalists take note
Curve fitting is a useful tool, even in non ML contexts like a simple linear regression. There's the hype, which will eventually die, and then there's the business/engineering aspect, which will likely stick around.
I guess what I mean is at a much larger scale. Like 1000s of networks or maybe more, like I presume you get in the brain. Is this already what’s happening currently?
Yes, it's done all the time. Suppose you have a model A that predicts some value for input x and you also have model B for the same problem, and they both work, but are not optimal for all cases, so they do the following:
I think intelligence has for some time already been boiled down to curve fitting, even for humans. Our current accepted definition of intelligence in schools is to get a score that is higher than the average to be considered sufficiently intelligent to proceed to the next grade.
I feel anything that we develop for AI would fundamentally always be inspired by our own experiences and hence curve fitting is something we understand to be the best metric to optimize for.
I agree. In our current neoliberal era, the ones in power have already been replacing various systems in our society with algorithms and computers, and formulating everything into an optimization problem. As a result of this, the reverse has happened: the systems are now shaping humanity into something that could be optimized.
We have been trying to manage governments, public services, and education the same as corporations, creating numerical targets for institutions to optimize for. Education itself was formulated as an optimization problem about how to create more jobs. Public services like healthcare were privatized and became a target for profit optimization. Half of the stock market is controlled by High-Frequency Trading supercomputers, which will do virtually anything to gain an upper-hand in profits. Those methods were all inherited from the management styles of corporations that began in the neoliberal era. As fundamental parts of our society are replaced by those systems, the society now curve-fits the systems rather than the systems curve-fitting the society. We now hyperoptimize ourselves to fit in this neoliberal landscape; our time is told as something to be optimized between work, socializing, exercising, and self-improving, with no space for "actual free time" of our own. We go to college not to learn but to pass exams and get ourselves a good job that can sustain us. And the faults of our systems are now blamed to be individual problems: "You didn't optimize towards the current trends of the job market, it's your fault." And from the view of the corporations, we are just AI agents waiting to be optimized for cash, and we're now becoming one through fitting our bodies and minds to the social media that tries to maximize engagement and ad revenue no matter the real societal cost.
Now, the real problem of AI compared to the algorithms of the past is its data-driven nature: it can only learn from what data you give it for training. We can only accumulate data from the past and never from the future, so the AI systems will just keep repeating the past, no matter what unseen change will come. We will lose the ability to imagine new political, economic alternatives, we will just be feeding ourselves the status quo, and societal advancement will stagnate at the hands of automated systems. The cancellation of the future: this is what I'm ultimately afraid of.
We certainly tend to fall into scripts and follow those scripts for many things in life. GPT-2 demonstrated how much online prose is glorified mad libbing IMO and yet it has no sense of a consistent long-term context nor do its articles make any sort of significant point. Reading its output is impressive but it reminds me of what I hear from someone with dementia.
that said I think if you made its context recurrent in some way between responses and queries you could probably build a really interesting chatbot that could ramble about just about anything and nothing but do so in a sufficiently coherent way to scare up venture capital. Just a guess but it is my guess.
> I think intelligence has for some time already been boiled down to curve fitting, even for humans.
No, the book actually makes an important point about this.
For instance, let's take a counterfactual. How do you know what would have happened if Barcelona had played Lionel Messi in goal over the latest season?
They've never done this, there are no data points for you to fit. The situation almost never arises that an outfield player plays in goal, and when they do it is always in a situation where someone's been sent off or injured, which is also rare.
And yet you and I and everyone else who can think knows this would result in Barcelona having a much worse season.
Just to be clear, it's not only because you'd be taking a top player out of offense where he is worth a lot, which you can surely fit some curves to show.
We can all guess that he'll be a worse keeper than Ter Stegen, but what curve would you fit that shows this? There's no data about Messi in goal.
Pearl does give a way to work it out via counterfactual analysis though.
If we say that goalkeeping requires certain skills, and being an outfielder also requires certain skills, these skills being graphed on a chart, its simple to see that Messi would not perform well as a goalkeeper, and this is still curvefitting. If it turns out that the goalkeeper and outfielder have a lot of overlapping skills, we might fall back and refer to a probability curve of things that normally happen; "players require practice to excel in their position" would seem to fall on that curve.
The fact is that everything in the world can be reduced down to curves. It's just a matter of your perspective.
> If we say that goalkeeping requires certain skills... If it turns out that the goalkeeper and outfielder have a lot of overlapping skills...
That's kinda the problem here. Once you have a model fitting curves is fine. But you need a structure from somewhere.
> players require practice to excel in their position
The problem with this is there's no data. Nobody gets to play a position they haven't practiced for. Yet you still somehow came to the right conclusion.
Anyway Pearl is much better at explaining this than I am.
Curve fitting is only adapted to analysing continuous data and properties.
Sparse data such as actual semantics of natural languages or an object oriented database are a mismatch for current machine learning.
The difference is that you have the metacognition. You can think about your fitting. It’s possible to say that such a process is just another set of parameters, but they impact retro actively. At that point the metaphor of curve fitting might break down.
Out of date. Reinforcement Learning makes truly ‘smart’ actions, courses of action. Calling something ‘curve fitting’ pooh poohs it (especially ‘just’ curve fitting) - calling it ‘distilling’ gives it the respect that it deserves.
Great article. Why does AI hype persist? Simply because we're seeing moderate gains?
Newsflash- We're likely to continue seeing gains from ML tech for decades. Basic techniques may become part of core CS. With all the private siloed data out there, ML will need to be applied uniquely over and over.
In decades to come, if marketing continues as-is, are salesmen going to keep laying claim to the moon (AGI) the whole time? It's hard to believe we can remain on this tilt for so long.
> Our machines are still incapable of independently coming up with a thought or hypothesis, testing it against others and accepting or rejecting its validity based on reasoning and experimentation, i.e. following the core principles of the scientific method.
A prime example of this is "Adam," an autonomous mini laboratory that uses computers, robotics and lab equipment to conduct scientific experiments, automatically generate hypotheses to explain the resulting data, test these hypotheses, and then interpret the results.
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[ 2.4 ms ] story [ 242 ms ] threadGeneral 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!
[1] https://www.youtube.com/watch?v=BGPGknpq3e0
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.
So an ML model running an input through a collection of other models to see if it gets a reasonable answer.
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.
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.
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?)
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.
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.
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.
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.
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?
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.
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.
This is has the advantage of recognizing how human intelligence can be automated and aggregated into system processes. And, the disadvantage that the boundaries of the concept start exploding.
I like cybernetics for providing a clear model of what constitutes intelligence -- a feedback loop between perception and action that achieves goals or lowers local entropy.
And, cybernetic systems can be artificial, natural or a mix
I think the end state for large corporations will be to automate away so much of the human input that they end up looking like what we think of as "AI" in a broader cultural context. But we already live in a world controlled by AI, and we have for 3+ decades.
Are corporations AI, or rather superintelligences, because they are groups of people bound by bylaws? If so, the whole damn world is AI through and through and machine learning is really just the tip of the iceberg.
I think this is why late-stage capitalism feels so bad to live under: these artificial systems (corporations), by design, stand in direct opposition to our humanity. They exist to prevent our humanity from getting in the way of their perpetual growth by abstracting any ethical problems away so that no human is faced with an ethical dilemma. Which explains why the world so often feels like a dystopian nightmare.
Experimentalists may have verified these ideas using some kind of curve fitting, but thinking in abstractions, aka, "a ball rolled out in the street, maybe a child will follow," is one of the things curve fitting can't do.
The Plank Equation for the black body https://en.wikipedia.org/wiki/Planck%27s_law was the most successful curve fitting in history. They need like 30 years to discover quantum mechanics and understand the details.
Taking a model trained in one subject, and reapplying it to build out new vertical is pretty much how children learn new things.
We'll have models using models to train new models sooner rather than later.
If a program can create it's own curve functions and decide which one to use to evaluate a problem, is it that much different than a brain?
You don't start from zero when you learn to paint if you already know how to draw. You take another model of behavior and use it as a baseline to develop your painting behavior.
The fundamental thing that you can do that the computer can't right now is decide when you need a new model, when to use an existing one and when to transfer off of something.
In math, via a reduction, human life is just a function of time with many inputs and many outputs. I.e. you can do curve fitting there as well...
There was no curve to fit into.
AI used to be a tight quirky community. Having the brain as inspiration led to all sorts of anthropomorphizing. This was ok. Researchers understood what was meant with "learning", "intelligence", "to perceive" in the context of AI. Nowadays, it is almost irresponsible to do this, not because you'll confuse your co-researchers, but because popular tech articles will write about chatbots inventing their own language and having to be shutdown.
Still, as a business research lab, it is good to get your name out there, so all the wrong incentives are there: Careful researchers avoid anthropomorphizing, and lose their source of inspiration -- you can not be careful with difficult unsolved problems, you need to be a little crazy and "out there". Meanwhile, profit-seeking business engineers and their PR departments, obfuscate their progress and basic techniques, all to get that juicy article with "an AI taught itself to X and you won't believe what happened next".
The researchers actually busy solving the hard problems of vision, natural language understanding, and common sense, do not have time to write books about how AI is not yet general. Nobody from the research community ever claimed that, nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.
All this quarreling about definitions and Venn diagrams and well-known limitations is dust in the wind. Go figure out what to call it on your Powerpoint presentation by yourself, and quit bothering the community.
My intuition is that there's a lot of important work to be done using logical representations of models and transforming them back and forth using well understood semantics operators. Deep functions will be part of said models, but the whole model does not necessarily need to be deep. We can already see hints of the field going in this direction in deep generative models [3].
[1] http://web.stanford.edu/class/psych209/Readings/LakeEtAlBBS....
[2] https://probmods.org/
[3] http://pyro.ai/examples/
I personally do not believe in AGI since I also do not believe in psychology, sociology or neurobiology being anywhere near understanding the holistic nature of our own intelligence. We are getting better at emulating human traits for specific tasks with ML. We lack the specific knowledge of what the algorithm should mimic to become equal to us in terms of our intellect though.
All this resulted from evolutionary processes. Any approximation of AI which will deal with other agents will develop something like that and more in order to be competitive, collaborate and survive.
How can we assume that a simulated evolutionary process of a simple mathematical model or some arbitrarily sized multi-dimensional matrices yields similar evolutionary results?
Just think of the ongoing debate about quantum entanglement effects inside the neural signaling process. On a rather onthological level, we are still unable to formulate a mere definition of our consciousness or things like creativity that lasts longer than a few academic decades..
Hi, I work at one of the intersections of machine learning with certain schools of thought in neuroscience. The following is based entirely on my own understanding, but is at least based on an understanding.
Your list here really only has three problems in it: causal reasoning, theory of mind, and "emotional intelligence". Emotional intelligence works in the service of "drive and desire", considered broadly. Creativity likewise works for the emotions. To be creative, you need aesthetic criteria.
Most of that, we're still really working on putting into mathematical and computational terms.
As a take on your interpretation of creativity: I would argue that the act of forming new and valuable propositions is not related to emotion or aesthetics per se.
Aesthetic theory is observing a very narrow subset of creative processes. And even there, our transition from modernism into the uncertainty of the post-modernist world defies any sound definition of the "aesthetic criteria". Yet we perceive aesthetic human-creativity all the time.
In similar vain is the application of generative machine learning that spurs debate about computational aesthetics today. Nothing proofs better the incapability of modern ML forming real creativity than the imitating nature of adversarial networks spitting out (quite beautiful) permutations of simplified data structures underlying the body of Bach's compositions.
Now we could start on the assumed role of complex neurotransmitters in the creative process of the brain and the trivial way reinforcement learning rewards artificial agents, but that would push the scope of this comment.
You can't really separate emotion and aesthetics from the neurotransmitters helping to implement them! They're considerably more complex than anyone usually gives credit for.
Likewise, to form a valuable proposition, you need a sense of value, which is rooted in the same neurological functionality that creates emotion and aesthetics.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2666711/
Point taken
I've come to terms with the hype. There are still researchers doing the hard theoretical work, and they will still be toiling away after the next economic downturn. We can all choose every day whether to find fulfillment through seeking attention from other people, money, or satisfying our curiosity to solve problems.
> Nobody from the research community ever claimed that [AGI], nobody came forward to claim they've solved these decade-old problems. It is people selling books railing against the popular reporting of AI. Boring, self-serving, and predictive, and you do not need to fit a curve to see that.
Hear hear! That said, this is a good article by a respected researcher. Here's what LeCun had to say about it,
> ...In general, I think a lot of people who see the field from the outside criticize the current state of affair without knowing that people in the field actively work on fixing the very aspects they criticize.
> That includes causality, learning from unlabeled data, reasoning, memory, etc. [1]
[1] https://www.facebook.com/yann.lecun/posts/10156387222842143
On the plus side, it makes it fairly easy to ask cocktail-party-caliber questions and quickly suss out whether you conversation partner knows what the hell they're talking about.
You can get involved in this, but it takes real work (i.e. time taken away from your research area) and an honest understanding that the policy issues their own deep specialty, and you are likely to be quite naive about it going in.
I’ve noticed at least as many people under-anthropomorphize as over. People who seem obsessed with human exceptionalism and are personally offended at the idea that plants and animals (and computers!) might have subjective experiences like our own.
But to me it seems obvious we are far more alike “lower” species than we are unlike them. I would say the cases of human exceptionalism are actually extremely rare. The main source of our uniqueness is that we amalgamate other species, not that we have transcended them.
My theory is that we are terrified that we might be simpler than we think, because socially we behave as if we are so singular. If we are simple, and animals and machines are like us, then maybe we should be treating them with more reverence.
But being afraid of that is OK for a random person. For a machine learning researcher I would hope they are more careful about what we have evidence for (the similarities between us) and what we don’t (that there is some ineffable magic about humans).
It's sort of like the color perception problem [1]. Dogs and machines do see colors, but what do they see?
1. https://newrepublic.com/article/121843/philosophy-color-perc...
I don't see a test that majorly distinguishes it from a human. It appears to be following the same process with a few tweaks around the edges. There are some exceptions in the 2-5 situations in Go where a human can actually use optimised logic to determine what will happen; but they aren't the meat of the game.
I don't recall ever reading in a technical paper, or in an interview, a leader in the field of ANNs claim they were thinking. If you have, I'd like to see a reference. Most are fairly honest about the differences between artificial neurons and real ones, and between human cognition and what ANNs are doing with data.
Probably the most cited paper regarding this debate is by Marc Bekoff, "Cognitive Ethology: Slayers, Skeptics, and Proponents" (http://cogprints.org/160/1/199709005.html). Your original comment would be categorized as a "slayer" a position which is widely criticized. In fact Bekoff's focus is on canines and he used your exact example with dogs, but to opposite affect.
I do wonder about the theoretical bird scientist trying to figure out the "fixed action patterns" of other animals. If anthropomorphism is the way to go, surely it goes in the other direction in some way.
But ML, AFAIK, is so simple; its literally a glorified polynomial functions. The only thing it get going for it is the large data set that we can train it on. It cannot "learn" anything from a small data set and extract any information out of it without a human imposing his/her knowledge on it.
For instance, take the concept of an even number. This simple knowledge is so powerful in solving algorithmic problems. But, its very hard to make a machine learn of this concept in general.
I think the problem is really overestimating how "intelligent" human are. We are only as intelligent with respect to our imagination. Its possible that there is an entire class of intelligent outside of our imagination that we cannot fully grasp its intelligent. Similarly, I am only conscious with respect to my own consciousness, but there may be another class of consciousness that is unimaginable to this monkey's brain.
c.f. 'eliza' for some of the issues.
You haven't proven this statement. It's possible within your own brain is nothing more than a rudimentary curve fitting algorithm that allowed you to see this pattern.
Stuart Russell recently published a non technical book on AI. I really hope tech journalists take note
Current AIs seem more like specific, fairly simple, brain regions. Maybe we need a level up.
But, I think that its a bit "hit and hope" - as an idea it doesn't really address any questions unless/until it is realised and works.
I feel anything that we develop for AI would fundamentally always be inspired by our own experiences and hence curve fitting is something we understand to be the best metric to optimize for.
I don’t even think educators mistake that for intelligence. It’s, at best, a proxy for intelligence mixed with other factors.
We have been trying to manage governments, public services, and education the same as corporations, creating numerical targets for institutions to optimize for. Education itself was formulated as an optimization problem about how to create more jobs. Public services like healthcare were privatized and became a target for profit optimization. Half of the stock market is controlled by High-Frequency Trading supercomputers, which will do virtually anything to gain an upper-hand in profits. Those methods were all inherited from the management styles of corporations that began in the neoliberal era. As fundamental parts of our society are replaced by those systems, the society now curve-fits the systems rather than the systems curve-fitting the society. We now hyperoptimize ourselves to fit in this neoliberal landscape; our time is told as something to be optimized between work, socializing, exercising, and self-improving, with no space for "actual free time" of our own. We go to college not to learn but to pass exams and get ourselves a good job that can sustain us. And the faults of our systems are now blamed to be individual problems: "You didn't optimize towards the current trends of the job market, it's your fault." And from the view of the corporations, we are just AI agents waiting to be optimized for cash, and we're now becoming one through fitting our bodies and minds to the social media that tries to maximize engagement and ad revenue no matter the real societal cost.
Now, the real problem of AI compared to the algorithms of the past is its data-driven nature: it can only learn from what data you give it for training. We can only accumulate data from the past and never from the future, so the AI systems will just keep repeating the past, no matter what unseen change will come. We will lose the ability to imagine new political, economic alternatives, we will just be feeding ourselves the status quo, and societal advancement will stagnate at the hands of automated systems. The cancellation of the future: this is what I'm ultimately afraid of.
that said I think if you made its context recurrent in some way between responses and queries you could probably build a really interesting chatbot that could ramble about just about anything and nothing but do so in a sufficiently coherent way to scare up venture capital. Just a guess but it is my guess.
No, the book actually makes an important point about this.
For instance, let's take a counterfactual. How do you know what would have happened if Barcelona had played Lionel Messi in goal over the latest season?
They've never done this, there are no data points for you to fit. The situation almost never arises that an outfield player plays in goal, and when they do it is always in a situation where someone's been sent off or injured, which is also rare.
And yet you and I and everyone else who can think knows this would result in Barcelona having a much worse season.
Just to be clear, it's not only because you'd be taking a top player out of offense where he is worth a lot, which you can surely fit some curves to show.
We can all guess that he'll be a worse keeper than Ter Stegen, but what curve would you fit that shows this? There's no data about Messi in goal.
Pearl does give a way to work it out via counterfactual analysis though.
The fact is that everything in the world can be reduced down to curves. It's just a matter of your perspective.
That's kinda the problem here. Once you have a model fitting curves is fine. But you need a structure from somewhere.
> players require practice to excel in their position
The problem with this is there's no data. Nobody gets to play a position they haven't practiced for. Yet you still somehow came to the right conclusion.
Anyway Pearl is much better at explaining this than I am.
Newsflash- We're likely to continue seeing gains from ML tech for decades. Basic techniques may become part of core CS. With all the private siloed data out there, ML will need to be applied uniquely over and over.
In decades to come, if marketing continues as-is, are salesmen going to keep laying claim to the moon (AGI) the whole time? It's hard to believe we can remain on this tilt for so long.
https://www.scientificamerican.com/article/robots-adam-and-e...
A prime example of this is "Adam," an autonomous mini laboratory that uses computers, robotics and lab equipment to conduct scientific experiments, automatically generate hypotheses to explain the resulting data, test these hypotheses, and then interpret the results.