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Artificial Intelligence Hits the Barrier of Meaning

213 points by mathgenius 8y ago

Barrin92 8y ago
Good article. I think issues like the mistranslation "The bear headed man needed a hat" really show that what we need is not an "AI algorithm" but AI agents. The lack of common sense that we see in machines is very much intended, it's what makes them machines in the first place. Humans don't make these mistakes because humans are able to create stories and place even a mundane translation in the deep context of a 'world' that must be coherent. Heads simply don't have bears on them, we wouldn't trust that translation even if we had heard it clearly, and we'd rather think we hallucinate before we'd drive on a road that goes nowhere in an impossible direction. Algorithms that are just glorified feature extraction machines, in my opinion, can never create a coherent story, so I'm very skeptical about the claim that we will have somehow solved intelligence in the next ten years. It seems to me like we are almost nowhere closer to it than we were decades ago.
- inasring 8y ago
  Isn't it also true that what we define as coherent is based on observed data that could be fed into these algorithms?
  - Barrin92 8y ago
    In a sense you can always assume that the answer to these problems is just 'make the neural net bigger' but I find this deeply unhelpful for two reasons: 1. This clearly does not seem to be the way humans learn. Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do). 2. It's essentially surrendering to the fact that we aren't able to understand how cognition works and build higher-level representations as a result. The goal of AI research can not just be to feed data blindly into enormous primitive structures, it must also be to get a grasp on what sort of complex structures are part of intelligent agents and how they interact.
    - chongli 8y ago
      Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do). That's because, contrary to the zeitgeist, humans are not a blank slate. Our brains are the result of billions of years of evolution. They are extremely well adapted to modelling the natural environment and the behaviour of other beings around us. This is in stark contrast to computers which we start from nothing and force feed a huge amount of data without context and then expect results. The fact that this approach works at all for some tasks is staggering.
  - sifoobar 8y ago
    Not an expert, but as long as there is no way of knowing WHAT it is the algorithm is learning it seems to me that it could never work reliably. It might look perfectly reasonable until you hit one of the triggers the algorithm used to segment the data. Someone somewhere shared a story about using machine learning to spot the difference between US and Russian tanks ; which apparently worked fine until field testing, where it failed miserably. What the algorithm had learned was the difference between great quality photos of US tanks and poor quality photos of Russian. True or not, this is exactly the kind of issues that will keep popping up. Plenty of people are spending plenty of time figuring out how to mess with facial recognition as we speak by taking advantage of the same fundamental weakness.
    - how-bizarre-u 8y ago
      Yeah, you have no idea what your talking about. Examples of people making silly mistakes can be found in any engineering field. A company I work with has a few Nest cameras with face recognition - the precision in a well lit office is 100%.
    - freeone3000 8y ago
      Oh, yep! It's super simple to induce systemic errors like that. Take https://github.com/kevin28520/My-TensorFlow-tutorials/tree/master/01%20cats%20vs%20dogs https://github.com/kevin28520/My-TensorFlow-tutorials/tree/m... . Lighten every dog by 20%. Darken every cat by 20%. Train. Take image of cat, lighten 20%, watch as it's transformed into a dog! For large corpora, it's impossible to know what features got selected. They probably aren't any feature a human would consider.
- glup 8y ago
  I agree with the general skepticism regarding AGI, but I think it's worth pointing out that there is some progress towards robust representations of abstract conceptual knowledge (http://science.sciencemag.org/content/331/6022/1279 http://science.sciencemag.org/content/331/6022/1279) that are far beyond feature extraction.
- staz 8y ago
  > Heads simply don't have bears on them https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPevc7Us3usObZEF6ykQBcsh2xTDBYR5friM7T2TTwhvvCChI https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPe...
  - ppod 8y ago
    Perhaps the man proceeding towards the grizzly had a negative economic outlook. There are literally hundreds of word-sense-disambiguation papers being published per year, and have there have been for about a decade. The state-of-the-art has been advancing relentlessly, and examples like the ones cited here are being zoomed past right now by the latest thing, which is even larger models pre-trained on unsupervised data. There will be a next thing, and for NLP this will be backgammon or chess to 2020s Go. Embodiment is a better criticism, but this article is still bad, and is written as though Intelligence were some essential soul-like property that agents either possess or don't. It is really amazing how often articles with this viewpoint repeat one particular trope: they put the word "understanding" in italics. Look out for it.
    - freeone3000 8y ago
      But if you do an error analysis on even ShotgunWSD (Betnaru 2017), you get errors that humans would not produce - archaic favors, missing recent terms, confusing opposing meanings, ... When the average researcher can easily select the correct sense. And that's a DETERMINISTIC algorithm! Swapping ELMO for pure WordNet gives us slightly better results in that sense but introduces literal random chance errors. And the older, Bayesian models are worse! While we'll probably solve WSD to a high degree of accuracy (such that engineering bugs cause more failures than the model), our current approach is still fundamentally flawed. We need a better method of imbuing causal relationships than correlation+chance.
      - ppod 8y ago
        > archaic favors, missing recent terms, confusing opposing meanings, Humans make errors like these all the time (e.g., I don't know what you mean by 'favor' here, and I'm sure many people wouldn't have the type "ELMO"), and the dictionary definition of the word shifts accordingly, except we can't really call them 'errors' when it's a natural phenomenon that we're trying to match. Humans make seemingly random errors sometimes too, confusing one word for a mother, I mean another. When we do it we infer (perhaps correctly) deep psychological causes. Current machines don't have personal identities and desires and goals like we do, but embodied agents with a natural selection process could have those things, there isn't an obvious technological barrier. And if you build in a language model that shares some of the general computation machinery we could reasonably expect it to make those kinds of errors.
scottyelich 8y ago
Bingo.
not_a_moth 8y ago
The wild Zuckerberg quote aside, I don't believe many people expect this current wave of AI to approach general intelligence. I would be surprised if we weren't all mostly in agreement with the author.
- hprotagonist 8y ago
  In my experience, domain practitioners who spend our days elbow-deep in tensors are well aware that what we call AI right now has nothing to do with AGI. That knowledge does not seem to generalize well, though. Even technically literate people who aren't in the trenches seem to have frankly insane ideas about AGI. This phenomenon is not helped by armchair philosophy, the frothy chatter of the tech-adjunct world, or the naive idea that "well, transistors are easy, so brains should be too!" that is all too common in people who haven't stuffed electrodes into brains and then spent weeks figuring out what the hell could be going on in there.
  - adrianN 8y ago
    You'd have a pretty hard time figuring out what a CPU does if all you could do is poking a small number of needles into it, cutting it into slices to study under a microscope and measuring which parts get hot while you use it.
    - hprotagonist 8y ago
      https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo... true fact.
  - thwy12321 8y ago
    The thing is that progress is so fast in some areas of ML, you dont really know when something could emerge. Who knows, maybe with enough training data and a deep enough network with the right supervision, something extraordinary pops out. Google google bert nlp. Google recently released an NLP algorithm that basically beats all predecessors, but the algorithm itself is extremely simple. Just lots of data and compute, no telling what will happen in ten years when the amount of data has exploded and GPUs are cheap.
hyperpallium 8y ago
Surprise! Meaning requires strong AI (and cultural understanding, much of which is only learnable from interaction).
- Retra 8y ago
  Meaning doesn't 'require' strong AI -- the kind of meaning that strong AIs like us prefer to interact with does. There's plenty of simple meaning in simple systems, and they are quite useful to us that way. They just don't encapsulate the complexities of human experience very well.
imh 8y ago
I've started thinking about the whole "do computers grok meaning" thing in terms of proof. If a computer tells me a true theorem, I don't know if I can ascribe "understanding" to it. If it tells me a true theorem and comes up with a proof, then I'm comfortable defining that as understanding within the context of that particular proof language. It passes the buck up a step. You define a new notion of understanding-in-a-given-language and then get to argue about whether that language/deduction system constitutes true "understanding."
- panic 8y ago
  The problem isn't deduction. It's getting things into the deductive system in the first place. You have to model the world, and your model will probably be incorrect somehow (https://www.student.cs.uwaterloo.ca/~cs492/11public_html/p18-smith.pdf https://www.student.cs.uwaterloo.ca/~cs492/11public_html/p18...).
- freeone3000 8y ago
  Okay, but what if it just made stuff up by trying everything until something came out true? We have theorum provers on this model today. Does such a system "understand" mathematics? It's probably more explainable than anything else, but the strength of the current generation of learning is application to cases with ambiguity present, and our current approaches here are completely unexplainable.
  - imh 8y ago
    I'm saying that we can define what constitutes understanding by defining what constitutes reasoning within a given inference system. That's a simpler task, but doesn't solve the original problem. It just passes the buck. Now instead of asking whether a given learner performs reasoning and understands, we could ask whether the language/logic constitutes understanding. I'd definitely say that the current theorem provers have some form of true understanding, even though it isn't the same form as we have. For the typical ML learners, I think it's more interesting to ask and taxonomize "in what ways is this reasoning/understanding" rather than just ask "is this reasoning/understanding?"
goldenkey 8y ago
Here's what I've realized both about the universe AND intelligence . Silicon cannot touch either of them. I didn't always think this though. I thought intelligent beings, but first smaller structures, could evolve in a large digital universe. I made a blog post: http://scrollto.com/blog/2017/04/11/life-a-universe-simulation/ http://scrollto.com/blog/2017/04/11/life-a-universe-simulati... It took a long time but I eventually created what I set out to do. Here is ScatterLife: https://github.com/churchofthought/ScatterLife/ https://github.com/churchofthought/ScatterLife/ But the result was far from what I wanted. Even with a Titan V, a world of only 4096×4096 could be handled at a reasonable update rate. I basically had spacetime foam under the ideas of Doubly special relativity, ie. Feynman checkerboard universe. https://en.m.wikipedia.org/wiki/Doubly_special_relativity https://en.m.wikipedia.org/wiki/Doubly_special_relativity https://en.m.wikipedia.org/wiki/Feynman_checkerboard https://en.m.wikipedia.org/wiki/Feynman_checkerboard If our own universe is digital, then it updates at C/plank_length times per second, over 10^40 hz. Not only that but it consists of over 10^185 planck lengths. Nothing interesting is really viewable or even extrapolatable from the smallest of truths or fundamentals. Thats why many predictions of string theory need higher energies to be tested. The smaller you delve, the higher energy needed. In any case, I realized that evolution itself has been fueled by orders of magnitude. Symmetries of matter and energy, planets stars and solar systems...same magnitudes required. Even the best silicon isnt going to have 10^20 transistors on it. We would have to somehow go analog..use chemicals or matter in a way that didnt require slow refining and construction. Chemical based computing.... Now about ML and AI, same issue is present. Brains have 100 billion neurons. The best GPUs have maybe 5000 cores. The only way forward is to maximize what each core does - as much as possible. I learned this with the cellular automatas. Black and White 2-state automatas are neat but are a huge waste of processing power. They hold little information. Better is integers. Even better is floats. Why stop there though? Lets use complex numbers. https://github.com/churchofthought/HexagonalComplexAutomata https://github.com/churchofthought/HexagonalComplexAutomata Magnitude is against carefully crafted silicon. If we want to achieve what magnitude can, with silicon, we need to make sure our fundamental neuron units arent unnecessarily sparse. Magnitudes can afford to use simple units. Silicon cant. 5000 maybe 100000 cores when advances in fab accelerate. But still not 100 billion. Still needs to use the advanced abilities of the cores to their full extent. A neuron cannot compute any universal function...but a gpu core can. Anyways, I am almost failing to mention that I dont believe we have anything to worry about. Unless more research is devoted to special purpose hardware (consider an i7 has 731 million transistors), the software side will have a really hard time compensating for low magnitude. Lets see what happens. Its going to be exciting none the less. I am doing ML work myself on Boltzman networks and RNNs snd Hopfield nets. This is a promising field and its emerging at breakneck pace. Cheers!
voidhorse 8y ago
Aside from the technical obstacles currently barring us from realizing AIs capable of human competency, we should be seriously considering the social question of whether or not such a technology is something we really need or want. The world is already saturated with technical systems and artifacts that have effectively flattened social relations, eradicated traditions, and generally reduced the field of humanitarian meaning and theory to nil. All our problems have become technical problems. We’ve even reached a point where we try to solve social problems with technical solutions, and when that doesn’t work we try to fix the boo-boo the naive and rampant application of technology has caused with, guess what, more technology. The philosophy of technology is perhaps the most important discipline of our time—there’s little left in life that technics hasn’t in some way enveloped, either insidiously or openly.
- nradov 8y ago
  Those seem mostly like arbitrary value judgments based on what you personally happen to be comfortable with. Ask more fundamental questions. Are we necessarily better off with traditions than without?
  - voidhorse 8y ago
    Sure. Imagine a world in which all tradition and culture was wiped out. Imagine a world in which all technology was wiped out. They're both absolutely horrible. And certainly, some traditions are better discarded, just as some technologies (the A-bomb being the prime example) are better left unexplored. I'm only trying to point out the tendency of our era is to forget humanitarian values entirely in the face of 'objective', 'practical', 'utilitarian' and technical progress. I am making a value judgement, and your dismissal of my points simply because they entail value judgements is exactly the sort of attitude that has led to the dissolution of culture and the near reemergence of fascist and populist thought in arguably the most technologically advanced country in the world. There's a reason technology couldn't stem the reemergence of such thinking, and in fact the social conditions rampant, unquestioned (or if so, not nearly enough) technological development has caused has contributed to the revival of such thought. Such an approach annihilates dialogue before it can begin. Living social subjects no longer endeavor to understand each other because they fall back on a ideology that tells them they it's "above" values. I am championing the philosophy of technology, and I am somewhat familiar with it, but I also genuinely believe it's important, and yes, that is a value, and one that I stand by. The sort of world I'm hoping will emerge in the future is dictated by those values. I just hope there's still enough folks in tech considering the future their current values might create.
rademacher 8y ago
This latest craze of "AI" research seems to be fueled by a sudden glut of computational power (GPUs) that wasn't available previously. I think that most technical people would agree that the mid 2020s is extremely ambitious. I'd also argue that we're actually more likely to experience another AI winter. The frightening part of the current deep learning research is how susceptible they are to adversarial attacks. Adding small amounts of noise causes misclassification in images, and some papers even explore the inevitability of adversarial examples [1]. This is especially frightening given the amount of autonomous vehicle work being done. I could imagine a situation in which the sensor noise varies just enough to cause such an error. Obviously, the systems will have redundancies built in, but I'm convinced the self-driving cars are still a ways off as well. EDIT: As others, have stated just adding noise is not enough and it is often used to generalize the model. The paper does discuss that the perturbations can be incredibly small to cause this deviation and that the set of such deviations may be larger than expected especially for complex images. Regarding the AI winter, I suppose I should have defined it as a reduction in the amount of research and the extent of the progress being made in the area rather than the utility of such research. [1] https://arxiv.org/abs/1809.02104 https://arxiv.org/abs/1809.02104
- njoubert 8y ago
  This is a common misconception. It is not a small amount of noise that causes misclassification of images. It is a carefully designed and quite unique pattern that causes misclassification. It only looks like noise to the human eye, but it really isn't. Yes, neural networks are susceptible to adversarial attacks. No, just adding noise to an image doesn't break neural networks.
  - pmoriarty 8y ago
    Adding small amounts of noise is actually sometimes used to improve the performance of various AI techniques. It helps prevent overfitting. In fact, if your technique or model is seriously affected by a little noise this is usually enough to brand it brittle and maybe even a failure, as it's a sign of overfitting. Anyone working in this field knows to look for this and will try to make what they create more robust. The design of visual captchas is one obvious indication of just how successful AI techniques have been at image recognition in the presence of noise. It's no longer enough to make them a little noisy. In order to resist being solved by mechanical means, visual captchas have to include so much noise that even humans have problems recognizing them.
evilturnip 8y ago
I don't know much about AI, but I wonder what AI people think of this theory: https://en.wikipedia.org/wiki/Embodied_cognition https://en.wikipedia.org/wiki/Embodied_cognition The idea being that if we want to replicate strong AI, it needs to be embodied, because a lot of our cognition is built on metaphors that are instantiated in our physical actions and perceptions.
- mingmecca 8y ago
  I believe this to be true (embodiment as a precursor to cognition). So much of our early years learning is exploring how our various actions lead to a corresponding result. Touching a hot stove vs petting a soft cat, for example. And instead of just training 1 neural system at a time we're training all of our senses at once, which I think gives a significant boost to the brain's 'understanding' of the result of the action.
- nradov 8y ago
  Embodied cognition probably matters a lot if you're trying to build a drop-in replacement for humans. But in principle if someone manages to build a disembodied AGI it would probably still be useful for many purposes including ones we haven't even thought of yet. Think of it like a special-purpose dedicated co-processor. We already have those for other purposes like math, physics, graphics, etc to perform specialized tasks that the CPU can't do efficiently. In practice we're nowhere close to being able to build any sort of AGI so this is just a thought exercise.
- versteegen 8y ago
  One (of many) reasons I agree is because determining causal relationships is far easier when you can perform 'experiments', i.e. make changes to the world see the results. Another reason is that nearly all of what we call "common sense" is just knowledge about the real world rather than being some kind of abstract reasoning ability. Note that embodied cognition doesn't require robotics. An agent can act in a simulated environment instead. Note also that Deep Mind is very heavily focused on embodied agents.
- mettamage 8y ago
  I think we don't need to go there yet. There needs to be a stage before that which is the ability to precisely debug the human brain. If you don't understand the brain, then embodied cognition will be impossible to understand. I think the theory needs to be tested in much more depth. Fortunately, you could create AI models to test the theory itself. AI can serve as a testbed of our understanding in biology. Let me dive in on the idea of debugging the brain. If we're able to fully record one's brain activity in a precise manner, then we'll understand much better how to create an intelligent system. This is because very strong advances have been made in machine vision through a similar idea. Scientists didn't need much precise granularity to understand the visual cortex. The structure of the physical cortex is quite understandable: it detects detailed features and integrate them in bigger concepts until you finally 'see'. But I think for more abstract things in the human brain we'd need more fine-grained data and the possibility to replay that data (in the future), so mapping and recreating the structure of a brain (digitally) will also be needed for when I am talking about "the ability to precisely debug the human brain." What is funny is that AI currently serves as a very crude check to see whether we really understand brains at all. Just rebuild the brain in AI and see if it produces the same result. So part of this ability to precisely debug the human brain comes from AI itself. Since AI can be used as a hypothesis to test our understanding of the brain. Couple of things: animal brains are cool too, AI can also progress without understanding the brain and this obviously isn't the only thing that will leap AI forward. But if human brains become more debuggable (either through questionable ethics or technological advances), then it will benefit AI immensely. Also the ability to have hardware that would be 10,000 times as fast and software that would be optimized for a 10,000 speedup would help. I know that sounds a bit clunky but it does.
mark_l_watson 8y ago
Great article by Melanie Mitchell (she gave me her code for the Copy Cat program from her book with Hofstaeder almost 30 years ago - she has been looking at creativity and common sense in AI for a long while) My day job is all about deep learning but personally I think we need to stop and take a deep breath and really solve problems with biased data sets and models, easily spoofed models, etc. I have worked through a few AI winters and we may be hitting another one. I would like to see care given to using deep learning models only where it is safe to do so.
- kopo 8y ago
  Yup. Dotcom era all over again where everyone and their grandmother need to be building a website.
  - rjtavares 8y ago
    Dotcom era gave us Amazon and Google. If AI craze gives us something like that again, I think that's fine.
    - thanatropism 8y ago
      There have been impressive advances in Computer Vision; they're just an outgrowth of the work that produced JPEG, rather than of the work that produced Lisp machines and expert systems. It's often said that we tend to take AI that really works and call it something else, but we also tend to call whatever is current and somewhat succesful (fuzzy logic, etc.) "AI".
    - mcguire 8y ago
      Universal surveillance and data collection? /Cynic
    - kulahan 8y ago
      I know I'm late to the party, but... Amazon abuses many of their workers, they've been dangerous to small businesses in any area they move into, and they're contributing significantly to the wealth inequality problem. Google knows a scary amount of information about you and everyone you know, they're supporting a horrific change in China allowing even tighter control of their citizens, and they were even looking at building AI for military drones at one point. In exchange, we get to buy cheap stuff with quick shipping and we can find resources on the internet slightly quicker. An AGI could have much more significant implications than either of those companies ever have. We need to figure out a solution to the AI control problem before we have a Dotcom-like burst of development.
symplee 8y ago
As if humans haven't also hit the barrier of meaning? And yet we've still made unimaginable discoveries. I almost added the word "progress" but I guess that depends on what you find meaningful. The article states: "But ultimately, the goal of developing trustworthy A.I. will require a deeper investigation into our own remarkable abilities and new insights into the cognitive mechanisms we ourselves use to reliably and robustly understand the world." Why limit the field to the capacity of humans? What the author calls "remarkable abilities" and "robustly understand[ing] the world" can also be seen as just reproducing our own innate and learned collective human biases. Our theories from observation, and their unprecedented ability to predict future events, is more about describing the world vs understanding it. Is there any topic in the world that doesn't have contrary interpretations? What quantifiable metric would we even use to gauge artificial intelligence's grasp of "meaning"? We don't even have one for our own.
- freeone3000 8y ago
  This is not philosophy. This is "apples are smaller than watermelons", "objects must be contiguous to be the same object", "the thing that makes an elephant an elephant is the presence of several large key features (ears, nose, trunk, legs) and not its color". If machines are going to meaningfully process our data, they're going to have to agree with us on certain base facts of reality.
- nathan_long 8y ago
  > What the author calls "remarkable abilities" and "robustly understand[ing] the world" can also be seen as just reproducing our own innate and learned collective human biases. From the article: > “The bareheaded man needed a hat” is transcribed by my phone’s speech-recognition program as “The bear headed man needed a hat.” If A.I. is going to work with humans, then yes, reproducing some of our "biases", such as the tendency to describe someone as "bearheaded" far more often that "bear headed", is a good goal. In an alternative universe of chimeras, a different bias would be needed. But in any case, the A.I. needs to "understand" that one of these transcriptions is much more likely to be correct than the other. So a level of processing beyond "sounds like this word" is needed. Take the famous absurd quote++: "Time flies like an arrow. Fruit flies like a banana." Reading the second sentence makes you, a human, double-take, and re-read the first sentence, and try reinterpreting it to see if it makes sense a different way. (The fact that it doesn't is what makes it funny.) An A.I. that has no "wait did that make sense?" step, no "is that funny?" step, is going to produce results that disappoint or confuse us humans who do. (++Interestingly, this quote seems to originate in A.I. research: https://quoteinvestigator.com/2010/05/04/time-flies-arrow/ https://quoteinvestigator.com/2010/05/04/time-flies-arrow/)
blueadept111 8y ago
Everything OP says is correct. And let's not forget that even in the absence of adversarial attacks, even with flawless associative mapping and incredible generalization, even hypothetically solving the problem of higher level ontological 'meaning', there's STILL absolutely no known way for AI to even begin to address the matter of the qualia - how to build something that's actually has a conscious perception (of pain, for example). We're groping in the dark without matches, and there will be many more seasons/winters to come.
- pas 8y ago
  Qualia is a red herring, just like the p-zombie topic. Consciousness is a program run on your brain to make you think you are conscious. (And as long as this explanation works and is simpler than all the others, we should go with it, instead of claiming how incredibly hard and unfathomable consciousness is.) What's hard is bootstrapping the whole thing. Evolution simply used brute force and copy-pastes a sort of working, already pre-wired, fine-tuned mishmash of faculties as a brain from the previous generation to the next. And uses the old gen to train the next gen.
  - Valmar 8y ago
    > Qualia is a red herring, just like the p-zombie topic. It is NOT a red herring ~ it is a real problem for reductionist physicalism. However, because it cannot explain qualia, it tries to ignore, dismiss, and belittle, the enormity of the problems presented by qualia for its attempts at trying to explain. > Consciousness is a program run on your brain to make you think you are conscious. This is a presumption with scientific evidence. This is merely reductionist physicalist philosophical dogma. We can be certain that we are individually conscious and aware ~ but not necessarily why or how. Consciousness cannot be reduced down to a bunch of changes of the brain's matter. Consciousness is qualitatively different to how the brain functions, even though consciousness can indeed be influenced by the changes in brain states. Even though literally no-one knows how. > (And as long as this explanation works and is simpler than all the others, we should go with it, instead of claiming how incredibly hard and unfathomable consciousness is.) It is not simpler ~ it makes a set of presumptions which reductionist physicalism must explain scientifically. And even now, no scientific explanations are forthcoming. The philosophy has had centuries to explain how consciousness arises from the material and physical, and yet no explanation has been offered as to how the brain can magically create consciousness. There are literally no models at all that demonstrate, solidly and irrevocably, how particular configurations of brain matter directly translate into consciousness. > What's hard is bootstrapping the whole thing. Evolution simply used brute force and copy-pastes a sort of working, already pre-wired, fine-tuned mishmash of faculties as a brain from the previous generation to the next. And uses the old gen to train the next gen. A bunch of presumptions and just-so hypotheses without scientific evidence.
peter303 8y ago
'Understanding' is a matter of degree. Humans do not know very well how their own brains generate ideas, thought or language. Yet they appear to be capable of understanding.
aabajian 8y ago
I agree with the author overall, but I think she picks a poor example for dictation. “The bareheaded man needed a hat” falls squarely in the domain of contextually-aware models and would likely be transcribed correctly using deep learning.
wiz21c 8y ago
Didn't check but, considering there are probably thousands of "small tasks" that are being solved by AI, can one build an AI that can choose the right "small task" to apply to a never-seen-before problem ?
js8 8y ago
I recently realized that meaning cannot be done using pattern matching only, which is what (current) machine learning is about. The key insight came from article on bullshit: https://news.ycombinator.com/item?id=17764348 https://news.ycombinator.com/item?id=17764348 I believe understanding (things having meaning) comes down to whether you can produce an example from the given description of the situation. If you can produce an example, then you can say you understand the description. The problem is, you can have situation descriptions that are contradictory (have no examples, thus have no meaning) yet they are arbitrarily close to situation descriptions that have examples (and so have meaning). A good example are those Escher-like impossible objects, which look very much like real objects, but humans can easily see they are meaningless (they cannot be interpreted, and thus imagined, in 3D). Another good examples are sentences from the above paper. The bullshit sentences are the ones for which you cannot construct a mental example in your head. I suspect this happens for the famous flaws in deep learning as well, the deep learning network cannot learn that some inputs are contradictory. I believe that this is actually ultimately related to the boolean satisfiability problem. In theory, one could determine whether some learning agent is only using pattern matching or is actually having understanding (is able to recognize logical inconsistency in the input) by teaching it different SAT instances, and the agent that would be able to learn the difference between arbitrarily similar SAT instances with and without solutions could be considered to have understanding.
hacknat 8y ago
It all comes down to the fact that we don’t really understand what intelligence is. We know it is an emergent property of several mechanisms, working together, and we are even getting a decent grip on what those mechanisms are and how they work, but we are still struggling to see the big picture of how intelligence actually emerges.
- qubax 8y ago
  We don't know if it is an emergent property. That's what we think it is likely. but we won't know until we find out what consciousness is. And we won't find that out until we crack the secrets of the brain. There once was a time when we thought our bodies moved because there was an "emergent property" called the soul that instructed our bodies to move. It took a very long time for humans to discover how our bodies through the science of physiology.
  - mcguire 8y ago
    "The soul" is (was?) explicitly not an emergent property.
guilamu 8y ago
The author is trying to make a point that AI is still truggling on basic understanding based on examples. Google Translate renders “I put the pig in the pen” into French as “Je mets le cochon dans le stylo” (mistranslating “pen” in the sense of a writing instrument). Well, Google is far from being the best at translation right now. If I try the author's example in Deepl for example "I put the pig in the pen" it translates into French as "J'ai mis le cochon dans l'enclos." which is the perfect translation based on context. So, I still have to read the rest of the article, but right from the bat, you're wrong mate (or at least you provided an example fo something "impossible for AI" which is already perfectly done by properly trained/programmed AI).
thallukrish 8y ago
This is the reason why chatbots did not live up to the hype. It is hard to have an agent that offers solution in a domain as well as being generally conversant.
kazinator 8y ago
Optimistic framing: "Machine learning algorithms, and domesticated animals such as dogs, don’t yet understand things the way humans do — with sometimes disastrous consequences.
bigbadgoose 8y ago
Turns out the primary use case of AI at the moment is duping low info voters into electing fascists. AI has an IQ of 87 in narrow situations, while the targets are working with 85 and below. One foot in front of the other, but the chasers got a little bit ahead of the horse here.

213 points by mathgenius 8y ago

Good article. I think issues like the mistranslation "The bear headed man needed a hat" really show that what we need is not an "AI algorithm" but AI agents. The lack of common sense that we see in machines is very much intended, it's what makes them machines in the first place. Humans don't make these mistakes because humans are able to create stories and place even a mundane translation in the deep context of a 'world' that must be coherent. Heads simply don't have bears on them, we wouldn't trust that translation even if we had heard it clearly, and we'd rather think we hallucinate before we'd drive on a road that goes nowhere in an impossible direction. Algorithms that are just glorified feature extraction machines, in my opinion, can never create a coherent story, so I'm very skeptical about the claim that we will have somehow solved intelligence in the next ten years. It seems to me like we are almost nowhere closer to it than we were decades ago.

inasring 8y ago
Isn't it also true that what we define as coherent is based on observed data that could be fed into these algorithms?
- Barrin92 8y ago
  In a sense you can always assume that the answer to these problems is just 'make the neural net bigger' but I find this deeply unhelpful for two reasons: 1. This clearly does not seem to be the way humans learn. Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do). 2. It's essentially surrendering to the fact that we aren't able to understand how cognition works and build higher-level representations as a result. The goal of AI research can not just be to feed data blindly into enormous primitive structures, it must also be to get a grasp on what sort of complex structures are part of intelligent agents and how they interact.
  - chongli 8y ago
    Humans can learn from very few examples, in entirely unguided environments, and they don't face the same issues that existing algorithms suffer from. (for example humans have no big problem with rotational invariance, whereas ML vision algorithms do). That's because, contrary to the zeitgeist, humans are not a blank slate. Our brains are the result of billions of years of evolution. They are extremely well adapted to modelling the natural environment and the behaviour of other beings around us. This is in stark contrast to computers which we start from nothing and force feed a huge amount of data without context and then expect results. The fact that this approach works at all for some tasks is staggering.
- sifoobar 8y ago
  Not an expert, but as long as there is no way of knowing WHAT it is the algorithm is learning it seems to me that it could never work reliably. It might look perfectly reasonable until you hit one of the triggers the algorithm used to segment the data. Someone somewhere shared a story about using machine learning to spot the difference between US and Russian tanks ; which apparently worked fine until field testing, where it failed miserably. What the algorithm had learned was the difference between great quality photos of US tanks and poor quality photos of Russian. True or not, this is exactly the kind of issues that will keep popping up. Plenty of people are spending plenty of time figuring out how to mess with facial recognition as we speak by taking advantage of the same fundamental weakness.
  - how-bizarre-u 8y ago
    Yeah, you have no idea what your talking about. Examples of people making silly mistakes can be found in any engineering field. A company I work with has a few Nest cameras with face recognition - the precision in a well lit office is 100%.
  - freeone3000 8y ago
    Oh, yep! It's super simple to induce systemic errors like that. Take https://github.com/kevin28520/My-TensorFlow-tutorials/tree/master/01%20cats%20vs%20dogs https://github.com/kevin28520/My-TensorFlow-tutorials/tree/m... . Lighten every dog by 20%. Darken every cat by 20%. Train. Take image of cat, lighten 20%, watch as it's transformed into a dog! For large corpora, it's impossible to know what features got selected. They probably aren't any feature a human would consider.
glup 8y ago
I agree with the general skepticism regarding AGI, but I think it's worth pointing out that there is some progress towards robust representations of abstract conceptual knowledge (http://science.sciencemag.org/content/331/6022/1279 http://science.sciencemag.org/content/331/6022/1279) that are far beyond feature extraction.
staz 8y ago
> Heads simply don't have bears on them https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPevc7Us3usObZEF6ykQBcsh2xTDBYR5friM7T2TTwhvvCChI https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcSzPe...
- ppod 8y ago
  Perhaps the man proceeding towards the grizzly had a negative economic outlook. There are literally hundreds of word-sense-disambiguation papers being published per year, and have there have been for about a decade. The state-of-the-art has been advancing relentlessly, and examples like the ones cited here are being zoomed past right now by the latest thing, which is even larger models pre-trained on unsupervised data. There will be a next thing, and for NLP this will be backgammon or chess to 2020s Go. Embodiment is a better criticism, but this article is still bad, and is written as though Intelligence were some essential soul-like property that agents either possess or don't. It is really amazing how often articles with this viewpoint repeat one particular trope: they put the word "understanding" in italics. Look out for it.
  - freeone3000 8y ago
    But if you do an error analysis on even ShotgunWSD (Betnaru 2017), you get errors that humans would not produce - archaic favors, missing recent terms, confusing opposing meanings, ... When the average researcher can easily select the correct sense. And that's a DETERMINISTIC algorithm! Swapping ELMO for pure WordNet gives us slightly better results in that sense but introduces literal random chance errors. And the older, Bayesian models are worse! While we'll probably solve WSD to a high degree of accuracy (such that engineering bugs cause more failures than the model), our current approach is still fundamentally flawed. We need a better method of imbuing causal relationships than correlation+chance.
    - ppod 8y ago
      > archaic favors, missing recent terms, confusing opposing meanings, Humans make errors like these all the time (e.g., I don't know what you mean by 'favor' here, and I'm sure many people wouldn't have the type "ELMO"), and the dictionary definition of the word shifts accordingly, except we can't really call them 'errors' when it's a natural phenomenon that we're trying to match. Humans make seemingly random errors sometimes too, confusing one word for a mother, I mean another. When we do it we infer (perhaps correctly) deep psychological causes. Current machines don't have personal identities and desires and goals like we do, but embodied agents with a natural selection process could have those things, there isn't an obvious technological barrier. And if you build in a language model that shares some of the general computation machinery we could reasonably expect it to make those kinds of errors.

scottyelich 8y ago

Bingo.

not_a_moth 8y ago

The wild Zuckerberg quote aside, I don't believe many people expect this current wave of AI to approach general intelligence. I would be surprised if we weren't all mostly in agreement with the author.

hprotagonist 8y ago
In my experience, domain practitioners who spend our days elbow-deep in tensors are well aware that what we call AI right now has nothing to do with AGI. That knowledge does not seem to generalize well, though. Even technically literate people who aren't in the trenches seem to have frankly insane ideas about AGI. This phenomenon is not helped by armchair philosophy, the frothy chatter of the tech-adjunct world, or the naive idea that "well, transistors are easy, so brains should be too!" that is all too common in people who haven't stuffed electrodes into brains and then spent weeks figuring out what the hell could be going on in there.
- adrianN 8y ago
  You'd have a pretty hard time figuring out what a CPU does if all you could do is poking a small number of needles into it, cutting it into slices to study under a microscope and measuring which parts get hot while you use it.
  - hprotagonist 8y ago
    https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005268 https://journals.plos.org/ploscompbiol/article?id=10.1371/jo... true fact.
- thwy12321 8y ago
  The thing is that progress is so fast in some areas of ML, you dont really know when something could emerge. Who knows, maybe with enough training data and a deep enough network with the right supervision, something extraordinary pops out. Google google bert nlp. Google recently released an NLP algorithm that basically beats all predecessors, but the algorithm itself is extremely simple. Just lots of data and compute, no telling what will happen in ten years when the amount of data has exploded and GPUs are cheap.

hyperpallium 8y ago

Surprise! Meaning requires strong AI (and cultural understanding, much of which is only learnable from interaction).

Retra 8y ago
Meaning doesn't 'require' strong AI -- the kind of meaning that strong AIs like us prefer to interact with does. There's plenty of simple meaning in simple systems, and they are quite useful to us that way. They just don't encapsulate the complexities of human experience very well.

imh 8y ago

I've started thinking about the whole "do computers grok meaning" thing in terms of proof. If a computer tells me a true theorem, I don't know if I can ascribe "understanding" to it. If it tells me a true theorem and comes up with a proof, then I'm comfortable defining that as understanding within the context of that particular proof language. It passes the buck up a step. You define a new notion of understanding-in-a-given-language and then get to argue about whether that language/deduction system constitutes true "understanding."

panic 8y ago
The problem isn't deduction. It's getting things into the deductive system in the first place. You have to model the world, and your model will probably be incorrect somehow (https://www.student.cs.uwaterloo.ca/~cs492/11public_html/p18-smith.pdf https://www.student.cs.uwaterloo.ca/~cs492/11public_html/p18...).
freeone3000 8y ago
Okay, but what if it just made stuff up by trying everything until something came out true? We have theorum provers on this model today. Does such a system "understand" mathematics? It's probably more explainable than anything else, but the strength of the current generation of learning is application to cases with ambiguity present, and our current approaches here are completely unexplainable.
- imh 8y ago
  I'm saying that we can define what constitutes understanding by defining what constitutes reasoning within a given inference system. That's a simpler task, but doesn't solve the original problem. It just passes the buck. Now instead of asking whether a given learner performs reasoning and understands, we could ask whether the language/logic constitutes understanding. I'd definitely say that the current theorem provers have some form of true understanding, even though it isn't the same form as we have. For the typical ML learners, I think it's more interesting to ask and taxonomize "in what ways is this reasoning/understanding" rather than just ask "is this reasoning/understanding?"

goldenkey 8y ago

Here's what I've realized both about the universe AND intelligence . Silicon cannot touch either of them. I didn't always think this though. I thought intelligent beings, but first smaller structures, could evolve in a large digital universe. I made a blog post: http://scrollto.com/blog/2017/04/11/life-a-universe-simulation/ http://scrollto.com/blog/2017/04/11/life-a-universe-simulati... It took a long time but I eventually created what I set out to do. Here is ScatterLife: https://github.com/churchofthought/ScatterLife/ https://github.com/churchofthought/ScatterLife/ But the result was far from what I wanted. Even with a Titan V, a world of only 4096×4096 could be handled at a reasonable update rate. I basically had spacetime foam under the ideas of Doubly special relativity, ie. Feynman checkerboard universe. https://en.m.wikipedia.org/wiki/Doubly_special_relativity https://en.m.wikipedia.org/wiki/Doubly_special_relativity https://en.m.wikipedia.org/wiki/Feynman_checkerboard https://en.m.wikipedia.org/wiki/Feynman_checkerboard If our own universe is digital, then it updates at C/plank_length times per second, over 10^40 hz. Not only that but it consists of over 10^185 planck lengths. Nothing interesting is really viewable or even extrapolatable from the smallest of truths or fundamentals. Thats why many predictions of string theory need higher energies to be tested. The smaller you delve, the higher energy needed. In any case, I realized that evolution itself has been fueled by orders of magnitude. Symmetries of matter and energy, planets stars and solar systems...same magnitudes required. Even the best silicon isnt going to have 10^20 transistors on it. We would have to somehow go analog..use chemicals or matter in a way that didnt require slow refining and construction. Chemical based computing.... Now about ML and AI, same issue is present. Brains have 100 billion neurons. The best GPUs have maybe 5000 cores. The only way forward is to maximize what each core does - as much as possible. I learned this with the cellular automatas. Black and White 2-state automatas are neat but are a huge waste of processing power. They hold little information. Better is integers. Even better is floats. Why stop there though? Lets use complex numbers. https://github.com/churchofthought/HexagonalComplexAutomata https://github.com/churchofthought/HexagonalComplexAutomata Magnitude is against carefully crafted silicon. If we want to achieve what magnitude can, with silicon, we need to make sure our fundamental neuron units arent unnecessarily sparse. Magnitudes can afford to use simple units. Silicon cant. 5000 maybe 100000 cores when advances in fab accelerate. But still not 100 billion. Still needs to use the advanced abilities of the cores to their full extent. A neuron cannot compute any universal function...but a gpu core can. Anyways, I am almost failing to mention that I dont believe we have anything to worry about. Unless more research is devoted to special purpose hardware (consider an i7 has 731 million transistors), the software side will have a really hard time compensating for low magnitude. Lets see what happens. Its going to be exciting none the less. I am doing ML work myself on Boltzman networks and RNNs snd Hopfield nets. This is a promising field and its emerging at breakneck pace. Cheers!

voidhorse 8y ago

Aside from the technical obstacles currently barring us from realizing AIs capable of human competency, we should be seriously considering the social question of whether or not such a technology is something we really need or want. The world is already saturated with technical systems and artifacts that have effectively flattened social relations, eradicated traditions, and generally reduced the field of humanitarian meaning and theory to nil. All our problems have become technical problems. We’ve even reached a point where we try to solve social problems with technical solutions, and when that doesn’t work we try to fix the boo-boo the naive and rampant application of technology has caused with, guess what, more technology. The philosophy of technology is perhaps the most important discipline of our time—there’s little left in life that technics hasn’t in some way enveloped, either insidiously or openly.

nradov 8y ago
Those seem mostly like arbitrary value judgments based on what you personally happen to be comfortable with. Ask more fundamental questions. Are we necessarily better off with traditions than without?
- voidhorse 8y ago
  Sure. Imagine a world in which all tradition and culture was wiped out. Imagine a world in which all technology was wiped out. They're both absolutely horrible. And certainly, some traditions are better discarded, just as some technologies (the A-bomb being the prime example) are better left unexplored. I'm only trying to point out the tendency of our era is to forget humanitarian values entirely in the face of 'objective', 'practical', 'utilitarian' and technical progress. I am making a value judgement, and your dismissal of my points simply because they entail value judgements is exactly the sort of attitude that has led to the dissolution of culture and the near reemergence of fascist and populist thought in arguably the most technologically advanced country in the world. There's a reason technology couldn't stem the reemergence of such thinking, and in fact the social conditions rampant, unquestioned (or if so, not nearly enough) technological development has caused has contributed to the revival of such thought. Such an approach annihilates dialogue before it can begin. Living social subjects no longer endeavor to understand each other because they fall back on a ideology that tells them they it's "above" values. I am championing the philosophy of technology, and I am somewhat familiar with it, but I also genuinely believe it's important, and yes, that is a value, and one that I stand by. The sort of world I'm hoping will emerge in the future is dictated by those values. I just hope there's still enough folks in tech considering the future their current values might create.

rademacher 8y ago

This latest craze of "AI" research seems to be fueled by a sudden glut of computational power (GPUs) that wasn't available previously. I think that most technical people would agree that the mid 2020s is extremely ambitious. I'd also argue that we're actually more likely to experience another AI winter. The frightening part of the current deep learning research is how susceptible they are to adversarial attacks. Adding small amounts of noise causes misclassification in images, and some papers even explore the inevitability of adversarial examples [1]. This is especially frightening given the amount of autonomous vehicle work being done. I could imagine a situation in which the sensor noise varies just enough to cause such an error. Obviously, the systems will have redundancies built in, but I'm convinced the self-driving cars are still a ways off as well. EDIT: As others, have stated just adding noise is not enough and it is often used to generalize the model. The paper does discuss that the perturbations can be incredibly small to cause this deviation and that the set of such deviations may be larger than expected especially for complex images. Regarding the AI winter, I suppose I should have defined it as a reduction in the amount of research and the extent of the progress being made in the area rather than the utility of such research. [1] https://arxiv.org/abs/1809.02104 https://arxiv.org/abs/1809.02104

njoubert 8y ago
This is a common misconception. It is not a small amount of noise that causes misclassification of images. It is a carefully designed and quite unique pattern that causes misclassification. It only looks like noise to the human eye, but it really isn't. Yes, neural networks are susceptible to adversarial attacks. No, just adding noise to an image doesn't break neural networks.
- pmoriarty 8y ago
  Adding small amounts of noise is actually sometimes used to improve the performance of various AI techniques. It helps prevent overfitting. In fact, if your technique or model is seriously affected by a little noise this is usually enough to brand it brittle and maybe even a failure, as it's a sign of overfitting. Anyone working in this field knows to look for this and will try to make what they create more robust. The design of visual captchas is one obvious indication of just how successful AI techniques have been at image recognition in the presence of noise. It's no longer enough to make them a little noisy. In order to resist being solved by mechanical means, visual captchas have to include so much noise that even humans have problems recognizing them.

evilturnip 8y ago

I don't know much about AI, but I wonder what AI people think of this theory: https://en.wikipedia.org/wiki/Embodied_cognition https://en.wikipedia.org/wiki/Embodied_cognition The idea being that if we want to replicate strong AI, it needs to be embodied, because a lot of our cognition is built on metaphors that are instantiated in our physical actions and perceptions.

mingmecca 8y ago
I believe this to be true (embodiment as a precursor to cognition). So much of our early years learning is exploring how our various actions lead to a corresponding result. Touching a hot stove vs petting a soft cat, for example. And instead of just training 1 neural system at a time we're training all of our senses at once, which I think gives a significant boost to the brain's 'understanding' of the result of the action.
nradov 8y ago
Embodied cognition probably matters a lot if you're trying to build a drop-in replacement for humans. But in principle if someone manages to build a disembodied AGI it would probably still be useful for many purposes including ones we haven't even thought of yet. Think of it like a special-purpose dedicated co-processor. We already have those for other purposes like math, physics, graphics, etc to perform specialized tasks that the CPU can't do efficiently. In practice we're nowhere close to being able to build any sort of AGI so this is just a thought exercise.
versteegen 8y ago
One (of many) reasons I agree is because determining causal relationships is far easier when you can perform 'experiments', i.e. make changes to the world see the results. Another reason is that nearly all of what we call "common sense" is just knowledge about the real world rather than being some kind of abstract reasoning ability. Note that embodied cognition doesn't require robotics. An agent can act in a simulated environment instead. Note also that Deep Mind is very heavily focused on embodied agents.
mettamage 8y ago
I think we don't need to go there yet. There needs to be a stage before that which is the ability to precisely debug the human brain. If you don't understand the brain, then embodied cognition will be impossible to understand. I think the theory needs to be tested in much more depth. Fortunately, you could create AI models to test the theory itself. AI can serve as a testbed of our understanding in biology. Let me dive in on the idea of debugging the brain. If we're able to fully record one's brain activity in a precise manner, then we'll understand much better how to create an intelligent system. This is because very strong advances have been made in machine vision through a similar idea. Scientists didn't need much precise granularity to understand the visual cortex. The structure of the physical cortex is quite understandable: it detects detailed features and integrate them in bigger concepts until you finally 'see'. But I think for more abstract things in the human brain we'd need more fine-grained data and the possibility to replay that data (in the future), so mapping and recreating the structure of a brain (digitally) will also be needed for when I am talking about "the ability to precisely debug the human brain." What is funny is that AI currently serves as a very crude check to see whether we really understand brains at all. Just rebuild the brain in AI and see if it produces the same result. So part of this ability to precisely debug the human brain comes from AI itself. Since AI can be used as a hypothesis to test our understanding of the brain. Couple of things: animal brains are cool too, AI can also progress without understanding the brain and this obviously isn't the only thing that will leap AI forward. But if human brains become more debuggable (either through questionable ethics or technological advances), then it will benefit AI immensely. Also the ability to have hardware that would be 10,000 times as fast and software that would be optimized for a 10,000 speedup would help. I know that sounds a bit clunky but it does.

mark_l_watson 8y ago

Great article by Melanie Mitchell (she gave me her code for the Copy Cat program from her book with Hofstaeder almost 30 years ago - she has been looking at creativity and common sense in AI for a long while) My day job is all about deep learning but personally I think we need to stop and take a deep breath and really solve problems with biased data sets and models, easily spoofed models, etc. I have worked through a few AI winters and we may be hitting another one. I would like to see care given to using deep learning models only where it is safe to do so.

kopo 8y ago
Yup. Dotcom era all over again where everyone and their grandmother need to be building a website.
- rjtavares 8y ago
  Dotcom era gave us Amazon and Google. If AI craze gives us something like that again, I think that's fine.
  - thanatropism 8y ago
    There have been impressive advances in Computer Vision; they're just an outgrowth of the work that produced JPEG, rather than of the work that produced Lisp machines and expert systems. It's often said that we tend to take AI that really works and call it something else, but we also tend to call whatever is current and somewhat succesful (fuzzy logic, etc.) "AI".
  - mcguire 8y ago
    Universal surveillance and data collection? /Cynic
  - kulahan 8y ago
    I know I'm late to the party, but... Amazon abuses many of their workers, they've been dangerous to small businesses in any area they move into, and they're contributing significantly to the wealth inequality problem. Google knows a scary amount of information about you and everyone you know, they're supporting a horrific change in China allowing even tighter control of their citizens, and they were even looking at building AI for military drones at one point. In exchange, we get to buy cheap stuff with quick shipping and we can find resources on the internet slightly quicker. An AGI could have much more significant implications than either of those companies ever have. We need to figure out a solution to the AI control problem before we have a Dotcom-like burst of development.

symplee 8y ago

As if humans haven't also hit the barrier of meaning? And yet we've still made unimaginable discoveries. I almost added the word "progress" but I guess that depends on what you find meaningful. The article states: "But ultimately, the goal of developing trustworthy A.I. will require a deeper investigation into our own remarkable abilities and new insights into the cognitive mechanisms we ourselves use to reliably and robustly understand the world." Why limit the field to the capacity of humans? What the author calls "remarkable abilities" and "robustly understand[ing] the world" can also be seen as just reproducing our own innate and learned collective human biases. Our theories from observation, and their unprecedented ability to predict future events, is more about describing the world vs understanding it. Is there any topic in the world that doesn't have contrary interpretations? What quantifiable metric would we even use to gauge artificial intelligence's grasp of "meaning"? We don't even have one for our own.

freeone3000 8y ago
This is not philosophy. This is "apples are smaller than watermelons", "objects must be contiguous to be the same object", "the thing that makes an elephant an elephant is the presence of several large key features (ears, nose, trunk, legs) and not its color". If machines are going to meaningfully process our data, they're going to have to agree with us on certain base facts of reality.
nathan_long 8y ago
> What the author calls "remarkable abilities" and "robustly understand[ing] the world" can also be seen as just reproducing our own innate and learned collective human biases. From the article: > “The bareheaded man needed a hat” is transcribed by my phone’s speech-recognition program as “The bear headed man needed a hat.” If A.I. is going to work with humans, then yes, reproducing some of our "biases", such as the tendency to describe someone as "bearheaded" far more often that "bear headed", is a good goal. In an alternative universe of chimeras, a different bias would be needed. But in any case, the A.I. needs to "understand" that one of these transcriptions is much more likely to be correct than the other. So a level of processing beyond "sounds like this word" is needed. Take the famous absurd quote++: "Time flies like an arrow. Fruit flies like a banana." Reading the second sentence makes you, a human, double-take, and re-read the first sentence, and try reinterpreting it to see if it makes sense a different way. (The fact that it doesn't is what makes it funny.) An A.I. that has no "wait did that make sense?" step, no "is that funny?" step, is going to produce results that disappoint or confuse us humans who do. (++Interestingly, this quote seems to originate in A.I. research: https://quoteinvestigator.com/2010/05/04/time-flies-arrow/ https://quoteinvestigator.com/2010/05/04/time-flies-arrow/)

blueadept111 8y ago

Everything OP says is correct. And let's not forget that even in the absence of adversarial attacks, even with flawless associative mapping and incredible generalization, even hypothetically solving the problem of higher level ontological 'meaning', there's STILL absolutely no known way for AI to even begin to address the matter of the qualia - how to build something that's actually has a conscious perception (of pain, for example). We're groping in the dark without matches, and there will be many more seasons/winters to come.

pas 8y ago
Qualia is a red herring, just like the p-zombie topic. Consciousness is a program run on your brain to make you think you are conscious. (And as long as this explanation works and is simpler than all the others, we should go with it, instead of claiming how incredibly hard and unfathomable consciousness is.) What's hard is bootstrapping the whole thing. Evolution simply used brute force and copy-pastes a sort of working, already pre-wired, fine-tuned mishmash of faculties as a brain from the previous generation to the next. And uses the old gen to train the next gen.
- Valmar 8y ago
  > Qualia is a red herring, just like the p-zombie topic. It is NOT a red herring ~ it is a real problem for reductionist physicalism. However, because it cannot explain qualia, it tries to ignore, dismiss, and belittle, the enormity of the problems presented by qualia for its attempts at trying to explain. > Consciousness is a program run on your brain to make you think you are conscious. This is a presumption with scientific evidence. This is merely reductionist physicalist philosophical dogma. We can be certain that we are individually conscious and aware ~ but not necessarily why or how. Consciousness cannot be reduced down to a bunch of changes of the brain's matter. Consciousness is qualitatively different to how the brain functions, even though consciousness can indeed be influenced by the changes in brain states. Even though literally no-one knows how. > (And as long as this explanation works and is simpler than all the others, we should go with it, instead of claiming how incredibly hard and unfathomable consciousness is.) It is not simpler ~ it makes a set of presumptions which reductionist physicalism must explain scientifically. And even now, no scientific explanations are forthcoming. The philosophy has had centuries to explain how consciousness arises from the material and physical, and yet no explanation has been offered as to how the brain can magically create consciousness. There are literally no models at all that demonstrate, solidly and irrevocably, how particular configurations of brain matter directly translate into consciousness. > What's hard is bootstrapping the whole thing. Evolution simply used brute force and copy-pastes a sort of working, already pre-wired, fine-tuned mishmash of faculties as a brain from the previous generation to the next. And uses the old gen to train the next gen. A bunch of presumptions and just-so hypotheses without scientific evidence.

peter303 8y ago

'Understanding' is a matter of degree. Humans do not know very well how their own brains generate ideas, thought or language. Yet they appear to be capable of understanding.

aabajian 8y ago

I agree with the author overall, but I think she picks a poor example for dictation. “The bareheaded man needed a hat” falls squarely in the domain of contextually-aware models and would likely be transcribed correctly using deep learning.

wiz21c 8y ago

Didn't check but, considering there are probably thousands of "small tasks" that are being solved by AI, can one build an AI that can choose the right "small task" to apply to a never-seen-before problem ?

js8 8y ago

I recently realized that meaning cannot be done using pattern matching only, which is what (current) machine learning is about. The key insight came from article on bullshit: https://news.ycombinator.com/item?id=17764348 https://news.ycombinator.com/item?id=17764348 I believe understanding (things having meaning) comes down to whether you can produce an example from the given description of the situation. If you can produce an example, then you can say you understand the description. The problem is, you can have situation descriptions that are contradictory (have no examples, thus have no meaning) yet they are arbitrarily close to situation descriptions that have examples (and so have meaning). A good example are those Escher-like impossible objects, which look very much like real objects, but humans can easily see they are meaningless (they cannot be interpreted, and thus imagined, in 3D). Another good examples are sentences from the above paper. The bullshit sentences are the ones for which you cannot construct a mental example in your head. I suspect this happens for the famous flaws in deep learning as well, the deep learning network cannot learn that some inputs are contradictory. I believe that this is actually ultimately related to the boolean satisfiability problem. In theory, one could determine whether some learning agent is only using pattern matching or is actually having understanding (is able to recognize logical inconsistency in the input) by teaching it different SAT instances, and the agent that would be able to learn the difference between arbitrarily similar SAT instances with and without solutions could be considered to have understanding.

hacknat 8y ago

It all comes down to the fact that we don’t really understand what intelligence is. We know it is an emergent property of several mechanisms, working together, and we are even getting a decent grip on what those mechanisms are and how they work, but we are still struggling to see the big picture of how intelligence actually emerges.

qubax 8y ago
We don't know if it is an emergent property. That's what we think it is likely. but we won't know until we find out what consciousness is. And we won't find that out until we crack the secrets of the brain. There once was a time when we thought our bodies moved because there was an "emergent property" called the soul that instructed our bodies to move. It took a very long time for humans to discover how our bodies through the science of physiology.
- mcguire 8y ago
  "The soul" is (was?) explicitly not an emergent property.

guilamu 8y ago

The author is trying to make a point that AI is still truggling on basic understanding based on examples. Google Translate renders “I put the pig in the pen” into French as “Je mets le cochon dans le stylo” (mistranslating “pen” in the sense of a writing instrument). Well, Google is far from being the best at translation right now. If I try the author's example in Deepl for example "I put the pig in the pen" it translates into French as "J'ai mis le cochon dans l'enclos." which is the perfect translation based on context. So, I still have to read the rest of the article, but right from the bat, you're wrong mate (or at least you provided an example fo something "impossible for AI" which is already perfectly done by properly trained/programmed AI).

thallukrish 8y ago

This is the reason why chatbots did not live up to the hype. It is hard to have an agent that offers solution in a domain as well as being generally conversant.

kazinator 8y ago

Optimistic framing: "Machine learning algorithms, and domesticated animals such as dogs, don’t yet understand things the way humans do — with sometimes disastrous consequences.

bigbadgoose 8y ago

Turns out the primary use case of AI at the moment is duping low info voters into electing fascists. AI has an IQ of 87 in narrow situations, while the targets are working with 85 and below. One foot in front of the other, but the chasers got a little bit ahead of the horse here.