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>Any model which has no explicit understanding of the causal behaviour of an environment is going to misbehave for one of those unseen data points (eg. an unsee

>Any model which has no explicit understanding of the causal behaviour of an environment is going to misbehave for one of those unseen data points (eg. an unseen road in a weather/lighting/etc. condition I disagree. That sounds like an older interpretation of behavior of simple deep nets like the inception family. My experience with auto encoders and GANs is such that the nets can and unquestionably do interpolate between training data points. What's more, the latent spaces display order reflecting logic - much in the way that you can perform arithmetic on BERT encodings in what amounts to a form of logic. This is the where SOTA is now and I think it's a strong step towards AI, though we're still far from it. The trick is in understanding the boundaries in high dimensional space that your training data represents. So there is some degree of covering all your bases, so to speak, and that requires a new kind of intuition. These are very exciting times in tech/ML.

mjburgess 7y ago
Again you're treating this as-if its a problem of finding a sufficiently smooth function over data points. The problem is that the right behaviour is not a model fitted to that data. It isn't "fitted" at all. When I turn the thermostat the temperature in the room increases. If the thermostat is broken, the temperature does not. The predicting the effect of the thermostat on the room requires intervening in the room to find out if it is broken; it requires having a model of the room, of the thermostat, etc. No system that is not in direct causal contact with its environment can adapt to it. The system -- as it is in contact --- needs to be explicitly modelling the causally relevant features of that environment. This isn't a statistical problem. You cannot learn a function over images to actions because the environment is absent from those images. An infinite number of 2D images contain no 3D information. An infinite number of 3D images contain no skeletons (ie., inner-structure of objects). An infinite number of images of object pieces contain no information on object behaviour. An infinite number of videos of an object behaving contains no information of its behaviour when broken. An infinite number of videos of all possible breakages in all existing environments contains no information about behaviour in new environments. Animals solve this problem by playing with objects, building models of those objects (their causal properties, ie., how they interact with other objects). That requires being-in an environment and explicitly modelling it. There cannot be a "non-bogus" system arrived at via ML. Statistics itself is deficient in providing tools to design systems that "do what they should". Any paradigm which makes the cartesian assumption that "behaviour is a function of data" is necessarily incapable of intelligent adaptation. Environments "as data" are infinities. ie., For statistics to work you need all the relevant variables of an environment to make the "correct decision", and all the data needed to train against those. That's infinite. The "child on road" column isn't going to be fed into the machine. The machine does not, and cannot, even model what a "child" is. Patterns among fractions of infinites is not a basis for saftey (, nor for intelligence).
- jdmichal 7y ago
  I think most of the systems do try to model the objects around them. Of course, that is also subject to its own statistical anomalies. See report of the Uber/pedestrian collision for what happens when that goes wrong. I don't mean to dismiss your points, though. I wholely agree that if we really want to be moving towards an AI that is anything like animal intelligence, things like play are crucial. I'm a big fan of the attention schema theory. And if that is right, then building systems that can generate such models for their own mental state and others is the way to "consciousness". https://en.wikipedia.org/wiki/Attention_schema_theory https://en.wikipedia.org/wiki/Attention_schema_theory It's all well and good to build systems that can look at faces and statistically say, "oh they're mad". And I think people have this idea that maybe if we build enough of these systems and jam them together, we'll make an SI. But until we can build systems that can model why that person is mad, based on the system's own experience and models, we're just making parlor tricks.
  - mjburgess 7y ago
    I mean a very specific sort of model: a causal model. A model of that kind has to be able to generate predictions given the agents interaction with the object. Ie., how will the thermostat behave if I turn on the air conditioning. The problem is that we acquire these models during an entire lifetime of being embedded in an environment, esp. a social one. A self-driving car is never going to predict human behaviour given its own behaviour without having lived a human life. The question of what it should do given how another driver/pedestrian/etc. behaves isn't something it can solve with "mere statistics". It might be that human behaviour around roads ends up being extremely predictable, I'm doubtful that's the case. I think a car is going to need a Theory-of-Mind to navigate complex social driving environments, ie., a causal model of the behaviour of other animals.
    - OnlineGladiator 7y ago
      > I think a car is going to need a Theory-of-Mind to navigate complex social driving environments As someone with a lot of experience in self-driving cars, my opinion has changed over the course of the last decade from "we can create smart enough models of these separate problems to create a statistically safer product" to "first you need to invent general AI." It becomes immediately obvious as you encounter more and more edge cases, but you would never even begin to think of these edge cases and you have no idea how to handle them (even when hard coding them on individual cases - and you couldn't anyway as there are too many) so you realize the car actually has to be able to think. What's worse, it has to be able to think far enough into the future to anticipate anything that could end badly. The most interesting part of self-driving cars is definitely on the prediction teams - their job is to predict the world however many seconds into the future and incorporate that into the path planning. As you can guess, the car often predicts the future incorrectly. It's just a ridiculously hard problem. I think the current toolbox of ML is just woefully, completely, entirely inadequate to tackle this monster.

mjburgess 7y ago

Again you're treating this as-if its a problem of finding a sufficiently smooth function over data points. The problem is that the right behaviour is not a model fitted to that data. It isn't "fitted" at all. When I turn the thermostat the temperature in the room increases. If the thermostat is broken, the temperature does not. The predicting the effect of the thermostat on the room requires intervening in the room to find out if it is broken; it requires having a model of the room, of the thermostat, etc. No system that is not in direct causal contact with its environment can adapt to it. The system -- as it is in contact --- needs to be explicitly modelling the causally relevant features of that environment. This isn't a statistical problem. You cannot learn a function over images to actions because the environment is absent from those images. An infinite number of 2D images contain no 3D information. An infinite number of 3D images contain no skeletons (ie., inner-structure of objects). An infinite number of images of object pieces contain no information on object behaviour. An infinite number of videos of an object behaving contains no information of its behaviour when broken. An infinite number of videos of all possible breakages in all existing environments contains no information about behaviour in new environments. Animals solve this problem by playing with objects, building models of those objects (their causal properties, ie., how they interact with other objects). That requires being-in an environment and explicitly modelling it. There cannot be a "non-bogus" system arrived at via ML. Statistics itself is deficient in providing tools to design systems that "do what they should". Any paradigm which makes the cartesian assumption that "behaviour is a function of data" is necessarily incapable of intelligent adaptation. Environments "as data" are infinities. ie., For statistics to work you need all the relevant variables of an environment to make the "correct decision", and all the data needed to train against those. That's infinite. The "child on road" column isn't going to be fed into the machine. The machine does not, and cannot, even model what a "child" is. Patterns among fractions of infinites is not a basis for saftey (, nor for intelligence).

jdmichal 7y ago
I think most of the systems do try to model the objects around them. Of course, that is also subject to its own statistical anomalies. See report of the Uber/pedestrian collision for what happens when that goes wrong. I don't mean to dismiss your points, though. I wholely agree that if we really want to be moving towards an AI that is anything like animal intelligence, things like play are crucial. I'm a big fan of the attention schema theory. And if that is right, then building systems that can generate such models for their own mental state and others is the way to "consciousness". https://en.wikipedia.org/wiki/Attention_schema_theory https://en.wikipedia.org/wiki/Attention_schema_theory It's all well and good to build systems that can look at faces and statistically say, "oh they're mad". And I think people have this idea that maybe if we build enough of these systems and jam them together, we'll make an SI. But until we can build systems that can model why that person is mad, based on the system's own experience and models, we're just making parlor tricks.
- mjburgess 7y ago
  I mean a very specific sort of model: a causal model. A model of that kind has to be able to generate predictions given the agents interaction with the object. Ie., how will the thermostat behave if I turn on the air conditioning. The problem is that we acquire these models during an entire lifetime of being embedded in an environment, esp. a social one. A self-driving car is never going to predict human behaviour given its own behaviour without having lived a human life. The question of what it should do given how another driver/pedestrian/etc. behaves isn't something it can solve with "mere statistics". It might be that human behaviour around roads ends up being extremely predictable, I'm doubtful that's the case. I think a car is going to need a Theory-of-Mind to navigate complex social driving environments, ie., a causal model of the behaviour of other animals.
  - OnlineGladiator 7y ago
    > I think a car is going to need a Theory-of-Mind to navigate complex social driving environments As someone with a lot of experience in self-driving cars, my opinion has changed over the course of the last decade from "we can create smart enough models of these separate problems to create a statistically safer product" to "first you need to invent general AI." It becomes immediately obvious as you encounter more and more edge cases, but you would never even begin to think of these edge cases and you have no idea how to handle them (even when hard coding them on individual cases - and you couldn't anyway as there are too many) so you realize the car actually has to be able to think. What's worse, it has to be able to think far enough into the future to anticipate anything that could end badly. The most interesting part of self-driving cars is definitely on the prediction teams - their job is to predict the world however many seconds into the future and incorporate that into the path planning. As you can guess, the car often predicts the future incorrectly. It's just a ridiculously hard problem. I think the current toolbox of ML is just woefully, completely, entirely inadequate to tackle this monster.