3 ms·

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/pe

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.
  - perl4ever 7y ago
    I happened to run across statistics (iihs I think) for fatalities to (human) drivers per registered vehicle-years, and the number is about 30 per million. You may or may not accept that this is the right type of metric, but it's interesting to think about what the number implies. I read that Uber had a fleet of 250 cars in testing. If an average human driver has a fatal accident once in ~33,000 years, then to demonstrate parity in self driving, Uber would have to operate their 250 vehicles for an average of 130 years between fatalities. Granted, this is driver fatalities, so hitting pedestrians would not count. But even so, it seems it is a high bar and you would need hundreds of years of testing at the current rate to be confident you are better than human drivers rather than having a statistical fluke.

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.
- perl4ever 7y ago
  I happened to run across statistics (iihs I think) for fatalities to (human) drivers per registered vehicle-years, and the number is about 30 per million. You may or may not accept that this is the right type of metric, but it's interesting to think about what the number implies. I read that Uber had a fleet of 250 cars in testing. If an average human driver has a fatal accident once in ~33,000 years, then to demonstrate parity in self driving, Uber would have to operate their 250 vehicles for an average of 130 years between fatalities. Granted, this is driver fatalities, so hitting pedestrians would not count. But even so, it seems it is a high bar and you would need hundreds of years of testing at the current rate to be confident you are better than human drivers rather than having a statistical fluke.