6 ms·

This is so misleading, it's not even funny. Predicting animation frames is something completely different than recreating a game engine. It's the difference bet

by dvt 9y ago

This is so misleading, it's not even funny. Predicting animation frames is something completely different than recreating a game engine. It's the difference between re-creating a movie frame-by-frame and building a movie camera. Obviously this is impressive in its own right, but we're a a long ways away from AI building general-purpose systems.

yters 9y ago
The constant overselling of AI is a negative indicator.
- jdietrich 9y ago
  I'd suggest that you review how computing was reported on in decades past. We made a lot of predictions that seemed utterly fantastical for some decades, but eventually came good when Moore's Law caught up. Many of these predictions drastically underestimated the impact of computing, particularly with regards to networking and mobile. I sincerely believe that the move towards deep learning is every bit as radical as the development of the microprocessor. We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. We're finding orders-of-magnitude improvements to difficult problems at an astonishing rate, often with remarkably modest resources. There's clearly a huge amount of hype happening at the moment, but genuine technological revolutions are usually preceded by a ludicrous hype bubble. https://www.youtube.com/watch?v=HW5Fvk8FNOQ https://www.youtube.com/watch?v=HW5Fvk8FNOQ
  - yters 9y ago
    I know no one wants to be that guy who said we only need 64k, but hindsight is 20/20. The DL hype is driven more by the singularitians than by actual usefulness of DL. The personal computer revolution was driven by the fact personal computers are very useful.
  - YeGoblynQueenne 9y ago
    >> We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. I think by "incredibly elegant solutions" you mean the typical black-box statistical machine learning algorithm that takes in some data as input and outputs an approximated function that "solves" whatever problem your data was representing. If that is indeed what you mean (apologies otherwise) I really struggle to see that as "elegant" -or even a solution. We start with some problem too complex to understand and build a complex system we don't quite understand, that solves the problem in a way we don't quite understand. What did we gain? A system that solves the problem - maybe, when it feels like it, depending on the problem, provided enough data etc etc. Our understanding of the problem hasn't changed and therefore our ability to solve it, hasn't. To give an analogy- it's like a kid at school who can't solve some arithmetic problem they have for homework asking their big sister to solve it for them. The kid now knows the answer to the problem but still doesn't know how to solve it, on their own. They didn't "find a solution"- they found someone who knows the solution. I think, if you look at the progress we've made as a civilisation in producing knowledge, you'll find that this was always driven by people who solved problems, themselves. And if you think of the kind of people who invoke some higher authority that has all the solutions, you're probably looking at religion.
    - jdietrich 9y ago
      Biological animals are amazingly good at crude statistical approximations. That's how we perform the vast majority of tasks - we make educated guesses that are reasonably close, then correct those guesses as new information becomes available. Computers have historically been very good at getting exact answers to simple problems, but very bad at approximating a solution to more complex problems. Computers that can interact with the real world are computers that can make quick, reasonably accurate guesses based on incomplete information. Hugely important tasks like speech recognition and autonomous driving are practically impossible without using approximate statistical methods. Machine learning is the key framework that allows us to attack those real-world problems.
    - thomashop 9y ago
      you're completely overlooking the fact that deep neural networks can generalize to unseen data. the kid analogy doesn't work at all.
  - rapind 9y ago
    We also made lots of fantastic predictions about space travel. I don't buy that hype guarantees future success. I'm not even sure it's relevant beyond possibly getting more people working in the domain and the resulting impact that may have.
    - rpedela 9y ago
      True, but some of those space predictions may come true sooner rather later now largely thanks to Elon and SpaceX. For example, a startup called Positron Dynamics has a serious proposal for an antimatter rocket. If it works, that would likely open up the solar system to human exploration. I doubt they would have gotten funding without the renewed excitement around space.
    - ThomPete 9y ago
      In AI it's not hype is trajectory. The fact that computers now can learn they way they do, is a very big deal it simply cannot be emphasized enough. We are only a few years in and haven't seen the limits of where it can be applied yet. It's Moores Law + Metcalfe's law. With space there are all sorts of hard limits because of physics and the goal is very specific. With deep learning there isn't some hard limit because there isn't very specific goals in the same way and not the same kind of laws hindering it. On the subject of space I am much more convinced that AI will be traveling the universe than humans.
      - rapind 9y ago
        To have a meaningful discussion I think we need to identify which specific AI we're talking about first. If it's general intelligence then I definitely think the hype is real. I don't think machine intelligence gets us any close to general intelligence than say a calculator has.
        ThomPete 9y ago
        Sure but I don't think anyone here is talking about general intelligence. You don't need general intelligence to replace a lot of ex. job functions. Ironically some of the lowest paid (cleaning) does require close to GI. But many other areas are quite far and are increasingly replacing human intellect. That is a big deal no matter how you look at it.
- agumonkey 9y ago
  They should get a lesson or two from IBM
kyberias 9y ago
But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). Think of the potential: we could create games just by giving the AI some example game play animations and it would learn the logic of the game (game engine).
- zeusk 9y ago
  > But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input.
  - kyberias 9y ago
    > No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! Game engine here _merely_ means the algorithm that takes user input, calculates the next state of the game and outputs it to the screen. No-one has claimed that this engine would be a generic engine capable of running a variety of games. No. Only this game. Whether the engine is written manually by a human or is a trained AI is irrelevant. It's still a game engine. The AI trained may have a different internal representation of the game state, but it still must have something like that. The whole POINT of this exercise is to find an automatic way of generating this algorithm without manually writing code.
    - pqh 9y ago
      I suppose, continuing this thought experiment, the generic game engine behind the AI-generated game would be the AI generating games.
    - zeusk 9y ago
      > Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! You don't seem to understand the difference between predicting what could be next compared to actually producing things from scratch. Think of this in terms of a movie where there is no user input and the state is linear (state is frame number and increments at a constant rate). Predicting next frame and saying that AI can recreate a movie is false and very misleading. Add in user input and non-linear state and your AI isn't very useful anymore, even with predicting next frames (you'll never reach perfect reproduction given the vast game state that is possible).

by dvt 9y ago

yters 9y ago

The constant overselling of AI is a negative indicator.

jdietrich 9y ago
I'd suggest that you review how computing was reported on in decades past. We made a lot of predictions that seemed utterly fantastical for some decades, but eventually came good when Moore's Law caught up. Many of these predictions drastically underestimated the impact of computing, particularly with regards to networking and mobile. I sincerely believe that the move towards deep learning is every bit as radical as the development of the microprocessor. We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. We're finding orders-of-magnitude improvements to difficult problems at an astonishing rate, often with remarkably modest resources. There's clearly a huge amount of hype happening at the moment, but genuine technological revolutions are usually preceded by a ludicrous hype bubble. https://www.youtube.com/watch?v=HW5Fvk8FNOQ https://www.youtube.com/watch?v=HW5Fvk8FNOQ
- yters 9y ago
  I know no one wants to be that guy who said we only need 64k, but hindsight is 20/20. The DL hype is driven more by the singularitians than by actual usefulness of DL. The personal computer revolution was driven by the fact personal computers are very useful.
- YeGoblynQueenne 9y ago
  >> We're starting to find incredibly elegant solutions to problems that have stymied computer scientists for decades. I think by "incredibly elegant solutions" you mean the typical black-box statistical machine learning algorithm that takes in some data as input and outputs an approximated function that "solves" whatever problem your data was representing. If that is indeed what you mean (apologies otherwise) I really struggle to see that as "elegant" -or even a solution. We start with some problem too complex to understand and build a complex system we don't quite understand, that solves the problem in a way we don't quite understand. What did we gain? A system that solves the problem - maybe, when it feels like it, depending on the problem, provided enough data etc etc. Our understanding of the problem hasn't changed and therefore our ability to solve it, hasn't. To give an analogy- it's like a kid at school who can't solve some arithmetic problem they have for homework asking their big sister to solve it for them. The kid now knows the answer to the problem but still doesn't know how to solve it, on their own. They didn't "find a solution"- they found someone who knows the solution. I think, if you look at the progress we've made as a civilisation in producing knowledge, you'll find that this was always driven by people who solved problems, themselves. And if you think of the kind of people who invoke some higher authority that has all the solutions, you're probably looking at religion.
  - jdietrich 9y ago
    Biological animals are amazingly good at crude statistical approximations. That's how we perform the vast majority of tasks - we make educated guesses that are reasonably close, then correct those guesses as new information becomes available. Computers have historically been very good at getting exact answers to simple problems, but very bad at approximating a solution to more complex problems. Computers that can interact with the real world are computers that can make quick, reasonably accurate guesses based on incomplete information. Hugely important tasks like speech recognition and autonomous driving are practically impossible without using approximate statistical methods. Machine learning is the key framework that allows us to attack those real-world problems.
  - thomashop 9y ago
    you're completely overlooking the fact that deep neural networks can generalize to unseen data. the kid analogy doesn't work at all.
- rapind 9y ago
  We also made lots of fantastic predictions about space travel. I don't buy that hype guarantees future success. I'm not even sure it's relevant beyond possibly getting more people working in the domain and the resulting impact that may have.
  - rpedela 9y ago
    True, but some of those space predictions may come true sooner rather later now largely thanks to Elon and SpaceX. For example, a startup called Positron Dynamics has a serious proposal for an antimatter rocket. If it works, that would likely open up the solar system to human exploration. I doubt they would have gotten funding without the renewed excitement around space.
  - ThomPete 9y ago
    In AI it's not hype is trajectory. The fact that computers now can learn they way they do, is a very big deal it simply cannot be emphasized enough. We are only a few years in and haven't seen the limits of where it can be applied yet. It's Moores Law + Metcalfe's law. With space there are all sorts of hard limits because of physics and the goal is very specific. With deep learning there isn't some hard limit because there isn't very specific goals in the same way and not the same kind of laws hindering it. On the subject of space I am much more convinced that AI will be traveling the universe than humans.
    - rapind 9y ago
      To have a meaningful discussion I think we need to identify which specific AI we're talking about first. If it's general intelligence then I definitely think the hype is real. I don't think machine intelligence gets us any close to general intelligence than say a calculator has.
      - ThomPete 9y ago
        Sure but I don't think anyone here is talking about general intelligence. You don't need general intelligence to replace a lot of ex. job functions. Ironically some of the lowest paid (cleaning) does require close to GI. But many other areas are quite far and are increasingly replacing human intellect. That is a big deal no matter how you look at it.
agumonkey 9y ago
They should get a lesson or two from IBM

kyberias 9y ago

But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). Think of the potential: we could create games just by giving the AI some example game play animations and it would learn the logic of the game (game engine).

zeusk 9y ago
> But if it can predict animation frames perfectly (it cannot), it is practically a game engine (for that particular game). No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input.
- kyberias 9y ago
  > No, it isn't - just like the subject in the Chinese room experiment cannot understand Chinese yet is able to reply with perfect language to given input. Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! Game engine here _merely_ means the algorithm that takes user input, calculates the next state of the game and outputs it to the screen. No-one has claimed that this engine would be a generic engine capable of running a variety of games. No. Only this game. Whether the engine is written manually by a human or is a trained AI is irrelevant. It's still a game engine. The AI trained may have a different internal representation of the game state, but it still must have something like that. The whole POINT of this exercise is to find an automatic way of generating this algorithm without manually writing code.
  - pqh 9y ago
    I suppose, continuing this thought experiment, the generic game engine behind the AI-generated game would be the AI generating games.
  - zeusk 9y ago
    > Oh, come on. This is silly semantics. Human written engine doesn't "understand" the game state any more than an AI does! You don't seem to understand the difference between predicting what could be next compared to actually producing things from scratch. Think of this in terms of a movie where there is no user input and the state is linear (state is frame number and increments at a constant rate). Predicting next frame and saying that AI can recreate a movie is false and very misleading. Add in user input and non-linear state and your AI isn't very useful anymore, even with predicting next frames (you'll never reach perfect reproduction given the vast game state that is possible).