Answers.Body: <p>Such a great question. I would concur with Dennis Soemers comment that humans are not great at thinking of random numbers (just think about any card trick). However, we are very good at creating randomness through our actions.</p>
  
  <p>If you consider moving a computer mouse, the stock market, or playing a lottery, humans are very good at creating randomness through our constructs or actions. </p>
  
  <p>I would pose that maybe randomness needs to be part of a AGI to make it more able to jump out of suboptimal valleys or change it’s topology rather than sticking always to the local minimax.</p>
  
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Answers.Text: It may be important to distinguish between quantum stochasticity (the appearance of uncertainty in nature where outcomes seem unrelated to causality,) and chaotic systems, which are deterministic but unpredictable. I'd agree that that AGI needs some form of counter-intuitivity, but wonder if more efficient methods than "monte-carlo" may emerge.  *(This stems from a belief that human creativity is not based on mere randomness;)*
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Answers.Body: <blockquote>
    <p>As it can be easily pointed out that true random numbers cannot be generated fully by programming and some random seed is required.</p>
  </blockquote>
  
  <p>This is true. In fact, it is impossible to solve using software. No software-only technique can generate randomness <em>without</em> an initial random seed or support from hardware.</p>
  
  <p>This is also true for AI software. No AI design that uses deterministic software can do this - e.g. any Turing machine without a magic unexplained "random" function can be shown to remain deterministic, no matter how complex. That's because any combination of deterministic functions is deterministic. It may not be predictable without following the process exactly, it may be "chaotic" and depend critically on initial conditions, but it is 100% deterministic and repeatable.</p>
  
  <blockquote>
    <p>On the other hand, humans can easily generate any random number independently of other factors.</p>
  </blockquote>
  
  <p><a href="http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041531" rel="noreferrer">Typically not high quality randomness</a>. It is not clear <em>how</em> we make random decisions, but entirely possible that internally we rely effectively on noisiness from the environment or our own internal "hardware" doing something simple such as racing decisions between neurons (timing will vary due to speed of electrical impulses, diffusion time of neuro-transmitters across synapses etc)</p>
  
  <blockquote>
    <p>Does this suggest that absolute random number generation an AI cocept?</p>
  </blockquote>
  
  <p>I think it is orthogonal issue. We can already produce very high quality artificial randomness - better than human quality used by humans for conscious decisions (such as "choose a random number between 1 and 10"). These artificial random number generating systems are part of modern cryptography and are tested thoroughly. </p>
  
  <p>Essentially "true" artificial randomness is a solved problem using hardware, and does not involve anything that has traditionally been called AI.</p>
  
  <p>In reverse, AI systems often rely on stochastic functions in order to break symmetry, break ties, regularise models etc. So it does look like some kind of RNG is necessary within an artificial agent. However, even  pseudo-random number generators (PRNGs) seem to be fine for this purpose. Mersenne Twister is a very common choice for generating random numbers inside neural networks for weight initialisation, dataset shuffling, dropout regularisation, or when simulating environments for RL, or taking exploratory actions. Despite the fact that it is not "true" random, a PRNG will work just fine for these purposes.</p>
  
  <hr>
  
  <p>Working definition of "true" randomness that I use: Even if you know the state of a system as accurately as modern physics allows, the output cannot be predicted with better accuracy than a fixed guess.</p>
  
  <p>Human randomness already fails this test. If you ask someone to choose a number randomly between 1 and 9, you will generally have a better than 1 in 9 chance of guessing the correct value, based on statistical analysis. If we were able to take good state measurements of brains, it might be possible to predict with high accuracy - although this is unknown and not possible with current technology.</p>
  
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Answers.Text: Great answer--concepts well explicated. *(Theoretically, if a search space is large enough, would pseudo-RNG start showing fllaws? )*   I've been using ["quantum" stochasticity](https://en.wikipedia.org/wiki/Hardware_random_number_generator#Physical_phenomena_with_random_properties), based on the idea that we only find true randomness in nature in certain quantum models.  Any thoughts on that terminology?
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Answers.Text: @DukeZhou I don't have any knowledge or opinion on the terminology. Our best models of quantum physics are stochastic to the core, but that could change as we haven't really got to the bottom of that rabbit hole yet.
Answers.UserDisplayName: Neil Slater
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Answers.Text: Re: the rabbit hole, many competing theories: https://en.wikipedia.org/wiki/Interpretations_of_quantum_mechanics#Comparison_of_interpretations  *(FYI for readers interested in randomness)*
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Body: <p>As it can be easily pointed out that true random numbers cannot be generated fully by programming and some random seed is required.</p>
  <p>On the other hand, humans can easily generate any random number independently of other factors.</p>
  <p>Does this suggest that absolute random number generation is an AI concept?</p>
  
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Comments.Text: I don't believe the notion that "humans can easily generate any random number independently of other factors" is correct. I think humans are notoriously *bad* at coming up with truly random numbers. For example, in some sports (tennis, penalties in football, etc.) players will try to really be random in selecting which corner of the goal to aim for, but only the best really manage to avoid being somewhat predictable.
Comments.UserDisplayName: Dennis Soemers
Tags: <randomness>
Title: Is true random number generation an AI concept?
UserDisplayName: Abhishek Choudhary
ThreadText: # Is true random number generation an AI concept?
  
  (Asked by: username_0 on 2018-07-23)
  
  As it can be easily pointed out that true random numbers cannot be generated fully by programming and some random seed is required.
  
  On the other hand, humans can easily generate any random number independently of other factors.
  
  Does this suggest that absolute random number generation is an AI concept?
  
  **Tags:** randomness
  > **Comments:**
  > **username_1**: I don't believe the notion that "humans can easily generate any random number independently of other factors" is correct. I think humans are notoriously *bad* at coming up with truly random numbers. For example, in some sports (tennis, penalties in football, etc.) players will try to really be random in selecting which corner of the goal to aim for, but only the best really manage to avoid being somewhat predictable.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_4 on 2018-07-23. Score: 6 | Accepted answer
  
  > As it can be easily pointed out that true random numbers cannot be generated fully by programming and some random seed is required.
  
  This is true. In fact, it is impossible to solve using software. No software-only technique can generate randomness *without* an initial random seed or support from hardware.
  
  This is also true for AI software. No AI design that uses deterministic software can do this - e.g. any Turing machine without a magic unexplained "random" function can be shown to remain deterministic, no matter how complex. That's because any combination of deterministic functions is deterministic. It may not be predictable without following the process exactly, it may be "chaotic" and depend critically on initial conditions, but it is 100% deterministic and repeatable.
  
  > On the other hand, humans can easily generate any random number independently of other factors.
  
  [Typically not high quality randomness](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0041531). It is not clear *how* we make random decisions, but entirely possible that internally we rely effectively on noisiness from the environment or our own internal "hardware" doing something simple such as racing decisions between neurons (timing will vary due to speed of electrical impulses, diffusion time of neuro-transmitters across synapses etc)
  
  > Does this suggest that absolute random number generation an AI cocept?
  
  I think it is orthogonal issue. We can already produce very high quality artificial randomness - better than human quality used by humans for conscious decisions (such as "choose a random number between 1 and 10"). These artificial random number generating systems are part of modern cryptography and are tested thoroughly.
  
  Essentially "true" artificial randomness is a solved problem using hardware, and does not involve anything that has traditionally been called AI.
  
  In reverse, AI systems often rely on stochastic functions in order to break symmetry, break ties, regularise models etc. So it does look like some kind of RNG is necessary within an artificial agent. However, even pseudo-random number generators (PRNGs) seem to be fine for this purpose. Mersenne Twister is a very common choice for generating random numbers inside neural networks for weight initialisation, dataset shuffling, dropout regularisation, or when simulating environments for RL, or taking exploratory actions. Despite the fact that it is not "true" random, a PRNG will work just fine for these purposes.
  
  ---
  
  Working definition of "true" randomness that I use: Even if you know the state of a system as accurately as modern physics allows, the output cannot be predicted with better accuracy than a fixed guess.
  
  Human randomness already fails this test. If you ask someone to choose a number randomly between 1 and 9, you will generally have a better than 1 in 9 chance of guessing the correct value, based on statistical analysis. If we were able to take good state measurements of brains, it might be possible to predict with high accuracy - although this is unknown and not possible with current technology.
  
  > **Comments:**
  > **username_3**: Great answer--concepts well explicated. *(Theoretically, if a search space is large enough, would pseudo-RNG start showing fllaws? )*   I've been using ["quantum" stochasticity](https://en.wikipedia.org/wiki/Hardware_random_number_generator#Physical_phenomena_with_random_properties), based on the idea that we only find true randomness in nature in certain quantum models.  Any thoughts on that terminology?  
  > **username_4**: @DukeZhou I don't have any knowledge or opinion on the terminology. Our best models of quantum physics are stochastic to the core, but that could change as we haven't really got to the bottom of that rabbit hole yet.  
  > **username_3**: Re: the rabbit hole, many competing theories: https://en.wikipedia.org/wiki/Interpretations_of_quantum_mechanics#Comparison_of_interpretations  *(FYI for readers interested in randomness)*  
  
  ---
  
  ### Answer by username_2 on 2018-07-23. Score: 2
  
  Such a great question. I would concur with Dennis Soemers comment that humans are not great at thinking of random numbers (just think about any card trick). However, we are very good at creating randomness through our actions.
  
  If you consider moving a computer mouse, the stock market, or playing a lottery, humans are very good at creating randomness through our constructs or actions.
  
  I would pose that maybe randomness needs to be part of a AGI to make it more able to jump out of suboptimal valleys or change it’s topology rather than sticking always to the local minimax.
  
  > **Comments:**
  > **username_3**: It may be important to distinguish between quantum stochasticity (the appearance of uncertainty in nature where outcomes seem unrelated to causality,) and chaotic systems, which are deterministic but unpredictable. I'd agree that that AGI needs some form of counter-intuitivity, but wonder if more efficient methods than "monte-carlo" may emerge.  *(This stems from a belief that human creativity is not based on mere randomness;)*  
  
  ---
  
  