Answers.Body: <p>The simplest case is copying the software. That is instant duplication of the learnings. In a similar, but less trivial way you can adjust pretrained neural network classifiers to new datasets by simply re-initializing the last layer.</p>
  
  <p>It gets more interesting when you have multiple agents and you want to combine their knowledge. This can be done with many techniques; the simplest is averaging the weights of identical neural network architectures. One example of this <em>combined learning</em> happened is <a href="https://www.popularmechanics.com/technology/robots/a20190/google-room-full-of-robot-arms/" rel="nofollow noreferrer">Google Has a Room Full of Robot Arms Learning Hand-Eye Coordination</a>. The relevant paper is <a href="https://arxiv.org/pdf/1603.02199.pdf" rel="nofollow noreferrer">Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection</a>.</p>
  
  <p>The most complicated case, where you want to transfer just the gist and prevent forgetting is -- up to my knowledge, and I'm relatively certain it's outdated in that area -- unsolved. The problem is <strong>post-hoc explanations</strong>. Humans decide to do something, observe the outcome and then explain why they did something / why it was good to do it. So we automatically generate hypotheses about the world. And sometimes we are able to formulate them in a way that others can understand them. That is not possible with current learning machines. We don't know yet how to abstract automatically from arbitrary problems.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-11-14T16:13:41.360Z
Answers.Text: I am confident transfer of learnings from one agent to the other will be a surer route to achieving artificial general intelligence.
Answers.UserDisplayName: Elisha Senoo
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-11-10T21:43:12.950Z
Answers.LastActivityDate: 2018-11-13T09:04:55.203Z
Answers.LastEditDate: 2018-11-13T09:04:55.203Z
Answers.UserDisplayName: Martin Thoma
Body: <p>One disadvantage or weakness of Artificial Intelligence today the slow nature of learning or training success. For instance, an AI agent might require a 100,000 samples or more to reach an appreciable level of performance with a specific task. But this is unlike humans who are able to learn very quickly with a minimum number of samples. Humans are also able to teach one another, or in other words, transfer knowledge acquired.</p>
  
  <p>My question is this: are Artificial Intelligence learnings or trainings transferable from one agent to the other? If yes, how? If no, why?</p>
  
Tags: <machine-learning><reinforcement-learning><training><intelligent-agent>
Title: Are artificial intelligence learnings or trainings transferable from one agent to the other?
UserDisplayName: Elisha Senoo
ThreadText: # Are artificial intelligence learnings or trainings transferable from one agent to the other?
  
  (Asked by: username_0 on 2018-11-10)
  
  One disadvantage or weakness of Artificial Intelligence today the slow nature of learning or training success. For instance, an AI agent might require a 100,000 samples or more to reach an appreciable level of performance with a specific task. But this is unlike humans who are able to learn very quickly with a minimum number of samples. Humans are also able to teach one another, or in other words, transfer knowledge acquired.
  
  My question is this: are Artificial Intelligence learnings or trainings transferable from one agent to the other? If yes, how? If no, why?
  
  **Tags:** machine-learning, reinforcement-learning, training, intelligent-agent
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-11-10. Score: 2
  
  The simplest case is copying the software. That is instant duplication of the learnings. In a similar, but less trivial way you can adjust pretrained neural network classifiers to new datasets by simply re-initializing the last layer.
  
  It gets more interesting when you have multiple agents and you want to combine their knowledge. This can be done with many techniques; the simplest is averaging the weights of identical neural network architectures. One example of this *combined learning* happened is [Google Has a Room Full of Robot Arms Learning Hand-Eye Coordination](https://www.popularmechanics.com/technology/robots/a20190/google-room-full-of-robot-arms/). The relevant paper is [Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection](https://arxiv.org/pdf/1603.02199.pdf).
  
  The most complicated case, where you want to transfer just the gist and prevent forgetting is -- up to my knowledge, and I'm relatively certain it's outdated in that area -- unsolved. The problem is **post-hoc explanations**. Humans decide to do something, observe the outcome and then explain why they did something / why it was good to do it. So we automatically generate hypotheses about the world. And sometimes we are able to formulate them in a way that others can understand them. That is not possible with current learning machines. We don't know yet how to abstract automatically from arbitrary problems.
  
  > **Comments:**
  > **username_0**: I am confident transfer of learnings from one agent to the other will be a surer route to achieving artificial general intelligence.  
  
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