5 ms·

Training agents to invent a language

163 points by runesoerensen 10y ago

gallerdude 10y ago
So many opportunities here! What if they let the agents just spout out random characters and let the other agents listen? They also talk about a lot of ways to get the agents to stop "cheating," but I think a better method would just be better goals.
- infogulch 10y ago
  Yes that's mostly what they did. E.g. they adjusted the goal to favor fast communication, and then to impose a cost on exponential vocabularies. Both of these sound very reasonable.
caublestone 10y ago
I really like the introduction of "cost". Have you considered having a multi-variable time decay distribution function for all learning so that the agents need to reinforce every lesson? Our brains forget over time because energy is limited and new connections take energy from older memories. So you could introduce a global set energy variable that is the ultimate denominator. Lastly, have you considered connecting your agents in a one to one public channel where a person interacts with an agent that asks the person about "meaning of objects" and collects opinions from people to create it's own opinion?
- halflings 10y ago
  Regarding memory/forgetting, this was published yesterday: https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/ https://deepmind.com/blog/enabling-continual-learning-in-neu...
JamilD 10y ago
It's interesting that a lot of added constraints mirror "real-world" scenarios. We don't have an intrinsic global co-ordinate system, and brevity/laziness is generally rewarded (which results in Zipf's Law in natural language).
imh 10y ago
Also interesting: https://github.com/iassael/learning-to-communicate https://github.com/iassael/learning-to-communicate and the paper: https://arxiv.org/abs/1605.06676 https://arxiv.org/abs/1605.06676 "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"
- ibgib 10y ago
  Yes, they both seem to be focused on the differentiable inter-agent communication aspect. I wonder if this is related to the recent articles on how honey bees communicate to each other the learning required for pulling strings, rolling balls, etc. (e.g. on HN https://news.ycombinator.com/item?id=13723645 https://news.ycombinator.com/item?id=13723645)
giardini 10y ago
Luc Steels was doing this years ago (since mid-1990s IIRC): https://www.google.com/search?client=opera&q=luc+steels+inventing+language&sourceid=opera&ie=utf-8&oe=utf-8&channel=suggest&gws_rd=ssl#spf=1 https://www.google.com/search?client=opera&q=luc+steels+inve... Ah, yes, I see that a paper referenced on the website: "A Paradigm for Situated and Goal-Driven Language Learning Jon Gauthier, Igor Mordatch" grants a footnote to one of Steels' 2012 publications, stating: "A related line of work in evolutionary linguistics constructs a similar language learning scenario entirely without fixed-language agents (Smith et al., 2003; Steels, 2012; Kirby et al., 2014). All of the agents in these environments construct a novel language simultaneously to accomplish some shared task. This is an interesting separate line of research, but ultimately a separate task from the understanding and acquisition problems discussed in this abstract."
- MrQuincle 10y ago
  There was one study together with Paul Vogt which also allows agents to establish which sensory channels to use for communication as well. This is language grounding in a very pure way. I think it's important not to restrict those channels. We communicate nonverbally, by touch, by smiling, by whistling, by scratching, by walking funny, by writing, by painting, by love.
- laretluval 10y ago
  It sounds like you're frustrated that deep learning is rediscovering stuff that your field discovered years ago. I've come to the conclusion that the only solution to that is to try to communicate more effectively; the reason they are not talking to us is that they can't understand what we're saying.
  - mackan_swe 10y ago
    Why the frustration? We are progressing, are we not? A sign of us making no progress would be when the new guys keep disproving the old theories.
    - laretluval 10y ago
deleted 10y ago
[deleted]
sidhantgandhi 10y ago
Implications: - Every Agent can have it's own language best suited for work in its domain. It can then auto-translate that to English at the end. This adds ambiguity once instead of at every step of the work if all of it was done in English. Like doing math with floating point then converting to int vs doing math in int the whole time. Concrete example: Law. - Agents could evolve a language that averages the Sapir-Whorf biases of known human languages. This new language would help humans understand each other better. Concretely, this language could be used as an intermediary in UN Meetings from which known languages could be auto-translated to while mitigating learned Sapir-Whorf biases. - As @amelius alluded to out, Agents could automate the process of learning alien languages: what we saw in the film Arrival (Dan's short story is so much better).
- taurath 10y ago
  I certainly would love to read a list of Sapir-Whorf biases included in English!
  - microcolonel 10y ago
    Biases against what baseline? A language optimized to express a finite set of things?
    - sidhantgandhi 10y ago
      Yeah there is no objective baseline. However, if the Agent could evolve a language that averages the biases of all human, that could be used as the baseline couldn't it?
      - microcolonel 10y ago
        Some humans can not make mouth sounds; some humans don't have arms. You already can't hit that baseline with one language. As somebody who has done a great deal of idle bloviation about ideal languages, I can say that there are many obstacles. For example, you need to add a true noun because a new material is discovered and it's getting very popular. You want a short and efficient name for it, but all the space is allocated. So you add it to your table of weights, and suddenly every prefix-coded word is pronounced and written completely differently. If you can't use prefix trees, how will you allocate the words efficiently?

163 points by runesoerensen 10y ago

So many opportunities here! What if they let the agents just spout out random characters and let the other agents listen? They also talk about a lot of ways to get the agents to stop "cheating," but I think a better method would just be better goals.

infogulch 10y ago
Yes that's mostly what they did. E.g. they adjusted the goal to favor fast communication, and then to impose a cost on exponential vocabularies. Both of these sound very reasonable.

caublestone 10y ago

I really like the introduction of "cost". Have you considered having a multi-variable time decay distribution function for all learning so that the agents need to reinforce every lesson? Our brains forget over time because energy is limited and new connections take energy from older memories. So you could introduce a global set energy variable that is the ultimate denominator. Lastly, have you considered connecting your agents in a one to one public channel where a person interacts with an agent that asks the person about "meaning of objects" and collects opinions from people to create it's own opinion?

halflings 10y ago
Regarding memory/forgetting, this was published yesterday: https://deepmind.com/blog/enabling-continual-learning-in-neural-networks/ https://deepmind.com/blog/enabling-continual-learning-in-neu...

JamilD 10y ago

It's interesting that a lot of added constraints mirror "real-world" scenarios. We don't have an intrinsic global co-ordinate system, and brevity/laziness is generally rewarded (which results in Zipf's Law in natural language).

imh 10y ago

Also interesting: https://github.com/iassael/learning-to-communicate https://github.com/iassael/learning-to-communicate and the paper: https://arxiv.org/abs/1605.06676 https://arxiv.org/abs/1605.06676 "Learning to Communicate with Deep Multi-Agent Reinforcement Learning"

ibgib 10y ago
Yes, they both seem to be focused on the differentiable inter-agent communication aspect. I wonder if this is related to the recent articles on how honey bees communicate to each other the learning required for pulling strings, rolling balls, etc. (e.g. on HN https://news.ycombinator.com/item?id=13723645 https://news.ycombinator.com/item?id=13723645)

giardini 10y ago

Luc Steels was doing this years ago (since mid-1990s IIRC): https://www.google.com/search?client=opera&q=luc+steels+inventing+language&sourceid=opera&ie=utf-8&oe=utf-8&channel=suggest&gws_rd=ssl#spf=1 https://www.google.com/search?client=opera&q=luc+steels+inve... Ah, yes, I see that a paper referenced on the website: "A Paradigm for Situated and Goal-Driven Language Learning Jon Gauthier, Igor Mordatch" grants a footnote to one of Steels' 2012 publications, stating: "A related line of work in evolutionary linguistics constructs a similar language learning scenario entirely without fixed-language agents (Smith et al., 2003; Steels, 2012; Kirby et al., 2014). All of the agents in these environments construct a novel language simultaneously to accomplish some shared task. This is an interesting separate line of research, but ultimately a separate task from the understanding and acquisition problems discussed in this abstract."

MrQuincle 10y ago
There was one study together with Paul Vogt which also allows agents to establish which sensory channels to use for communication as well. This is language grounding in a very pure way. I think it's important not to restrict those channels. We communicate nonverbally, by touch, by smiling, by whistling, by scratching, by walking funny, by writing, by painting, by love.
laretluval 10y ago
It sounds like you're frustrated that deep learning is rediscovering stuff that your field discovered years ago. I've come to the conclusion that the only solution to that is to try to communicate more effectively; the reason they are not talking to us is that they can't understand what we're saying.
- mackan_swe 10y ago
  Why the frustration? We are progressing, are we not? A sign of us making no progress would be when the new guys keep disproving the old theories.
  - laretluval 10y ago

deleted 10y ago

[deleted]

sidhantgandhi 10y ago

Implications: - Every Agent can have it's own language best suited for work in its domain. It can then auto-translate that to English at the end. This adds ambiguity once instead of at every step of the work if all of it was done in English. Like doing math with floating point then converting to int vs doing math in int the whole time. Concrete example: Law. - Agents could evolve a language that averages the Sapir-Whorf biases of known human languages. This new language would help humans understand each other better. Concretely, this language could be used as an intermediary in UN Meetings from which known languages could be auto-translated to while mitigating learned Sapir-Whorf biases. - As @amelius alluded to out, Agents could automate the process of learning alien languages: what we saw in the film Arrival (Dan's short story is so much better).

taurath 10y ago
I certainly would love to read a list of Sapir-Whorf biases included in English!
- microcolonel 10y ago
  Biases against what baseline? A language optimized to express a finite set of things?
  - sidhantgandhi 10y ago
    Yeah there is no objective baseline. However, if the Agent could evolve a language that averages the biases of all human, that could be used as the baseline couldn't it?
    - microcolonel 10y ago
      Some humans can not make mouth sounds; some humans don't have arms. You already can't hit that baseline with one language. As somebody who has done a great deal of idle bloviation about ideal languages, I can say that there are many obstacles. For example, you need to add a true noun because a new material is discovered and it's getting very popular. You want a short and efficient name for it, but all the space is allocated. So you add it to your table of weights, and suddenly every prefix-coded word is pronounced and written completely differently. If you can't use prefix trees, how will you allocate the words efficiently?