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I feel like it's fairly well-established that these text-only models cannot not have the capability of common sense reasoning because they don't incorporate sen

by ilaksh 6y ago

I feel like it's fairly well-established that these text-only models cannot not have the capability of common sense reasoning because they don't incorporate sensorimotor/spatial/temporal/etc. knowledge. They can't because they are not trained with any of that, just text.

madsbuch 6y ago
Multi modal learning is the new kid on the block.
unityByFreedom 6y ago
Read the post. It's an incremental improvement on existing language models to incorporate numerical understanding. It's more than just the title.
- ilaksh 6y ago
  I did read the post. I guess I should have been more explicit. These types of incremental improvements seem to have limited utility and researchers who want to improve the models substantially may find more payoff in pursuing the integration of other types of data.
  - unityByFreedom 6y ago
    It's a clever technique, not data, so anyone can incorporate it. As for improvement the post shares its metrics and anyone could experiment on their own tasks to see if it brings improvement or not.
nothis 6y ago
I find it interesting how hard it is to think of a question that truly cannot be answered by just reading lots of text. Stuff like "how can you carry water without a vessel?" (you freeze it!), which might stump a lot of AI models. But then you think... there should be texts that explain that ice is water and that ice can be carried. Similarly, the problem with numbers (lenghth, height, etc) isn't a lack of data, it's messy notation which is still easily parsed by human readers and probably could be solved. If anything, I've become more convinced that text alone has enough information to solve some eerily specific problems. It's quite a powerful tool.
- selestify 6y ago
  > Stuff like "how can you carry water without a vessel?" (you freeze it!), which might stump a lot of AI models. What? Even I couldn't answer that. I thought it was a brainteaser. "Water" in common parlance refers to its liquid form, as opposed to ice or steam.
  - rangibaby 6y ago
    It is a brain teaser and you explained why
- k_sze 6y ago
  In an alternate universe, a really “smart” AI would be able to answer the “how do you carry water without a vessel” question, still not in the normal human way (you freeze it), but in the same vein as what you would find in Randall Munroe’s “How To” book.
- unityByFreedom 6y ago
  That question is hard for current language models. Theoretically you could get there but you'd need to connect a lot of dots in your algorithm. Throwing more text at existing algorithms won't reliably answer this kind of question.
- mannykannot 6y ago
  One answer can be found in the issue being addressed here, which is that the models were not, just from reading lots of text, performing well on questions involving scale.
Peritract 6y ago
Fairly well-established, but not widely accepted; discussion around language models tends to ascribe all sorts of complex capabilities to them without evidence.
mrfusion 6y ago
I think it’s an open question. Why do you say it’s well established? Aren’t people working on combining language and visual models now? It will be interesting to see how those do.
- RugnirViking 6y ago
  Yes. The combination of the two is quite a difficult task in itself, known as "grounding" - a mapping of an observation to a language concept. Progress is slow but it's being worked on (including by me)
  - mrfusion 6y ago
    Wow can you tell us more?
    - RugnirViking 6y ago
      I'm just starting to write my master's in robotics, and this is the topic I'm working on. Basically, grounding is the idea of trying to correlate (draw a connection between) a visual set of features (outputs from a vision system) and a set of identifiers (outputs from a language model). There are several techniques to do this! A good starting point is a system that has a pretrained list of objects it can recognise. For example, we can train our image model what a book is, so when a user asks for a book, we can correlate the "bookness" feature (trained part of the output vector from the vision system) to it 1-1, and connect the bounding box from the vision system to the named entitity "book". However, say a user instead asked for a "novel". While this is also a book, we need a way for the two to be connected. For this, we use a quite different type of language model to connect the named entity "novel" to a list of known objects, one of which is "book". It does this by scoring how similar words are in the language model, and picking the closest match that is in the list of known objects. Over some threshold of similarity (measured by distance in abstract word-vector space), it is determined that this is a new, unknown object. What happens next varies, but is usually some variation of the system guiding the user to teach it what the object is that they meant (something like: point to the object, or put the object on top of a special evenly lit blank background for the visual system to learn it's features), so it is known for next time, and given an identifier from what the user called it. This is the most basic form, but (as with everything) it gets more complicated. Two areas I am looking into at the moment are past reference language learning, which is about learning new visual features from users in operation. For example: user refers to this particular book as an "open textbook", later asks for a "closed textbook". Model knowns "open" and "closed" are mutually exclusive, so looks for a second match for book that is dissimilar in some visual features to the first, and learns some weak relation in those features to "open" and "closed", possibly to be reinforced later by repeated user expressions. The other area I am looking at is learned spacial relation grounding. That is, trying to be able to resolve something like "The third book from the left" or "the book on top of the blue table". This is interesting because it breaks a lot of current grounding approaches, in that it requires some recursion (you can chain spatial descriptors in natural language e.g. "The book on the table in the room in the leftmost house on the hill...") and self-referential techniques. Ways of doing it aren't as well researched, and we're working on a method for doing it atm :)

by ilaksh 6y ago

madsbuch 6y ago

Multi modal learning is the new kid on the block.

unityByFreedom 6y ago

Read the post. It's an incremental improvement on existing language models to incorporate numerical understanding. It's more than just the title.

ilaksh 6y ago
I did read the post. I guess I should have been more explicit. These types of incremental improvements seem to have limited utility and researchers who want to improve the models substantially may find more payoff in pursuing the integration of other types of data.
- unityByFreedom 6y ago
  It's a clever technique, not data, so anyone can incorporate it. As for improvement the post shares its metrics and anyone could experiment on their own tasks to see if it brings improvement or not.

nothis 6y ago

I find it interesting how hard it is to think of a question that truly cannot be answered by just reading lots of text. Stuff like "how can you carry water without a vessel?" (you freeze it!), which might stump a lot of AI models. But then you think... there should be texts that explain that ice is water and that ice can be carried. Similarly, the problem with numbers (lenghth, height, etc) isn't a lack of data, it's messy notation which is still easily parsed by human readers and probably could be solved. If anything, I've become more convinced that text alone has enough information to solve some eerily specific problems. It's quite a powerful tool.

selestify 6y ago
> Stuff like "how can you carry water without a vessel?" (you freeze it!), which might stump a lot of AI models. What? Even I couldn't answer that. I thought it was a brainteaser. "Water" in common parlance refers to its liquid form, as opposed to ice or steam.
- rangibaby 6y ago
  It is a brain teaser and you explained why
k_sze 6y ago
In an alternate universe, a really “smart” AI would be able to answer the “how do you carry water without a vessel” question, still not in the normal human way (you freeze it), but in the same vein as what you would find in Randall Munroe’s “How To” book.
unityByFreedom 6y ago
That question is hard for current language models. Theoretically you could get there but you'd need to connect a lot of dots in your algorithm. Throwing more text at existing algorithms won't reliably answer this kind of question.
mannykannot 6y ago
One answer can be found in the issue being addressed here, which is that the models were not, just from reading lots of text, performing well on questions involving scale.

Peritract 6y ago

Fairly well-established, but not widely accepted; discussion around language models tends to ascribe all sorts of complex capabilities to them without evidence.

mrfusion 6y ago

I think it’s an open question. Why do you say it’s well established? Aren’t people working on combining language and visual models now? It will be interesting to see how those do.

RugnirViking 6y ago
Yes. The combination of the two is quite a difficult task in itself, known as "grounding" - a mapping of an observation to a language concept. Progress is slow but it's being worked on (including by me)
- mrfusion 6y ago
  Wow can you tell us more?
  - RugnirViking 6y ago
    I'm just starting to write my master's in robotics, and this is the topic I'm working on. Basically, grounding is the idea of trying to correlate (draw a connection between) a visual set of features (outputs from a vision system) and a set of identifiers (outputs from a language model). There are several techniques to do this! A good starting point is a system that has a pretrained list of objects it can recognise. For example, we can train our image model what a book is, so when a user asks for a book, we can correlate the "bookness" feature (trained part of the output vector from the vision system) to it 1-1, and connect the bounding box from the vision system to the named entitity "book". However, say a user instead asked for a "novel". While this is also a book, we need a way for the two to be connected. For this, we use a quite different type of language model to connect the named entity "novel" to a list of known objects, one of which is "book". It does this by scoring how similar words are in the language model, and picking the closest match that is in the list of known objects. Over some threshold of similarity (measured by distance in abstract word-vector space), it is determined that this is a new, unknown object. What happens next varies, but is usually some variation of the system guiding the user to teach it what the object is that they meant (something like: point to the object, or put the object on top of a special evenly lit blank background for the visual system to learn it's features), so it is known for next time, and given an identifier from what the user called it. This is the most basic form, but (as with everything) it gets more complicated. Two areas I am looking into at the moment are past reference language learning, which is about learning new visual features from users in operation. For example: user refers to this particular book as an "open textbook", later asks for a "closed textbook". Model knowns "open" and "closed" are mutually exclusive, so looks for a second match for book that is dissimilar in some visual features to the first, and learns some weak relation in those features to "open" and "closed", possibly to be reinforced later by repeated user expressions. The other area I am looking at is learned spacial relation grounding. That is, trying to be able to resolve something like "The third book from the left" or "the book on top of the blue table". This is interesting because it breaks a lot of current grounding approaches, in that it requires some recursion (you can chain spatial descriptors in natural language e.g. "The book on the table in the room in the leftmost house on the hill...") and self-referential techniques. Ways of doing it aren't as well researched, and we're working on a method for doing it atm :)