Part of my series of notes from ICLR 2019 in New Orleans.
- the problem of induction – how do people learn so much from so little?
- need learning mechanism to go from examples to concepts
- assuming “language of thought” to express complex concepts (somewhat provocatively…)
- example: grammar for boolean concepts, priors + noisy labelling to learn rules
- this can actually already explain human concept learning pretty damn well
- starts to struggle with many complex concepts, with many features, from lots of data
- stronger priors?
- neural networks?
The Cultural Ratchet
- human concept learning actually doesn’t scale super well…
cultural ratchet to accumulate knowledge over generations
- amplifies limited individual learning
- see Tomasello 1999
- requires faithful transmission of concepts in a way that’s easier than directly learning
- maybe: language?!
- experiment to compare concept learning from language alone (teacher/student setup) vs. from examples
- language seems to be effective (learn concepts) and efficient (takes less time)
- some modeling to understand the “teaching utterances” people use
- v. small dataset – 365 utterances
- pretty simple model with LSTM + CNN encoders
- agreement with teacher similar as for humans
- doesn’t seem to be doing “the right thing” though
- what kind of language do teachers use?
- language of generalisation
- baseline to compare – reference game (describe which object to choose – concretely rather than concepts)
- e.g. find that bare plural generics are more prevalent in concept teaching setting
- aside: reference games are super nice for grounded language data
- hypothesis: cultural ratchet arises specifically from ability of language to convey generalisations
- how do we learn from generics?
- our interpretation of them differs weirdly, wtf is going on
- generics as minimal examples
- but also, teaching has social force – someone is intending it as an example
- two models
- rational speech acts – to model minimal example viewpoint
- pragmatic listener – to model social force viewpoint
- collect some data…
- prior expectations of prevalence are different depending on the property
- e.g. true or false: “Birds lay eggs” vs. “Birds are female”
- social model better accounts for data => supports generics as intended minimal examples
- open question: how do people learn about properties?
Conclusion and Q&A
- hypothesis: language prepares you for future learning
- regularizer for transfer learning
- logical language of thought? really?
- might be sufficient in the cultural ratchet setting, but who knows
- specifically interested in the gap between baby humans and baby monkeys
- they both learn a ton, but monkeys don’t build rocket ships