If — as Efros has pointed out — there are a lot more conceptual patterns than words can describe, then do words constrain our thoughts? This question is at the heart of the Sapir-Whorf or Linguistic Relativity Hypothesis, and the debate about whether language completely determines the boundaries of our cognition, or whether we are unconstrained to conceptualize anything — regardless of the languages we speak.
In its strongest form, the hypothesis posits that the structure and lexicon of languages constrain how one perceives and conceptualizes the world.
One of the most striking effects of this is demonstrated in the color test shown here. When asked to pick out the one square with a shade of green that’s distinct from all the others, the Himba people of northern Namibia — who have distinct words for the two shades of green — can find it almost instantly. The rest of us, however, have a much harder time doing so.
The theory is that — once we have words to distinguish one shade from another, our brains will train itself to discriminate between the shades, so the difference would become more and more “obvious” over time. In seeing with our brain, not with our eyes, language drives perception.
With machine learning, we also observe something similar. In supervised learning, we train our models to best match images (or text, audio, etc.) against provided labels or categories. By definition, these models are trained to discriminate much more effectively between categories that have provided labels, than between other possible categories for which we have not provided labels. When viewed from the perspective of supervised machine learning, this outcome is not at all surprising. So perhaps we shouldn’t be too surprised by the results of the color experiment above, either. Language does indeed influence our perception of the world, in the same way that labels in supervised machine learning influence the model’s ability to discriminate among categories.
And yet, we also know that labels are not strictly required to discriminate between cues. In Google’s “cat-recognizing brain”, the network eventually discovers the concept of “cat”, “dog”, etc. all by itself — even without training the algorithm against explicit labels. After this unsupervised training, whenever the network is fed an image belonging to a certain category like “Cats”, the same corresponding set of “Cat” neurons always gets fired up. Simply by looking at the vast set of training images, this network has discovered the essential patterns of each category, as well as the differences of one category vs. another.
In the same way, an infant who is repeatedly shown a paper cup would soon recognize the visual pattern of such a thing, even before it ever learns the words “paper cup” to attach that pattern to a name. In this sense, the strong form of the Sapir-Whorf hypothesis cannot be entirely correct — we can, and do, discover concepts even without the words to describe them.
Supervised and unsupervised machine learning turn out to represent the two sides of the controversy’s coin. And if we recognized them as such, perhaps Sapir-Whorf would not be such a controversy, and more of a reflection of supervised and unsupervised human learning.
- More Here
In its strongest form, the hypothesis posits that the structure and lexicon of languages constrain how one perceives and conceptualizes the world.
One of the most striking effects of this is demonstrated in the color test shown here. When asked to pick out the one square with a shade of green that’s distinct from all the others, the Himba people of northern Namibia — who have distinct words for the two shades of green — can find it almost instantly. The rest of us, however, have a much harder time doing so.
The theory is that — once we have words to distinguish one shade from another, our brains will train itself to discriminate between the shades, so the difference would become more and more “obvious” over time. In seeing with our brain, not with our eyes, language drives perception.
With machine learning, we also observe something similar. In supervised learning, we train our models to best match images (or text, audio, etc.) against provided labels or categories. By definition, these models are trained to discriminate much more effectively between categories that have provided labels, than between other possible categories for which we have not provided labels. When viewed from the perspective of supervised machine learning, this outcome is not at all surprising. So perhaps we shouldn’t be too surprised by the results of the color experiment above, either. Language does indeed influence our perception of the world, in the same way that labels in supervised machine learning influence the model’s ability to discriminate among categories.
And yet, we also know that labels are not strictly required to discriminate between cues. In Google’s “cat-recognizing brain”, the network eventually discovers the concept of “cat”, “dog”, etc. all by itself — even without training the algorithm against explicit labels. After this unsupervised training, whenever the network is fed an image belonging to a certain category like “Cats”, the same corresponding set of “Cat” neurons always gets fired up. Simply by looking at the vast set of training images, this network has discovered the essential patterns of each category, as well as the differences of one category vs. another.
In the same way, an infant who is repeatedly shown a paper cup would soon recognize the visual pattern of such a thing, even before it ever learns the words “paper cup” to attach that pattern to a name. In this sense, the strong form of the Sapir-Whorf hypothesis cannot be entirely correct — we can, and do, discover concepts even without the words to describe them.
Supervised and unsupervised machine learning turn out to represent the two sides of the controversy’s coin. And if we recognized them as such, perhaps Sapir-Whorf would not be such a controversy, and more of a reflection of supervised and unsupervised human learning.
- More Here
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