Most of us benefit every day from the fact computers can now understand us when we speak or write.
Human language is full of ambiguity and double meanings.
For instance, consider the potential meaning of this phrase: I went to project class.
Without context, its an ambiguous statement.
Computer scientists and linguists have spent decades trying to program computers to understand the nuances of human language.
And in certain ways, computers are fast approaching humans ability to understand andgenerate text.
Through these subtle, everyday interactions, machine learning is influencing our culture.
Are we ready for that?
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I created an online interactive work for theKyogle Writers Festivalthat lets you explore this technology in a harmless way.
What is natural language processing?
The field concerned with using everyday language to interact with computers is called natural language processing.
Last year, this technologys potential became clear when the Generative Pre-trained Transformer 3 (GPT-3) was released.
It set a new benchmark in what computers can do with language.
It does this by building on machine-learning models, including two widely adopted models calledBERT and ELMO.
How is this technology affecting culture?
But during this process,a modelmay absorb a distorted or problematic worldview from its training data.
This can lead to serious exclusion and inequality, as detailed in the recent documentaryCoded Bias.
In other words, it lets you use a computer to change the meaning of a piece of text.
You cantry it yourself.
ScreenshotHeres an example of the Australian national anthem subjected to some automated revision.
For instance, if I ask you: man is to king what woman is to?
you might answer queen.
Thats an easy one.
But I could ask a more challenging question such as: rose is to thorn what love is to?
There are several possible answers here, depending on your interpretation of the language.
When asked about these analogies, GLoVe will produce the responses queen and betrayal, respectively.
A such, it can perform calculations with words, adding and subtracting words as if they were numbers.
Approaches to making machine learning models more transparent are afocus of much current research.
Some analogies will surprise you with their poignancy, while others may well leave you bewildered.
Yet, every association was inferred from a huge corpus of a few billion words written by ordinary people.
Having entire news feeds populated by machine-written text is no longer the stuff of science fiction.
This technology isalready here.
And the cultural footprint of machine-learning models seems to only be growing.
