Building a no-code toxicity classifier by talking to GitHub Copilot

This is really pretty impressive. I think Copilot for these kinds of one-off analysis tasks where specific data manipulation rather than structuring abstractions makes a lot more sense. Structuring libraries or building UI requires a lot more understanding of potential users - in that case, writing the requirements is honestly the harder part.

Impressive BUT.

Who is defining toxic speech? Where is that data being taken from?

This is the definition of using AI to set what the edges of “speech” should be based on potentially flawed data.

This is a clown world.

There is nonetheless something extremely valuable about being able to write at different levels of abstraction when developing code.

Copilot lets you do that in a way that is way beyond what a normal programming language would let you do, which of course has its own, very rigid, abstractions.

For some parts of the code you'll want to dive in and write every single line in painstaking detail. For others `# give me the industry standard analysis of this dataset` is maybe enough for your purposes. And being able to have that ability, even if you think of it as just another programming language in itself, is huge.

> In this example, we’re using the Copilot extension for Visual Studio Code, and a free toxicity dataset that we built;

(Emphasis mine)

Following that link:

> Surge AI is a data labeling platform and workforce. Our labeling team pored over tens of thousands of social media comments to build this toxicity dataset. Each comment was then evaluated by multiple members of our team to determine its severity level.

I thought at first this was a classifier for the toxicity of no-code solutions.

For instance, Microsoft Power Automate should rank highly.