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Article questioning the usefulness and legality of “the AI pair programmer”

Follow up to https://kbin.social/m/technology@lemmy.ml/t/700738

cross-posted from: https://derp.foo/post/544012

There is a discussion on Hacker News, but feel free to comment here as well.

The New York Times is suing OpenAI and Microsoft for copyright infringement, claiming the two companies built their AI models by “copying and using millions” of the publication’s articles and now “directly compete” with its content as a result.

As outlined in the lawsuit, the Times alleges OpenAI and Microsoft’s large language models (LLMs), which power ChatGPT and Copilot, “can generate output that recites Times content verbatim, closely summarizes it, and mimics its expressive style.” This “undermine[s] and damage[s]” the Times’ relationship with readers, the outlet alleges, while also depriving it of “subscription, licensing, advertising, and affiliate revenue.”

The complaint also argues that these AI models “threaten high-quality journalism” by hurting the ability of news outlets to protect and monetize content. “Through Microsoft’s Bing Chat (recently rebranded as “Copilot”) and OpenAI’s ChatGPT, Defendants seek to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment,” the lawsuit states.

The full text of the lawsuit can be found here

Note: I am not affiliated with the project

I got interested in this question a few years ago, when I started writing about the “denominator problem”. A great deal of social media research focuses on finding unwanted behavior – mis/disinformation, hate speech – on platforms. This isn’t that hard to do: search for “white genocide” or “ivermectin” and count the results. Indeed, a lot of eye-catching research does just this – consider Avaaz’s August 2020 report about COVID misinformation. It reports 3.8 billion views of COVID misinfo in a year, which is a very big number. But it’s a numerator without a denominator – Facebook generates dozens or hundreds of views a day for each of its 3 billion users – 3.8 billion views is actually a very small number, contextualized with a denominator.

The paper this post describes can be found here
Abstract:

YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.