Suchir Balaji: “When does generative AI qualify for fair use?”

Fair use is defined in Section 107 of the Copyright Act of 1976, which I’ll quote verbatim below:

Notwithstanding the provisions of sections 106 and 106A, the fair use of a copyrighted work, including such use by reproduction in copies or phonorecords or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright. In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include—

1. the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes;
2. the nature of the copyrighted work;
3. the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and
4. the effect of the use upon the potential market for or value of the copyrighted work.

The fact that a work is unpublished shall not itself bar a finding of fair use if such finding is made upon consideration of all the above factors.

Fair use is a balancing test which requires weighing all four factors. In practice, factors (4) and (1) tend to be the most important, so I’ll discuss those first. Factor (2) tends to be the least important, and I’ll briefly discuss it afterwards. Factor (3) is somewhat technical to answer in full generality, so I’ll discuss it last.

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None of the four factors seem to weigh in favor of ChatGPT being a fair use of its training data. That being said, none of the arguments here are fundamentally specific to ChatGPT either, and similar arguments could be made for many generative AI products in a wide variety of domains.

Suchir Balaji

Interesting analysis by a former OpenAI researcher who left the company and publicly spoke against their business practices, going as far as an interview with The New York Times – a publication which last year sued OpenAI (and Microsoft) for copyright infringement, so naturally they would want to distribute Balaji’s views. Moreover, in November he became a potential witness in this trial after the Times’ attorneys named him in court filings as having material helpful to their case, along with at least twelve people, including past or present OpenAI employees.

PetaPixel: “Why Samsung should have Stuck it Out in Photography”

The buzz and hype were mainly down to the NX1, an APS-C mirrorless that was set to lead the charge for Samsung, but in truth the momentum had been building for a while, with a succession of innovative and impressive cameras that weren’t afraid to pioneer new technology. With a future looking so bright, it seems such a shame that, as we all know, Samsung stepped back from the photo industry and the fairytale came to an abrupt end. Here’s why I think Samsung could have (and should have) stuck it out in the photography game.

Let’s start with that flagship camera, the NX1. Now, despite this camera being launched a decade ago, the specs list would still put some of today’s cameras to shame. Built around that 28.2-megapixel APS-C sensor, the NX1 achieved a very clever trick of marrying together features that appealed to a great number of photographers. Landscapers were reassured by that resolution, enabling large prints, while sports and wildlife photographers could take advantage of a Hybrid AF system that made use of 205 Phase Detection points and a 15 frames per second burst rate.

Matty Graham

An interesting question to consider: what would the camera market have looked like today if Samsung had continued and expanded this NX-camera line? The answer I think depends on what the main differentiator for a successful camera company is.

Adobe Blog: “Removing window reflections in Adobe Camera Raw”

In this blog we describe Reflection Removal, a new technology that can eliminate reflections from photographs taken through windows with a single click. Our technology is powered by AI, but it’s not generative AI. This first iteration of the tech is designed to address only one kind of reflection — from plate glass windows that cover most or all of your field of view. It’s not designed to remove reflections from windows that are small or far away, or where the window frame is within the field of view, or reflections from objects like wine glasses, car bodies, or clouds reflected in a lake. We might address some of these applications in later updates. Our goal is to help you turn a photo you might otherwise delete into one that is good enough to share. Reflection Removal is available now in Camera Raw as a Technology Preview, to get feedback from the community, and will be coming soon to Lightroom.

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That said, removing window reflections is a hard problem, and this is our first stab at it, so there are inevitably some rough edges. For example, we don’t currently do well on cityscapes at night. In fact, removing reflections is what mathematicians call an ill-posed problem, meaning that for a given photograph it’s not possible to decide with certainty which objects are in the original scene versus the reflected scene. One polluted photo could have many plausible separations. The key is training our model to understand scenes that are likely to exist in the real world. We’re constantly improving the model, so stay tuned!

Marc Levoy, Eric Kee & Adam Pikielny

Here’s a genuinely positive use of AI – machine learning to be more exact. Though the results are impressive, the use case seems fairly limited for me. There are multiple methods to minimize reflections at capture time, as this blog post admits, and in many cases reflections can be put to great creative use. The only situation where I would always apply this tool are images taken through airplane windows. Nevertheless, it’s good to see Adobe investing in these sort of niche applications of AI – maybe someday they will get around to flare removal as well.