Absolutely, but also the attorney from Devil’s Attorney

Absolutely, but also the attorney from Devil’s Attorney

Absolutely, but also the attorney from Devil’s Attorney

Absolutely, but also the attorney from Devil’s Attorney

Oh yeah, the 365 version is terrible. And post of the time, it could have been a Python Gradio interface or similar simple implementation without having to fight so much to make basic things work. Most of what I want Excel to do it just isn’t efficient enough for; particularly with lets and lambdas, it’s gotten quite powerful as a programming paradigm where you can visualize and manipulate your data spatially in a kind of Logo / NetLogo style way which is really interesting, but the second you reference a few thousand cells a few times even a solid CPU starts screaming.

I use Excel for a decent number of tasks and can do some magic with it, but only ever really for work where it’s easier to share a weird Excel sheet than it is to pass around a Python script (which given I teach Python, isn’t actually as often as most people experience).

But what about those of us in R1C1 mode using lambdas to do recursive cell operations across data pulled from multiple sheets? Am I anywhere near the kinda of Eldritch horrors discussed? I’ve also written indirect references based on Sheet name to populate filters from web scraped tables. I just don’t know how deep the pit goes at this point.

As someone who researched AI pre-GPT to enhance human creativity and aid in creative workflows, it’s sad for me to see the direction it’s been marketed, but not surprised. I’m personally excited by the tech because I personally see a really positive place for it where the data usage is arguably justified, but we either need to break through the current applications of it which seems more aimed at stock prices and wow-factoring the public instead of using them for what they’re best at.

The whole exciting part of these was that it could convert unstructured inputs into natural language and structured outputs. Translation tasks (broad definition of translation), extracting key data points in unstructured data, language tasks. It’s outstanding for the NLP tasks we struggled with previously, and these tasks are highly transformative or any inputs, it purely relies on structural patterns. I think few people would argue NLP tasks are infringing on the copyright owner.

But I can at least see how moving the direction toward (particularly with MoE approaches) using Q&A data to support generating Q&A outputs, media data to support generating media outputs, using code data to support generating code, this moves toward the territory of affecting sales and using someone’s IP to compete against them. From a technical perspective, I understand how LLMs are not really copying, but the way they are marketed and tuned seems to be more and more intended to use people’s data to compete against them, which is dubious at best.

Not to fully argue against your point, but I do want to push back on the citations bit. Given the way an LLM is trained, it’s not really close to equivalent to me citing papers researched for a paper. That would be more akin to asking me to cite every piece of written or verbal media I’ve ever encountered as they all contributed in some small way to way that the words were formulated here.

Now, if specific data were injected into the prompt, or maybe if it was fine-tuned on a small subset of highly specific data, I would agree those should be cited as they are being accessed more verbatim. The whole “magic” of LLMs was that it needed to cross a threshold of data, combined with the attentional mechanism, and then the network was pretty suddenly able to maintain coherent sentences structure. It was only with loads of varied data from many different sources that this really emerged.

This mouse? Believe it or not, file.