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.

My guess was that they knew gaming was niche and were willing to invest less in this headset and more in spreading the widespread idea that “Spatial Computing” is the next paradigm for work.

I VR a decent amount, and I really do like it a lot for watching TV and YouTube, and am toying with using it a bit for work-from-home where the shift in environment is surprisingly helpful.

It’s just limited. Streaming apps aren’t very good, there’s no great source for 3D movies (which are great, when Bigscreen had them anyways), they’re still a bit too hot and heavy for long-term use, the game library isn’t very broad and there haven’t been many killer app games/products that distinct it from other modalities, and it’s going to need a critical amount of adoption to get used in remote meetings.

I really do think it’s huge for given a sense of remote presence, and I’d love to research how VR presence affects remote collaboration, but there are so many factors keeping it tough to buy into.

They did try, though, and I think they’re on the right track. Facial capture for remote presence and hybrid meetings, extending the monitors to give more privacy and flexibility to laptops, strong AR to reduce the need to take the headset off - but they’re first selling the idea, and then maybe there will be a break. I’ll admit the industry is moving much slower than I’d anticipated back in 2012 when I was starting VR research.