cpc

There is a good case to be made that a big model does lossy compression on the training data. Plenty of science to back it up. No idea about the legal consequences of such an approach.

This might have been the 256GB SSD version. Mine is the 1TB version which I recall was something like 4k euro with VAT this side of the pond, or whatever that was in GBP at the time. Not sure that paying significantly more than this is wise, seeing as the batteries in most of these aren't particularly young. 🙂

I've yet to look up current pricing, but I'm not looking to make a profit, so around what these went for in 2017 would be alright. Mine was bought in the UK immediately after Bolex announced discontinuation. Not sure about shipping to the other side of the world though. 🙂

Bulgaria, which is a tiny market for niche gear, hence probing for places with higher exposure.

What's the current state of online marketplaces for gear in Europe? I've been out of the loop for quite some time. I think it is time my basically new Bolex D16 goes to someone that'll actually use it, but ebay doesn't seem like the right place for that.

Around 1.6:1 for BM, around 2:1 for the others. Will vary with image content, lossless compression being variable bit rate.

Early BM cameras, early Kinefinity cameras and many DJI cameras do losslessly compressed raw.

This might be premature. Red has a bunch of newer raw/compression related patents which are "continuation" of the old patents or merge a few of the old patents in a new one. E.g., 10531098 (issued 2020), 11076164 (issued 2021), 11503294 (issued 2022), 11818351 (issued 2023), etc. I have no knowledge of the legal implications of these, but won't be surprised one bit if they actually extend the in-camera raw compression monopoly.

Most issues come from the fact that VND is not ND. Which should be obvious: 2xPola is anything but ND, except someone with an inclination for marketing thought it clever to call 2xPola "Variable ND". Pola filters are special purpose filters and it makes no sense to use them for general purpose light levels reduction. There are too many variables involved in filtering polarized light, starting with light falling angles and reflective surface characteristics, for this to be a reliable levels reducing method. Not to mention the adverse side effects of filtering out some reflected light more than other, e.g. preferentially filtering out skin subsurface scattered light otherwise known as "skin glow".

In their peak years Nikon filed a couple of thousands of patents a year. Their patents portfolio is definitely in the tens of thousands. Good chances that they have dug out a few that RED infringed upon. After all, protection is the main reason corporations amass patents in the first place.

"When you’re fundraising, it’s AI. When you’re hiring, it’s ML. When you’re implementing, it’s linear regression." Replace "fundraising" with "marketing" and the truth value doesn't change. It is "artificial intelligence" as much as your phone or watch is "smart". Which is none. So the answer is "No, it isn't", but it largely depends on definitions and heavily overloaded semantics. "AI" certainly doesn't "think", nor "feel", but it can "sense" or "perceive" by being fed data from sensors, and it can represent knowledge and learn. The latter two are where the usefulness comes from, currently. A model can distill structure from a dataset in order to represent knowledge needed for solving a specific task. It is glorified statistics, is all. But anthropomorphizing is in our DNA, we have a sci-fi legacy imprinted on us, and model design itself has long been taking cues from neurobiology, so you'll never be able to steer terminology in the field towards something more restrained.

Let's not forget that ChatGPT is built entirely on Google developed science. Google still have many of the top machine learning R&D people. It is premature to write them off. I'm absolutely certain that Google can deploy and optimize LLMs for scale in a way few other companies can. All they need is incentive, and now they also have that. re: Bard model errors It is certainly a failure to have this happen in a presentation, but ChatGPT makes stupid mistakes all the time. Here is a good writeup with some blatant examples by Wolfram: https://writings.stephenwolfram.com/2023/01/wolframalpha-as-the-way-to-bring-computational-knowledge-superpowers-to-chatgpt/

The Kodak sensor also appears to have thicker filter dyes which results in rich color and excellent color separation. Later sensors may have been optimized for sensitivity, particularly cheaper sensors. If you look at images from the BM Pocket, they have more compressed color with hues mаshed together.

Did it win, though? Apparently Red settled with Sony after Sony counter sued for infringement. And Apple's case was dismissed for what looks, at least partially, like procedural reasons (basically due to incompleteness -- the decision uses the word "unclear" multiple times in relation to Apple's rationale and says literally "In sum, Petitioner’s obviousness challenge is unclear and incomplete"). That is, Apple lost, but it is not clear if the patent won.

Companies can be clueless about these matters. And Nikon comes from a stills background. On the other hand, Nikon's patents portfolio includes tens of thousands of patents, including thousands in the US. They can probably dig into it for counter infringements, if they are forced to. I don't recall resolution specifics in the Sony case, but I wouldn't be surprised if Sony did exactly this to fend off Red.