Rhaedas

There is a solution.

Models are geared towards seeking the best human response for answers, not necessarily the answers themselves. Its first answer is based on probability of autocompleting from a huge sample of data, and in versions that have a memory adjusts later responses to how well the human is accepting the answers. There is no actual processing of the answers, although that may be in the latest variations being worked on where there are components that cycle through hundreds of attempts of generations of a problem to try to verify and pick the best answers. Basically rather than spit out the first autocomplete answers, it has subprocessing to actually weed out the junk and narrow into a hopefully good result. Still not AGI, but it’s more useful than the first LLMs.

Relative scale is worse. Not only that the temperatures keep going up and aren’t a fixed “red”, but that there’s few to no blues now, meaning it’s always going up everywhere. And this is actually a more calming way to present it that the usual exponential spike chart.

There’s no question I wrote the couple of things I’ve done for work to automate things, but I swear every time I have to revisit the code after a long while it’s all new again, often wondering what the hell was I thinking. I like to tell myself that since I’m improving the code each time I review it, each new change must be better overall code. Ha. But it works…

Same here, although I thought VGA. Dealt with too many parallel cables in the past and that didn’t look wide enough.

I try to be impartial and let them dig the hole themselves. Silence so far is damning.

gen-x boomer

As a Gen-X I have to ask what exactly is meant by that. That you openly admit cyber bullying gives some hint, but I’d rather get a self definition than to assume.

Because before Firefox I used Netscape. It’s had its ups and downs but it’s never been bad to me to the point of considering moving to anything else. So a combo of legacy and that it works.

True, and then because it’s a black box there wouldn’t be a known issue at all. Or maybe it would be much less of an issue because the words might have blended others into the mix, and his style wouldn’t be as obvious in the outputs, and/or it would be easier to dismiss. Did the training involve actual input of his name, or was that pulled from the source trained on? How much control was in the training?

I totally agree with you on protecting the basics of creativity and growth. I think the core issue is using “imitate” here. Is that what the LLM is doing, or is that an anthropomorphism of some sense that there’s intelligence guiding the process? I know it seems like I’m nitpicking things to further my point, but the fact that this is an issue to many even outside artwork says there is a question here of what is and isn’t okay.

And yet the artist’s name is used to push the weights towards pictures in their style. I don’t know what the correct semantics are for it, nor the legalities. That’s part of the problem, the tech is ahead of our laws, as is usually the case.

they charge money to generate his style of art without compensating him.

That’s really the big thing, not just here but any material that’s been used to train on without permission or compensation. The difference is that most of it is so subtle it can’t be picked out, but an artist style is obviously a huge parameter since his name was being used to call out those particular training aspects during generations. It’s a bit hypocritical to say you aren’t stealing someone’s work when you stick his actual name in the prompt. It doesn’t really matter how many levels the art style has been laundered, it still originated from him.

This isn’t really about the tool but the general idea of what to move. Like many I don’t feel most of my posts alone were that valuable, but what was lost was the chain of conversation that they were part of. I had requested and received my data from Reddit after a while and looking through it I realized this. I suppose I could weed through the Excel file (!!) and grab longer comments that I’ve made in ten years of discussion, and it’s always there to search if a memory is sparked, but I see no personal reason to dump it somewhere else.

I guess this is more a caution to not use a tool to mass spam just because you can. Not all posts are worth repetition, especially out of context.

That’s how language models work. It’s grouped into AI as is so many things, but it’s not AGI. It could open the doors to AGI as a component, but isn’t actually thinking about its answers. And those probabilities are driven by training reinforcement which includes the bias of giving an answer the human will receive well. Of course it’s going to “lie” or make up things if that improves the acceptance of the answer given.

Must have posted via UDP and wasn’t sure it made it the first time.

That happens a lot here too. Except the trash part, if it’s not in the trash or recycle container, it will still be there later. Anything large you have to take yourself to the community waste dump.

I didn’t either, actually. It seems to me that where LLMs excel is in situations where there will be a large consensus of a topic, so the training weights hit close to 100%. Anyone who has read through or Googled for answered for programming in the various sources online has seen how among the correct answers there are lots of deviations which muddy the waters even for a human browsing. Which is where the specialized training versions that hone down and eliminate a lot of the training noise come in handy.

There are a number of them now, but I’ve put the Vicuna 13B one on my Windows side before. Trying to get it on Ubuntu so it can use the GPU, but it’s being difficult. Look up TheBloke on github, they have a large selection that can be used through the text-generation UI coding.

I may have misspoke saying “better”, as it looks like it’s a few percentages below on comparisons. I thought I had seen some varieties of local compared that rated higher though, such as on AI Explained’s channel.

That’s exactly what a LLM is, an advanced autocomplete using a huge training database to determine the probabilities. It never was anything more, even with the unexpected characteristics from further development. The models that are being fine-tuned for their training (stepping them back to a more narrow field LLM) do a lot better than a general purpose one.

The generic ChatGPT is far too error-prone and limited compared to the many variations of other GPTs out there. It was a fad for those who weren’t going to fine tune a use that worked well or are doing actual research in better tactics. How many who are knowledgeable on computer systems have moved to smaller locally installed versions that work just as well or better?