Image/video upscaling via neural net is something I would pay for.

I currently use freeware/open source alternatives, but I am planning to get a copy of Topaz Video AI in the future.

Even the freeware/open source algorithms are incredible in terms of quality. You do get artifacts and it doesn’t handle certain things very well (I get problems with watermarks); but that might be my lack of knowledge.

When I first did a successful SD upscale (x2 resolution), it almost felt like magic.

I had seen some pretty impressive neural net upscaling before (Arrival of a Train at La Ciotat, Lumière Brothers, 1896), but it’s a whole different feeling when you can do it yourself with pretty solid results.

these chatbots are novelty toys at this point when stopping them from generating complete garbage isnt feasible with current methods

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I dont really think so. I mean yea, too much money gets dumped into AI, but the dot com comparison doesn’t really work. The dot com bubble burst because investors realized that a ton of small companies(aunt-emmas-flowers.com or something), had no strategies and unsustainable business models. They were all massively overvalued. But Microsoft, Google, Tencent, Baidu are all large companies, they aren’t comparable and unlikely to suffer much if one of their investment fail. Additionally, AI is incredibly young and essentially still in beta, just because it works and can be used (and be profited from) doesn’t mean the current versions are more than mometized research projects. Yes that’s a problem if these are sold as full products, but that’s want they are. All users are currently testers for the AI companies. A lot of companies managed to get some of that sweet VC but that’s always been possible with the hype of that time. Now its just AI, gullible Investors have lost money since the invention of investing.

LLMs are trained on vast amounts of human-generated text which is grounded in real-world concepts, allowing the models to build rich representations that go beyond surface-level word associations. Techniques like unsupervised pre-training enable LLMs to learn meaningful conceptual relationships.

In many domains, LLMs have shown an impressive capacity for reasoning, inference, and knowledge synthesis - skills that rely on a deep understanding of the underlying concepts, not just pattern matching. Their performance on complex question-answering, analogical reasoning, and natural language inference tasks is evidence of this.

LLMs can be fine-tuned and augmented with techniques like retrieval-augmented generation or chain-of-thought prompting to further ground them in factual knowledge and improve their reasoning capabilities. This allows more robust conceptual mappings.

Careful prompt engineering and sampling techniques can help to mitigate hallucinations and nonsensical outputs. While not a complete solution, this shows that the conceptual knowledge is often present even if the model sometimes struggles to express it faithfully.

The major difference is that we don’t see an influx of insanely overvalued startups nobody heard of before.

That was the norm in the dotcom bubble and nobody remembers the “major players” of that time now.

The AI boom is pushed by the well established big tech which are also highly profitable and can afford the AI expense. Dotcom startups never were profitable.

Sokay, they’ll just steal the secrets to accelerate their programs as usual.

… if their own AI efforts didn’t fall so short. They‘re seeing silicon valley raising massive amounts of money…

Successful AI =/= grifting massive amounts of money

“i like your shoelaces”

“thanks i stole them from sam altman”

You may jest, but too many hammers and not enough nails is what led us here.