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Cause they got your SSN from the dark web. Come on, man, think!
This is another good use case for gAI. Copy/paste the comment into a GPT and tell it to re-write the content at the desired reading or technical level. Then it’s available for follow-up clarification questions.
That’s how I communicate my intention to pay a parking ticket. “Bowing to regulatory pressure”
Sure, thanks for your interest. It’s an incomplete picture, but we can think of LLMs as an abstraction of all the meaningful connections within a dataset to a higher dimensional space - one that can be explored. That alone is an insane accomplishment that is changing some of the pillars of data analysis and knowledge work. But that’s just the contribution of the “Attention is All You Need” paper. Many implementations of modern generative AI combine LLM inference in agentic networks, with GANs, and with rules-based processing. Extracting connections is just one part of one part of a modern AI implementation.
The emergent properties of GPT4 are enough to point toward this exponential curve continuing. Theory of mind (and therefore deception) as well as relational spatial awareness (usually illustrated with stacking problems) developed solely from increasing the parameter count describing the neural network. These were unexpected capabilities. As a result, there is an almost literal arms race on the hardware side to see what other emergent properties exist at higher model sizes. With some poetic license, we’re rending function from form so quickly and effectively that it’s seen by some as freeing and others as a sacrilege.
Some of the most interesting work on why these capabilities emerge and how we might gain some insight (and control) from exploring the mechanisms is being done by Anthropic and by users at Hugging Face. They discovered that when specific neurons in Claude’s net are stimulated, everything it responds with will in some way become about the Golden Gate Bridge, for instance. This sort of probing is perhaps a better route to progress than blindly chasing more size (despite its recent success). But only time will tell. Certainly, Google and MS have had a lot of unforced errors fumbling over themselves to stay in what they think is the race.
I’m happy to take the time to alter your perspective, if you are open to new information.
Rabbit season
Our Fall is quite autumnal.
I understand this perspective, because the text, image, audio, and video generators all default to the most generic solution. I challenge you to explore past the surface with the simple goal of examining something you enjoy from new angles. All of the interesting work in generative AI is being done at the edges of the models’ semantic spaces. Avoid getting stuck in workflows. Try new ones regularly and compare their efficacies. I’m constantly finding use cases that I end up putting to practical use - sometimes immediately, sometimes six months later when the need arises.
I just meant I work for a corporation. I produce videos for marketing, been doing it for 25 years.
What tasks are you thinking about?
It saves me 10-20 hours of work every week as a corpo video producer, and I use that time to experiment with AI - which has allowed our small team to produce work that would be completely outside our resources otherwise. Without a single additional breakthrough, we’d be finding novel ways to be productive with the current form of generative AI for decades. I understand the desire to temper expectations, and I agree that companies and providers are not handling this well at all. But the tech is already solid. It’s just being misused more often than it’s being wielded well.
Don’t blame CEO tomfoolery on generative AI. Generative AI is amazing.
Train LLMs on large bulks of data that meet criteria for deletion, thereby shrinking like 100 petabytes to a terabyte, albeit imperfectly. That way, you have a collection of AI bots that you can chat with about all the deleted data. And I suppose the threshold for deletion is, “How disastrous could a hallucination about this be?”
That’s for its own benefit. It has to talk through it (or alternatively code through it) to work out the answer. S’just showing its work.
I hear good things about Krita.
So I work in a creative industry (video production), and have for like three decades. If A.I. can do a lot of the work I do just as well, no part of me wants to continue to do that work. Most of what I get paid for is not “art” in the sense that it expresses some fundamental drive in me. But I do love collaborating with A.I.s to create things that I would’ve never been able to do on my own (and that A.I. would have never been able to do without me). This is where things are going, and I totally grant that greedy corpos doing greedy corpo shit is not to be lauded. But that’s an Ubisoft problem, not a gen AI problem. People are the issue with A.I.
This sounds like a semantic argument, so… definitions.
Authoritarian - 1) of, relating to, or favoring blind submission to authority
Slavery is blind submission. Forbidding authoritarianism isn’t authoritarian. Kinda like how destruction of the self (suicide) cannot be selfish, despite what some will argue.
I understand them both well enough to implement them in my projects. I don’t see why people are anything other than excited about the implementation of more capable AI in games. Are these initial implementations garbage? Probably, but that’s just growing pains, So what is it about gen AI that actually bothers people?