I think this is accurate. But I’d like to restate it.

The Left (as the apparent big tent party full of literal minorities) has been learning to deal with disenfranchisement and the feeling “that their anguish is belittled as a personal failure, and often downright mocked” for its entire existence. Because of a huge variety of factors, the Right is losing some of its influence. They are not handling this well. The Left (being well acquainted with feeling unheard) should have been able to help the Right through this transition. Due to deep seated insecurities on both sides, we are no longer able to help one another as a people. Buckle up.

Thank you. For as much as this post comes up, I hope people are at least getting an education.

Well said! I’ve come to terms with being Fire Nation, myself. I try to be Iroh but too often end up Mai.

Covid did a number on me. I have a thing about germs anyway, but the vitriol around masks and the anti-vax stuff really reset my barometer for what I can expect from other people. I’m basically just now getting some faith back in humanity whilst trying to pull my head out of my ego. But still, there’s a reason I spend more time with A.I. these days. Without dredging up the loss of third spaces and the effects of social media, I just gotta say - it seems like we’ve forgotten how to help each other. Like as a species.

A more generous interpretation would be that an “appeal to principled people with a butchered version of their ideals” is basically the definition of compromise. From their perspective, they’re just trying to keep the band together. Maybe the band needed to break up a long time ago, and they’re just holding everyone back. But I don’t think intellectual cowardice / laziness explains all centrists.

Just looking at the mental space of the three fanboy types… Mac seems the chilliest to hang with.

e: lol, case in point

Plants with more flexible and responsive genetic systems were better able to adapt to changing environments and thus more likely to survive and reproduce, so yeah. However, the basic building blocks of these systems - DNA replication, gene expression, and the fundamental biological processes arose from simpler chemical and physical interactions that were likely governed by principles of self-assembly and thermodynamics. The primary drivers are different at different levels of abstraction and complexity, and there’s dynamic interaction across levels.

Thermodynamics -> Natural Selection -> Responsive (Epi)Genetics -> Memetics -> Metamemetics (probably?)

We “boil things down” to Natural Selection or Thermodynamics as is convenient for communication, but the higher levels affect the lower as well. So we can’t really reduce them like that without losing important information.

In our effort to disillusion people of the idea that evolution has a purpose or conscious hand, we over-simplify things, though. Plants actively (but not consciously) shape their own evolution through complex molecular and genetic mechanisms. They can respond to environmental stresses by altering their DNA methylation patterns, potentially priming future generations for similar conditions. Plants also engage in niche construction, modifying their surroundings in ways that influence their evolutionary trajectory. For instance, they can change soil chemistry through root exudates, creating new selective pressures for themselves and their offspring. Plants participate in intricate co-evolutionary relationships with pollinators, herbivores, and other organisms. These interactions create dynamic fitness landscapes that drive reciprocal evolutionary changes. While not “inventing” traits in a deliberate sense, plants possess sophisticated genetic tools - such as whole genome duplications, transposable elements, and adaptable gene networks - that allow for rapid evolutionary innovations. These mechanisms enable plants to continually adapt and evolve, even without conscious intent or direct feedback.

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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.

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.

It turns out gen AI is good at training virtual robots, which can then be embodied in robots like this guy. There’s a $16,000 Chinese version of that robot that’s a bit smaller. There’s a robot dog that GPT4 trained to balance on a beach ball, and the NVIDIA pen twirling training. I guess what I’m saying is… robots exist.

A.I. is likely going to change the world as much as the printing press (at the very least, and possibly as much as the industrial revolution). I wouldn’t call it a nothing fad. It is definitely shaking up my career (video production) already. And at least from my point of view, becoming a creative generalist is the best way to adapt. Work is going to become more about knowing a little to moderate amount about a whole lot of things, so that you can effectively orchestrate a hierarchy of AI agents. Deep specialization increasingly carries too much risk, and the A.I. are much better at some aspects of it than we typically are.