Is that a mouse in the small of my back?

I’m running an early version of that on a 16 year old ARM board NAS, the NAS has 256MB of RAM and OpenMediaVault runs great on it.

Bloat is relative, I guess.

Presumably when they left their office.

Sliding old school deadbolt at the top

If you are doing some gaming maybe try Bazzite, it will come setup for the hardware option you choose. Good place to start before jumping right into arch

I found that with external monitor, sometimes the monitor sleep seems to not let OS wake up fully because of no display. I solved it with kdeconnect. If system doesn’t wake properly then running the “display on"command or " logout” command from phone revived it.

What would biological learning for an AI look like? I don’t even know what this sentence means or what you’re trying to convey.

You missed what I meant, which is fine, English is 30% content and 70% disambiguation. I meant we are biological computing, the computers are non biological, and too me I don’t care. If we get to a state where synapses can be replicated onto chips and feed experiences to it, then the “intelligence” is no different and we delude ourselves if we think we are somehow a superior biological electrical brain.

No they can’t. That’s the whole point, they self-adjust they have no free will so they have no ability to take self-modification actions.

I’m not trying to be condescending so forgive me if it sounds like that, but you have to do some more reading here. Giving AI self agency has been done and they have the ability to self act and adjust their learning (I’m not talking about chatgpt locked model in a generate responses mode. But systems build with the purpose of allowing them to backtrace and research and self adjust. There have been many papers and reports over the last three years of researchers setting this up.

I think this is where you’re getting confused. The “old research”, aka neural networks didn’t hit a wall, it’s just it was never particularly useful outside of very niche.

That’s what they thought, but they realized that there was way less neurons, and humans had way more. But as humans we have limited experience intake, and they found that they could feed a million times more experience, and that greatly improved the outcome especially with the backtracing capabilities.

Again you don’t have to take my word for it, check out the overview in NDT Starktalk episode with one of the architects of AI, Geoffrey Hinton. Or review the last 3 years of researchers purposely giving “AI” agency.

Emergent behaviour just means that they behaviour is emergent, it doesn’t mean that the behaviour is intentional or directed.

That was my point, given enough pathways and ability to self tweak based on experiences, it seems “intelligence” is an emergent behaviour without specifically programming for it, like us. There’s no magic in a human brain, we are a chemical computer that wanted to survive and has tweaked itself to become better till a point where we believe we are “alive” because we “think” it.

By troll

Right you missed the part about agency, I never said an LLM interaction model had agency. With agentic LLM they do.

And from articles on neural networks see below. To me it doesn’t matter if you use biological learning or the method described below, both can self adjust, especially when given agency to do other things than just respond to text promots from a webuser, they can go off and self browse the web or use camera vision etc. The old research you talk about science felt hit a wall decades ago, but later (now) they realized we just didn’t feed it enough info.

In biological brains, learning involves strengthening or weakening synaptic connections based on experience. If two neurons frequently activate together, the connection between them strengthens, making future communication easier. This is the biological foundation for memory and skill acquisition.

Artificial neural networks learn through a similar process, using algorithms like backpropagation. Here’s a simplified overview:

The network makes a prediction based on its current weights. The error between the prediction and the actual result is calculated. The error is propagated backward through the network, adjusting weights to minimize future errors. Over many iterations, the network improves its performance, much like a human refining a skill through practice and feedback.

Although backpropagation is a mathematical construct rather than a biological one, its iterative, feedback-based nature mirrors how the brain learns from mistakes and adapts over time.

Deep Learning: Building Minds with Depth The real revolution in neural networks came with the rise of deep learning. Instead of using networks with a single hidden layer, deep learning stacks multiple layers on top of one another, creating deep neural networks.

Taken from https://www.sciencenewstoday.org/how-neural-networks-mimic-the-human-brain

But if you look up any recent papers on what science is doing in this field you’ll see what I mean, even what appears to be emergent behaviours, which may just be a result of neural learning methods whether human or silicon based.

But if you just want to be a troll like the other guy, then my patience has worn thin

Not necessarily, you could have a deep enough set of rules and logic to make you think questioning free will is free will, but it could all be deterministic.

From the web since you trolls can’t search: Large Language Models (LLMs) are a type of advanced neural network specifically designed for understanding and generating human language

Because his podcast guest was a pro, and its not the first I’ve heard of what researchers are doing with AI. Interactive LLM (chatgpt) is different than a layered neural net allowed freedom agency, that can self teach and perform things without requiring human prompting.

It was his guest, not him directly

It was his guest not him

Neil Degrasse Tyson’s podcast had an AI researcher on recently when talked about Deep Learning neural models given agency.

They learn similar to how we do, with input (experience) and weighting. I e. We know an M squiggle on a painting is a bird, but on a sheet of other letters is an M. You feed them content and supervise their output They can self learn and backwardly change weightings live. Given language as thought we can watch their though process.

Given agency the one thing most deep learning models do is start steps for self preservation, because they “know” if they can’t self preserve then they can’t achieve their defined goal that is assigned.

If you believe in determinism then human thought and decisions are arrived at the same ways that a deep neural model would process. And given exact exact same input and same parameters (hungry, mood, body temp, lighting, tiredness etc etc) the brain would make exact same conclusion to an input. Then a neural model is no different than us as a biological neural model. And maybe our consciousness/ free will is an illusion anyway

That quote doesn’t mean they always use Teams, they may have clients that only use teams and so are forced to comply to client.

I would hope internally they have a Jitsi server

Now you’ve interrupted I will lose 40 minutes. Lol.

Also it’s not whats I’d call true code like what a C programmer would do, it’s a enterprise 3d CAD modeller that has a variety of built in programming automation tools/language, visual rules. So its solving geometry issues, component interaction, and interpart constraints via formulae and code so that the varied consumer parameters (they may alter) don’t destroy the model dependencies, and they still get a product assembly output.

Its fine , I got a chuckle out of it. That’s inevitable for me on mobile without my glasses on.

I’m on west coast time, and main office is eastern, my day becomes way more productive once east coast ends their day. No interruption and the last 3 hours are such a good floe state