Jeez, calm your tits, I think I asked politely enough so I wouldn’t deserve this kind of response.
My question was what do you think this particular model does, not what is achievable with AI in general. And I’m asking because a model that weights 4GB is not some trivial thing that every Chrome user wants or needs loaded in memory.
What do you think this nano model actually achieves?
Because I know why someone would want to eat a burger, or fill up a tank, but why would anyone want this running in their computers?
I didn’t say that. I said that F1 has attacking and defending strategies, e.g. using the slipstream to increase speed, or closing an attacking car trajectory in a turn.
Given that some tech bros are measuring AI productivity in lines of code, yes, yes it does.
Correlation doesn’t imply causation.
If you lay off 10% of your workforce, mostly juniors, and your KPIs are loose enough, there’s no reason to think that companies won’t succeed in pushing senior employees to meet them, mostly through overwork.
Also, certain companies track AI usage as a KPI in itself, so there’s a circular logic component to it.
Good. Solar is critical infrastructure.
Most people don’t have a PC laying around, just so it can be hooked to a TV.
Even more true, most people don’t know how to do what you just described.
This looks more like senior staff being forced to pick up more work with the same pay.
You don’t need to be like that, but if you do, go nuts!
Guys, guys, it’s 2026, gamers don’t need to look like unhygienic basement dwellers anymore, get on with the times!
I can think of a ton of times where increasing welfare does not impact crime statistics and increasing cop spending does and vice versa.
The point is not just to increase it, but to make it enough for people to not resort to commit crimes.
But I’m curious about this take. Scientific literature vastly supports the idea of welfare as a societal stabilizer. What it does not support as much is increased enforcement as a way to curb crime in developed and democratic countries. Which concrete examples can you show to support your point?
Kind of inspired on Surreal Numbers.
You have made your point. You don’t really care about provable facts. Maybe you shouldn’t pretend that you are trying to argue in good faith at all?
Provable assertions about the physical world require measurable observations, not personal beliefs.
I’m panpsychist enough to believe matrix multiplication has qualia
According to this, any sufficiently skilled high school student could, with just pen and paper and enough time, build an entity from nothing that can experience pain.
I’m concerned that the training process, which involves back-propagation to adjust synapse weights, may be an unpleasant experience for the ANN.
This assumption is not based on facts. It’s pretty much like saying that matrix multiplication can have feelings, or that heat stressed silicon is equivalent to pain.
But if this is actually a concern, RNNs have been widespread since the late 90s. Any advanced search engine, translation engine, or weather forecast model, make use of these.
Regardless, it’s all a moot point because we have lots of other reasons not to use LLMs.
This may be true, but it’s absolutely outside of the scope of your original point. You dragged the conversation around claiming to be concerned about how models are “treated”, wrapping speculation with philosophical arguments that cannot be applied here, since none of your “what ifs” are remotely based on scientific consensus.
Any non factual philosophical argument is debatable. We could forever discuss if AI models could construct sensations and thought from perceptions, but we would then need to ignore the fact that models don’t, and cannot do, that, simply because there is no way for them to learn from direct experience as a whole, i.e. outside of a particular session, and without being “forcibly coerced”, i.e. they require specific refinement mechanisms to temporary “memorize” external instructions, which in LLM engineering just means to extend their context.
This all doesn’t even take into account that models are, in essence, non deterministic, and given the same input, there’s no guarantee that subsequent outputs will be the same. In other words, today Claude may tell you that summer sunsets make it happy, tomorrow it would say that they make it sad, etc.
Anyway, there’s barely any debate in academia, as in computer scientists, about AI being sentient or giving clues of qualia. Maybe a paper here and there, little more than curiosities. Outside of it? Yeah, sure, barely science fiction, and pretty uninteresting unless we are talking about conspiracy theories or just wild speculation.
I’m pretty sure races have attacking and defending strategies, though.
AI isn’t conscious. Feedback loops and subsequent responses in LLMs are grounded purely on training datasets, thus any “internal dialogue” emulated by a LLM is just echoes from someone else’s data.
They don’t need AI for that. I was handed the exact same deal in 2008.
If it succeeds, then great, people will have a faster, safer Bun.
If it fails, then great, one less JS runtime.
It’s a win-win!