The general problem is how to ensure quality in a low-competitive-pressure environment, especially public services which are natural monopolies. Quality only appears randomly if the right people happen to be in charge.
Look at what competition does in the AI model space. Imagine there was just OpenAI vs the actual case where there's Anthropic and others which pressure and copy each other.
I wonder if there's any solution to this, I couldn't think of any.
> I mean if your profit metric is showing all green and growing you have to be really cold to say we need to fire 1/4 people now why? (to maximize shareholders short term value of course)
Yes, it's cold, but in the grand scheme of things, it's good for the society if companies are efficient and the economy is efficient. An efficient economy means that we can produce and consume more per hour worked.
Such behavior by companies may not be in my self-interest but that's another matter.
Why amoral? If a an employee costs the company more than they make, it means the owner of the company is essentially donating his money to the employee. Is it amoral to not do that? What if the owner wants to donate the money to cancer research instead?
To expand on this, a company’s purpose should go beyond simply generating profit for its owners, imho; among other things, it should also include a duty of care toward its employees. Society instills a strong work ethic in us from childhood, teaching us that contributing to the greater good is our individual duty. Before the plague of neoliberalism, there was an expectation that companies would have a positive impact on society, too. Nothing, except greed, forbids to return to those values.
There is this HoMM3: The Succession War total conversion mod for HoMM3 in the style of Heroes 2 which happens to have new factions for what looks like Heroes of M&M II.
What if the filter is that other civilizations don't have an incentive to expand?
The core reason why we want to consume more resources is that we want to be happier, which really means we want to make certain physical changes in our brains. If we cut the middlemen and find a way to directly control how we feel, there would be no need to expand, because it wouldn't increase our happiness.
Edit: Just found out someone else has made the exact same point in another comment before me.
My (very limited and human-centered) theory is that all carbon civilizations would at least in their large part of civ-life would face the problem of:
Growing needs of resources >> resources acquired from current technology
Again I'm probably wrong and this is just my conjecture. So I can't really answer this question, as I presume that all civilizations want to expand (or collapse).
I think the term is wire-heading and it does feel like an inevitable development when you think about it, assuming the technology gets there.
It's also possible that our expanding understanding of ourselves and our desires would lead to entirely different outcomes that are impossible to anticipate from our current POV.
The text was hand written, but reviewed by AI and light edits were made. The CAD models that drive a lot of the animations were either sourced or built based on pictures of historical machines. As called out in the credits, the interactive figures were built by myself and Claude Fable 5 (mostly - a bit of Opus 5.5 in there now).
It's a code-first CAD kernel/toolkit specifically to help LLMs with CAD stuff, it's currently AI usable but not very human usable yet since I'm working on the editor UI. Get your Claude to clone it and give it a try and see if it's easier than your current workflow.
What CAD do you use for the models, and does it have a CAM simulator (for example, you might actually be able to simulate the tread cutting in Fusion 360 CAM's simulator.
The weights in the fly's brain are unknown, the connectome doesn't contain such data. Not sure what exactly these demos do, but it's certainly not a simulation of the fly's brain.
You are technically correct. The weights of the connectome are not the same as the parameters of an ai model, but FlyWire absolutely does provide a weighted directed connectivity graph, where the edge weight is the number of synapses between neurons. The FlyWire literature itself calls those values “edge weights” or “connection weights”, and treats them as a proxy for synaptic strength
And agreed that this isn’t a faithful simulation of a fly brain. I’m using the connectome as the network structure/parameters for a computational model, then using its outputs as the teacher for the classifier. Not sure how the ChessFly uses it
Edit: in any case, these are just fun demos, they aren’t research papers trying to claim accurate physiological fly brain software simulations
That sounds like you're saying that they got you on a minor technicality only. That's not the case; they're right, you're wrong – you did not "download a real fly's brain's weights".
I wonder if this should be considered some super-quantized version of the fly brain. Sub 1 bit level. Think like taking a 400GB model and reducing it to 400MB level of quantization. In a standard NN, this isn't really possible as it changes the shape of the weight rather than just the information for weight, so it isn't called quantization anymore. But with actual neurons, the amount of data needed to represent the actual brain enough to capture what is the fly really doing is so massive that reducing it down to the model might keep the shape but has quantized the model to a degree far beyond anything we see with LLMs. Maybe if we imagine an LLM trained where each weight is ~1KB (with sufficient training to actually make sure of those bits) is then quantized down to a 1 bit per weight model, but I'm guessing the IRL Fly to FlyWire is still orders of magnitude more quantization. So does it even count as quantization is a bit hard to argue.
They're a proxy yeah, but it's also somewhat loose.
There's a lot more going on at synaptic clefts, so just knowing the number of synapses doesn't tell you enough to model anything. You still need to know which neurotransmitters are used, how much, second-order effects like G proteins, basal firing rates, distance to the axon hillock, the shape of the neuron's effect on potential decay, etc.
And that's all to predict whether one neuron will fire. You could get very different behavior between different two neuronal pairs having the same synaptic count.
Look at what competition does in the AI model space. Imagine there was just OpenAI vs the actual case where there's Anthropic and others which pressure and copy each other.
I wonder if there's any solution to this, I couldn't think of any.
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