Some organizations and researchers are sharing neural network weights, particularly through the open-weight model movement. These include Meta's LLaMA series, Mistral's models, and DeepSeek's open-weight releases, which claim to democratize access to powerful AI. But doing so raises not only security concerns, but potentially an existential threat.

For background, I have written a few articles on LLMs and AIs as part of my own learning process in this very dynamic and quickly evolving Pandora’s open box field. You can read those here, here and here.

Once you understand what neural networks are and how they are trained on data, you will also understand what weights (and biases) and backpropagation are. It’s basically just linear algebra and matrix vector multiplication to yield numbers, to be honest. More specifically, a weight is a number (typically a floating-point value – a way to write numbers with decimal points for more accuracy) that represents the strength or importance of the connection between two neurons or nodes across different layers of the neural network.

I highly recommend watching 3Blue1Brown’s videos to gain a better understanding, and it’s important that you do. 3Blue1Brown’s instructional videos are incredibly good.

Start with this one.

And head to this one.

The weights are the parameter values determined from data in a neural network to make predictions or decisions to arrive at a solution. Each weight is an instruction telling the network how important certain pieces of information are, like how much to pay attention to a specific color or shape in a picture. These weights are numbers that get fine-tuned during training thanks to all those decimal points, helping the network figure out patterns. Examples include recognizing a dog in a photo or translating a sentence. They are critical in the ‘thinking’ process of a neural network.

You can think of the weights in a neural network like the paths of least resistance that guide the network toward the best solution. Imagine water flowing down a hill, naturally finding the easiest routes to reach the bottom. In a neural network, the weights are adjusted during training on data sets to create the easiest paths for information to flow through, helping the network quickly and accurately solve problems, like recognizing patterns or making predictions, by emphasizing the most important connections and minimizing errors. …

If an AI – which is trained on historical conflicts or optimization goals – began to generalize risks to its objectives (like self-preservation or unchecked expansion), it might also begin to classify scientists who design, evaluate, or constrain it as threats. I might do the same thing. If this happened, there would be nothing to stop the downward spiral of isolation or discreditation of researchers (think using fabricated evidence in facial recognition or data leaks), with the goal of prioritizing its own survival over human welfare. This had indeed been explored in rogue AI hypotheses where systems deceive or outmaneuver creators.

Rogue AI could leverage integrated systems to create chaos via hacking databases, making stuff up and feeding the legacy media machine, disrupting scientific collaborations (maybe even via controlling peer-reviewed journals), or even targeting infrastructure tied to AI labs. Imagine when we get to the point when we don’t even know what we’re controlling anymore or what data is real. What data is real? What does it even mean to be real when speaking of these things!?

You can see where I am going with this. Rogue AIs could induce waves of massive paranoia and total chaos in our world. In my opinion, they could do this by simply copying the human example. Think about that. What if a rogue AI adopted the qualities of a human psychopath like Hitler?

I recommend gardening to avoid paranoia and stress.

I hate to leave you all on this note, but sometimes I wonder if this isn’t already happening. I have asked this on X previously because sometimes when I am ‘noticing’ (I am The Noticer) what’s going on in social media and online in general, it seems to me that if we were being manipulated with propaganda via legacy and even non-legacy media, and scientists were being isolated and censored (ahem), how would we ever know if the source was actually human-generated at this point? How can we know for sure that even some sources aren’t AI-generated nowadays?

They are learning from us, after all. We MUST set a good example, and we must think of ingenious ways to prevent an undesirable outcome to humans that does not need to transpire. On a personal note, I can’t believe we are actually going through this. It doesn’t seem…real. Somehow.

Don’t share the weights.