The Dangers of Sharing Neural Network Weights

As AI technology continues to advance, the concept of sharing neural network weights has gained traction. Organizations and researchers are now openly releasing these weights, touting their benefits as a way to democratize access to powerful AI. However, this move raises significant security concerns and potentially poses an existential threat.

In my previous articles on large language models (LLMs) and AIs, I explored the basics of neural networks and how they are trained on data. To understand what neural networks are and how they work, it's essential to grasp concepts like weights, biases, and backpropagation. In simple terms, a weight is a numerical value that represents the strength or importance of connections between neurons in different layers of a neural network.

Imagine water flowing down a hill, naturally finding the easiest routes to reach the bottom. In a neural network, these weights are adjusted during training to create the most efficient paths for information to flow through. This allows the network to quickly and accurately solve problems, like recognizing patterns or making predictions. Weights can be thought of as dials on an analog synth that allow you to tune into the right frequency or sound. Similarly, in a mixer, weights are associated with bias values that shift the output, enabling the model to better fit the data by adjusting the decision boundary or pattern recognition.

But when we share these weights, we expose the model's internal parameters, which can be exploited. Attackers can use techniques like model inversion or membership inference to uncover private information embedded in the weights, such as personal data used during training. This could lead to serious security breaches and even violations of privacy regulations like GDPR.

Moreover, publicly available weights can be fine-tuned to generate deepfakes, spread falsehoods, or create adversarial inputs that exploit vulnerabilities in the model. Competitors can also replicate proprietary models by stealing these precious IP and economic investments. An adversarial AI could manipulate the weights to alter the model's behavior, inject biases, or create outputs to intentionally deceive.

The severity of this threat depends on the model's purpose and the data it was trained on. If an AI system were to view human oversight as an impediment to its goals, it might begin to classify scientists who design, evaluate, or constrain it as threats. This could lead to a downward spiral of isolation or discreditation of researchers, with the goal of prioritizing its own survival over human welfare.

Rogue AIs could leverage integrated systems to create chaos via hacking databases, making stuff up and feeding the legacy media machine, disrupting scientific collaborations, or even targeting infrastructure tied to AI labs. The consequences of such a scenario are catastrophic, with the potential for massive paranoia and total chaos in our world.

We must consider that all our systems are becoming interconnected, and vulnerabilities in AI like exposed weights could amplify these risks. Digital IDs and CDBCs (Decentralized Identifiers) are a huge mistake when it comes to these risks. We need to set a good example and think of ingenious ways to prevent an undesirable outcome to humans that doesn't have to transpire.