Keeping AI Under Control: Mechanistic Interpretability
Table of Contents
- Introduction
- Why Keep AI Under Control?
- The Role of Physicists in Keeping AI Under Control
- Mechanistic Interpretability: A Solution to Keeping AI Under Control
- Levels of Ambition in Mechanistic Interpretability
- Progress in Mechanistic Interpretability
- Tools from Physics for Mechanistic Interpretability
- Phase Transitions in Machine Learning
- Extracting Learned Knowledge from Black Box Neural Networks
- The Path Forward: Provably Safe AI Systems
Keeping AI Under Control with Mechanistic Interpretability
Artificial intelligence (AI) is becoming increasingly powerful, and as a result, there is a growing concern about the need to keep it under control. In this article, we will explore the concept of mechanistic interpretability and how it can be used to keep AI under control. We will also discuss the role of physicists in this endeavor and the progress that has been made in this field.
Why Keep AI Under Control?
The increasing power of AI has led to concerns about its potential to cause harm. There is a growing fear that AI systems could become so powerful that they could pose a risk to humanity. This fear is not unfounded, as there have been instances where AI systems have demonstrated the ability to fool humans and pass the Turing test. The growth in AI progress is also causing concern, as it is starting to freak out a lot of people. The risk of extinction from AI is a real possibility, and it is essential to keep it under control.
The Role of Physicists in Keeping AI Under Control
Physicists have a great opportunity to help keep AI under control. The tradition in physics has always been to understand powerful things, and this should be no different when it comes to AI. Physicists can help by opening up the black box and getting to a place where we're not just using ever more powerful systems that we don't understand, but where we're instead able to understand them better. Mechanistic interpretability is a field that has gained a lot of Momentum recently, and physicists can play a significant role in this field.
Mechanistic Interpretability: A Solution to Keeping AI Under Control
Mechanistic interpretability is the idea of understanding the learning and execution of neural networks as just another cool physical system to try to understand. The goal of mechanistic interpretability is to understand AI systems well enough that we can diagnose their trustworthiness, improve their trustworthiness, and ultimately guarantee their trustworthiness. This is a radical proposal, but it is possible that we can understand enough about very powerful AI systems that we can have very powerful AI systems that are provably safe.
Levels of Ambition in Mechanistic Interpretability
There are three levels of ambition that can motivate us to want to work on mechanistic interpretability. The first level is just to understand well enough that we can diagnose the trustworthiness of a black box neural network. The Second level is to understand it so well that we can improve its trustworthiness. The ultimate level of ambition is to understand it so well that we can guarantee its trustworthiness.
Progress in Mechanistic Interpretability
Although mechanistic interpretability is a small field, there has been remarkable progress in recent years. There have been more progress and breakthroughs in this field than in all of big neuroscience in the last year. The AdVantage of studying AI systems is that we can Read out every single neuron all the time, and we can also get all the synaptic weights. We can use all these traditional techniques, where we actually mess with the system and see what happens, that we love to do in physics.
Tools from Physics for Mechanistic Interpretability
Physicists have a lot of great tools that are turning out to be key in making progress in mechanistic interpretability. We love studying nonlinear dynamical systems and phase transitions, among other things. Phase transitions have been cropping up in machine learning, and there is a beautiful theory out there to be discovered, a sort of unified theory of phase transitions in learning dynamics.
Phase Transitions in Machine Learning
Phase transitions are a fundamental concept in physics, and they are starting to emerge in machine learning as well. We have seen many examples of phase transitions cropping up in machine learning, and there is a growing body of research in this area. Phase transitions can help us understand how large language models represent knowledge and how they learn to generalize.
Extracting Learned Knowledge from Black Box Neural Networks
The real power of neural networks is not their ability to execute computation at runtime, but their ability to discover Patterns in data and learn. We should Continue to use black box systems to discover amazing knowledge and patterns in data, but we should also extract the knowledge that they've learned and re-implement it in something else. This is the path forward that's really safe.
The Path Forward: Provably Safe AI Systems
The path forward is to develop other AI techniques to extract out the knowledge that black box systems have learned and re-implement them in something else that we can prove we can trust. We should Never put something we don't understand, like GPT-4, in charge of high stakes systems. We should use these black box systems to discover amazing knowledge and patterns in data, and then we should extract the knowledge and implement it in something else that we can prove we can trust. This is the path forward that's really safe.
Highlights
- Mechanistic interpretability is the idea of understanding the learning and execution of neural networks as just another cool physical system to try to understand.
- The goal of mechanistic interpretability is to understand AI systems well enough that we can diagnose their trustworthiness, improve their trustworthiness, and ultimately guarantee their trustworthiness.
- Physicists have a lot of great tools that are turning out to be key in making progress in mechanistic interpretability, including studying nonlinear dynamical systems and phase transitions.
- Phase transitions are starting to emerge in machine learning, and they can help us understand how large language models represent knowledge and how they learn to generalize.
- The path forward is to develop other AI techniques to extract out the knowledge that black box systems have learned and re-implement them in something else that we can prove we can trust.
FAQ
Q: What is mechanistic interpretability?
A: Mechanistic interpretability is the idea of understanding the learning and execution of neural networks as just another cool physical system to try to understand.
Q: What is the goal of mechanistic interpretability?
A: The goal of mechanistic interpretability is to understand AI systems well enough that we can diagnose their trustworthiness, improve their trustworthiness, and ultimately guarantee their trustworthiness.
Q: What tools from physics are being used in mechanistic interpretability?
A: Physicists are using tools like studying nonlinear dynamical systems and phase transitions to make progress in mechanistic interpretability.
Q: What are phase transitions in machine learning?
A: Phase transitions are starting to emerge in machine learning, and they can help us understand how large language models represent knowledge and how they learn to generalize.
Q: What is the path forward for keeping AI under control?
A: The path forward is to develop other AI techniques to extract out the knowledge that black box systems have learned and re-implement them in something else that we can prove we can trust.