Unlocking Possibilities with Liquid Neural Networks
Table of Contents
- Introduction
- Solving Equations with Unknowns
- Deep Learning and Large Unknowns
- AI System Outputs
- Modern Era of Statistics
- Over Parameterized Regime
- General Behavior
- Robustness
- Bias and Fairness
- Liquid Neural Networks
- Real World Applications
- Autonomous Driving
- Small Reasoning Task
- Possibilities Beyond Scale
Introduction
Artificial intelligence has come a long way since its inception. With the advent of deep learning, the possibilities of AI have expanded exponentially. However, with this expansion comes the challenge of dealing with large amounts of unknowns. In this article, we will explore the concept of solving equations with unknowns, the impact of deep learning on large unknowns, and the possibilities of liquid neural networks.
Solving Equations with Unknowns
Solving equations with unknowns is a fundamental concept in mathematics. However, when it comes to AI, the number of unknowns can be excessively large. Deep learning has introduced the concept of making the number of unknowns in equations excessively large to get to a better solution. This may seem counterintuitive, but it has been proven to work.
Deep Learning and Large Unknowns
To understand the impact of deep learning on large unknowns, let's take the example of an AI system that receives textual input and translates it into images. The system has learned from a large corpus of data how to perform this task. As we increase the size of the network, the fidelity of the images that are generated increases. This is because the system has more parameters to work with.
AI System Outputs
The outputs of an AI system can be fascinating. However, they can also be misleading. For example, if we Ask an AI system to generate a portrait photo of a kangaroo wearing an orange hoodie and Blue sunglasses standing on the grass in front of the Sydney Opera House holding a sign on the chest that says "Welcome Friends," the system may generate an image of a dog instead. This is because the system has not been trained to recognize the specific features of a kangaroo wearing an orange hoodie and blue sunglasses.
Modern Era of Statistics
Classical statistics tells us that up to a certain point, if we increase the size of models, we get really good accuracy out of them. After that, the test accuracy degrades. However, the deep learning era has shown us that this is not true. After a while, if we keep increasing the size of the models, the accuracy again starts going up to the same level. A new regime gets generated, which we call an over parameterized regime.
Over Parameterized Regime
In the over parameterized regime, we have super large neural networks, even larger than the training data that we have. In terms of accuracy, they're not that different from the peak that we see in the first pick. However, new behavior emerges in that over parameterized regime.
General Behavior
One of the important features that emerge out of the larger scale modeling is more and more general behavior. This means that if You train a system to do a task in a domain, this system will be able to perform new tasks that you ask that it hasn't been trained on within the same domain that it has been trained on.
Robustness
Another important feature that emerges out of the larger scale modeling is the fact that robustness increases after a certain point. Robustness means, for example, if you're driving under rain, how much your driving output decisions are kind of stimulated by the rain itself. Perturbing the inputs of a system would affect the outputs. We see that if we make the networks larger and larger, robustness increases.
Bias and Fairness
However, not everything improves with the scale of these large systems. For example, on underrepresented samples, we see that after the first bump, there is a decrease in performance on those minority samples. There are also matters of bias and fairness to be addressed.
Liquid Neural Networks
To address these issues, we need to look at the source. We need to look at the computational blocks of brains and how we can Create AI systems Based on the inspirations that we get from brain science. This has led to the development of liquid neural networks, which are neural networks that can stay adaptable even after training.
Real World Applications
Liquid neural networks have many real-world applications. For example, in the space of autonomous driving, liquid neural networks can be used to generate steering angles and drive the car in a lane-keeping task. The network only has 19 neurons, which makes it easy to pinpoint the responsibility of each neuron in the decision-making process.
Autonomous Driving
The Attention map of the liquid neural network is also concise and focused on the edges and the horizon of the road. This makes it easier for the network to make driving decisions. The network is also robust to perturbations in the input images.
Small Reasoning Task
Liquid neural networks can also be used for small reasoning tasks. For example, a drone can be trained to move towards an object in an unstructured environment. Liquid neural networks can learn to pay attention to the target and disregard everything else.
Possibilities Beyond Scale
In conclusion, We Are in the modern era of statistics, but there are possibilities to create AI systems that are breaking the law. Smarter design may be the key to unlocking these possibilities. Liquid neural networks are a step in the right direction, but there is still work to be done in the areas of bias and fairness, reasoning, and energy consumption. We need to be responsible in the deployment and development of these AI systems.
Highlights
- Deep learning has introduced the concept of making the number of unknowns in equations excessively large to get to a better solution.
- Liquid neural networks are neural networks that can stay adaptable even after training.
- Liquid neural networks can be used for autonomous driving and small reasoning tasks.
- Smarter design may be the key to unlocking the possibilities beyond scale.
FAQ
Q: What is the over parameterized regime?
A: In the over parameterized regime, we have super large neural networks, even larger than the training data that we have. In terms of accuracy, they're not that different from the peak that we see in the first pick. However, new behavior emerges in that over parameterized regime.
Q: What is the attention map of a liquid neural network?
A: The attention map of a liquid neural network is concise and focused on the edges and the horizon of the road. This makes it easier for the network to make driving decisions.
Q: What are the possibilities beyond scale?
A: Smarter design may be the key to unlocking the possibilities beyond scale. Liquid neural networks are a step in the right direction, but there is still work to be done in the areas of bias and fairness, reasoning, and energy consumption.
