Liquid Neural Networks: Adept Invention for Adaptable Self-Driving Cars

Hey, fellow tech enthusiasts. Are you contemplating and envisioning your autonomous vehicle

on the road being able to automatically adjust and adapt as per the traffic scenarios and road conditions ahead, without you having to interfere or be concerned?

Well, that has always been the intention of innovators and AI technologists. However, there have been concerning faults and/or rather room for improvement pertaining to self-driving cars.

Well, to make self-driving cars all the more safe, sound, and smart, we have the most intelligent technology – Liquid Neural Networks.

In this article, we will delve deeper to know how the challenges of safety and longevity identified in self-driving cars to date can be controlled using liquid neural networks. Also, how LNNs make use of already present training datasets

What Are Liquid Neural Networks?

In a nutshell, NNs are machine learning algorithms that mimic the structure and capability of the human brain. They are used to recognize patterns by analyzing training data.

In addition to recognizing faces, understanding natural languages, and predicting the future, neural networks are capable of performing complex tasks without the intervention of humans through their network of interconnected artificial neurons.

Liquid Neural Networks VS Traditional Networks

Traditional neural networks are regarded as one of the most powerful Artificial Intelligence tools. But it comes along with a few limitations, majorly:

1. Training data, which must be annotated and labeled in a substantial amount.

2. Also, they have been found to be inefficient at utilizing the enormous amount of input data to tackle real-time scenarios, due to the non-sequentiality of their processing

In order to overcome these two major challenges, Ramin Hasani and Mathias Lechner were the two researchers in MIT's computer science and AI lab who invented Liquid Neural Networks.

They found their inspiration in a 1 mm long work that has an impressive structured nervous system and can perform tasks as complex as searching for food, going off to sleep, and most importantly, learning briskly by observing the surrounding environment.

Similarly, LNNs are an advanced type of neural network that learns on the go and takes necessary action on the spot.

While most traditional networks perform by the data they are fueled with during the training period, LNNs have proved to be all the more adaptable. LNNs are able to read, learn, and respond on the go, on the spot by ‘OBSERVING’ the impromptu input.

Liquid Neural Network

• Dynamic Architechture
• Self-Expressive
• Interpretable, ability to take instant action
• Ability to learn continuously and on the fly

Traditional Neural Network

• Static Architecture
• Express only what is taught
• Non-interpretable, take action as per training input only
• Limited learning - only during the training period

How do Liquid Neural Networks Facilitate Autonomous Vehicles Production?

Liquid Neural Networks are undoubtedly an elegant, fast, and reliable alternative to traditional neural networks. It’s like a creature living in real conditions - understands what’s happening at present, can predict the near future, and act accordingly.

Liquid Neural Networks for Autonomous Vehicles Uses Cases

Imagine getting into a self-driven vehicle without having to worry about the anonymity of the input training data. You know the car will adapt and adjust as per the situation on the road.

You can gleefully hop onto your driver-less car and enjoy your ride to your destination:

• No worrying about swimming cautiously through the sea of unusual traffic.

• Moving through uneven roads and reaching unknown destinations will become easier

•Problems like crossing speed limits and getting into a ‘no-Uturn’ area unintentionally, will decline.

•Roads, drivers, and driving styles will almost be the same for one and all.

•Difference between the rich and poor will decrease, as everyone will automatically follow the same rules.

•The camaraderie between insurance companies and can owners will improve as conditions and situations will become transparent.

Wrapping It Up

Claiming Liquid Neural Networks to be a boon for the self-driving vehicle industry will not be an overstatement. It will not only increase the production and sales of autonomous vehicles but also make the life of vehicle owners and drivers easy.

Therefore, self-driving vehicles will not only become more efficient but also gain immense popularity with time.