Liquid Neural Networks: Adept Invention for Adabtable Self-driving Cars

Hey fellow tech enthusiasts. Are you contemplating and envisioning your autonomous vehicle on the road being able to be adjust, adapt as per the traffic scenarios, and road conditions ahead, without you having you 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 identified. Well, to make self-driving cars all the more safe, sound, and smart we have the most 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 can be controlled using liquid neural networks. Also, how LNNs make use of already present training datasets.

What are Liquid Neural Networks?

LNNs 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. however it has certain limitations, including:

1. Training data must be annotated and labeled in a substantial amount.
2. 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.

Ramin Hasani and Mathias Lechner were the two researchers in MIT’s computer science and AI lab invented Liquid Neural Networks in order to overcome these two major challenges. They found their inspiration in a 1 mm long work that has a impressive structured nervous system and can perform tasks as complex as searching food, going off to sleep, and most importantly learning by observing the surrounding environment. Similarly, LNN’s is 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 training period, LNN’s have proved to be all the more adabtable. LNN’s 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 Architechture
Express only what is taught
Non-interpretable, take action as per training input only
Limited learning — only during the training period

How Liquid Neural Networks Facilitate Autonomous Vehicles Production?

Liquid Neural Networks are undoutedly an elegant, fast, and reliable alternative to traditional neural networks. It’s like a creature living in real conditions, understands what’s happening, predicts the near future, and acts accordingly.

Liquid Neural Networks for Autonomous Vehicles Uses Cases

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

You can gleefully hop onto your driverless car and enjoy our ride to your destination:

• No worrying about swimming cautiously through sea of unusual traffic.
• Moving though 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 style 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 camaredie 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 self-driving vehicle industry will not be a overstatement. It will not only increase production and sales of autonomous vehicles but also make the life of vehicle owners easy. Therefore, self-driving vehicles will not only become more efficient but also gain immense popularity.