An Introduction to “Liquid” Neural Networks

Several kinds of research and implementations have led to the discovery of numerous best-performing artificial intelligence systems in the past years.

The idea behind deep learning was to amplify and replicate the brain’s structure and a function called artificial neural networks.

Nowadays, every other individual is familiar with artificial intelligence, but few know that deep learning is basically a new name for Neural networks.

Neural networks are older than most people think, and the idea was first proposed by Warren McCullough and Walter Pitts in 1944. Since then, the technology has been constantly tested and improved for 77 years now.

Both of the Neural networks’ inventors were students of the University of Chicago and moved to MIT in 1952 as the founding members of the first-ever “Cognitive Science Department.”

At first glance, Neural networks were being considered a very innovative and viable idea, and major researches in the neuroscience and computer science field were observed until 1969.

When MIT mathematicians Marvin Minsky and Seymour Papert became the co-director of the new MIT Artificial intelligence lab, the research was stopped. Hence, the technology was in a resting state until the 1980s when Neural networks were reborn. However, the technology then again fell into eclipse.

It was the dawn of computers that helped bring the technology back again, and this time it was fueled by the processing power of graphic chips. After its resurgence, Neural networks became the talk of the town.

After constant improvements and testing, a new type of Neural network technology was born. This technology is capable of adapting its underlying behavior during the training phase.

The new term given to this technology was “Liquid” Neural networking technology devised by MIT Computer science and artificial intelligence Lab’s Ramin Hasani and his team.

Ramin Hasani’s team’s efforts had turned the tides and bought us several revolutionary technologies like autonomous driving, robot control, and enhanced medical diagnostics.

This new technology became the leading cause of several innovations due to its sophisticated analytical algorithms and innovative analytical abilities.

After the technology training phase, the Neural network algorithms are provided with a large volume of relevant data that they analyze, understand, and store to hone their inference capabilities and optimize these devices’ performance.

However, the “Liquid” Neural network could analyze and adapt to the success metrics over time and constantly harness new information to increase performance.

The small number of neurons within the technology made it easier to understand and communicate information, which led to better decision making and performance.

The main difference in the methodology introduced by Hasani and his team was that it focused on time-series adaptability. This means that rather than analyzing and understanding data based on a few numbers of images or static moments, the “Liquid” Neural technology could consider time-series data or sequences of images to better cultivate the information.

The system’s design also enables researchers to further study and observe the network compared to traditional Neural networks. This kind of AI was generally called the “Black box.”

The term “Black box” was given because researchers who were developing the algorithm knew the best way to input information and the criteria for determining a successful outcome.

However, they could not identify the patterns within the Neural network to determine what was leading it to success. The “Liquid” Neural network model, on the other hand, gave the researchers more transparency, and it was also affordable when it came to implementing computing because it relied on fewer but more sophisticated computing nodes.

There were literally many reasons why this “Liquid” Neural network technology was being considered viable in the future. The performance results derived from the technology also indicated that the “Liquid” Neural network offered better accuracy than its alternatives, and it could be used to more efficiently predict the future values of known data sets.

Let’s analyze how Neural networks or neural nets work today after being modernized for so many years. The Neural nets are basically computer programs that are assembled from millions of units. These units were designed to work as an artificial neuron.

Above, we spoke about “Training a Neural network.” This training is linked to data integration. When a Neural network is being trained, it is actually fed information. This feeding allows it to recognize structures and patterns.

Some examples of these recognitions can be spotting a familiar face in photos or identifying how to hit a ball.

Moreover, with feedback and a constant interchanging of information, neural networks further modify their performance and information processing. This network actually learns over time and improves its performance.

When training is complete, the Neural sets are all set to solve numerous problems instantly and effectively.

They can notice deviations in historical patterns and automatically recognize trigger points in patterns, which generally means that they can identify, analyze and solve complex problems.

Instant Problem Identification & Solving
It is usually easy for a machine to follow a strict set of rules and regulations to solve a problem or even decipher a code.

However, when speech patterns and medical diagnostics need identification or solving, more variables are required to do the job.

The machines need to understand what they’re looking for and how they can distinguish from other similar results that can alter the final outcome.

And this part is exactly where “Liquid” Neural Networks come into play. This network is amazingly good at solving big problems, and nowadays, they’ve surpassed even humans in some aspects.

Feature engineering is the process of Neural networks to analyze and create more possibilities, categories, and considerations that allow the user to understand what more needs to be done to ensure success.

For example, if you’re training or teaching a machine to learn how to play Flappy Bird, you might have to teach it an algorithm so that it could learn how to play it.

However, there is generally very little to no chance of ensuring that every tap on the phone by a machine would ensure successful passage through the given spaces.

To better understand and cope with such conditions, the “Liquid” Neural networking technology gives you more categories that you need to focus on to program the machine to win at all costs.

The power of flexibility is that neural networks can be applied to almost every technology today. The seamless integration of this technology enables people to leverage it in any way they deem necessary.

Whether you want this technology to solve a Rubik’s cube or you want it to recognize patterns or improve air traffic patterns to efficiently manage air traffic control – Neural nets can seamlessly recognize and identify patterns in almost any domain.

The “liquid” Neural network has an extremely promising future. What seemed like a dream has now become a reality.

This technology has become one of the most important programming paradigms ever invented. Gone are the times when programmers used to tell the computers about the problems.

The conventional approach of precisely defining a task to the computer so that it could solve it has been minimized by “Liquid” Neural Network. We’re constantly driving towards a future where a computer does not require precise information to solve our problems.

Instead, it learns from training and observing the data and figures out the solution to the problem all by itself without human intervention.

The number of highly expressive neurons in a Neural network makes it more feasible and easier for humans to interpret decisions and solve problems through technology.

Hasani said, “Just changing the representation of neurons can enable you to explore new categories and solve extremely sophisticated problems that otherwise would’ve not been possible.”

Moreover, experts say that we’ll have a more sophisticated and complex network inspired by nature in the future. The current model of the network is just the beginning of technological advancements.

One of the most remarkable feats of the “Liquid” Neural network is its ability to work with insufficient knowledge.

In a world where you have to program/feed every little piece of information into the machine to ensure maximum efficiency and performance, neural networks surpass every other technology.

Its ability to work with insufficient knowledge, parallel processing, distributed memory, and fault detection and fixing.

This seamless integration of information through the “Liquid” Neural network is making machines more robust and smarter.

With improved resilience to unwanted or noisy data – “Liquid” neural networks are making machines more accurate.