Why Does AI Need a Different Kind of Brain?
Normal Neural Networks vs. Liquid Neural Networks:
Note - Suitable for: Beginners who are starting their journey in Artificial Intelligence, Machine Learning, or Deep Learning. No prior knowledge of neural network architectures or mathematics is required. If you have heard terms like neural networks, Transformers, or ChatGPT but aren’t entirely sure how they work, this article is for you.
First, why do we even need different types of neural networks?
Imagine building two AI systems.
The first one needs to identify whether an image contains a cat or a dog.
The second one needs to control a drone flying through unpredictable weather, where wind speed, direction, and obstacles keep changing every second.
Would you design both systems in exactly the same way?
Probably not.
Although both problems involve Artificial Intelligence, the nature of the problems is very different.
One primarily needs to recognise patterns in images. The other needs to continuously respond to a changing environment.
This is one of the reasons we have different neural network architectures.
Before getting into Liquid Neural Networks, let’s understand the conventional neural networks we already know.
1. What are normal neural networks?
When we talk about neural networks, we are referring to computational systems loosely inspired by the way biological brains process information.
They consist of interconnected units called neurons, arranged in layers.
A simplified neural network looks something like this:
Input → Hidden Layers → Output
Let’s take an example.
Suppose we want to build an AI model that predicts house prices.
We provide information such as:
- Number of bedrooms
- Number of bathrooms
- Location
- House area
- Property age
The neural network processes these inputs through its internal layers and produces a predicted house price.
But how does it know which factors matter more?
This is where weights and parameters come in.
During training, the network learns numerical values called weights. These values determine how strongly different inputs influence its predictions.
For example, the area of a house might have a stronger influence on its price than the age of the property.
The network learns these relationships by examining training examples and adjusting its weights to reduce prediction errors.
Once training is complete, the learned weights are generally fixed during inference (when the model is being used).
The model still produces different outputs for different inputs, but its learned parameters do not automatically change every time a new input arrives.
This is how many conventional neural networks work.
However, not every neural network is designed this way. Recurrent Neural Networks (RNNs), for example, maintain an internal state that carries information across a sequence.
And this brings us to something interesting.
2. What are Liquid Neural Networks?
Liquid Neural Networks (LNNs) are a type of neural network designed to handle continuously changing information.
They were introduced by researchers at MIT and collaborators, with the original research published around 2020.
The interesting part is how they process information.
Unlike a conventional feedforward neural network, which computes outputs through a fixed sequence of operations, Liquid Neural Networks use mathematical equations that describe how their internal states evolve over time.
In simple terms, their internal behaviour can change dynamically as new information arrives.
Think about a glass of water.
When you pour water into a glass, its shape is determined by the container. Pour it into a different container, and it takes a different shape.
The water itself hasn’t changed into a different substance. Its behaviour adapts to the surrounding conditions.
This is where the name Liquid comes from.
It describes the adaptable, time-dependent behaviour of the network.
What makes them technically different?
Traditional neural networks commonly use mathematical operations involving weights, inputs and activation functions.
Liquid Neural Networks use differential equations to describe how their internal states change over time.
Don’t worry if you haven’t studied differential equations.
You only need to understand one idea:
A differential equation describes how something changes.
For example:
- How does temperature change over time?
- How does the speed of a moving vehicle change?
- How does the position of a drone change as wind affects its movement?
Liquid Neural Networks use this kind of mathematical approach to model the changing internal state of neurons.
Instead of treating every input as an isolated event, they can maintain and update information as time progresses.
That makes them particularly interesting for systems dealing with continuous streams of information.
3. Let’s compare them using real-world applications
Understanding the architecture is useful, but understanding where to use it is even more important.
Let’s look at some practical examples.
Application 1: Image classification
Imagine uploading a photograph and asking an AI model:
“Is this a cat or a dog?”
A conventional Convolutional Neural Network (CNN) can be trained to identify visual patterns such as:
- Ears
- Eyes
- Fur patterns
- Facial structures
- Body shapes
After learning these patterns from thousands of images, it can classify new images.
Would a Liquid Neural Network be necessary here?
Usually not.
Image classification is primarily a visual pattern-recognition problem. CNNs and Vision Transformers are established choices for this task.
LNNs can be incorporated into visual systems, especially when temporal information matters, but they are not automatically better at classifying individual images.
Application 2: Self-driving cars
Now imagine a car driving through a busy city.
The environment is constantly changing.
- A pedestrian suddenly crosses the road.
- Another vehicle changes lanes.
- Rain reduces visibility.
- The road becomes slippery.
- A traffic signal changes.
A self-driving system must continuously interpret its surroundings and make decisions.
This is where the distinction becomes interesting.
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A conventional neural network can identify pedestrians, vehicles, traffic signs and other objects.
A Liquid Neural Network can be used as part of a control system that processes changing information over time and helps determine how the vehicle should respond.
Notice something important?
We don’t necessarily need to choose one architecture and discard the other.
A self-driving system can use conventional neural networks for perception and a Liquid Neural Network for certain control or temporal-processing tasks.
Different components can solve different problems.
Application 3: Drones
Consider a drone flying outdoors.
It receives information from multiple sensors:
- Accelerometer
- Gyroscope
- GPS
- Camera
- Altitude sensors
Now imagine a sudden change in wind direction.
The drone needs to adjust its movement to maintain stability.
A model that can continuously process changing sensor information is particularly useful here.
Liquid Neural Networks have demonstrated promising capabilities in robotic control tasks, including work involving autonomous navigation.
Their ability to model changing internal states makes them an interesting choice for resource-constrained autonomous systems.
Application 4: Financial time-series prediction
Stock prices, currency values and market indicators change over time.
A financial prediction system might receive:
- Historical stock prices
- Trading volumes
- Market volatility
- Price movements
- Economic indicators
Traditional approaches such as LSTMs, Transformers and other time-series models are widely used for these problems.
Liquid Neural Networks can also model evolving temporal patterns.
However, this doesn’t mean they automatically predict markets more accurately. Financial prediction is extremely difficult, and model performance depends heavily on the data, validation methodology and market conditions.
Application 5: Industrial automation
Imagine a machine operating inside a manufacturing plant.
It continuously generates sensor readings:
- Temperature
- Pressure
- Vibration
- Motor speed
- Energy consumption
A sudden increase in vibration might indicate that a machine component is wearing out.
An AI system can monitor these readings and identify unusual behaviour.
Liquid Neural Networks are worth exploring for such applications because they are designed to handle evolving temporal information and can be computationally efficient in certain settings.
They could be useful in predictive maintenance, adaptive control and robotics.
4. Normal Neural Networks vs. Liquid Neural Networks
Let’s bring everything together.
One important clarification: Liquid Neural Networks are themselves neural networks. They are not an entirely separate category of AI.
Also, conventional neural networks aren’t necessarily incapable of adapting to changing environments. Recurrent networks, Transformers, adaptive models and control systems can all handle temporal information in different ways.
The distinction is in how the models represent and process changing information.
5. Does this mean Liquid Neural Networks are better than Transformers?
Not necessarily.
Let’s take ChatGPT as an example.
When you ask ChatGPT a question, it needs to understand the relationship between words, identify relevant context, connect information across sentences and generate a meaningful response.
Transformers are exceptionally good at modelling these relationships using an attention mechanism.
Now consider a robot navigating a room.
It needs to continuously process sensor information, maintain an understanding of its current state and adjust its movements as the environment changes.
This is a different challenge.
Liquid Neural Networks offer an interesting approach to these problems.
Neither architecture is universally superior. Their usefulness depends on the task.
In fact, combining different architectures can be more practical than expecting one architecture to solve everything.
6. Where is this technology heading?
Liquid Neural Networks are part of a broader effort to build AI systems that are more efficient, adaptive and capable of operating in real-world environments.
One particularly interesting development is the work from Liquid AI, a company founded by researchers associated with the original MIT Liquid Neural Network research.
The company has developed Liquid Foundation Models (LFMs), extending ideas from liquid neural networks into foundation-model architectures.
This is worth distinguishing from the original small Liquid Neural Networks: an LFM is not simply the same architecture made bigger.
The broader research direction is exciting because the future of AI won’t necessarily be determined by making language models larger and larger.
We also need AI systems that can:
- Operate efficiently on edge devices.
- Make decisions with limited computing resources.
- Respond to continuously changing environments.
- Work alongside physical machines and robots.
- Process real-world sensor information.
Liquid Neural Networks are one interesting direction towards these goals.
Final thoughts
When we first learn about Artificial Intelligence, it is easy to associate neural networks primarily with chatbots, image generators and language models.
But AI is much bigger than that.
Some AI systems need to understand language. Others need to recognise objects, predict failures, control robots or respond to changes in the physical world.
Different problems require different approaches.
Conventional neural networks have given us extraordinary capabilities in pattern recognition, language processing and much more.
Liquid Neural Networks introduce another interesting way of modelling information, particularly when that information changes continuously over time.
The exciting part isn’t deciding which architecture will replace the other.
It’s understanding when each one makes sense, and how we can combine them to build better AI systems.
Because the future of AI isn’t just about building models that know more. It’s also about building systems that can respond better to the world around them.
