Revolutionary Water-Based Computer Outperforms Digital Computer in Predicting Future Events

Can a computer learn from the past and anticipate what will happen next, like a human? You might not be surprised to hear that some cutting-edge AI models could achieve this feat, but what about a computer that looks a little different – more like a tank of water?

Reservoir Computers vs. Digital Computers: Reservoir Computer Outperforms Digital Computer

Scientists have built a small proof-of-concept computer that uses running water instead of a traditional logical circuitry processor, and forecasts future events via an approach called “reservoir computing”. In benchmark tests, the analog computer did well at remembering input data and forecasting future events – and in some cases, it even did better than a high-performance digital computer.

So How Does It Work?

Let’s understand through a simple example.

Imagine two kids, Abdul and Basir, playing at the edge of a pond. Bob throws big and small stones into water one at a time, seemingly at random.

Big and small stones create water waves of different size. Abdul watches the water waves created by the stones and learns to anticipate what the waves will do next – and from that, he can have an idea of which stone Basir will throw next. Reservoir computers copy the reasoning process taking place in Abdul’s brain. They can learn from past inputs to predict the future events. Although reservoir computers were first proposed using neural networks – computer programs loosely based on the structure of neurons in the brain – they can also be built with simple physical systems.

Reservoir computers are analog computers. An analog computer represents data continuously, as opposed to digital computers which represent data as abruptly changing binary “zero” and “one” states. Representing data in a continuous way enables analog computers to model certain natural events – ones that occur in a kind of unpredictable sequence called a “chaotic time series” – better than a digital computer.

How To Make Predictions?

To understand how we can use a reservoir computer to make predictions, imagine you have a record of daily rainfall for the past year and a bucket full of water near you. The bucket will be our “computational reservoir”.

We input the daily rainfall record to the bucket by means of stone. For a day of light rain, we throw a small stone, for a day of heavy rain, a big stone. For a day of no rain, we throw no rock. Each stone creates waves, which then slosh around the bucket and interact with waves created by other stones.

At the end of this process, the state of the water in the bucket gives us a prediction. If the interactions between waves create large new waves, we can say our reservoir computer predicts heavy rains. But if they are small then we should expect only light rain.

It is also possible that the waves will cancel one another, forming a still water surface. In that case, we should not expect any rain.

The reservoir makes a weather forecast because the waves in the bucket and rainfall patterns evolve over time following the same laws of physics. So do many other natural and socio-economic processes. This means a reservoir computer can also forecast financial markets and even certain kinds of human activity.

Enhancing Reservoir Computing: Using Solitary Waves

The “bucket of water” reservoir computer has its limits. For one thing, the waves are short-lived. To forecast complex processes such as climate change and population growth, we need a reservoir with more durable waves. One option is “solitons”. These are self-reinforcing waves that keep their shape and move for long distances.

In the computer, a thin layer of water flows over a slightly inclined metal plate. A small electric pump changes the speed of the flow and creates solitary waves. A fluorescent material is added to make the water glow under ultraviolet light, to precisely measure the size of the waves. The pump plays the role of falling stones in the game played by Alice and Bob, but the solitary waves correspond to the waves on the water surface. Solitary waves move much faster and live longer than water waves in a bucket, which lets the computer process data at a higher speed.

How Does It Perform?

The computer’s ability is tested to remember past inputs and to make forecasts for a benchmark set of chaotic and random data. The computer not only executed all tasks exceptionally well but also outperformed a high-performance digital computer tasked with the same problem.

Future Implications: Miniaturizing the Computer and Wide Application Potential

With the help of Andrey Pototsky, Scientists also created a mathematical model that enabled us to better understand the physical properties of the solitary waves.

Next, the plan to miniaturize the computer as a microfluidic processor. Water waves should be able to do computations inside a chip that operates similarly to the silicon chips used in every smartphone.

In the future, the computer may be able to produce reliable long-term forecasts in areas such as climate change, bushfires, and financial markets – with much lower cost and wider availability than current supercomputers.

And the computer is also naturally immune to cyber-attacks because it does not use digital data.

Vision for the Future: Bringing Data Science to Rural and Remote Communities

Scientists envision a future where a cutting-edge soliton-based microfluidic reservoir computer revolutionizes data science and machine learning, making them accessible to rural and remote communities worldwide. While this vision is yet to be fully realized, researchers are diligently continuing their work in this promising field.