Artificial intelligence, or AI, is ubiquitous and integrated into almost any field or application. Progress made over the last few decades has been astounding, with achievements—such as DeepMind’s AlphaGo defeating the world’s foremost Go player in 2016 and the application of LinearFold to predict the secondary structure of the SARS-CoV-2 RNA sequence in just 26 seconds—demonstrating the ever-growing capabilities of these systems.
There is a caveat though; running AI requires large amounts of energy and data, and computer hardware can’t keep up. Integrated circuit chips are reaching capacity even as structures on the chips and circuit components become smaller. There is a limit to how far we can physically take this
Semiconductor structures on computer chips are now approaching the size of several nanometers and the quantum uncertainty starts to break the electrical insulation, which causes the failure of the chip,” explained Li Lin, a post-doctoral researcher in the Department of Mechanical and Aerospace Engineering at George Washington University, focusing on plasma physics, plasma chemistry, and machine learning. “It’s quite challenging to keep making smaller and smaller chips.”
Lin and his advisor, Michael Keidar, professor of engineering at George Washington University, think that alternative hardware can be explored, and for this, they turned to chemistry.
During a chemical reaction, the number of reactive species within a system is defined and leads to set products. “A chemical system naturally has a network of all its chemical reactions, namely the ‘chemical pathway network’,” said Lin. “This network can be trained like a regular artificial neural network. One could make a material with chemical behaviors that can be applied to AI.”
Any matter with a temperature higher than –273 °C contains random movements of molecules and when molecular collisions occur, this can lead to chemical reactions. This occurs constantly, everywhere in the universe, from star formation to our own biological processes.
To build AI from these systems, we need to manipulate and maintain the probabilities of the collision,” said Lin. “In other words, chemical reactions are a type of data processing and nature can keep running it automatically, we just needed to find a way to use it.”
This could hypothetically convert any type of matter with substantial chemical complexity into an alternative hardware carrier for AI. In other words, AI could be built without digital computers.
A winning AI plasma
In a study published in Advanced Intelligent Systems, the team trained a low-temperature helium plasma to play the game tic-tac-toe.
The concept is based on creating a data processing unit from a network of chemical reactions taking place within an isolated chemical system — such as a plasma. The scientists need to find a set of chemical parameters for the system, such as pressure or temperature, so that the system can spit out proper information in real-time according to a dynamic input, making the system a programmable analog computer that functions on a molecular level, and can process complicated information in nanoseconds. The chemical parameter sets are thus the “software” in such an analog computer, to determine the chemical reactions. In other words, the thinking process in the computer.
However, writing the program for it is quite different from coding a conventional computer. The scientists consider the map of chemical reactions in the system, namely the “chemical pathway network”, as an artificial neural network. The chemical parameters are the weights of the network, and the concentrations of species are the neuron values. Using modern machine learning techniques, the hardware can be trained (programmed) for specific missions. The training purpose is to find the chemical parameter set for the mission. Once it is acquired, the programming is completed. When using the system after training, the users can switch the chemical parameter set among different missions, just like running different software on a computer.
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