Artificial intelligence (AI) has greatly aided in research in scientific laboratories. From cancer to genetic biology, drug development to food quality control, AI technologies are used in a myriad of research areas.
Today, AI plays an essential role in the labs, where researchers develop and test new virtuous paths every day.
The origins of artificial intelligence can be traced back to the 1940s, when British mathematician and computer scientist Alan Matheson Turing began to question whether machines could think. Eventually he himself developed an experiment called the Turing Test, designed to determine whether machines can be “smart” based on the answers formulated by questions.
If the machine can simulate human responses and convince the person that they are another human, it will pass the test and be considered intelligent. Turing colloquially refers to testing as an “imitation game” and is widely seen as the grandfather of artificial intelligence.
Over the past few decades, artificial intelligence has become a common denominator in laboratories around the world. It is used today as an umbrella term to describe an arsenal of technologies used to replicate human intelligence and perform complex tasks.
Artificial intelligence has come a long way since the Turing era. Advances in artificial intelligence technologies have enabled analysts to collect massive amounts of data, using next-generation software and algorithmic techniques used to transform this data into actionable information.
Here are some of the biggest AI breakthroughs of the past few decades.
Convolutional Neural Networks, also known as CNNs or ConvNets, allow AI systems to analyze visual images and solve problems by determining importance and discrimination. Complex networks are made up of neurons and are inspired by the communication patterns and neural pathways found in the human brain, particularly the visual cortex.
Reinforcement learning sees AI systems explore a variety of different scenarios and possibilities. Transferred learning sees previously learned concepts used in new and unknown scenarios.
Developed by artificial intelligence scientist Ian Goodfellow in 2014, Aggressive Generative Networks (GANs) pit a pair of neural networks against each other. By creating a “zero-sum game” scenario, researchers can force machines to learn new technologies and generate valuable data.
For the scientific research sector, artificial intelligence has been a major factor in many discoveries. Researchers from multiple disciplines rely on the tool to extract and analyze data using information used to gain insights, discover patterns, and predict outcomes.
Here is a look at how AI is currently being used in some of the world’s science research laboratories:
Artificial intelligence in genomic research
Artificial intelligence has emerged as a valuable tool for genomic research, with data used to predict complex protein structures. By predicting the different shapes that protein structures acquire and how they affect the human body, researchers can develop a deeper understanding of disease progression. Data from AI is also used to improve diagnosis and develop new treatments. In addition to posing technical challenges, using genetic sequences to predict the shape of a protein is incredibly labor intensive.
100,000 genomes project
The now-completed 100,000 genomes project overseen by Genomics England shows just how complex genetic sequencing can be. Using patient data from the National Health Service, the project sequenced the entire genome with the goal of recording more than 100,000 known structures and developing a better understanding of common cancers, as well as rare and infectious diseases.
The first hurdle was the identification of variants, which consisted in comparing the millions of potential differences between the reference genome and the unique genome of a patient. Annotations, the process of extracting meaning and significance from each difference, was also a major challenge. Instead of attempting these complex tasks manually, the researchers used artificial intelligence and machine learning techniques to analyze DNA sequences and predict 3D protein structures.
But that’s not all: AI is also involved in the study of climate. Indeed, climate change is an urgent issue and artificial intelligence is being used to help environmental scientists uncover important data. From monitoring air quality patterns to recording changes in average monthly precipitation, environmental science relies heavily on data. Complex systems are essential to analyze this data and use it to make informed decisions locally, nationally, and globally.
In short, AI is able to bridge the gap between the myriad of available information and the potential impacts that environmental changes can have on the planet.
Renewable energy is also involved in artificial intelligence. Indeed, as the planet is committed to combating climate change and switching to renewable energy, experts expect AI to play an important role. The technologies are already being used to manage decentralized renewable energy networks and distribute supplies.
From an operational point of view, AI is used to monitor the condition and performance of renewable energy infrastructure, including wind turbines, solar panels, and hydroelectric machinery. By improving efficiency, reducing downtime and reducing maintenance costs, operators can keep prices as low as possible and accelerate the global spread of green energy.
Artificial intelligence does not only fuel scientific research here on Earth. Machine learning is also used to detect patterns in astronomical data and to develop a deeper understanding of outer space. For example, NASA uses AI to discover existing data sets to discover compact, rotating magnetars known as pulsars. Machine learning is also used to map the physical properties of stars, supernovae, and classify galaxies.
In short, AI is constantly evolving, with new developments always pushing the boundaries of scientific research. In the coming years, it is certain that artificial intelligence will continue to play a fundamental role in advancing modern science and revolutionizing research from an increasingly interdisciplinary perspective.