Science Labs of the Future: Perspectives and Predictions
Photo by Walter Otto
On AI, Automation and the evolving role of the scientist
Laboratories are on the brink of a technological revolution. The implementation of Artificial Intelligence (AI) and automation into the laboratory environment will connect hardware, software and teams increasing productivity and ensuring an increased level of expediency and efficiency in the laboratory. It is expected that these technologies will shape the way research and development are conducted to the extent that the quality and yield of research will increase.
Yet as machines and software begin to assume the roles that were previously in the domain of the scientist, the question of whether the role of the scientist will be diminished by these technological advancements needs to be addressed.
What is different about the introduction of automation and AI into the laboratory environment is simply the scale of change that is expected to occur. Laboratories are structured, organised environments that are designed to ensure the precision and reproducibility of research. It is the nature of the laboratory environment which makes it the perfect candidate for automation and AI implementation.
The current system relies on scientists manually handling equipment and recording data. Large volumes of repetitive tasks are still the responsibility of the scientist. This can create an enormous strain on the scientist, who is not undertaking tasks that will further their research, but a large volume of menial, repetitive tasks that don’t require much thought or effort but take up a lot of time.
However, in the labs of the future, most tasks will be solved through the use of laboratory automation which includes the connection of instruments, devices and software. Information collected by the lab automation devices can then be processed and analysed by AI, which can make basic decisions in the laboratory to ensure experiments are run under the optimal conditions according to the results that have been analysed.
So what will this mean for the role of the scientist? Will the occupation be replaced by the combination of AI and autonomous devices eventually?
Co-bots are also incredibly efficient at undertaking high volumes of repetitive tasks, they will work alongside scientists carrying out small manual labour tasks such as making small testing batches or retrieving certain items from an inventory. With the manual labour tasks transferred to the Co-bots, scientists will be able to dedicate their time to their more pressing benchwork duties.
The Materials Acceleration Platform stated in a research paper that “in scientific research, robotic technologies have spurred gains in speed and efficiency”.
Put simply, automation will allow scientists to work smarter not harder.
In order to reach laboratory automation’s full potential, it needs to be combined with an AI or machine learning system. AI consists of statistical algorithms that learn with experience. These algorithms enhance the capabilities of automated systems enabling them to perform tasks that have traditionally required human cognition. It has progressed to the extent that now AI software can aid an experiment from inception through to its conclusion.
During the experiment, AI and machine learning can derive meaningful information that goes beyond just recording the outcome of an experiment. Machine learning provides a systematic, less biased analysis that can be used to determine what laboratory procedures should be used in future experiments.
Through gathering and analysing relevant secondary data from the autonomous machines, AI can help maximise materials, laboratory items and researcher time. It uses continuous data from multiple monitors to determine how the experiment should be run, e.g. what environment or time will yield the best result.
Once the experiment has taken place AI can analyse the data generated and produce new hypotheses from the experiment results. AI predictive abilities can also predict which materials will work best in the experiments, ensuring greater efficiency, precision and yields in the laboratory. In this way AI streamlines the entire experimental process, driving innovation and creating tremendous opportunities for those in research and development.
AI technologies are progressing at a phenomenal rate. Many complex tasks can now be carried out by AI algorithms, which minimise the risk of error and perform tasks quicker and more efficiently. AI is not something to be feared, it will not replace scientists. Rather, researchers need to harness the abilities AI offers to further scientific progression and alleviate the burden on scientists.
Laboratory automation and machine learning software will not diminish the role of the scientist. Instead, technologies will enhance it. AI and automation create effective methods for better AI-human interaction and collaboration. By eliminating the repetitive aspects of research, technologies will increase productivity levels, allowing scientists to direct their efforts to their research. With its growing prevalence, it is not surprising that AI and automation implementation has met some backlash, due to the breadth of sectors that it will affect, combined with the perception that these machines could eventually replace us.
However, trends suggest that in the research and development realm, the integration of AI and surrounding disciplines will augment human ability resulting in a greater number of global opportunities and an increase in scientific breakthroughs.