By providing access to the computing resources needed to run advanced artificial intelligence models, we are equalizing the playing field for scientific researchers.
Scientific researchers are underappreciated for their lifelong endeavors to make discoveries that change the world. Without them, many of the tools, foods, medicines, and other daily conveniences we take for granted would not exist.
That being said, scientific research is an incredibly competitive industry, and many are prevented from making these life-changing discoveries, but the culprit might not be what you’d expect. Researchers’ biggest adversary is not their fellow scientists but rather funding (or, more accurately, a lack thereof). The truth of the scientific industry is that researchers often lack the money they need to invest in valuable resources.
As with everything else in life, time is money, and working with limited resources often comes at the expense of efficiency. Fortunately, one innovation promises to revolutionize the scientific research process and allow researchers to become much more efficient: artificial intelligence. From automating routine processes to enabling simulations on a massive scale beyond what was once thought possible, AI will help scientists streamline their processes.
Some of the most exciting ways scientific researchers can use AI technology include:
Data analytics: The most common application of artificial intelligence across industries is data analytics. An AI model can analyze a vast data set nearly instantaneously, much more efficiently than any human worker could. In scientific research, this functionality can help researchers quickly identify outliers, extract features, and normalize datasets — all without requiring an extensive data science background. This allows researchers to focus more on what matters most: making discoveries.
Perfecting experiment design: Artificial intelligence technology can be used to power advanced predictive analytics, which can be invaluable during the experiment design process. Experiments are costly to run, and if a mistake is made that requires an experiment to be scrapped and restarted, the costs incurred can be damaging or even crippling. Predictive analytics give researchers a better way to analyze any obstacles that may come up in the experiment process, allowing them to address problems before they arise.
Expanding capabilities: One of the most innovative use cases of AI technology in scientific research is expanding the capabilities of researchers, allowing them to test hypotheses that would otherwise be difficult or impossible to test. For example, AI can be used to run simulations of conditions that cannot be achieved in a laboratory setting, such as in deep outer space or an early Earth setting. This opens the door to many new possibilities for exploration and innovation.
Unfortunately, even though artificial intelligence has such massive potential to expand the frontiers of science, the reality of this technology is that access to the computing resources researchers need to take advantage of it is anything but democratic. Private corporations own most of the high-power data centers, meaning that this infrastructure primarily serves private interests, not the public good. Public institutions, such as universities and labs, often do not have the budget to purchase and maintain the equipment they would need to take advantage of the power of artificial intelligence technology.
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Solving the access issue with AI in scientific research
The answer to this problem is public-private partnerships that allow those with access to computing technology and hardware to share their resources with those who need them. With computing hardware, so much of the technology’s power is left unused. Reports find that even the most efficient data centers often operate at only 50% of their capacity. This is because many private companies purchase dedicated machines for single-use cases, not realizing that each use does not necessarily require its own machine.
So, what happens to the other 50% of the power that is not being used? Even when idling, this hardware consumes valuable resources. Idle hardware takes up space and requires power to run and be maintained, which can cost companies that own it thousands of dollars or more. However, some are beginning to realize they can lease out their unused computing power, recouping some of their own costs and giving other researchers much-needed access.
One of the benefits of this strategy is that it eliminates technical overhead for individual scientific researchers. Because these computing resources are managed by someone else and delivered via the cloud, researchers can hit the ground running, getting directly to their experiments. Researchers don’t have to worry about taking the time (or the headache) of setting up the cloud.
Better yet, these tools are compatible with some of the platforms most commonly used by scientific researchers, such as Jupyter, Python, and Kubernetes. As a result, scientists don’t have to worry about learning an entirely new skill or platform to take advantage of the power of these resources. After all, researchers’ jobs are already hard enough — why should we complicate them further by giving them another tool they must learn to use?
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Empowering researchers to focus on science
Ultimately, by providing scientific researchers with access to the computing resources they need to run advanced artificial intelligence models, we are equalizing the playing field for scientific researchers. Now, the brightest minds in science across the private and public sectors can come together to innovate and find solutions to the world’s most pressing problems. More researchers will have access to the power they need to supercharge their experiments.
One of the main benefits of this equalized access to computing resources is that it will enable greater collaboration between researchers. After all, collaboration is a critical part of the scientific process — discoveries depend on peer reviews and further testing by other researchers to determine their validity. Not only does artificial intelligence ensure that researchers can work with the same tools, eliminating the possibility of knowledge gaps, but it also allows researchers to share their data seamlessly with their peers.
However, at a core level, this technology is designed to enhance productivity and efficiency, allowing researchers to do their jobs faster and more effectively. With the help of these high-powered computing resources, scientists can expect to conduct as many as five times the number of experiments that they would without the aid of this technology because they can now train and deploy AI models effortlessly. This is a major help when trying to streamline workflows.
Artificial intelligence technology will enable scientists to revolutionize their productivity and conduct more and more intensive experiments than they have ever conducted before. By giving researchers the tools they need to take advantage of this powerful innovation, we are helping them change the world better, faster, and more efficiently.
