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Science is Benefiting from AI Too Featured

"Sometimes you just have to look up." "Sometimes you just have to look up."

Artificial intelligence, machine learning, and big data are revolutionizing manufacturing, marketing and supply chain. The same is happening to scientific research where AI is proving to be a critical technology in scientific discovery. For instance, it is proving indispensable in predicting the outcomes of experiments, an example being quantum systems. Fully incorporated with fundamental laws of physics, AI can easily predict scientific simulations. Materials science, just like other experimental science branches such as drug discovery, leverage AI to model sophisticated experiments from data that is collected.

A deep learning algorithm developed by a collaboration of the University of Warwick, University of Luxemburg and Technical University of Berlin can now help predict molecules’ quantum states, functions, and determine all the properties of these molecules. AI does this by learning how to solve essential equations in quantum mechanics. After thoroughly learning this, it solves the equations with ease as compared to traditional methods that take a long time and require many computing resources. This new approach can deliver accurate results within a few seconds on mobile phones or computers.

Advances in artificial intelligence are likely to alter every aspect of human life, ranging from transport, business, and agriculture to healthcare. Similarly, science and research are also set to reap big with this technology as countries and institutions position themselves to take the lead. By mid 2020s, AI will be ubiquitous for carrying out experiments while computers will control the logging of data and instruments used in scientific research. This shift will not only ease scientific experimentation but will also enable an enhanced analysis of data that is obtained from experiments and even suggest what to experiment next. Currently, sophisticated AI systems are exploring reinforcement learning and Bayesian analysis.

Advancement in AI, mainly within the aspect of machine learning, is allowing scientists to automate processes by learning based on data that is collected during different experiments and respective findings. Through such learning, the cost of finding out a specific scientific phenomenon is reduced. AI systems can also be used to optimize the system to meet specific criteria. Through analysis of results in different experiments and the subsequent discoveries, researchers can alter their approaches, saving not only time and money while enhancing outcomes.  

Advanced equipment needed

Scientific AI techniques will also be applied in areas such as quantum technologies, nanoelectronics, and screening of materials and microscopy where computer vision will be required. Despite this, some types of AI and machine learning that will aid scientific discovery will require sophisticated computing hardware that is not currently available that includes Intelligence Processing Unit (IPU). Through these advanced IPU’s, new scientific innovations will be developed, and a new type of researchers that know how to capitalize on technology will emerge in science and engineering.

As climate change continues ravaging the planet, scientists in this area will require new approaches and modeling solutions on how carbon footprints can be reduced. AI can quickly learn climate patterns and predict future occurrences. Such prediction will allow professionals to come up with the best course of action to solve emerging issues or prepare in advance for the same.

AI continues to be one of the biggest areas of conversation in tech circles that draws mixed reactions. Although some may view it as the cause of job losses as humans get replaced by machines, others see it as an opportunity to enhance efficiency. Science, for example, is benefiting from AI that has once and again shown its potential to improve results of research and make a better societal impact. It will allow humans researchers to understand and explore how new techniques and materials should be used.

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Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

Find his portfolio here and his personal bio here

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