Recent research on AI vs Humans

Artificial Intelligence has made advancements in all fields ranging from the military to the healthcare sector. It has provided an easy way for humans to handle the work efficiently without putting in hours and thousands of effort.

We are always curious to know if humans are more intelligent or AI. No doubt humans are the most intelligent as AI is developed by humans only. But as today's generation is technology-dependent, machines are considered to be more intelligent. Considering both scenarios, humans, and machines both are intelligent at their levels. 

Keywords: AI, humans, proteins, self-assemble, predict, machine-learning system

A recent study has researched upon comparing AI and humans.  

Proteins are made up of amino acids that are joined from end to end. The chains of proteins fold up to form three-dimensional molecules with complex shapes. The shape of protein along with amino acid determines the work done by protein. 

In this study, researchers focus on who can do a better job in predicting which protein sequences would combine most successfully are out. Basically, the scientists conducted an experiment involving a human - one with a profound and intuitive understating of protein design and self-assemble compared to artificially intelligent computers.

Scientists are always interested in protein self-assembly. According to them, understanding it better could help them in designing various products useful for medical and industrial uses such as Artificial human tissue for wounds and catalysts for new chemical products.

For conducting this research Vikas Nanda, a professor in the Department of Biochemistry and Molecular Biology at Rutgers Robert Wood Johnson Medical School was on the top of the list. He said, "Despite our extensive expertise, the AI did as good or better on several data sets, showing the tremendous potential of machine learning to overcome human bias. Understanding protein self-assembly is fundamental to making advances in many fields, including medicine and industry". 

For this experiment, Nanda along with 5 other colleagues was given a list of proteins. They were asked to predict which ones were more likely to self-assemble. They made their observations based on protein behavior in experiments, including patterns of electrical charges and degree of aversion to water. After observations, they chose 11 proteins that would self-assemble. The computer program was based on an advanced machine-learning system. It chose nine proteins.

The humans were correct for six out of the 11 proteins and the computer program was correct for six out of the nine proteins. Thus, the computer program earned a higher percentage. It was observed that humans favored some amino acids which lead them to incorrect choices. 

Expert Nanda said, "We're working to get a fundamental understanding of the chemical nature of interactions that lead to self-assembly, so I worried that using these programs would prevent important insights. But what I'm beginning to really understand is that machine learning is just another tool, like any other".

This study has given us more proof to increase our belief in technology.