The Biotech industry is booming lately with the progress being made in Artificial Intelligence and Machine Learning. However, these two terms that are often confused with one another.
In the simplest of terms, Artificial Intelligence is the capability of a machine to imitate human behavior and Machine Learning is the ability to make predictions and improve tasks based on data.
Although AI has been around for quite some time, recent advances are launching both AI and Machine Learning onto a new platform. In the Life Sciences field we are seeing more ground breaking treatments and medications through the use of AI and Machine Learning which is helping narrow the gap and allowing for faster data analysis.
A recent article in Forbes magazine does a great job at outlining these key differences, noting:
As technology, and, importantly, our understanding of how our minds work, has progressed, our concept of what constitutes AI has changed. Rather than increasingly complex calculations, work in the field of AI concentrated on mimicking human decision making processes and carrying out tasks in ever more human ways.
Artificial Intelligences – devices designed to act intelligently – are often classified into one of two fundamental groups – Applied or General.
Applied AI is far more common – systems designed to intelligently trade stocks and shares or maneuver an autonomous vehicle would fall into this category.
Generalized AIs – systems or devices which can in theory handle any task – are less common, but this is where some of the most exciting advancements are happening today. It is also the area that has led to the development of Machine Learning. Often referred to as a subset of AI, it’s really more accurate to think of it as the current state-of-the-art.