Join INFORMS community of data, analytics, operations research, and statistics professionals and tackle the future together. With nearly 13,000 members around the world, INFORMS is the largest international association for data science professionals.
As you probably know, many breakthroughs in machine learning and related disciplines can be attributed to the rapid advances in computing power and data capacity made in the last several decades. However, there are many fields outside of computer engineering and programming that have played and continue to play a role in the development of machine learning. And even within computing, there are bleeding edge topics with significance for AI and machine learning just starting to gain steam. Quantum computing, for instance, can potentially help address a major issue with current machine learning models – namely, the data going into them. Quantum computing moves beyond classical computing’s binary data, which provides opportunities to use multidimensional datasets, which should yield higher-fidelity models.
If data quality is paramount, data science is then, naturally, another component field of machine learning. Simon Lee, Chief Analytics Officer at Waitr Inc., argues that, contrary to the public’s conception of their role, data science professionals spend most of their time gathering and cleaning data and finding data artifacts. “Farmers don’t just spend their time harvesting the crops and selling them. Most of their time is spent in the dirt,” he says.
Lee’s colorful metaphor recalls yet another related field: linguistics. Writes Joseph Byrum, chief data scientist at Principal Financial Group, “Linguist Noam Chomsky once held out the possibility of a universal grammar, which, if properly analyzed, could bring human and computer language closer. The idea was that there were innate properties of communication shared between the thousands of different languages spoken around the world .” Natural language processing remains one of the buzziest sub-disciplines of machine learning, and linguistics experts continue to be consulted in experimental efforts.
There are many more fields and subfields with ties to machine learning and INFORMS is proud that the expertise of its members spans much of this incredibly diverse space. If you’re excited about saving lives, saving money, and solving problems, join our community of likeminded data and analytics professionals, programmers, statisticians, and operations researchers. Let’s tackle the future together.
With nearly 13,000 members around the world, INFORMS is the largest international association for data science professionals. INFORMS provides unique and valuable opportunities for individual professionals and organizations to better use a wide variety of big data, analytics, and operations research methods to drive strategic visions and achieve better outcomes.