AI and Corporate Culture

illustrations

AI and Corporate Culture

Published on Feb 22, 2020 by Ashok Kasilingam

post-thumb

Big Data business consulting for huge multinational corporations and governments have been around since early 2000. Typical use cases included hospitals which have been keeping huge amount of records of diagnosis and the survival rates, insurance companies which have been covering accidents and catching frauds and many use cases in financial sector in general. But until recently traditional businesses had a hard time making meaningful correlations from the data they have to derive better results.

The widespread adoption of Deep Learning around 2013 turbocharged these capabilities and gave birth to lot of startups vying to compete for their share in this space. Those startups which provides solution for specific scenarios which traditional businesses are trying to solve gets acquired by major Internet companies. Apple has recently acquired an AI start-up Xnor.ai for about $200 million to bolster AI at the edge (can be thought of “on device AI”). We will discuss about edge computing and the crucial role it plays in IoT and AI another time in detail.

But for Deep Learning to make meaningful correlations of data we need labeling of available data - in other words a well structured data. Hence businesses which usually tend to have these well structured corporate data sets can now use Deep Learning to improve fraud detection, make smarter trades and uncover inefficiencies in supply chains.

This highlights the significance of individual organization culture playing a very crucial role for adaptation of AI and Deep Learning by those companies. Companies which invest heavily in enterprise software for storing inventory, customer relationship management (CRM) and other large amounts of data in a well structured formats will definitely have a lead over other companies who don't follow this.

This also means when government/private institutions like hospitals which have well structured data and have the willingness to harness the power of Deep Learning for good with their data will have a dramatic impact on medical diagnosis. Until now, medical knowledge and the power to deliver accurate diagnosis is pretty much contained within a small number of very talented humans.

By utilizing Deep Learning, algorithms are being developed that are on par with doctors at diagnosing specific illness for example pneumonia through chest x-ray data and skin cancer through images. And with a broader adaption of such algorithms in medical field, AI can handle the entire diagnosis process for a wide variety of illness.