Big Data and Artificial Intelligence (AI) Need Each Other
Artificial intelligence (AI) has become pervasive in every industry - be it - health care, media, advertising, human resources, finance, security and more. Organizations are embracing artificial intelligence technologies to train algorithms in areas of image recognition, speech recognition and various other applications. The growth of artificial intelligence is to a great extent being empowered by big data. The availability of the sheer volume of data is what is fueling the rapid improvements in artificial intelligence technologies.
Big data plays a crucial role in modern day enterprises and when combined with artificial intelligence it can become a robust tool for proactive and automated management. AI has seeped into our daily lives through the Internet, without many of us even being aware of it. For instance, when an ecommerce website recommends or suggests you products; it is based on your previous purchase patterns and behavior. This is artificial intelligence and its effectiveness is correlated to the availability of relevant data and accurate predictive analysis.
Artificial Intelligence technologies are not only dependent on huge amounts of data, but are only as good as the data they work with or train on. Today there is availability of large data sets be it in forms of images, weather data, logistics data, health data, transactions, etc. and we also have the ability to now process these huge quantities of data which previously required extremely expensive hardware or software systems. And this ease of technology and availability of data is what is enabling AI and machine learning to scale which was otherwise dormant for decades due to lack of availability of data.
There are still limitations to what technology can do. To create sophisticated and valuable algorithms, large data sets are critical. But that is not all – as those large data sets need to be first collected, organized, cleansed, annotated, classified, tagged, labeled etc. for the AI technologies to effectively work. Generally speaking, AI companies and startups all require accurate data for specialized applications and seemingly little things such as classifying, tagging, de-duplicating etc., demand accuracy. Thus, the ability to collect and organize high quality data is not only a prerequisite but plays a key role in determining the competitiveness of high growth companies which are building services on top of artificial intelligence technologies.