With rapid development of IT industry including high-capacity data storage devices, the world is increasingly becoming digitalized these days. People are staying online 24/7 and generate incredible amounts of media, which is extraordinary valuable. Such amounts of data can no longer be covered by human analysts in order to extract meaningful insights. Instead – machines are replacing humans and Artificial Intelligence is becoming a disruptive innovation driving economies around the globe.
Any technological evolution has a spiral effect, it consists of cycles with further advancement at each stage. AI-based media analysis develops in the same way. Behavioral analysis of Internet users has become one of the most successful applications of Artificial Intelligence: Google Search and Google AdWords for targeting potential customers, Netflix and Amazon with their recommender engines, etc. These companies have already made billions of dollars out of AI technologies, and their solutions have become best practice cases.
However, as of now, the industry is entering a new cycle. This is caused by the new achievements in building IT infrastructures of Big Data analysis: corporations collected and structured large datasets which can be used for Machine Learning. New methods for media content analysis, like Deep Learning,Embedding, Named Entity Recognition, Sentiment Analysis, Recurrent and Convolutional Neural Networks, have been developed.
The Big Data infrastructure and technology solutions for Natural Language Processing
are of a great interest among corporations. We @Fayrix
witness an increased interest towards AI-based methods for media content analysis especially among PR managers, procurement managers, HR managers, and investors today.
For business to stay successful and competitive there is no better strategy than learning about their clients and turning this knowledge into real actions. This basically means that all profitable companies are divided in just into 2 types:
- One that already use data to stay efficient and grow revenues,
- Ones that haven't used data yet.