Transforming AI with Big Data
Today, Artificial Intelligence (AI) is being employed by financial companies around the world to advise customers on financial decisions that they should make; digital assistants are finding their ways onto our smartphones. Another important area where AI can be successfully implemented is in data analysis of specifically big data. The biggest barrier to this in the past has been the requirement of computational capacity, and until recently, large-scale cluster computing has always been termed as being too costly and time-consuming. At a time when Nanoseconds are the bar quick processing, today’s CPUs and GPUs are able to process huge amounts of data at capacity in real-time significantly faster than what was previously thought possible.
AI applications are agile and work with the data in real-time and iteration-based data discovery. This has prompted many organizations to move away from the traditional hypothesis-based approach towards data. Natural language processing (NLP) technologies are also being implemented to compute human datasets. As these are voluminous due to the variety of languages and dialects, NLP helps AI significantly when finding the relevant content and when summarizing large volumes of content to obtain insights that would have otherwise taken weeks to glean. The NLP can also be used to reveal trends and patterns across different sources of data.