Recently, “The Economists” emphasized on the fact that data has become the most valuable commodity held by people. When small chunks of data are combined on a large scale, then it’s termed as Big Data. While we are busy in securing Big Data from attacks, it is quietly contributing towards the growth of Artificial Intelligence. You ask how? Well, Machine Learning, a section of AI is making exponential improvements and can be termed as “the information escalated strategy.” Simply put, huge chunks of data are required to make, test and prepare AI.
There is no denying the fact that AI has immense potential to boost various sectors. It is being leveraged by financial firms, automobile industry, legal offices, and what not! Thus, possession of data and its analysis using AI have become essential for businesses that are looking forward to competing each other. If we trust the reports of ‘Centre for Artificial Intelligence and Robotics’ then AI is not something which has been discovered recently! It has been around us since 1986. The capabilities of AI and Machine Learning have remained a mystery for quite long because we lacked large volumes of data that have been collected from multiple sources. As they were crucial for making our AI machines learn, no significant development could be done. But now, the scenario has changed and we not only have great volumes of data but also the capability to analyze data sets. And thus the developments in ‘Big Data’ have drastically altered and transformed the scope and future of AI significantly. You don’t agree? Read further to know about reasons for concluding the same!
Source: betanews.com
1. Computing Power
Computational capacity can transform Big Data from a burden to business asset and the same has been started. Earlier it used to take a lot of time and investment, but today, we just need Nanoseconds to process millions of datasets or Big Data. The credit for this goes to exponential rise in the speed of computing. The advancements sequential and parallel computing now help in processing data in real-time. Furthermore, it derives set of guidelines for AI-based applications.
2. Adequate approach
The ready to access and fast retrieval of Big Data or the large volumes of data is leading a revolution. If we consider the scenario of a decade back, then data scientists and statisticians had to limit their work to ‘sample datasets’. This has changed drastically now as they can now work fearlessly with the real data as well. Also, now Iteration-based data and predictive analytics tools are available, and thus more organizations are moving towards data-first approach to hypothesis-based approach, eventually giving a boost to AI.
Source: martechtoday.com
3. Natural Language Processing
Natural Language Processing (NLP) technologies are leveraged in several interactive applications. A few examples include Siri, online banking service bots, Alexa and others. Moreover, Learning from human interaction is a crucial part of AI and NLP as Big Data has capability to find relevant information in large volumes of data in order to obtain collective insights. Also, Big Data can help in identifying and revealing patterns across data sources which will prove fruitful for AI.
4. Cost and performance
There is an endless battle going on between cost and performance. Memory devices are now making it possible to efficiently store and retrieve Big Data and we need these in abundance! Keeping this in mind, Upmem, a popular French organization, has introduced a method to offload processing to DRAM for AI workloads. It is found out that by connecting thousands of such units to a traditional processor, the workload will run twenty times faster. However, implementing this requires a lot of investment. And therefore we cannot make cost and performance go hand in hand; we’ll have to compromise on one for sure.
Source: codekul.com
There is no denying the fact that Big Data’s influence will go beyond our expectations. The waves of innovation are expected to get heightened by combination of AI and Big Data. We can say so because these two are the most promising technology paths on which the businesses will rely upon in the future. Let’s not forget that the first wave of Big Data was concentrated on increasing the flexibility and speed for uploading and downloading data, and this has been achieved. However, we might take long enough to attain second wave that will leverage AI by understanding the convergence and interdependence with respect to Big Data. We hope that you liked reading this blog post, let us know your views in the comments section below!