Can Artificial Intelligence Solve The Complexity Of Big Data?

 

  • Google processes more than 40,000 search queries per second.
  • Twitter users generate over 500 million tweets every day, and a similar amount of images is uploaded to Facebook.
  • Facebook users send about 31.25M messages and watch 2.77M videos a minute.
  • As per reports by 2020 about 1.7MB of data is slated to be generated every second for every person on earth. They rightly say “Data never sleeps”.

 

Handling the massive amount of Data: Big Data Analytics

 

Businesses on a daily basis generate large volumes of data. Earlier, most of this data went unutilised as we had no way of analysing it and working on it. Advances in information technology in the last few years has allowed users to capture, communicate, aggregate, store and analyze enormous pools of data which we know today as “Big Data” (Manyika et al., 2011).

According to Analytics, Business Intelligence and Data Management , there’s no single technology that encompasses big data analytics. Of course, there’s advanced analytics that can be applied to big data, but in reality, several types of technology work together to help get the most value from your information.

 

Artificial Intelligence: Not a recent discovery

 

Artificial Intelligence(AI) isn’t a new-age discovery. The term was coined way back in 1956 by John McCarthy. In India, the Centre for Artificial Intelligence and Robotics (a DRDO Organization) was established as early as 1986.

 

From Big Data to Big Artificial Intelligence

 

The world is growing at an exponential rate, so is the size of data amassed across the globe. Massive amounts of data necessitates massive storage capacity and special techniques to analyze big data.

With the invention of computers with super processing powers, the problem has now shifted from collecting massive amounts of data to comprehend it or in other words turning the data into knowledge, conclusions, and actions.

 

“Big Data” is the body of technology—algorithms, programming systems, and hardware, that stress our abilities to handle the data. A related, and apparently more modern term is “Data Science”, which really means the same thing, but includes the application areas to which “big-data” technology is applied. What it DOESN’T mean is “AI” or “Machine Learning” or “Statistics done right”, as some have claimed (Jeffrey D. Ullman, Stanford, USA)

 

Big Data Analytics has provided machines “Big Data” to help emulate humans or train them to perform repetitive tasks with elan so much so, people are now fearful of getting replaced by them in this age of Industry 4.0.

The previously unutilized data has become the turn key to many successful endeavours, think self driving cars.

 

The path leading up to there, not so easy say experts.

 

#1

The last few years have shown that in many cases, businesses don’t have sufficient historical data in required quantities with the required QCs done, needed for current Machine Learning approaches.

Even in cases where sufficient data might be available, a massive amount of effort is required to be put for data engineering, data analysis, feature engineering, predictive modeling, model selection, and verification before even landing at the initial algorithm.

#2

Big Data is a “body of technology” they say, not just algorithms but also the architecture. Algorithms need to be developed keeping in mind the architecture of the machines which ultimately analyze the data in order to speed up the data analytic processing.

#3

In a paper titled, societal implications of Big Data the authors argue that data and social scientists should work together to develop concepts of regulation, which is essential to cope with the challenges and risks posed by Big Data.

 

Big Data Engineers believe, poor scale-up behavior from algorithms designed based on models of computation that are no longer realistic for handling massive amounts of data can heed the growth of Big Data. If we are able to overcome challenges like “algorithmic exploitation of parallelism (multicores, GPUs, parallel and distributed systems, etc.), handling external and outsourced memory as well as memory-hierarchies (clouds, distributed storage systems, hard-disks, flash-memory, etc.), dealing with large scale dynamic data updates and streams, compressing and processing compressed data, approximation and online processing respectively mining under resource constraints, increasing the robustness of computations (e.g., concerning data faults, inaccuracies, or attacks) or reducing the consumption of energy by algorithmic measures and learning”, only then Big Data can open up unprecedented opportunities for both scientific discoveries and commercial exploitation across fields and sectors. Only then we can achieve Big AI.

 

 

Artificial Intelligence to the Rescue

 

Simply put, there cannot be AI without Big Data and no Big Data without AI. Many technologies today, such as self-driving cars for example, heavily depend on the combination and intertwining of these two technologies. AI is here to stay, and it is poised to disrupt not just how we live our personal lives but how we work and the broader business landscape as we know it.

To sum it all up, Big Data has tremendous potential to take business initiatives to the next level. But the tremendous benefits of Big Data do not come without a catch — and in this case, the catch is added complexity.

AI definitely completes the task that Big Data started. Without AI, Big Data would surely be overwhelming and chaotic. But by incorporating AI into Big Data analytics the technology becomes useful, more lucrative, and has the ability to drive businesses into the future.

If you want to train yourself in Big Data Analytic softwares like Hadoop, Spark, MapReduce please feel free to get in touch with us at IvyPro School and enrol in a comprehensive Big Data certification course today.

  

 

REFERNCES:

 

  • https://learning.linkedin.com/blog/top-skills/the-5-most-up-and-coming-jobs-in-the-world–and-how-to-get-them?trk=lilblog_04-22-19_free-resources-future-of-work_learning
  • Kersting, K. & Meyer, U. Künstl Intell (2018) 32: 3. https://doi.org/10.1007/s13218-017-0523-7
  • https://info.aiim.org/aiim-blog/a-brief-history-of-artificial-intelligence
  • https://link.springer.com/article/10.1007/s13218-017-0520-x

 

 

– Shromona Kahali, Content Strategist, Ivy Pro

 

 

 

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