Challenges of Using Artificial Intelligence in Big Data World


Artificial Intelligence (AI) is echoing in every walk of life. Now, this power lies in your handsets in the form of Google Assistant. Just call what you intend to command for. The icon of a microphone starts exposing its artificial intelligence. The best way to overcome challenges of AI in the big data world is to master both technologies through Intellipaat Artificial Intelligence course and big data course.

Is AI a device? Or, is it a miracle?

Frankly speaking, it is no magic. The data scientists have called the revolution to train algorithms. This trend is in. Microsoft, Google, IBM, and Amazon are carrying it for evolving machine learning models. Such discovery through machine learning sets such data trends that could simplify data entry and research work. There are a few core  The effort to replicate a human mind is still on. Machines have started thinking instinctively, although this process is in the nascent stage.

How does artificial intelligence work?

The machine learning (ML), which is inextricably woven with AI and big data, has shown its excellence by integrating the senses into devices. Various data scientists are frequently churning big data to trace unique models. These models reflect the circumstances and the doable acts (reaction(s) over an action), which are tapped through demographics, habits, interests, and transactions. Upon analyzing and testing their viability, machine intelligence is exaggerating its capacities. You can feel it in the smart devices, such as Home, Echo, and Alexa.

Simply put, the process to make machines recognize to and think naturally is sluggishly gaining the momentum. The day is not so far when a device will compete with your cognitive thinking. It will show its presence of mind, as you do.

But, it is not as simple as it sounds. Several AI challenges emerge to question its effective viability. 

Challenges of Artificial Intelligence:

  • Data is Ambiguous: Big data are really big. Typically say, the data sizing more than one TB is big data. Being voluminous, it carries lots of ambiguities. Simply put about ambiguity, the data that need to undergo cleansing and reformatting to attain usability is ambiguous data.

    Let’s say, a data solutions outsourcing company deploys AI tool for tapping data entry trends. Being had multiple layouts of source data, running a single function cannot hit the bull’s eye. And, if it executes many complex functions, more time and money apart from putting efforts will be required to incur on the data collection. The data warehousing process will be run thereafter to achieve a desirable uniform format for discovering patterns to train algorithms. In the nutshell, machine intelligence does not know how to counter ambiguity in a wink. 
  • Data Cleansing is Time & Money Consuming: AI will work incredibly well if it gets clear parameters to pass through the defined functions. But, this technology needs to up-skill. Unfortunately, the big data consist of lots of sedimentary data, which require hard-core efforts to achieve granularity. 

    During F2F interview with The Wall Street Journal, Krishna-IBM’s senior VP of cloud and cognitive software claimed that 80% of the AI work is based on data collection& cleaning, which requires to have a big pocket. If somehow any organization shows interest to bear it, the time flies in data mining and edifying algorithms. Thereby, running out of patience is no surprise. Being short of time and unable to bear the loss, the IBM clients have suspended AI projects.       
  • Blue-Collar Army is Required: The editorial director Jason Hiner of CNET revealed, “One of the ‘dirty little secrets’ of AI is that a lot of companies that are working on AI the most are hiring armies of human beings to cleanse the data, to curate the data and to crunch the data before you feed it into AI.”

    His revelation boosts the fact that the blue-collar army needs to be essentially appointed for cleansing data. This is the only way to insert the granular data so that the AI-supported systems could get clear parameters. Hence, the virtual assistant will be on the ball while being aware of what is happening around and how to deal with it quickly and intelligently.
  • Subjectivity is Missing: AI makes decisions according to the data labeled to provide an explanation of the questions being raised. What if it does not have the data corresponding to the question? This is where this technology falls flat. There are certain qualities that are influenced by personal feelings, tastes, and opinion. Some decisions are made spontaneously. The subjective characteristics of this technology can develop this instinct. But, feeding such characteristics is an uphill climb. 


Artificial intelligence is facing challenges in the big data world. These challenges are based on the data labels that are fed to answer the question being raised around. Lack of subjectivity and having sedimentary data are barriers to up-skill AI-powered virtual assistants. Though we have numbers of companies, agencies, universities, and individual AI & ML professional working on various Artificial Intelligence and Machine learning development in various fields of business and day to day life. We hope for a better and prosperous future with AI and ML.

Author Bio:

Lovely Sharma is a veteran digital analyst, associated with Eminenture. Every new update in AI creates a sensation. The curiosity to absorb what is new in data mining and analysis feeds his appetite for up-skilling. There are several write-ups in his name, carrying solutions for big data challenges and related topics.