Deriving Wisdom From Data: A Technological Perspective

Samiya Khan, Senior Researcher, Jamia Millia Islamia

2019-07-23 08:45:45

Credit: Samiya Khan

Credit: Samiya Khan

“Data is the new oil. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc. to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.” – Clive Humpy

In these words, Clive Humpy, a famous Mathematician and architect of Tesco’s Clubcard, pretty much summed up all that data is and all we expect from it. The motive behind any analysis or processing is to derive value from raw data. This course of extracting value from raw facts has completely reformed with the advent of artificial intelligence. While we were only extracting information from data until recently, the present-day technologies allow us to generate new information, model knowledge and develop wisdom from data.

Data includes raw inputs collected from real-world systems and processes. These responses may or may not be organized and may not even be collected with the vision to solve a problem or answer a question. When we organize data to create a report, for example, the ocean of data is transformed from raw inputs to information. From a technological viewpoint, databases and database management systems that allow efficient storage and retrieval of data allow seamless transformation of raw facts from ‘data’ to ‘information’. NoSQL stores and decentralized storage are aimed towards making this transformation effective and efficient, in view of the ‘big’ data scenario.

Moving up the pyramid, modern-day systems generate new information and model knowledge using artificial intelligence. Data mining and machine learning techniques are torchbearers when it comes to generation of information. While classification tools and multi-dimensional vector analysis allow custom data categorization, technologies like Natural Language Processing (NLP) derive inferences using text summarization. A common example of NLP at work is assessing the mood of the writer from provided text. 

The use of computer vision coupled with neural networks and deep learning has revolutionized image analysis to generate varied forms of information, from human face detection to motion monitoring. Evidently, the applications of such synergistic technological usages are diverse, ranging from facial recognition systems to traffic management and forecasting.

To sum it up, the information generation technique or model contains knowledge that is driven by data. A NLP model that tells the mood of a writer has the knowledge of how a mood is demonstrated in the form of text and a facial recognition system has the knowledge of how a human face looks and is different from other objects. Advancing this into complex artificial intelligence allows modeling of procedural knowledge. For example, data can be used to map the fastest route to reach a destination, which takes this technology to development of abstract systems like self-driven cars.

Beyond the realm of machine learning and deep learning lies experiential learning, which allows systems to learn from past experiences and relate current situations with existing knowledge to come up with solutions. In other words, whenever faced with a situation, the system must be able to recall a similar situation that it had faced in the past and act in accordance with it. Wisdom is a decision-making ability that requires knowledge and experience, clubbed together. For this, technologies like deep learning and experiential learning need to be applied in an integrated manner.

Although, we are swiftly moving towards the age of ‘wise’ systems, their level of wisdom is questionable. Most of the contemporary systems are aimed to make humans wiser. In other words, they assist human wisdom. One such example is IBM’s Watson that answers questions asked in natural language. These systems require human wisdom to operate and interpret them. Moreover, they are merely improving human capabilities and are not capable enough to replace them.

In the current scenario, it can be established that human wisdom will always be required to comprehend, use and unravel the true potential of ‘wise’ systems. Therefore, modern systems must be used collaboratively with human wisdom to derive value out of data and use data science to its true potential.