Into the Next Zone
AI industry gradually shifting from data-centric to knowledge-centric models to meet future requirements
As the world becomes more reliant every passing day on self-learning algorithms and data-based models of logic and machine reasoning, Artificial Intelligence (AI) has become a household term. The tech industry too is rising high on automation and data-driven AI models. But some experts have already began to doubt whether data has served its day, and if it is now time for AI technology to go beyond petty ‘data’ and look into the much wider domain of ‘knowledge’. What significance does this new school of thought hold for the future of data science? Let’s take a quick tour of the basic concepts to understand.
It has been over 50 years now that AI had burst onto the technology arena. Back then, possibilities were endless, and the enthusiasm percolated to all walks of society – with much interest in robots and automation all round. This craze was ably fuelled by sci-fi writers and filmmakers. Sci-fi writing witnessed a boom between 1950s and 1990s, riding high on the wings of two technological wonders of the day – robotics and space missions. Hollywood took the cue and churned out one blockbuster after another on similar themes.
However, AI still remained at the theory level except for very niche purposes. It was only after computing technology began to advance at breakneck speed in the 1990s, and data-crunching became unbelievably easy, that AI models could really be put to actual use. Then came the Information and Communication Technology (ICT) boom which made possible dissemination, collection and collation of data in real time. Ever since, data-based algorithms have been taking over nearly every repetitive task that businesses require. And now that idea has extended to the Internet of Things (IoT) where every device can be interconnected and stay in sync real time, bridging distances both in terms of space and time.
However, with the generation, collection and analysis of data forming the backbone of all business algorithms, the question of data ownership arises. Companies build their decision models on proprietary data. However, with time and ever burgeoning competition, proprietary data will not remain as unique a business asset and AI strategies based on such data will lose their edge. According to experts, this is where AI-based businesses would need to shift their focus to remain sustainable –from data-based AI strategies, to knowledge-based AI strategies.
It has been the current trend among businesses, especially start-ups, to place data acquisition at the heart of their business strategy. The data sets they gather and their long-term strategy for acquiring additional proprietary data – feed the AI-based tools and models they use. This approach has been the backbone of commercially developed of AI models. Fundamentally, sophisticated AI models voraciously feed on such big data to analyse and derive knowledge and insights, and they need a critical mass of big data for machine-training and optimization of algorithm. Companies such as Google and Netflix have developed and curated massive and authoritative data sets over a long period of time. However, as public data becomes abundantly available, it would no longer be possible by any single player to hold on to such data as proprietary. With more players developing capability and collaborative data-sharing gaining acceptance, experts feel proprietary data will run out of steam within the next ten years. But the AI-based models would still need the input to run on – and this new input would now be ‘knowledge’-based.
What, then, is this knowledge? As has been famously said, currently “we are drowning in information but starved for knowledge”. Instead of piecemeal raw data, new decision-making models would be deriving inputs from more meaningfully processed inputs – customized to the needs of the company that uses it. This will lead to the development of innovative and new frameworks and business models. The new approach will require collaboration between diverse stakeholders to bring together data, information, AI models, storage, and computing power – and the resultant output would be creation of ‘knowledge’ – the new proprietary asset.
A good example is the Israeli Innovation Authority, which had already launched a pilot program for knowledge-based cooperation between hospitals and technology start-ups in 2019. It facilitated the exchange of raw healthcare among the hospitals, and between hospitals and start-ups to facilitate generation of new knowledge based on the inputs. Analysts predict that the industry will soon be witnessing changes aimed at this transition. More organisations would be laying the foundations for a knowledge-centric era, in which “asking the right questions, looking for the most relevant predictions and designing the most disruptive AI-based applications” would be the game changers.
