Today Artificial Intelligence (AI) is possibly the most widely discussed technology trend. There are strong views about the applications and benefits of AI, while some feel that it will bring in the apogalactic day of machine controlling human beings; a few others believe that AI will be extremely beneficial for humans.
In the near term, AI and machine learning is becoming a handy tool to solve important business and social problems. In many cases, machine intelligence is being used as a supplement to human decision making, thereby “augmenting” human intelligence. In this context ‘Augmented Intelligence’ is proving to be more critical rather than Artificial Intelligence.
In recent times, several business and social applications are using the power of machine intelligence to solve day-to-day complex challenges. Examples include identification of crop failure and de-forestation from satellite images, recognition of disease and anomaly identification from medical images, predicting failure of machines from sensor data, real time product recommendations, identification of fraud and cyber threat and many more.
Hence there is no question about the immense benefits of machine intelligence (whether it is being used as artificial intelligence or augmented intelligence). There are five key components that influence the development and usage of machine intelligence; these are:
- Quantified monetary or social benefit of machine intelligence
- Humans, who develop, use or are impacted by machine intelligence
- The technology which is used to gather data and apply machine intelligence
- The algorithms which are used to extract machine intelligence form data
Out of the above components, data is the most critical element for building and applying machine intelligence. Without data it is impossible to have machine intelligence. Data is really the heart or the primary input to machine intelligence.
For machine intelligence to work, one needs historical data to build the intelligence and current data (or data as of the present moment) to apply the intelligence. Extensive research in academia and industry have demonstrated that higher volumes of data and availability of multiple sources of data is the most important driver in developing more accurate machine intelligence. Hence, data is the competitive edge for an organization and for a nation to build an edge in this space.
However, the data in most organizations (including Government agencies) are extremely disaggregated. Putting all the data together and resolving data quality challenges is the first and the most difficult step in building machine intelligence. In addition, the ability of tapping into new data sources or obtaining external data remains the key differentiator in building strong machine intelligence that is difficult for a competitor to replicate.
Technology and algorithms are the other key enablers for building machine intelligence. Previously, most of the technology required for building machine intelligence used to be encapsulated in proprietary tools and hence, were prohibitively costly and was out of reach for the mid-to-small sized organizations. However, the open source revolution has broken that barrier and has made very sophisticated technologies available at nearby zero costs. This consequently fuelled the adoption machine learning across SME’s and has helped democratise machine intelligence.