AI DEVELOPMENTS IN INDIA

WHY IN NEWS:

• The Ministry of Electronics and Information Technology (MeitY) has reinitiated consultations with ministries and government departments on the use of AI, expressly for the implementation of state-run services.

WHAT IS ARTIFICIAL INTELLIGENCE (AI):

• AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making.
• Initially conceived as a technology that could mimic human intelligence, AI has evolved in ways that far exceed its original conception.

GLOBAL DEVELOPMENTS IN AI:

• Countries around the world are becoming increasingly aware of the potential economic and social benefits of developing and applying AI.
• For example: China and U.K. estimate that 26% and 10% of their GDPs respectively in 2030 will be sourced from AI-related activities and businesses.
• Most of the governments have established / utilised existing centralised umbrella body for budgetary planning of AI interventions. For example
  • U.K. has a dedicated department "Office of AI" to collaborate with multiple departments, ministries and other stakeholders to deliver AI projects
  • Strategic Council for AI technologies in Japan.

FACTORS AIDING AI:

• Unlimited access to computing power:
  • The worldwide public cloud services market is projected to grow 21.4% in 2018 according to Gartner, Inc.
  • The access is amplified by rapid increase in computational power. 
• Huge fall in cost of storing data:
  • Hard drive cost per gigabyte of data falling exponentially >> down from USD 500,000 a gigabyte in 1980 to 2 cents a gigabyte in 2017.

POTENTIAL APPLICATION OF AI:

• Intelligent automation:
  • It is the ability to automate complex physical world tasks that require adaptability and agility across industries
• Labour and capital augmentation:
  • Enabling humans to focus on parts of their role that add the most value, complementing human capabilities and improving capital efficiency
• Innovation diffusion:
  • Propelling innovations as it diffuses through the economy

SECTOR-WISE APPLICATION IN INDIAN CONTEXT:

• Healthcare:
  • AI helps to address issues of high barriers to access to healthcare facilities, particularly in rural areas that suffer from poor connectivity and limited supply of healthcare professionals.
  • AI driven diagnostics, personalized treatment, early identification of potential pandemics, and imaging diagnostics
  • Example: Sensely’s ‘Molly’, an AI-powered nurse used by UK’s NHS to interact with patients
• Agriculture:
  • AI holds the promise of driving a food revolution and meeting the increased demand for food
  • AI has the potential to address challenges such as inadequate demand prediction, lack of assured irrigation, and overuse/misuse of pesticides and fertilisers
  • Improvement in crop yield through real time advisory, advanced detection of pest attacks, and prediction of crop prices to inform sowing practices
  • AI also aids in enhanced farmers’ income, increased farm productivity and reduction of wastage
  • Examples: PEAT – Machine Vision for Diagnosing Pests/Soil Defects
• Education:
  • Augmenting and enhancing the learning experience through personalised learning, automating and expediting administrative tasks,
  • Predicting the need for student intervention to reduce dropouts or recommend vocational training.
• Smart Cities and Infrastructure:
  • Integration of AI in newly developed smart cities and infrastructure could also help meet the demands of a rapidly urbanising population and providing them with enhanced quality of life.
  • Traffic control to reduce congestion and enhanced security through improved crowd management.
• Smart Mobility and Transportation:
  • Potential use cases in this domain include autonomous fleets for ride sharing, semi-autonomous features such as driver assist, and predictive engine monitoring and maintenance.
  • AI improves autonomous trucking and delivery, and improved traffic management. 
  • Smarter and safer modes of transportation and better traffic
  • Example: Intelligent traffic signals at Pittsburgh, U.S
• Retail:
  • Use of AI applications improves user experience by providing personalised suggestions, preference-based browsing and image-based product search.
  • Customer demand anticipation, improved inventory management, and efficient delivery management
• Manufacturing:
  • AI enables 'Factory of the Future' through flexible and adaptable technical systems to automate processes and machinery to respond to unfamiliar or unexpected situations by making smart decisions.
  • Impact areas include engineering (AI for R&D efforts), supply chain management (demand forecasting), production (AI can achieve cost reduction and increase efficiency), maintenance (predictive maintenance and increased asset utilisation), quality assurance (e.g. vision systems with machine learning algorithms to identify defects and deviations in product features), and in-plant logistics and warehousing.
• Energy:
  • Energy system modelling and forecasting to decrease unpredictability and increase efficiency in power balancing and usage
  • In renewable energy systems, AI can enable storage of energy through intelligent grids enabled by smart meters
  • Improve the reliability and affordability of photovoltaic energy
  • AI can be deployed for predictive maintenance of grid infrastructure.
• Banking and financial services sector:
  • Improved customer interaction through personalised engagement, virtual customer assistance, and chat bots
  • Improved processes through deployment of intelligent automation in rule based back-office operations.
  • Development of credit scores through analysis of bank history or social media data.
  • Fraud analytics for proactive monitoring and prevention of various instances of fraud, money laundering, malpractice.
  • Prediction of potential risks.
  • AI helps in wealth management viz. robo-advisory, algorithmic trading and automated transactions.
  • India’s competence in IT combined with opportunities, such as interoperability between multiple languages, provides the much needed impetus for finding scalable solutions for problems that have global implications, such as NLP.

AI DEVLOPMENTS IN INDIA:

• Business:
  • There has been an increase in AI focused start-ups
  • According to a PwC research, 36 percent large financial establishments in India have invested in AI technologies
  • Artificial Intelligence Industry in India is currently estimated to be $180 million annually in revenues
• Defence:
  • Center for Artificial Intelligence and Robotics (CAIR) in DRDO conducts research in artificial intelligence
  • Indian Army already has Wheeled Robot with Passive Suspension, Snake Robot etc.
• Health Sector:
  • NITI Aayog is working with Microsoft and Forus Health to introduce a technology for early detection of diabetic retinopathy as a pilot project
• Agriculture:
  • NITI Aayog and IBM have partnered to develop a crop yield prediction model using AI to provide real time advisory to farmers.
• Education:
  • Andhra Pradesh government has collaborated with Microsoft to predict drop-outs and address the issue
• Urban infrastructure and Transport:
  • Pune Street Light Project- energy efficient street lights: can be remote controlled through a Supervisory Control and Data Acquisition (SCADA) systems.
  • Surat has collaborated with Microsoft to develop solutions for water management and urban planning.

SCOPE:

• India has the necessary building blocks to develop a thriving AI research and development ecosystem such as:
  • Availability of highly educated talent pool - India produced a whopping 2.6 million STEM graduates in 2016.
  • World class educational institutes
  • Illustrious list of top notch IT companies dominating the global IT landscape.
  • High data generation due to world’s second largest internet using population, smartphone users etc.

CHALLENGES:

• Lack of expertise:
  • Lack of broad based expertise in research and application of AI
  • Inadequate availability of AI expertise, manpower and skilling opportunities.
• Poor ecosystem:
  • Absence of enabling data ecosystems – access to intelligent data
• Cost:
  • High resource cost and low awareness for adoption of AI
• Privacy issues:
  • Privacy and security, including a lack of formal regulations around anonymisation of data
  • Unclear privacy, security and ethical regulations
• Lack of collaboration:
  • Absence of collaborative approach to adoption and application of AI.
  • Technical feasibility, availability of structured data, regulatory barriers, privacy considerations, ethical issues, preference for human relationship
• Poor research:
  • Low intensity of AI research and low budgetary allocation
  • Challenges in transforming core research into market applications
• IPR issues:
  • Unattractive Intellectual Property regime to incentivize research and adoption of AI
• Adopting a narrow view:
  • Adopting a narrow view and focusing on the challenges for a specific sector, the barriers to developing a robust set of AI applications may seem contextual and limited to that sector.
  • Taking healthcare sector as an example, while India has adopted electronic health record (EHR) policy, sharing of data between various hospital chains still remains a work in progress, since different hospital chains have adopted different interpretations of ‘digitising records’
• Not welfare driven:
  • AI technology adoption till date has been driven primarily from a commercial perspective.

WAY FORWARD:

• A two-tiered structure for promoting AI research:
  • Centre of Research Excellence (CORE) focused on developing a better understanding of existing core research and pushing technology frontiers through creation of new knowledge
  • International Centres of Transformational AI (ICTAI) for developing and deploying application-based research. Private sector collaboration is considered to be a key aspect of ICTAIs.
• Kamakoti Committee recommendations:
  • Set up digital data banks, marketplaces and exchanges to ensure availability of cross-industry information
  • Data ombudsman: to address data-related issues and grievances.
  • Ensure availability of funds for R&D
  • Setting up National Artificial Intelligence Mission (N-AIM)
• Government data sharing:
  • Government of India has large amounts of data lying in silos across ministries.
  • The government can launch a mission of making all these data available for public good after undertaking proper privacy checks.
  • For example – climate data, non-strategic remote sensing data, regional language speech (from All India Radio), soil health data etc. 
• Corporate data sharing:
  • Corporates based in India may be mandated to share their data for social good.
  • For example, sharing transportation pattern of individuals/mass transits, collected by service providers and aggregators, can help the city planners help in planning routes, predicting and managing traffic.
• Digitised and crowd-sourced collection of data by government:
  • Huge amounts of money and time is spend every few years to carry out the household consumption survey.
  • A mechanism, as adopted by online social networks, to incentivise individuals to share details of their consumption pattern via an app can greatly reduce the cost of manual surveys and lend itself to big data analysis and AI applicability. 

FIVE ESSENTIAL PILLARS OF AI ECOSYSTEM: 

BEST PRACTICE:

• EU’s Robotics Public Private Partnership, 2013:
  • One of the biggest civilian research programme - has helped Europe in emerging among top robot manufacturers.

PRACTICE QUESTION:

Q. How India can leverage the AI based technologies to ensure social and inclusive growth?