Context:
The NITI-Aayog has put forward a discussion paper on artificial intelligence in India
What is Artificial intelligence (AI)?
- Artificial Intelligence is a way of making a computer, a computer-controlled robot, or software perform human-like tasks.
- It refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making.
- There are two subsets under the umbrella term AI: Machine learning and Deep learning
- Machine Learning involves the use of algorithms to parse data and learn from it. This enables making a determination or prediction.
- Deep learning is technique for implementing machine learning.
- The term was coined in 1956 by John McCarthy
Evolution of AI
Categorization of AI:
Advantages of AI
- No leisure time required
- Lower error rate compared to humans. Better precision and accuracy. Eg: Robotic radio surgery
- Better speed
- Not affected by surrounding environment
- Replace humans in repetitive, tedious tasks
- Better user experience through predictive technology e.g. Help in predicting what a user will type, ask, search, and do. Can easily act as assistants and recommend actions.
- Interact with humans for entertainment or a task. E.g. Sophia robot
- Logical – devoid of emotions. Can make rational decisions with less or no mistakes.
Disadvantages:
- Initial cost too high
- Cannot take decisions if they encounter a situation unfamiliar to them.
- Repair costs are high
- May lead to destruction, if put in the wrong hands. E.g. terrorists
- May have Impact on employment
- Ethical concerns:
Application of AI:
- Agriculture:
Application of AI in agriculture can help in increasing crop yield by providing real-time advisory, early detection of pest attacks, prediction of crop prices, precision farming etc.
Examples:
- PEAT – Machine Vision for Diagnosing Pests / Soil Defects
Berlin-based agricultural tech start-up PEAT has developed a deep learning application called Plantix that reportedly identifies potential defects and nutrient deficiencies in soil.
- CROPTIX- diagnose crop diseases in the field and alert rural farmers in Kenya
It is a new mobile app that uses artificial intelligence to accurately diagnose crop diseases in the field
- Microsoft -ICRISAT AI Sowing App: The app sends sowing advisories to participating farmers on the optimal date to sow
- Healthcare:
Can be used in diagnosis, treatment design, imaging diagnosis, early detections of disease outbreaks, robot assisted surgeries, virtual nurse assistants etc.
Examples:
- Sensely’s “Molly”– an AI-powered nurse used by UCSF and the UK’s NHS to interact with patients
- Recently, researchers at an Oxford hospital developed AI that can diagnose scans for heart disease and lung cancer
- Education:
- Can be used for developing tools for customised learning, interactive and intelligent tutoring systems, and predicting tools- for example predicting dropouts
- Can also be used in developing automated teacher posting and transfer systems, using analytics based on demand – supply gaps across schools
Example:
Pearsons’ WriteToLearn software:
It uses natural language processing technology to give students personalised feedback, hints, and tips to improve their writing skills
- Urban planning:
Can be used for optimizing infrastructure in cities, service delivery, crowd management, cyber security, public safety and water and waste management
Examples:
Bandicoot
In Kerala, engineers have developed sewer-cleaning robots to put an end to manual scavenging
- Transportation:
- Can be used in developing AI-based traffic management system including sensors, CCTV cameras, automatic number plate recognition cameras, speed detection cameras, signalised pedestrian crossings
- Can help in predictions in public transport journeys
- Can help in optimising parking
Example:
Intelligent traffic signals at Pittsburgh
Smart parking garages in USA
- Retail
Can be used in customer demand anticipation, improved inventory management, efficient delivery management, interaction with customers etc
Example:
Japan’s Pepper:
It is a humanoid robot that can interact with customers and “perceive human emotions”.
- Manufacturing:
Can be used in supply chain management, predictive maintenance, logistics, and quality assurance
Example:
General Electric’s Brilliant Manufacturing:
Designed to make the entire manufacturing process more efficient and thus reduce costs.
- Energy
Can be used in energy system modelling, predictive analysis, demand and infrastructure management, renewable management, building energy efficient buildings, etc
Example:
EWeLiNE, Germany:
It can work as an early-warning system for grid-operators to assist them in calculating renewable-energy output over the next 48 hours using AI
- Defence:
Areas of application of AI in defence include:
- Intelligent and Autonomous Unmanned Weapon Systems
- Tactical artificial intelligence: Tactical AI analyzes the battlefield and acts on information by creating a Course of Action that exploit the weaknesses in enemy’s position
- AI-Enabled Data Fusion, Information Processing, and Intelligence Analysis
- Cyber defence and cyber warfare
Examples:
AI powered planning tool, USA
Global Developments:
Various initiatives taken by countries to promote and develop artificial intelligence
Artificial Intelligence in India
Why does India need AI?
- To reap economic benefit
- According to a research by Accenture, AI can boost India’s annual growth rate by 1.3 percentage points by 2035.
- Business opportunities: Can host enterprises and institutions globally to develop AI technology which can be easily implemented in the rest of the developing and emerging economies
- Social development and inclusive growth
AI developments 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, Legged Robot, Wall-Climbing Robot, and Robot Sentry,
- CAIR has been working on a project to develop a Multi Agent Robotics Framework (MARF)- will have array of robots functioning as human soldiers
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
- 3nethra developed by Forus Health for detecting common eye problems
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.
- Traffic Management System based on radar-based monitoring with the help of AI to be used in Delhi
- Kamakoti Committee:
AI Task Force headed by V.Kamakoti was set up to to explore possibilities to leverage AI for development across various fields.
Key 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)
10 domains need attention:
- Manufacturing
- Fintech
- Healthcare
- Agriculture
- Education
- Retail/Customer engagement
- Public utility services
- Aid for differently-abled persons/Accessibility technology
- Environment
- National security
- Education and awareness
National strategy on artificial intelligence
- NITI Aayog in its recent discussion paper addresses the national strategy on artificial intelligence.
- It identifies 5 cores areas for application of artificial intelligence:
- Healthcare: increased access and affordability of quality healthcare
- Agriculture: enhanced farmers’ income, increased farm productivity and reduction of wastage
- Education: improved access and quality of education
- Smart Cities and Infrastructure: efficient and connectivity for the burgeoning urban population
- Smart Mobility and Transportation: smarter and safer modes of transportation and better traffic and congestion problems
Challenges to Adoption in India
- Unavailability of proper data ecosystem
- Insufficient funding and research in AI.
- Inadequate availability of AI expertise, manpower and skilling opportunities
- High resource cost
- Low awareness for adopting AI in business processes
- Lack of proper privacy, security and ethical regulations
- Unattractive Intellectual Property regime. This hinders research and adoption of AI
- Unemployment issues
Way Forward
- Research:
- Two-tiered structure for 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.
- Increased public sector spending.
- Private investment
- Achieve excellence in AI research
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.
- Skilling and Re-skilling of workforce, help in adoption of AI
- Facilitating creation of large foundational annotated data sets
- Partnership of Industries and Academia
- Supporting start-ups
- Spreading awareness on advantages of AI
- Taxing of Robots: Bill Gates had suggested that governments should tax companies using robots. This tax could be used for funding human services.