Question map
With the present state of development, Artificial Intelligence can effectively do which of the following ? 1. Bring down electricity consumption in industrial units 2. Create meaningful short stories and songs 3. Disease diagnosis 4. Text-to-Speech Conversion 5. Wireless transmission of electrical energy Select the correct answer using the code given below :
Explanation
The correct answer is Option 2 (1, 3 and 4 only). This question, appearing in the UPSC Civil Services Prelims (2020), evaluates the practical and industrial applications of Artificial Intelligence (AI) at its current stage of development.
- Statement 1: AI optimizes power grids and industrial machinery through predictive analytics, significantly reducing electricity consumption.
- Statement 3: AI algorithms excel in medical imaging and pattern recognition, enabling accurate disease diagnosis (e.g., detecting tumors or retinal diseases).
- Statement 4: Text-to-Speech conversion is a mature AI technology used widely in virtual assistants and accessibility tools.
Why Option 2 is correct: While AI can generate text, creating truly "meaningful" stories and songs (Statement 2) involves human-level creativity and consciousness, which remains a limitation. More importantly, Statement 5 (Wireless transmission of electrical energy) is a matter of physics and electrical engineering (like Inductive Coupling), not an AI function. Since Statement 5 is scientifically incorrect in the context of AI, options 1, 3, and 4 are excluded, making Option 2 the most accurate choice.
PROVENANCE & STUDY PATTERN
Guest previewThis is the classic 'Possibility Framework' question. It does not rely on a single book but on understanding the fundamental nature of AI: Optimization (Statement 1, 5), Generation (Statement 2, 4), and Pattern Recognition (Statement 3). If the tech involves data processing or system control, the answer is 'Yes'.
This question can be broken into the following sub-statements. Tap a statement sentence to jump into its detailed analysis.
- Statement 1: As of 2020, can artificial intelligence effectively reduce electricity consumption in industrial units?
- Statement 2: As of 2020, can artificial intelligence generate meaningful short stories and songs?
- Statement 3: As of 2020, can artificial intelligence effectively perform disease diagnosis?
- Statement 4: As of 2020, can artificial intelligence effectively perform text-to-speech conversion?
- Statement 5: As of 2020, can artificial intelligence be used to perform wireless transmission of electrical energy (wireless power transfer)?
Notes rapid growth in energy demand and that power failures/load-shedding hurt industrial production, implying large industrial sensitivity to energy use and potential gains from demand reduction.
A student could infer that targeting industrial energy use (e.g., via AI-driven load management) would meaningfully affect reliability and output, so they should look for AI case studies in load-shedding reduction.
Describes heavy industrial uses of electricity (high-temperature electric furnaces in steelmaking), identifying specific, high-consumption processes that could be optimized.
A student could reason that AI applied to process control or predictive maintenance in such electric-heating processes might cut consumption and then search for measured savings in those applications.
States over two-thirds of the world's electricity supply comes from thermal plants mainly from industrial areas, indicating industry is a major electricity consumer globally.
Knowing industry is a dominant load, a student could estimate potential system-level impact if AI reduced industrial consumption by a plausible percent and compare that to national/sector energy figures.
Gives an example (daylight saving) of a policy change that reduces electricity use for lighting, showing that operational or scheduling changes can lower demand.
By analogy, a student can consider AI as an operational/scheduling tool (e.g., shifting loads, optimizing lighting/heating schedules) and seek evidence of similar demand reductions from AI interventions.
Mentions governmental initiatives aiming to 'decrease the power consumption', implying organized interventions can change consumption patterns.
A student could view AI as one type of intervention governments or firms might deploy and then investigate whether policy-driven or tech-driven programs (including AI) reported consumption decreases.
Describes Industry 4.0 as driven by 'big data, high computing capacity, artificial intelligence and analytics' enabling creation of new virtual/digital worlds.
A student could combine this with the basic fact that language and media live in the digital domain to infer AI systems might be capable of producing creative digital artifacts (text/music) by 2020 and then look for demonstrations from that period.
Shows AI applied to complex, contextβdependent tasks (weather, seasonal forecasting, decision timing) using historic and farm-specific data.
One could generalize that if AI handles complex patterning and context in data, similar patterning could be applied to language sequences to produce narratives or lyrical sequences, prompting a check of textual generation examples up to 2020.
Details AI use in analysing varied data (weather, soil, images) and mentions image recognition collaborationsβevidence AI systems can model unstructured inputs and produce actionable outputs.
A student could extend this rule to note that modelling unstructured inputs (images, time series) is akin to modelling unstructured language, suggesting feasibility of AI generating coherent text or melodies and then verify by seeking contemporaneous generative AI work.
Specifically cites deep learning and deploying deep learning-based applications for image analysis and disease identification.
From this, one could infer deep learning architectures were in practical use and could be repurposed for sequence generation (text/music), so the student might search for deep learning-based text/music generation examples by 2020.
Encourages asking creative and varied questions and mentions songs/essays as creative forms, highlighting creativity as a definable task.
A student could combine the idea that creativity can be framed as a task with the above evidence of AI handling complex tasks to hypothesize AI could be asked to produce creative outputs and then check whether AI produced meaningful stories/songs by 2020.
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- Explicitly describes a system (Krishi Megh) equipped with artificial intelligence and deep learning for image analysis.
- Specifically cites use of these tools for disease identification in livestock, a diagnostic application.
- Mentions deployment and building of deep learningβbased applications, implying practical diagnostic use.
- States that Precision Farming uses AI to aid in detecting diseases in plants.
- Notes AI sensors can detect pests, poor nutrition and target interventionsβfunctions aligned with diagnosis and identification.
- Describes drone technology with AI-enabled cameras used to monitor crops and identify disease spots.
- Highlights ability to collect focused data from affected areas to stop disease spread, indicating effective diagnostic monitoring.
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- Explicitly lists "text-to-speech" among AI-powered technologies used in voice-enabled applications.
- Places text-to-speech alongside real-time translation, automatic speech recognition and speech-to-text, implying it is a practical component of AI systems for natural language interactions.
- Context describes these technologies supporting end-to-end buyer interactions, indicating operational use rather than just research.
Describes Industry 4.0 driven by 'high computing capacity, artificial intelligence and analytics' enabling end-to-end digitisation and creation of a 'virtual world'.
A student could infer that mature AI plus high compute in 2020 supports complex generative tasks (like TTS) and then check contemporaneous TTS systems and compute availability.
Gives concrete examples of AI being applied to real-world, data-driven tasks (weather and farm forecasting), showing practical deployment of ML/AI in 2020-era contexts.
One could generalise that if AI handled nuanced prediction and modelling tasks in production, similar ML methods could be adapted to audio generation (TTS), then compare published TTS research/products from 2020.
Mentions policy drive to 'Make AI in India and Make AI work for India' and establishment of AI centres of excellence, indicating institutional investment in AI capabilities around that time.
A student might deduce that government and institutional support accelerates development and deployment of AI technologies, and then look for institutional or commercial TTS offerings circa 2020.
States that use of AI can add substantially to GDP by 2025, implying wide economic adoption and applicability of AI technologies across sectors.
From broad economic impact claims, a student could reason that commercially useful AI applications (including TTS for accessibility and services) were likely viable by 2020 and seek marketplace examples.
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Shows AI being used to process sensor/IoT data and make operational decisions (e.g., timing irrigation, forecasting) β an example of AI controlling physical systems.
A student could infer that similar AI models and sensor networks might be applied to control, optimise or coordinate wireless power systems and then check technical literature on AI in power electronics or WPT control.
States the prevailing pattern: electrical energy is normally transported via wires and plugs over long distances β defining the conventional baseline for power transfer.
A student could use this baseline to contrast wired transmission with wireless methods and then investigate whether AI has been proposed or used to manage nonβwire transmission.
Emphasises that electricity supply typically reaches consumers via wires and sockets and lists generation methods β clarifies typical system architecture an AI would have to interface with.
A student could use knowledge of existing wired distribution topology to reason about what new control or safety roles AI would need to perform for wireless power transfer and then search for studies applying AI to WPT safety/control.
Describes institutional and grid structures (Ministry, power corporations, Power Grid) responsible for generation and transmission β indicating where control/management technologies like AI would be deployed.
One could infer that any practical wireless power deployment would need integration with grid operators, so a student could look for AI pilot projects within utilities or regulatory documents concerning WPT and AI.
Explains the existence of automated trading and platformisation in the electricity sector (IEX) β an example where software/automation manages electricity flows and markets.
From this, a student might reasonably hypothesise that automation/AI is already accepted in power systems and then check whether similar automation has been applied to manage wireless power transfer systems or markets.
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- [THE VERDICT]: Sitter (with the 'Science Heuristic'). While 'Wireless Transmission' looks exotic, it is a control-system problem, which AI solves. Source: General Awareness + Logic.
- [THE CONCEPTUAL TRIGGER]: Science & Technology > Emerging Technologies > Artificial Intelligence (Applications).
- [THE HORIZONTAL EXPANSION]: Map the 'Verbs' of Emerging Tech: 1. AI/ML: Optimizes, Predicts, Generates. 2. Blockchain: Decentralizes, Verifies. 3. CRISPR: Edits, Targets. 4. IoT: Senses, Connects. 5. Quantum Computing: Solves (Optimization/Crypto).
- [THE STRATEGIC METACOGNITION]: Do not hunt for specific news articles for every option. Instead, apply the 'Function Test': Does the task involve analyzing data, optimizing a flow, or generating a pattern? If yes, AI can do it. Wireless power is just 'optimizing energy flow without wires'.
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Industrial growth and rising per-capita income drive rapid increases in electricity demand, causing power failures and production losses when supply is inadequate.
High-yield for UPSC: questions often link industrial development to energy demand, supply constraints and economic outcomes. Mastering this helps answer policy and infrastructure questions, and connects to topics like industrial policy, energy security and regional development.
- Geography of India ,Majid Husain, (McGrawHill 9th ed.) > Chapter 8: Energy Resources > ENERGY CRISIS > p. 30
- Certificate Physical and Human Geography , GC Leong (Oxford University press 3rd ed.) > Chapter 27: Fuel and Power > World production and distribution of electricity. > p. 274
Electricity comes from thermal, hydro, fossil fuels and renewables, and per-capita electricity use is a standard index of socio-economic development.
Core concept for UPSC: understanding generation sources informs debates on energy transition, climate policy and regional energy planning. It aids answers on energy security, renewable integration and development indices.
- Geography of India ,Majid Husain, (McGrawHill 9th ed.) > Chapter 8: Energy Resources > Electricity > p. 17
- NCERT. (2022). Contemporary India II: Textbook in Geography for Class X (Revised ed.). NCERT. > Chapter 5: Print Culture and the Modern World > Electricity > p. 115
Technological routes such as waste-to-energy (incineration, pyrolysis, gasification, biomethanation) can generate electricity and reduce waste disposed to landfills.
Relevant for UPSC sections on renewable energy and urban/environmental management: explains alternative supply options, mitigation of waste issues, and links to policy instruments for sustainable energy. Useful for questions on decentralized generation and circular economy.
- Environment, Shankar IAS Acedemy .(ed 10th) > Chapter 22: Renewable Energy > 22.8 WASTE TO ENERGY > p. 294
- Geography of India ,Majid Husain, (McGrawHill 9th ed.) > Chapter 8: Energy Resources > ENERGY CRISIS > p. 30
AI is actively applied to predict sowing times, irrigation, fertilizers and to build seasonal forecasting models for farming decisions.
High-yield topic for UPSC: demonstrates concrete, policy-relevant uses of AI in primary sector modernization and rural development. Connects to questions on technology adoption in agriculture, climate-smart farming, and digital public goods. Enables answer patterns comparing domain-specific AI uses with broader capabilities.
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Application of Technology in Agriculture: > p. 357
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Application of Technology in Agriculture: > p. 358
- Indian Economy, Nitin Singhania .(ed 2nd 2021-22) > Chapter 9: Agriculture > X Krishi Megh > p. 332
AI is a core driver of Industry 4.0 and end-to-end digitisation of physical assets and manufacturing ecosystems.
Important for questions on economic transformation, manufacturing policy, and technological change. Helps link AI to industrial policy, employment impacts, and digital infrastructure debates β useful for essays and economy/GS mains answers.
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 7: Indian Economy after 2014 > Fourth Industrial Revolution (Industry 4.0): Present > p. 232
Deep learning and image-recognition systems are used for tasks like disease identification in livestock and camera-enabled weed recognition, illustrating AI's pattern-recognition strengths.
Practically useful for UPSC answers on AI capabilities and limits: explains why AI excels at pattern-based tasks and where it is deployed operationally. Enables comparisons between task-specific AI systems and claims about general creative abilities.
- Indian Economy, Nitin Singhania .(ed 2nd 2021-22) > Chapter 9: Agriculture > X Krishi Megh > p. 332
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Application of Technology in Agriculture: > p. 358
AI systems are used to detect diseases in plants and livestock via image analysis and tailored models.
High-yield for questions at the technologyβagriculture interface: explains how digital tools impact farm productivity, animal health and food security. Links to policy issues on adoption, scalability, and rural technology deployment; useful for case-based and policy critique questions.
- Indian Economy, Nitin Singhania .(ed 2nd 2021-22) > Chapter 9: Agriculture > X Krishi Megh > p. 332
- Indian Economy, Vivek Singh (7th ed. 2023-24) > Chapter 11: Agriculture - Part II > Application of Technology in Agriculture: > p. 359
Discover the small, exam-centric ideas hidden in this question and where they appear in your books and notes.
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Generative Adversarial Networks (GANs). Since 'creating stories' (Generative AI) was tested here, the specific architecture (GANs) or its misuse (Deepfakes) is the next logical sibling fact to master.
The 'Science is Limitless' Rule: In UPSC Prelims, if the question asks 'Can Technology X do Y?', and Y is not scientifically impossible (like 'Perpetual Motion' or 'Time Travel'), the answer is almost always YES. Avoid restrictive options.
GS-3 (Infrastructure & Security): AI in energy (Statement 1 & 5) links to 'Smart Grids'. A Smart Grid reduces AT&C losses but increases vulnerability to Cyber Warfare (Critical Infrastructure attacks).
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