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GS Paper: GS3-17.Awareness in the fields of IT, Space, Computers, Robotics, Nano-technology, Bio-technology and issues relating to Intellectual Property Rights.

  • Drone revolution and modern warfare

    Why in the News?

    The Ukraine War, the Israel-Hezbollah conflict, and broader West Asian confrontations demonstrate that mass-produced unmanned aerial systems (UAS) have become central to modern warfare. For the first time, relatively inexpensive, commercially derived drones have challenged the dominance of traditional military platforms such as tanks, artillery, combat aircraft, and precision-guided missile systems.

    Why has the traditional model of military superiority been challenged?

    1. Conventional Military Paradigm: Battlefield superiority historically depended on combat aircraft, tanks, artillery, warships, air-defence systems, precision-guided missiles, and advanced intelligence networks.
    2. Resource Advantage: Large military budgets enabled technologically advanced states to dominate battlefields.
    3. Asymmetric Warfare: Smaller states and non-state actors relied on guerrilla tactics, ambushes, and unconventional warfare to offset conventional disadvantages.
    4. Paradigm Shift: Commercially derived drones have disrupted this model by providing low-cost precision strike capabilities at scale.
    5. Persistent Battlespace: Modern battlefields no longer provide safe rear areas as drones can detect, track, and engage targets across the operational depth.

    How has the Ukraine War become the laboratory of industrial-scale drone warfare?

    1. Rapid Adaptation: Ukraine converted commercially available drones originally designed for photography, mapping, and surveillance into military platforms.
    2. Transformation of Role: Drones evolved from intelligence-gathering tools into active strike systems.
    3. Full Integration: By 2024, drones became integrated across almost every layer of Ukrainian combat operations.
    4. Operational Functions: Drones support battlefield surveillance, frontline targeting, artillery correction, logistics interdiction, and deep-strike missions.
    5. Replication Effect: Ukraine’s drone warfare model has subsequently influenced conflicts across West Asia.
    6. Historic First: Ukraine represents the world’s first industrial-scale, drone-intensive conflict.

    How did FPV drones revolutionise battlefield operations?

    First-Person View (FPV) drones allow you to fly while wearing specialized video goggles that stream a live, real-time feed directly from the drone’s onboard camera. Unlike standard camera drones that fly via GPS stabilization, FPV flying offers total acrobatic freedom and an immersive, cockpit-like experience.

    1. FPV (First Person View) Technology: Uses onboard cameras transmitting live video feeds to operators through virtual-reality-style goggles.
    2. Operational Advantage: Ensures precision, manoeuvrability, responsiveness, and low operational costs.
    3. Combat Variants: Includes strike drones, bombers, interceptors, and long-range attack systems.
    4. Cost Asymmetry: Systems costing only a few hundred dollars can destroy armoured vehicles and equipment worth millions.
    5. Expanded Combat Envelope: Thermal-imaging and night-vision variants enable round-the-clock operations.
    • Examples
      • Vampire Hexacopter (“Baba Yaga”): Heavy-lift drone used for combat missions.
      • FPV Kamikaze Drones: Quadcopters carrying explosive payloads such as: Rocket-propelled grenade (RPG) warheads. and Purpose-built munitions.

        How has Ukraine developed a layered drone ecosystem?

        1. Loitering Munitions
          1. RAM II: Short-range precision loitering munition used alongside reconnaissance drones.
          2. UJ-31 Zozulya: Aerially deployed “parasite drone” carried by the UJ-22 Airborne UAV to extend operational reach.
        2. Reconnaissance Systems
          1. Shark Drone: Provides reconnaissance support.
          2. PD-2: Supports surveillance and targeting missions.
        3. Bomber Drones
          1. DJI Mavic 3 Adaptations: Converted from civilian applications to military bomber roles.
          2. DJI Matrice 300 RTK Adaptations: Modified to carry Grenades, Anti-tank mines and Other munitions.
          3. Operational Benefit: Survive missions and conduct multiple sorties unlike kamikaze drones.
        4. Deep Strike Systems
          1. Pegasus FPV Strike Drone: Supports tactical strike operations.
          2. One-Way Attack Drones: Conduct deep strikes against:
            1. Logistics hubs.
            2. Airbases.
            3. Critical infrastructure.
        5. Parasite Drone Concept: UJ-31 Zozulya is carried by the UJ-22 Airborne UAV and released mid-air, extending operational range and penetration capability.

        Why are fibre-optic drones considered a major battlefield innovation?

        A fiber-optic drone is an unmanned aerial vehicle (UAV) that tethers to a ground controller via a thin, hair-like optical fiber cable. Deployed primarily as first-person view (FPV) loitering munitions or reconnaissance craft, they transmit control signals and high-bandwidth video through light, rendering them completely immune to electronic warfare (EW) jamming.

        1. Electronic Warfare Resistance: Conventional drones rely on radio-frequency links vulnerable to jamming.
        2. Fibre-Optic Guidance: Uses physical fibre-optic cables spooled during flight.
        3. Reduced Vulnerability: Ensures mission continuity despite electronic warfare interference.
        4. Operational Advantage: Enables operations in heavily contested electromagnetic environments.
        5. Strategic Significance: Restores drone effectiveness where conventional systems would fail.

        How does Hezbollah employ drones in its military strategy?

        Iranian Supply Chain: Relies heavily on Iranian-origin drone platforms.

        Key Platforms

        1. Ababil Series: Supports ISR and strike missions.
        2. Mohajer Series: Provides medium-range reconnaissance capabilities.
        3. Shahed Series: Performs surveillance and attack functions.

        Specific Systems

        1. Mohajer-4: Provides ISR coverage.
        2. Shahed-129: Supports medium- to long-range ISR missions.
        3. Shahed-136: Functions as a dedicated one-way strike loitering munition.

        Technological Adaptation

        1. Fibre-Optic FPV Drones: Adopted to overcome Israeli electronic warfare measures.

        How has Israel responded to the drone challenge?

        1. Layered Counter-Drone Architecture
          1. Electronic Warfare Systems: Supports drone detection and disruption.
          2. Specialised Radar Arrays: Improves low-altitude drone tracking.
        2. Emerging Technologies/AI-Enabled Iron Drone Raider:
          1. Neutralises drones through kinetic interception.
          2. Uses net capture mechanisms.
          3. Employs direct collision tactics.
          4. Reduces reliance on expensive missile interceptors.
        3. Integrated UAV Force Structure
          1. Heron Systems: Provide long-endurance ISR coverage.
          2. Armed Drones: Support precision strike missions.
          3. Loitering Munitions: Enable rapid reconnaissance-strike integration.

        How does Iran represent a distinct model of drone warfare?

        1. Strategic Integration: Uses drones as instruments of national deterrence and power projection, not merely battlefield weapons.
        2. Proxy Warfare Network: Supplies drone capabilities to allies and proxy groups across Iraq, Syria, Lebanon, and Yemen.
        3. IRGC-Led Doctrine: Integrates drone development and deployment into the Islamic Revolutionary Guard Corps’ military strategy.
        4. Indigenous Production: Manufactures Shahed-series drones domestically, ensuring scalability and strategic autonomy.
        5. Low-Cost Regional Influence: Projects military power and threatens adversary assets across West Asia without maintaining expensive conventional air forces.

        Why is the drone revolution fundamentally an economic revolution?

        1. Cost Efficiency: Cheap unmanned systems replace expensive military platforms.
        2. Production Scale: Industrial manufacturing capacity increasingly determines battlefield success.
        3. Attrition Advantage: Large-scale drone production offsets losses.
        4. Battlefield Economics: Few hundred-dollar drones can destroy million-dollar platforms.
        5. Industrial Endurance: Success depends on continuous production and adaptation.
        6. Technological Adaptability: Drone systems are rapidly reconfigured for evolving battlefield requirements.

        Conclusion

        Modern warfare is transitioning from a platform-centric model to a drone-centric ecosystem characterised by low-cost precision, continuous reconnaissance, and rapid innovation. As drones become central to deterrence, power projection, and battlefield operations, military advantage will increasingly depend on the ability to build, deploy, adapt, and neutralise unmanned systems at scale.

        Value Addition

        Revolution in Military Affairs (RMA)

        1. Integration of emerging technologies into warfare.
        2. Alters doctrine, force structure, and operational concepts.
        3. Comparable to:
          1. Gunpowder Revolution.
          2. Mechanised Warfare.
          3. Nuclear Revolution.
          4. Information Warfare.

        Emerging Technologies in Warfare

        Artificial Intelligence

        1. Autonomous targeting.
        2. Swarm coordination.
        3. Decision support systems.

        Electronic Warfare

        1. Jamming.
        2. Spoofing.
        3. Signal disruption.

        Autonomous Systems

        1. Loitering munitions.
        2. Unmanned combat aerial vehicles.

        Network-Centric Warfare

        1. Real-time ISR integration.
        2. Sensor-to-shooter connectivity.

        PYQ Relevance

        [UPSC 2023] The use of unmanned aerial vehicles (UAVs) by our adversaries across the borders to ferry arms/ammunitions, drugs, etc., is a serious threat to internal security. Comment on the measures being taken to tackle this threat.

        Linkage: The PYQ examines the security implications of the growing use of drone technology. The article discusses how drones have become central to modern warfare, highlighting the need for advanced counter-drone capabilities to address emerging military and internal security threats.

      1. Is a text AI-aided? Science, limits of detection tools 

        Why in the News?

        Allegations of AI-generated writing surfaced after three winners of the Commonwealth Short Story Prize were flagged by AI-detection tools, including Pangram, which classified one story as “100% AI-generated.” The controversy has reignited debate over whether AI detectors can reliably distinguish human-written content from AI-generated text. 

        Why is the Human vs AI Binary Becoming Obsolete?

        1. Collaboration Model: Increasingly, writing exists on a spectrum ranging from fully human-written to AI-assisted and heavily AI-generated.
        2. Hybrid Authorship: Writers often use AI for brainstorming, editing, structuring, or refining content.
        3. Future Challenge: Determining acceptable levels of AI assistance may become more important than identifying AI use itself.
        4. Example: The article cites categories such as lightly assisted, moderately assisted, and heavily assisted writing

        What is the machine learning foundation behind AI detection?

        1. Machine Learning (ML): Uses large datasets and statistical patterns to train systems to distinguish AI-generated text from human-written text.
        2. Training Data: Requires massive datasets containing both AI-generated and human-written content.
        3. Pattern Recognition: Learns recurring features such as vocabulary, sentence structure, punctuation, and stylistic patterns.
        4. Classification Function: Assigns probability scores indicating whether content appears AI-generated or human-authored.
        5. Example: Models may learn that AI systems frequently use formal verbs such as “delve”, “imperative”, or “devolve”.

        How are AI detectors trained to recognise AI-generated writing?

        1. Dataset Feeding: Large volumes of labelled human and AI text are fed into detection models.
        2. Statistical Learning: Models identify correlations and recurring linguistic features.
        3. Annotation-Based Training: Human annotators and data vendors classify examples to create training datasets.
        4. Behavioural Modelling: Since many frontier AI systems are trained on internet text, detectors attempt to identify common writing behaviours reproduced by these systems.
        5. Industry Dependence: Most training datasets are created by large technology firms, researchers, and annotation platforms.

        How is AI Detection Different from Plagiarism Detection?

        1. Plagiarism Detection: Identifies copied content by matching text with existing sources.
        2. AI Detection: Attempts to infer whether a text resembles AI-generated writing based on statistical patterns.
        3. Key Difference: AI detection relies on probability, whereas plagiarism detection relies on direct textual matches

        Linguistic signals that AI detectors rely upon

        Which ‘AI tells’ are commonly identified by detectors?

        1. Uncommon Vocabulary: Frequent use of words and phrases rarely encountered in ordinary conversation.
        2. Dash Usage: Excessive use of em dashes (—), often highlighted as a stylistic indicator.
        3. Structured Formatting: Frequent use of bullet points accompanied by descriptive headings.
        4. Neat Conclusions: Tendency to end content with highly organised summary paragraphs.
        5. Negative Parallelism: Repeated rhetorical structures such as “Not X, but Y.”
          1. Example: “These headphones are not just hearing devices, but sound-cancelling devices.”

        Why are these indicators not reliable proof of AI authorship?

        1. Overlap of Styles: Human writers can naturally employ the same stylistic features.
        2. Professional Writing Norms: Academic and journalistic writing often uses structured formatting and formal language.
        3. False Attribution Risk: Presence of a pattern does not establish authorship.
        4. Statistical Nature: Detection relies on probabilities rather than certainty.

        What are the inherent limitations of AI detectors?

        1. Low-Entropy Text: Text that is highly predictable and information-poor provides fewer linguistic signals, making AI detection less accurate.
          1. Example: Short responses, formulaic writing, or heavily edited text may be difficult to classify reliably
        2. Insufficient Signals: Short or highly edited content may not contain enough indicators for reliable classification.
        3. Probability-Based Judgments: Models provide likelihood estimates rather than definitive proof.
        4. Absence of Ground Truth: Detectors cannot directly observe whether a human or AI produced the text.
        5. Generalisation Problem: If a detector has not been specifically trained on outputs from a model such as Claude, it can only make an educated guess rather than a definitive classification.
        6. Implication: Detection tools struggle to keep pace with rapidly evolving AI models.

        How does editing affect detection accuracy?

        1. Mixed Authorship Challenge: Human-written text edited by AI, or AI-generated text edited by humans, creates ambiguity.
        2. Slight Modifications: Even limited editing can alter detectable patterns.
        3. False Positives: Human-written content may be incorrectly flagged as AI-generated.
        4. False Negatives: AI-generated content may evade detection after revision.

        Reliability of current AI-detection technologies

        Can AI detectors provide definitive evidence of AI use?

        1. False Positive Rate: Pangram reports a false-positive rate of 0.01%, equivalent to 1 error per 10,000 cases.
        2. Independent Validation: The figure has reportedly been supported by some independent studies.
        3. Operational Reliability: Suitable for risk assessment but not for conclusive judgment.
        4. Expert Assessment: Developers acknowledge that models cannot achieve 100% accuracy.

        Why is perfect detection technologically difficult?

        1. Continuous AI Evolution: New language models constantly improve linguistic sophistication.
        2. Human-AI Convergence: AI-generated text increasingly resembles human writing.
        3. Spam Detection Analogy: Similar to email spam filters, detection systems reduce risk but cannot eliminate errors.
        4. Adaptive Behaviour: AI systems learn to avoid patterns commonly targeted by detectors.

        Implications for writers and publishers

        How can false positives affect genuine authors?

        1. Reputational Damage: Writers may face allegations despite producing original work.
        2. Creative Discouragement: Fear of misclassification may discourage experimentation in writing styles.
        3. Publishing Risks: Manuscripts may be rejected based on uncertain evidence.
        4. Trust Deficit: Excessive dependence on detection tools can undermine confidence in evaluation systems.

        What challenges do publishers face in the AI era?

        1. Verification Difficulty: Establishing authorship becomes increasingly complex.
        2. Transparency Requirements: Growing demand for disclosure regarding AI assistance.
        3. Editorial Standards: Need for clear policies defining acceptable AI use.
        4. Reader Trust: Publishers must maintain credibility while adapting to technological change.

        Should AI assistance be treated differently from AI authorship?

        1. Spectrum of Use: Writing may be fully human-written, AI-assisted, moderately AI-assisted, or heavily AI-generated
        2. Collaborative Creation: Many authors increasingly use AI for brainstorming, editing, and research assistance.
        3. Policy Challenge: Institutions must determine acceptable levels of AI involvement.
        4. Binary Classification Problem: Human-versus-AI framing often oversimplifies modern writing practices.

        How does the issue intersect with ethics and regulation?

        1. Accountability: Establishes responsibility for content creation and originality.
        2. Intellectual Property: Raises questions regarding ownership of AI-assisted works.
        3. Academic Integrity: Challenges traditional plagiarism and authorship norms.
        4. Due Process: Prevents punitive actions based solely on probabilistic detection tools.Transparency: Encourages disclosure-based approaches rather than purely detection-based approaches.

        Should Transparency Replace Detection as the Primary Governance Tool?

        1. Disclosure-Based Regulation: Encourages authors to declare AI use.
        2. Reduced False Accusations: Minimises harm caused by false positives.
        3. Practical Governance: More feasible than attempting perfect detection.
        4. Institutional Trust: Builds confidence among publishers, educators, and readers.

        Conclusion

        AI-detection tools can serve as useful indicators but not definitive arbiters of authorship. The future of AI governance in publishing and academia will depend less on achieving perfect detection and more on developing credible standards for disclosure, accountability, and ethical human-AI collaboration.

        Value Addition

        AI Governance Frameworks

        UNESCO Recommendation on the Ethics of AI (2021)

        1. Promotes transparency, accountability, fairness, and human oversight.
        2. Calls for responsible deployment of AI technologies.

        OECD AI Principles

        1. Supports trustworthy AI.
        2. Emphasises explainability and human-centric design.

        G7 Hiroshima AI Process

        1. Develops international guardrails for advanced AI systems.
        2. Focuses on safety, transparency, and risk management.

        EU AI Act

        1. Adopts a risk-based regulatory framework.
        2. Imposes transparency obligations for certain AI applications.

        AI and India

        IndiaAI Mission

        1. Strengthens domestic AI capabilities.
        2. Supports compute infrastructure, datasets, innovation, and skill development.

        Digital Personal Data Protection Act, 2023

        1. Provides safeguards for personal data used in AI ecosystems.

        National Strategy for Artificial Intelligence

        1. Identifies AI applications in education, healthcare, agriculture, smart mobility, and governance.

        PYQ Relevance

        [UPSC 2023] Introduce the concept of Artificial Intelligence (AI). How does AI help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of AI in healthcare?

        Linkage: The PYQ examines the opportunities and challenges associated with Artificial Intelligence and its growing societal impact. The article highlights the limitations of AI systems and the need for transparency, accountability, and responsible AI governance.

      2. Prime Minister Research Chair (PMRC) Scheme 2026

        Why in the news?

        The Department of Higher Education under the Ministry of Education launched applications for the Prime Minister Research Chair (PMRC) Scheme 2026 to attract global Indian talent into India’s research and innovation ecosystem.

        Key Highlights

        • The scheme aims to connect:
          • Indian origin researchers and professionals working abroad
            with:
          • India’s higher education and research institutions.
        • Focus areas include:
          • Research
          • Innovation
          • Technology development.

        Objectives of PMRC Scheme

        • Strengthen: India’s research ecosystem.
        • Promote: International academic collaboration.
        • Enhance: Innovation in strategic sectors.
        • Support: Mission oriented research in national priority areas.

        Thematic Areas Covered

        The scheme focuses on 13 national priority sectors including:

        • Artificial Intelligence
        • Quantum Computing
        • Semiconductors
        • Cybersecurity
        • Biotechnology
        • Healthcare and MedTech
        • Space and Defence
        • Advanced Materials
        • Blue Economy
        • Atomic Energy
        • Climate Change and Sustainability.

        [2018] Consider the following statements :
        Human capital formation as a concept is better explained in terms of a process which enables
        1. individuals of a country to accumulate more capital.
        2. increasing the knowledge, skill levels and capacities of the people of the country.
        3. accumulation of tangible wealth.
        4. accumulation of intangible wealth.
        Which of the statements given above is/are correct?

        [A] 1 and 2

        [B] 2 only

        [C] 2 and 4

        [D] 1, 3 and 4

      3. The genie of synthetic biology is out, and with it comes power and peril

        Why in the News?

        Advances in synthetic biology, genome sequencing, artificial intelligence, and genome synthesis are rapidly giving humans the ability not only to read DNA but also to design and create new biological systems. This marks a historic shift from understanding life to engineering life.

        What is Synthetic Biology?

        1. Definition: Synthetic biology is the application of engineering principles to biology to design, modify, or create organisms, cells, genes, or biological systems with desired functions.
        2. Objective: Moves beyond studying life to actively engineering biological systems.
        3. Approach: Combines genetics, molecular biology, biotechnology, computer science, artificial intelligence, and engineering.
        4. Applications: Drug development, vaccines, biofuels, industrial chemicals, climate-resilient crops, and environmental remediation.
        5. Significance: Enables scientists to redesign existing life forms or create biological systems that do not exist in nature.

        What is DNA?

        1. DNA (Deoxyribonucleic Acid): The hereditary molecule that stores genetic information in living organisms.
        2. Building Blocks: Consists of four nucleotide bases:
          1. Adenine (A)
          2. Thymine (T)
          3. Guanine (G)
          4. Cytosine (C)
        3. Function: Contains instructions for building and maintaining an organism.
        4. Location: Found in nearly every cell of living organisms.
        5. Importance: Acts as the biological code that determines traits, growth, development, and cellular functions.

        What is a Genome?

        1. Definition: A genome is the complete set of DNA present in an organism.
        2. Contents: Includes:
          1. Genes that code for proteins
          2. Regulatory DNA that controls gene activity
        3. Role: Serves as the complete biological blueprint of an organism.
        4. Human Genome: Contains about 22,000 protein-coding genes.
        5. Significance: Differences in genomes explain biological diversity among species.

        What is the Genomic Revolution?

        1. Definition: The rapid advancement in genome sequencing technologies that has dramatically increased the ability to read and analyse DNA.
        2. Trigger: Massive reduction in sequencing costs and time.
        3. Human Genome Project Comparison:
          1. Took over a decade
          2. Cost nearly $3 billion
          3. Involved thousands of scientists
        4. Today:
          1. Genome sequencing can be completed in hours
          2. Costs have fallen to a few hundred dollars
        5. Major Outcomes:
          1. Mapping evolutionary history
          2. Understanding diseases
          3. Identifying genetic adaptations
          4. Personalized medicine
          5. Genome engineering
          6. Synthetic biology
        6. Significance: The genomic revolution has transformed biology into a data-driven science and laid the foundation for synthetic biology.

        How Has Understanding DNA Transformed Humanity’s Ability to Engineer Life?

        1. DNA as the Language of Life: DNA stores genetic information through four nucleotides, A, T, G, and C, which determine biological structure and function.
        2. Genome as Biological Blueprint: Every cell contains a genome comprising thousands of genes and regulatory sequences.
        3. Protein Synthesis: Genes encode proteins that perform structural, regulatory, metabolic, and physiological functions.
        4. Regulatory Architecture: Complexity arises not merely from gene numbers but from when, where, and how genes are expressed.
        5. Transcription Factors: Specialized proteins switch genes on or off, creating diverse biological outcomes.
        6. Phenylketonuria Example: Understanding genetic disorders has enabled dietary interventions that allow affected individuals to live normal lives.

        Why Does Gene Number Alone Not Explain Biological Complexity?

        1. Limited Difference in Gene Count: Humans possess approximately 22,000 genes, compared with:
          1. Escherichia coli: ~4,300 genes
          2. Fruit fly: ~17,000 genes
          3. Mouse: ~22,000 genes
          4. Water flea (Daphnia): ~31,000 genes
        2. Regulation Over Quantity: Biological complexity depends largely on gene regulation rather than the absolute number of genes.
        3. Expression Dynamics: Variations in timing, location, intensity, and interaction of gene expression create complexity.
        4. Cellular Specialization: Identical genomes produce diverse cell types through differential gene expression.

        How Has the Genomic Revolution Expanded Human Knowledge About Life?

        1. Reconstruction of Evolutionary History
          1. Evolutionary Mapping: Genome sequencing reconstructs the tree of life and evolutionary relationships among organisms.
          2. Complement to Fossils: Genomic evidence fills gaps where fossil records are absent.
          3. Historical Precision: Provides unprecedented accuracy in tracing biological evolution over millions of years.
        2. Understanding Adaptation and Natural Selection
          1. Adaptive Evolution: Genetic variations reveal how organisms adapt to environmental conditions.
          2. Human Diabetes Example: Genes predisposing populations to Type-II diabetes may have evolved under conditions of fluctuating food availability but become maladaptive under modern abundance.
          3. Selection Processes: Genome studies reveal how mutations are preserved or eliminated through natural selection.
        3. Building Comprehensive Cellular Maps
          1. Cellular Atlases: Sequencing enables identification of:
            1. Gene expression patterns
            2. Protein localization
            3. Cellular functions
            4. Regulatory interactions
          2. Big Data Biology: Massive biological datasets are enabling integrated understanding of cellular systems.
          3. Systems Biology: Facilitates comprehensive models of life processes rather than isolated gene studies.

        How Is Artificial Intelligence Accelerating Synthetic Biology?

        1. Computational Design: AI enables analysis of large-scale biological and environmental data.
        2. Genome Engineering: Scientists can increasingly design sections of genomes or entire genomes digitally.
        3. Predictive Biology: AI supports prediction of biological outcomes before laboratory implementation.
        4. Design Optimization: Accelerates identification of desirable genetic traits and functions.
        5. Reduced Costs: Improves accessibility and efficiency of biological engineering.
        6. Current Limitation: Biological systems often resist simplistic in silico predictions, requiring experimental validation.

        What New Possibilities Does Synthetic Biology Create?

        1. Designer Cells
          1. Biomanufacturing: Engineered cells produce chemicals, drugs, fuels, and advanced materials. Example: Genetically modified yeast is used to manufacture insulin and other therapeutic proteins.
          2. Industrial Biotechnology: Supports sustainable production systems. Example: Engineered microbes are used in the production of bioethanol and biodegradable plastics.
          3. Novel Biological Products: Enables creation of compounds not found naturally. 
        2. Engineered Organisms
          1. Genome-Wide Engineering: Modification extends beyond individual genes to entire genomes.
          2. Agricultural Applications: Facilitates development of improved crops and livestock.
          3. Biomedical Applications: Supports advanced therapeutics and regenerative medicine.
        3. Creation of Synthetic Life
          1. Artificial Genomes: Scientists can synthesize complete genomes and insert them into living cells.
          2. Novel Organisms: Opens possibilities for entirely new biological entities.

        Why Was Craig Venter’s Experiment a Historic Turning Point?

        1. Synthetic Genome Creation: In 2010, J. Craig Venter and his team chemically synthesized a complete bacterial genome.
        2. Genome Transplantation: The synthetic genome was inserted into a bacterial cell whose native DNA had been removed.
        3. Digitally Created Life: The experiment represented the first major demonstration of a cell controlled by a synthetic genome.
        4. Biological Watermarking: Non-coding DNA regions contained encoded quotations from:
          1. James Joyce: “To live, to err, to fall, to triumph, to recreate life out of life.”
          2. Richard Feynman: “What I cannot create, I do not understand.”
          3. J. Robert Oppenheimer: “See things not as they are, but as they might be.”
        5. Future Potential: Genome synthesis may eventually allow creation of larger synthetic genomes and engineered organisms.

        How Does Bottom-Up Synthetic Biology Attempt to Recreate the Origin of Life?

        1. Bottom-Up Synthetic Biology: Seeks to construct living systems from scratch using non-living chemical components. Instead of modifying existing organisms, it attempts to recreate the earliest stages through which life may have emerged on Earth.
        2. Scientific Objective: Examines one of biology’s fundamental questions, how non-living molecules transformed into self-replicating living systems approximately 4 billion years ago.
        3. Protocell Construction: Researchers build simplified cell-like structures called protocells, which mimic some characteristics of primitive life forms but are not fully living organisms.
        4. Jack Szostak’s Research: Developed fatty-acid membrane structures that can spontaneously assemble, encapsulate RNA molecules, grow by incorporating surrounding molecules, and divide into smaller daughter structures.
        5. Origin of Life Studies: Such experiments help scientists understand how the first biological cells may have formed before the evolution of complex organisms.
        6. Future Possibilities: Success in creating self-replicating protocells could eventually enable the development of entirely new forms of artificial life designed for specific purposes.
        7. Example: Jack Szostak’s protocell experiments demonstrated that simple fatty-acid vesicles can spontaneously form membrane-bound compartments capable of enclosing RNA and undergoing growth and division, providing a possible model for the earliest stages of life on Earth.

        Why Does Synthetic Biology Create Unique Governance Challenges?

        1. Self-Replicating Systems: Unlike machines, living organisms can reproduce and evolve.
        2. Unpredictability: Biological systems exhibit emergent properties and complex interactions.
        3. Biosecurity Risks: Potential misuse for harmful biological applications.
        4. Ecological Risks: Release of engineered organisms may alter ecosystems.
        5. Ethical Concerns: Raises questions regarding ownership, modification, and creation of life.
        6. Dual-Use Nature: Technologies useful for medicine and industry may also pose security threats.

        How Should Society Balance Innovation and Regulation in Synthetic Biology?

        1. Scientific Freedom: Advances require open research and innovation.
        2. Risk-Based Regulation: Governance frameworks must evaluate risks proportional to applications.
        3. Global Coordination: Biological risks transcend national boundaries.
        4. Responsible Innovation: Ethical oversight should accompany technological development.
        5. Precautionary Principle: Requires anticipation of future risks before deployment.
        6. Adaptive Governance: Regulations must evolve alongside technological progress.

        Conclusion

        Synthetic biology marks a transition from decoding life to designing life. The convergence of genomics, artificial intelligence, and genome synthesis offers unprecedented opportunities in healthcare, agriculture, industry, and environmental sustainability. However, because biological systems can self-replicate and evolve, governance challenges are fundamentally different from those associated with conventional technologies. The future of synthetic biology will depend on balancing scientific innovation with robust ethical, biosafety, and biosecurity safeguards.

        PYQ Relevance

        [UPSC 2021] What are the research and developmental achievements in applied biotechnology? How will these achievements help to uplift the poorer sections of society?

        Linkage: The PYQ examines the transformative potential of biotechnology and its socio-economic applications. With the new advancements, a question on synthetic biology can be asked next. The article extends the biotechnology discourse from genetic modification to genome engineering, synthetic genomes, and artificial life.

      4. Webb Telescope Captures Weather on Exoplanet WASP-94A b

        Why in the news?

        Scientists using the James Webb Space Telescope observed weather patterns on the exoplanet WASP-94A b located nearly 700 light years away from Earth.

        Key Highlights

        • The study was published in the journal Science on May 21, 2026.
        • Scientists detected:
          • Cloud formation
          • Atmospheric circulation
          • Dynamic weather cycles on the exoplanet.
        • The exoplanet studied is:
          • WASP-94A b.

        About WASP-94A b

        • It is a Gas giant exoplanet.
        • Nearly Twice the size of Jupiter.
        • It has about half Jupiter’s mass.
        • Completes one revolution around its star in Four days.

        What are Hot Jupiters?

        • “Hot Jupiters” are Massive gas giant exoplanets orbiting very close to their host stars.
        • Characteristics:
          • Extremely high temperatures
          • Tidally locked rotation
          • One side permanently facing the star.

        Tidally Locked Planets

        A tidally locked planet has:

        • Rotation period equal to revolution period.
        • As a result:
          • One side remains permanently day side.
          • The other side remains permanently night side.

        Weather on WASP-94A b

        Scientists observed:

        • Morning side: Clouds of magnesium silicate, iron and magnesium sulphide.
        • Evening side: Relatively clear skies.

        Clouds form on the cooler night side and move towards the hotter day side through powerful winds before dissipating.

        How Were the Atmospheres Studied?

        Scientists used:

        • Spectroscopy
        • Transit method

        Spectroscopy

        • Light from the host star is split into wavelengths.
        • Atmospheric gases absorb specific wavelengths.
        • This helps identify chemical compounds present in the atmosphere.

        Transit Method

        • The exoplanet passes in front of its host star.
        • The atmosphere absorbs part of the starlight.
        • Scientists analyse these changes to study atmospheric composition.

        About the James Webb Space Telescope (JWST)

        • Launched: December 2021.
        • Joint project of:
          • NASA
          • ESA
          • CSA.
        • Purpose:
          • Study the early universe
          • Exoplanets
          • Infrared astronomy.

        About Extremely Large Telescope (ELT)

        • Being built by the European Southern Observatory.
        • Location: Northern Chile.
        • Importance: May help discover more Earth like exoplanets and planetary systems.

        [2017] What is the purpose of ‘evolved Laser Interferometer Space Antenna (eLISA)’ project?

        [A] To detect neutrinos

        [B] To detect gravitational waves

        [C] To detect the effectiveness of missile defence system

        [D] To study the effect of solar flares on our communication systems

      5. SkyCast System

        Why in the news?

        Jitendra Singh inaugurated India’s first SkyCast System at Indira Gandhi International Airport under Mission Mausam.

        What is SkyCast?

        SkyCast is an advanced integrated aviation weather monitoring and forecasting system that provides:

        • Real-time weather intelligence
        • Fog monitoring
        • Turbulence detection
        • High-impact weather forecasting

        Key Features

        • Helps reduce:
          • Flight delays
          • Diversions
          • Cancellations
        • Provides short-term weather alerts to pilots and air traffic controllers
        • Monitors atmosphere up to nearly 3 km above airport

        Technologies Used

        SkyCast integrates:

        • Radar Wind Profiler
        • SODAR
        • Microwave Radiometer
        • Ground-based Fog Aerosol Spectrometer (GFAS)
        • Lidar-based Ceilometer

        [2025] GPS-Aided Geo Augmented Navigation (GAGAN) uses a system of ground stations to provide necessary augmentation. Which of the following statements is/are correct in respect of GAGAN?
        I. It is designed to provide additional accuracy and integrity.
        II. It will allow more uniform and high quality air traffic management.
        III. It will provide benefits only in aviation but not in other modes of transportation.
        Select the correct answer using the code given below.

        [A] I, II and III

        [B] II and III only

        [C] I only

        [D] I and II only

      6. The Physics of Thermometers, Temperature and Cold Atoms

        Why in the News?

        An article in The Hindu explained the scientific principles behind thermometers, temperature scales, absolute zero, and ultra-cold atomic physics, highlighting how measurement of temperature evolved from mercury thermometers to quantum physics-based studies of cold atoms.

        What is Temperature?

        • Temperature is a measure of the average kinetic energy of atoms and molecules in a substance.
        • When heat is supplied:
          • Atoms and molecules move faster.
        • When heat is removed:
          • Their motion slows down.
        • Thus, temperature reflects the degree of atomic agitation inside matter.

        Celsius Scale

        • Developed by: Anders Celsius

        Basis of the Scale

        • 0°C: Freezing point of water
        • 100°C: Boiling point of water
        • The interval between these two points is divided into 100 equal parts.

        Mercury Thermometers

        Why Mercury is Used

        • Uniform expansion on heating
        • Easily visible liquid metal
        • Good thermal conductor

        Digital Thermometers

        • Modern digital thermometers use Semiconductor materials
        • Principle
          • Semiconductors conduct limited electricity.
          • Higher temperature releases more free electrons.
          • Increased electric current is measured electronically and converted into temperature readings.

        [2021] In a pressure cooker, the temperature at which the food is cooked depends mainly upon which of the following?
        1. Area of the hole in the lid
        2. Temperature of the flame

        3. Weight of the lid
        Select the correct answer using the code given below.

        [A] 1 and 2 only

        [B] 2 and 3 only

        [C] 1 and 3 only

        [D] 1, 2 and 3

      7. Google’s new ‘information agents’ are a privacy and web infrastructure problem

        Why in the News?

        Google recently introduced “information agents,” AI assistants capable of continuously monitoring the web on behalf of users. These agents aim to automate information gathering, recommendations, and decision-making by integrating data across Google’s ecosystem such as Search, Gmail, Maps, Chrome, YouTube, Android, and Calendar. 

        What are Information Agents?

        Google Information Agents are AI-powered assistants, announced at Google I/O 2026, designed to run continuously in the background of Google Search to monitor the web, synthesize information, and act on your behalf 24/7. They act as an evolution of Google Alerts, proactively providing updates on topics like apartment hunting or price tracking.

        Key Features & Capabilities

        1. Proactive Monitoring: Instead of waiting for a manual query, agents constantly check the web for updates tailored to specific goals.
        2. Synthesis & Action: Agents gather data from multiple sources, provide insights, and can trigger actions (e.g., booking, alerting).
        3. “AI Mode” in Search: Activated within the Google App, where users can set up and track these agents.
        4. Personalization: Agents use user-provided details (budget, location, preferences) to provide personalized, actionable results.

        Why Do Google’s Information Agents Represent a Structural Shift in the Nature of Internet Use?

        1. Passive-to-Autonomous Transition: Traditional search depends on active human input where users consciously search for information. Information agents shift this model toward persistent AI monitoring that continuously scans the internet without repeated user intervention.
        2. Continuous Monitoring: Agents remain active over time rather than responding to one-time prompts. They monitor categories such as housing, travel, stocks, health, or shopping preferences.
        3. Cross-Ecosystem Integration: Google integrates information from Search, Gmail, Maps, Chrome, Calendar, YouTube, and Android, enabling deeper behavioural profiling than standalone AI assistants.
        4. Predictive Personalization: Agents function by collecting increasing amounts of personal data because improved recommendations depend on richer behavioural information.
        5. Machine-to-Machine Internet: The article highlights a structural change where digital interactions increasingly occur between automated systems instead of humans directly browsing websites.

        How Could Information Agents Intensify Data Privacy and Surveillance Concerns?

        1. Behavioural Profiling: Agents require intimate personal details to function effectively. A housing-monitoring request may reveal location preference, family size, budget, commuting constraints, timeline, and travel plans.
        2. Sensitive Data Accumulation: Users may unintentionally disclose religious beliefs, political preferences, sexual orientation, medical history, and financial behaviour, expanding risks of sensitive profiling.
        3. Indefinite Data Storage: Information collected for agentic services may remain stored for prolonged periods, increasing risks of misuse or surveillance.
        4. Data Concentration: Google already possesses vast datasets through existing platforms. Information agents deepen concentration by linking fragmented behavioural data into unified user profiles.
        5. Limited Regulatory Protection: Current frameworks remain underdeveloped regarding liability if AI agents influence financial or personal decisions that later harm users.

        Can AI Information Agents Overload the Internet’s Infrastructure?

        1. Bot Traffic Expansion: AI-driven internet activity is already increasing sharply.
        2. Striking Data: The article cites the Thales 2026 Bad Bot Report, which estimates bots account for 53% of global web traffic.
        3. Sharp Increase in Attacks: AI-driven bot attacks reportedly increased 15 times in 2025.
        4. Blocked Requests Surge: Daily blocked bot requests reportedly increased from 2 million to 25 million within a year.
        5. Exponential Crawling: A conventional Google search may trigger one crawl after a query. Information agents repeatedly scan websites, potentially generating hundreds of automated fetches daily per user.
        6. Infrastructure Burden: Millions of subscribers using persistent agents could impose enormous computational and bandwidth costs on websites.

        Example 

        1. Housing Listings: An agent monitoring apartment prices continuously would repeatedly crawl real-estate websites to detect changes.
        2. Stock Monitoring: Persistent stock monitoring may generate frequent automated queries throughout the day.

        How Could Information Agents Threaten the Economic Sustainability of the Open Web?

        1. Publisher Revenue Erosion: AI agents may summarize content directly instead of redirecting users to publisher websites, reducing click-through traffic.
        2. Server Cost Burden: Publishers would continue bearing infrastructure costs while AI systems scrape and synthesize content.
        3. Content Extraction Problem: Information harvesting without proportional traffic or revenue could weaken incentives for quality journalism.
        4. Potential Publisher Pushback: Websites may increasingly block Google crawlers or restrict access to AI scraping.
        5. Negative Feedback Loop: Reduced publisher incentives may degrade content quality, weakening the informational ecosystem itself.

        Comparative Contex

        1. AI Search Platforms: Similar debates have emerged around AI-generated search summaries reducing website visits.
        2. Media Compensation Models: Countries such as Australia introduced bargaining mechanisms between digital platforms and news publishers.

        Does the Rise of Information Agents Deepen Market Concentration and Digital Inequality?

        1. Platform Entrenchment: Google’s advantage lies in unmatched digital infrastructure across search, email, navigation, devices, and browsing behaviour.
        2. Lock-In Effect: Users embedded in Google’s ecosystem may find switching increasingly difficult due to personalized AI assistance.
        3. Subscription Divide: The information agents may initially launch for Google AI Pro and Ultra subscribers, creating differentiated access.
        4. Informational Inequality: Wealthier users may gain persistent AI assistants while others continue manual searches, widening informational asymmetries.
        5. Market Power Consolidation: Persistent agents could further strengthen dominance of already large digital platforms.

        Are Existing Legal and Governance Frameworks Adequate for AI Agents?

        1. Liability Gap: No clear framework exists regarding responsibility if an AI agent nudges users toward harmful financial or medical outcomes.
        2. Assistant-versus-Advisor Problem: Companies classify agents as “assistants” rather than advisors, limiting accountability.
        3. Regulatory Lag: Technology deployment currently outpaces legal adaptation.
        4. Need for Algorithmic Transparency: Users require clarity regarding how recommendations are generated and monetized.
        5. Data Governance Deficit: Existing laws inadequately address persistent behavioural monitoring by autonomous systems.

        Possible Governance Measures

        1. Consent Architecture: Ensures granular and revocable consent mechanisms.
        2. Transparency Mandates: Requires disclosure regarding data collection, recommendation logic, and commercial influence.
        3. Publisher Compensation: Develops fair economic arrangements for AI-generated content extraction.
        4. AI Liability Standards: Establishes responsibility for harmful outcomes from automated recommendations.
        5. Bot Governance Framework: Regulates autonomous web crawling and infrastructure burden.

        Conclusion

        Google’s information agents represent a transformative shift from search-based internet use to persistent AI-mediated interaction. While the model promises convenience and efficiency, it intensifies concerns relating to privacy, concentration of digital power, infrastructure strain, and publisher sustainability. The challenge for policymakers lies in balancing technological innovation with data protection, platform accountability, fair digital markets, and preservation of an open web ecosystem.

        Important Value Additions for UPSC MainsKey ConceptsAgentic AI: AI systems capable of autonomous action, monitoring, and decision-making.Surveillance Capitalism: Monetization of behavioural data for predictive commercial outcomes.Platform Monopoly: Dominance arising from control over infrastructure, data, and network effects.Data Colonialism: Extraction and monetization of user data at scale.Algorithmic Governance: Decision-making increasingly shaped through digital systems.

        PYQ Relevance

        [UPSC 2018] Data security has assumed significant importance in the digitized world due to rising cyber crimes. The Justice B.N. Srikrishna Committee Report addresses issues related to data security. What, in your view, are the strengths and weaknesses of the Report relating to protection of personal data in cyberspace?

        Linkage: The PYQ reflects UPSC’s focus on institutional and legal frameworks governing personal data in the digital age. Google’s information agents intensify concerns discussed in the PYQ by enabling persistent behavioural tracking and integrated profiling across digital ecosystems.

      8. New Crystal Discovered in Debris of First Nuclear Explosion

        Why in the News?

        Scientists discovered a previously unknown crystal in trinitite, the glass formed after the 1945 Trinity nuclear test conducted by the United States in New Mexico.

        Key Highlights

        • Study published in: Proceedings of the National Academy of Sciences
        • Researchers identified a rare cage-like crystal called a Clathrate

        What is Trinitite?

        • Glassy green material formed when the nuclear blast melted desert sand.
        • Created during the Trinity test on July 16, 1945.

        About the New Crystal

        • Composed of:
          • Calcium
          • Copper
          • Silicon
        • Classified as a Type-I clathrate

        Features

        • Silicon atoms form cage-like structures trapping other elements inside.
        • First clathrate discovered from a nuclear explosion product.

        How Was it Formed?

        The crystal formed under extreme conditions:

        • Temperature Above 1,500°C
        • Pressure Up to 8 gigapascals
        • Rapid cooling preserved the crystal structure.

        Link with Quasicrystals

        The study followed earlier discovery of a Quasicrystal in red trinitite (2021)

        Quasicrystals

        • Have ordered but non-repeating atomic patterns.
        • Earlier believed impossible in nature.
        • Researchers found Clathrates and quasicrystals formed separately during the blast.

        Scientific Importance

        The findings suggest:

        • Extreme environments can create entirely new forms of matter.
        • Nuclear blast conditions may help scientists develop novel synthetic materials.

        [2013] The efforts to detect the existence of Higgs boson particle have become frequent news in the recent past. What is /are the importance/importances of discovering this particle?
        1. It will enable us to understand why elementary particles have mass.
        2. technology to transferring matter from one point to another without traversing the physical space between them.
        3. It will enable us to create better fuels for nuclear fission.
        Select the correct answer using the codes given below:

        [A] 1 only
        [B] 2 and 3 only
        [C] 1 and 3 only
        [D] 1, 2 and 3

      9. Using DNA Maps to Trace Pangolin Trafficking

        Why in the News?

        Scientists have developed advanced “DNA maps” to identify the origin and trafficking routes of illegally traded pangolins, helping expose international wildlife smuggling networks.

        Key Highlights

        • Study published in PLOS Biology on May 7, 2026.
        • Researchers mapped trafficking routes of:
          • White-bellied pangolin
          • Sunda pangolin
          • Chinese pangolin

        How the DNA Mapping Works

        • Scientists analysed 671 specific locations in the pangolin genome that differ across populations.
        • Used:
          • Museum specimens
          • Recent pangolin samples
        • Created a large geo-referenced genetic database to identify the origin of trafficked pangolins.

        Major Findings

        • Researchers found evidence of trafficking routes from: Arunachal Pradesh and Assam
        • feeding illegal trade networks through Yunnan in China.

        Significance

        • Helps identify poaching hotspots accurately.
        • Assists enforcement agencies in tracking wildlife crime networks.
        • Can improve international cooperation against illegal wildlife trade.

        About Pangolins

        • Pangolins are scaly mammals threatened by:
          • Habitat loss
          • Illegal trafficking
        • Hunted mainly for:
          • Scales
          • Meat

        Conservation Status

        • Protected under: Schedule I of the Wild Life (Protection) Act, 1972
        • Listed under: Appendix I of Convention on International Trade in Endangered Species of Wild Fauna and Flora
        [2022] Consider the following statements: DNA Barcoding can be a tool to: 
        1. Assess the age of a plant or animal. 
        2. Distinguish among species that look alike. 
        3. Identify undesirable animal or plant materials in processed foods. 
        Which of the statements given above is/are correct? 
        [A] 1 only [B] 3 only [C] 1and 2 [D] 2 and 3