THE CONTEXT: The accelerating pace of Artificial Intelligence (AI) developments has reignited discourse around Artificial General Intelligence (AGI) — a theoretical form of AI capable of performing any intellectual task a human can. While mainstream AI today remains narrow and task-specific, AGI envisions machines with general cognitive capabilities, including reasoning, learning, and problem-solving across diverse domains.
CONCEPTUAL FOUNDATIONS AND EVOLUTION:
A. Historical Roots
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- Alan Turing (1950): Proposed the Turing Test as a measure of machine intelligence — if a human cannot distinguish machine responses from human ones, it is “intelligent”.
- John McCarthy’s Dartmouth Conference (1956): Defined AI as a field that assumes all aspects of learning and intelligence can, in principle, be simulated by machines.
- Marvin Minsky (1970): Predicted near-term emergence of human-equivalent AI, indicating early AGI optimism.
B. Emergence of AGI as a Term
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- Coined by Mark Gubrud (1997) to refer to AI systems rivaling or surpassing human intelligence in complexity, reasoning, and versatility.
- Popularised by Shane Legg and Ben Goertzel (2001–2007) as a new subfield of AI focused on human-level cognitive abilities.
- Differentiated from narrow AI by its breadth, adaptability, and self-improving capacities.
DEFINITIONS AND DEBATES AROUND AGI
| Scholar/Organisation | Definition or View of AGI |
|---|---|
| Goertzel & Legg | Systems capable of performing human cognitive tasks |
| Murray Shanahan (DeepMind) | AI that can learn to perform a broad range of tasks |
| OpenAI (2015 Charter) | “Highly autonomous systems that outperform humans at most economically valuable work” |
| DeepMind (2023) | Five AGI levels from Emerging to Superhuman |
| Yann LeCun (Meta) | Rejects AGI term: Intelligence is not general but modular |
| Dario Amodei (Anthropic) | Prefers term “powerful AI” over AGI due to hype |
DRIVERS OF THE AGI RACE
1. Moore’s‑Law‑plus: Specialised chips (TPUs, Cerebras Wafer‑Scale Engine) → 10^6× compute jump since 2012.
2. Data Abundance: 180 zettabytes by 2025; foundation models pre‑train on >10 trn tokens.
3. Algorithmic Innovations: Transformers, Retrieval‑Augmentation, Diffusion networks.
4. Capital Surge: Global AI investment crossed USD 227 bn (2024, Stanford AI Index).
5. Geo‑tech Competition: CHIPS & Science Act (US), China’s Next‑Gen AI Plan 2030, India’s ₹ 10,300 cr AI Mission (2024‑25 Budget) .
OPPORTUNITIES:
| Sector | Illustrative Upside | Data |
|---|---|---|
| Health | Drug discovery in weeks (e.g., Insilico Medicine’s AI generated anti fibrotic drug in Phase II). | Could shave 30% off R&D cost. |
| Climate & Energy | AlphaFold 2 protein folding; grid scale optimisation. | IPCC AR6 cites AI for 10 15 GtCO₂ mitigation potential. |
| Governance | Predictive service delivery (e Courts, PM GatiShakti). | Estonia’s “Kratt” AI handles 99% tax filings. |
| Defence | ISR swarms, adaptive cyber response. | Pentagon’s Replicator initiative aims 1000 AI drones by 2026. |
CONCERNS, RISKS, AND ETHICAL DILEMMAS
1. Existential and Catastrophic Risks
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- DeepMind (2024): AGI may cause “severe harm”, including loss of human control, social disruption, or economic inequality.
- Princeton researchers (Narayanan & Kapoor): Warn against overhyping AGI as an existential threat without grounding in current technological capability.
2. Conceptual Uncertainty
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- Lack of universal definition or consensus on benchmarks for AGI.
- Disagreement on whether scaling up current models (LLMs) can lead to AGI or if entirely new paradigms are needed.
3. Socioeconomic & Security Risks
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- Potential misuse by state and non-state actors.
- Amplification of existing societal inequities (e.g., job displacement, surveillance).
- AI as General-Purpose Technology (GPT) — like electricity or the internet — raises regulatory and ethical challenges.
POLICY & GOVERNANCE IMPLICATIONS:
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- Need for a global governance framework (akin to AI Geneva Conventions).
- India’s Perspective:
- Draft National Strategy on AI (NITI Aayog): Focus on “AI for All” for inclusive development.
- MeitY’s recent Digital India Bill seeks to regulate AI systems in the absence of comprehensive AI legislation.
- Absence of specific regulation on AGI-level threats or thresholds.
- Recommendations from Scholars:
- Focus on specific, assessable risks (bias, misinformation, privacy) rather than speculative AGI futures.
- Develop multi-stakeholder standards and technical safety mechanisms (e.g., alignment, interpretability).
INDIA’S PREPAREDNESS & POLICY GAPS
Strengths
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- Digital Public Goods (DPGs) – Aadhaar, UPI, ONDC enable inclusive datasets.
- India AI Mission to create 8,000 GPU compute‑cloud; India‑EU Trade & Tech Council.
- DPDP Act 2023 lays data fiduciary duties; draft Digital India Act includes algorithmic accountability.
Gaps
1. Siloed R&D – <0.8% of GDP on GERD vs OECD avg 2.3%.
2. Talent drain – 70% of top Indian AI PhDs work abroad (Brookings 2024).
3. Compute deficit – India hosts <2% of world’s AI supercomputing power.
4. Sectoral Ethics Codes still voluntary; no independent AI Safety Regulator.
THE WAY FORWARD:
1. National Compute & Data Commons: Public‑funded BharatGPT cloud under PPP; open “lighthouse” datasets with differential‑privacy guards.
2. AI Safety Sandbox: Regulatory pilots under MeitY + DPIIT for frontier models; mandatory red‑teaming & interpretability audits.
3. Skilling Pivot: Integrate AGI literacy in NEP 2020’s School Coding clubs; “AI‑4‑All” MOOCs for civil servants.
4. Sectoral AI Codes → Unified Law: Move from voluntary ethics to binding Responsible AI Act harmonised with DPDP & Competition Law.
5. Global Norm Entrepreneurship: Leverage G‑20 presidency legacy to champion ‘Jaipur Principles on Safe AI for the Global South’.
6. Public‑Interest Compute Grants: Similar to NSF “Access Credits”, fund academia & startups for safety‑alignment work.
7. Ethics‑by‑Design in Governance: Mandate Explainable AI (XAI) in critical domains (health, policing); citizen grievance portals.
THE CONCLUSION:
AGI sits at the intersection of promise and peril. For India, the task is to steer innovation towards Atmanirbhar, inclusive and ethically‑aligned AI while anticipating systemic risks. A calibrated, evidence‑based, and human‑centric policy architecture is thus imperative.
UPSC PAST YEAR QUESTION:
Q. “The application of Artificial Intelligence as a dependable source of input for administrative rational decision-making is a debatable issue.” Critically examine the statement from the ethical point of view. 2024
MAINS PRACTICE QUESTION:
Q. Analyze the concept of Artificial General Intelligence (AGI) and discuss how its emergence could reshape the ethical, social, and regulatory landscape of technological governance.
SOURCE:
https://indianexpress.com/article/explained/graham-staines-murder-case-recall-odisha-9951143/
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