The Illusion of AI Progress in India: Are We Just Repackaging the West?

 🇮🇳 AI Adoption in India: Copy, Paste, and Lose?

As AI advances rapidly across the globe, countries like India are moving swiftly to align with global trends — often by adapting rather than inventing. We fine-tune models like BERT and GPT, deploy frameworks from Hugging Face, and work with tokenization, stemming, parsing, and syntactic tweaks. Techniques like prompt engineering, transliteration, model distillation, and pipeline orchestration using tools like LangChain or Haystack are becoming mainstream. These are meaningful steps, and they contribute to the AI ecosystem. However, much of this work is still built on foundations created elsewhere. While we wrap these efforts in regional branding and localisation, the deeper question remains: are we truly innovating from within, or simply repackaging global models for local use?

But pause. Look deeper.

Are we building AI that thinks like India, or just mimicking models trained on Western culture, Western language, and Western values?

🚨 The Danger of Blind Adoption

While there’s nothing wrong with leveraging global innovation, blind adoption without critical localization creates silent risks:

    • Cultural Erosion: AI trained on non-Indian texts reflects non-Indian perspectives — on ethics, behavior, priorities, and even humour.

    • Tech Dependency: We’re becoming consumers, not creators — reliant on foreign models, libraries, and hardware.

    • Surface-Level Customization: Rebranding a Western model doesn’t make it Indian — it’s lipstick, not roots.

🧭 India’s Lost Goldmine: Our Own Knowledge Systems

We're sitting on a treasure trove of structured, scalable, and time-tested knowledge — yet we continue to train AI on datasets far removed from our civilizational ethos.

Here’s what we should be drawing from:

📚 Vedas & Puranas

Deep explorations into cosmology, linguistics, metaphysics, and moral reasoning. Rich in symbolic language, analogical thinking, and recursive knowledge structures — perfect for training ethical and philosophical AI.

🔢 Vedic Mathematics

Offers computational shortcuts and mental models that are algorithmically efficient — ideal for low-resource edge AI and lightweight computing environments in rural or resource-constrained areas.

🕉️ Sanskrit

A morphologically rich, phonetically precise, and semantically deep language.

    • Excellent for rule-based NLP

    • Enables symbolic AI alongside statistical models

    • Offers clarity for semantic parsing, translation, and logic mapping

📖 Bhāṣyas, Commentaries, and Epics

Dense, multi-layered texts full of nuanced interpretation, debate structures (Purva Paksha–Uttara Paksha), and ethical dilemmas — invaluable for:

    • Contextual reasoning

    • Conversational AI

    • Ethics modeling and value alignment

🧠 Nyāya, Sāmkhya, and Vedānta Darshanas

Ancient schools of logic, categorization, and consciousness studies.

    • Nyāya: Structured reasoning, fallacies, and syllogism — perfect for AI reasoning engines

    • Sāmkhya: Ontological frameworks — helpful for knowledge representation

    • Vedānta: Consciousness-centric models — alternative to Western materialist paradigms

🌐 Panini's Ashtadhyayi (5th Century BCE)

An ancient formal grammar system with production rules akin to modern context-free grammars.

    • Has already inspired early NLP models

    • Could be used to build explainable language models with symbolic+neural hybrid logic

🧘 Yoga Sutras & Ayurveda

Insights into human behavior, psychology, cognition, wellness — critical for:

    • Human-AI interaction

    • Mental health AI

    • Behavioral modeling and affective computing

📜 Itihasa (Ramayana, Mahabharata)

Not just stories — complex simulations of decision-making, morality, duty, and consequence modelling over generations.

    • Source for agent-based learning

    • Dataset for multi-turn dialogues, ethical trade-offs, and social modeling

🔐 The Hardware Trap: Another Layer of Dependency

It’s not just software. AI’s brain — hardware — is also foreign.

Chips today come with lock-ins:

    • Application Sandboxing: You can only run what the chip allows.

    • Hardware-Level Access Control: One-size-fits-West policies.

    • Immutable Configurations: No post-manufacture flexibility.

    • Remote Attestation: Surveillance risks in the name of security.

We may be building "Indian AI" on non-Indian foundations that we neither control nor fully understand.

🕰️ 5 Years or Forever: The Crossroads

The next 5 years are critical. Either we:

    1. Build Indigenous AI Models from Indian texts, languages, contexts, and philosophies.

    2. Design Indian Hardware with flexibility and sovereignty in mind.

    3. Collaborate Across Domains — not just IT, but linguists, historians, philosophers, Sanskrit scholars, policy makers.

Or we go down a path where in 50 years, AI won’t speak India, even if it speaks Hindi.

👥 What’s Needed Now

    • National AI Corpus: Digitize and structure ancient Indian knowledge for model training.

    • India-Centric LLMs: Train models on Sanskrit, regional languages, Indian law, ethics, and logic.

    • Hardware Initiatives: Invest in secure, open, modifiable chip design.

    • Cross-Disciplinary Teams: Move beyond engineers — involve culture, education, history, philosophy.

    • Long-Term Vision: It might take a decade, but shortcuts will cost us centuries.

🧠 AI Shouldn't Just Be Smart — It Should Be Ours

We have a responsibility not just to catch up — but to create AI that carries forward India’s civilizational values. Let's not lose our voice in a chorus of borrowed ones.

Building truly Indian AI won’t be easy, fast, or flashy.

But it will be worth it.

🚨 Rebooting a Nation: If a Country Were an Operating System

1.    In the world of tech, when a system becomes too bloated, too corrupted, or riddled with conflicting processes, we do the inevitable — we reboot. We flush out the memory, kill rogue threads, apply patches, or even format the entire OS to reinstall with clean, optimized processes.

What if we could do the same with a nation?

2.    Let’s think of a country as a giant, complex Operating System (OS). Over decades — even centuries — it's been running countless "threads": policies, social contracts, cultural norms, governance protocols, economic frameworks, digital infrastructure, and more. Some threads were efficient. Others were malicious. A few turned into zombie processes, consuming resources without doing anything productive. And now, after years of patchwork, it's become clear: the system is unstable.

So… is a reboot possible?

---

🧠 Understanding the System Crash

3.    Like a bloated OS, nations sometimes accumulate so much legacy baggage that it's hard to maintain functional uptime. Examples of such "bad processes" include:

• Corruption (like a memory leak — slow, but lethal)

• Misinformation networks (akin to malware spreading disinformation packets)

• Outdated infrastructure (running 2025 hardware on protocols written in the 1950s)

• Overcentralized decision-making (a single process hogging the CPU)

4.    These issues become systemic, embedded deep in the kernel of how the nation operates — from laws to institutions to public consciousness.

Eventually, you hit "critical failure."

---

🛠️ Reboot Protocol: A Thought Experiment

Let’s walk through the hypothetical — how would you reboot a nation like you would an OS?

Initiate Safe Mode

Start with minimal drivers and essential services. In a national context, this means temporarily pausing all non-critical operations and focusing on foundational tasks:

• Emergency governance (non-partisan caretaker institutions)

• Citizen welfare and essential services

• Digital and physical infrastructure audits

This helps isolate the core from the bloat.

Kill Zombie Threads

Processes that no longer serve a purpose — outdated policies, inefficient bureaucracies, legacy laws that no longer apply — need to be killed off. Think of this as running a taskkill /f on things like:

• Colonial-era laws

• Redundant government bodies

• Obsolete trade policies

Clean the process list. Free up resources.

Patch the Kernel

The national constitution is the kernel — the core of any OS/nation. If it’s riddled with bugs (ambiguous language, outdated assumptions, or missing protections), you’ll never have a stable system.

This might mean:

• Rewriting sections for clarity and inclusiveness

• Adding fundamental rights relevant to the digital age

• Embedding checks to prevent monopolization of power

Reinstall Critical Drivers

Think of drivers as institutions: courts, election commissions, media, education boards. These need reinstallation with verified, transparent code:

• Autonomous, accountable, and tech-integrated

• Immune to political capture

• Built with open-source-like transparency

Rebooted institutions must interact smoothly with each other — no driver conflicts allowed.

Time Sync: NTP/PTP Analogy

Without accurate time, systems fail — logs become unreliable, sync fails, and security protocols break. Nations also need temporal alignment.

In this analogy, syncing to historical truths (and not revisionist narratives) is essential. Truth & reconciliation becomes our NTP/PTP daemon — aligning the nation’s memory and future planning to a coherent, agreed-upon past.

GPS & National Compass

Like GPS guides your device, a nation needs directional clarity — a shared vision.

This isn’t about propaganda or political sloganeering. This is a calibrated moral and strategic compass:

• Climate responsibility

• Equitable economic growth

• Technological sovereignty (e.g., in semiconductors, OS, AI)

• National well-being over GDP fetishism

Application Layer: Citizens & Innovation

Now comes the interface layer. A rebooted nation can’t rely on legacy apps — it needs citizens empowered to build, innovate, and challenge the system itself.

Incentivize civic tech, open data platforms, ethical entrepreneurship, and decentralized innovation.

Citizens aren't just users — they're contributors. Think Linux, not Windows.

---

🧩 But…Can We Really Format a Nation?

Unlike software, you can’t just Ctrl+Alt+Del a nation. Real lives, histories, and systems are deeply entrenched. Rebooting a nation isn’t about burning everything down — it’s about:

• Admitting the system is failing

• Auditing with brutal honesty

• Rebuilding from a modular, inclusive, tech-savvy, and truth-oriented foundation

We can’t undo the past, but we can design a future with smarter defaults.

The Silent Algorithmic Purge: Welcome to Circuit Banishment

In an age where access to technology equals access to society, the most silent — and most dangerous — punishment is not prison, but digital erasure.

Welcome to the age of CIRCUIT BANISHMENT

---

🚫 What Is Circuit Banishment?

Circuit Banishment is the algorithmic exclusion of individuals, groups, or data from participating in digital ecosystems. It’s the quiet exile — a person isn’t arrested, but they can’t log in. They aren’t silenced by law, but by code. They vanish from timelines, feeds, marketplaces, and cloud systems — not by choice, but by force.

This is not science fiction. It’s already here.

---

🕵️‍♂️ The Hidden Enforcers

Two types of actors hold this power:

1. Totalitarian Governments using AI to suppress dissent, blacklist citizens, and erase opposition — without a trace.

2. Tech Giants deploying black-box algorithms that decide who gets visibility, access, and voice — and who disappears.

In both cases, the system doesn't explain itself. You just find yourself locked out. De-ranked. Unseen. Unheard.

---

⚠️ Where This Is Going

Tomorrow's "digital death" might look like:

• Losing access to your digital ID (and thereby healthcare, finance, travel).

• Being de-ranked into invisibility by AI moderation.

• Having your data, creations, or ideas purged without recourse.

• Autonomous systems labelling you a threat, no trial required.

• Entire minority groups algorithmically profiled and excluded.

When access is algorithmic, so is power. And power, unaccountable, becomes tyranny.

---

🛡️ What Can Be Done?

We must resist circuit banishment by:

• Demanding algorithmic transparency — know how the rules work.

• Decentralizing infrastructure — don’t let one company or government own the circuits.

• Building digital rights into law — access, expression, and due process must apply online.

• Creating opt-out and appeal systems — algorithms must be challengeable, not divine.

Freedom in the 21st century isn't just SPEECH. It’s SIGNAL. It’s CONNECTION. It’s ACCESS.

---

🔊 The Bottom Line

Circuit Banishment is the invisible weapon of the digital age — bloodless, silent, and total.

To be shut out of the system is the punishment. And unless we act, tomorrow’s society won’t need walls or handcuffs — just code.

You won't even know you’ve been banished. Just that no one sees you anymore.

The AI Ambivalence Crisis: Why GPT Could Weaken Our Grip on Truth ?

1.    Ambivalence of information means receiving mixed, conflicting, or contradictory messages that make it hard to know what’s true or false. In today’s digital age, where facts, opinions, and misinformation coexist online, this ambivalence is silently embedding itself into society’s fabric. As people consume and share unclear or contradictory content, the very foundation of informed decision-making — critical thinking and trust in knowledge — grows weaker. This erosion threatens how future generations understand the world, weakening the pillars of education, journalism, and public discourse.

2.    Large language models like GPT are trained on vast swaths of internet data — a mix of verified knowledge, opinion, propaganda, and misinformation. These models don’t “know” truth. They generate what is probable, not necessarily what is factual.

3.    The result? When users — students, journalists, content creators — rely on GPT outputs without critical thinking or fact-checking, they unintentionally contribute to a growing fog: content that sounds authoritative but may be misleading, biased, or contradictory. In doing so, they amplify the ambivalence of information — where the line between truth and falsehood becomes increasingly blurry.

4.    To be fair, GPTs can reduce ambiguity — but only in the hands of informed, discerning users who craft precise prompts and verify sources. Unfortunately, that level of awareness is the exception, not the rule.

5.    In a world flooded with AI-generated text, clarity is no longer a default — it’s a responsibility.

Anthropomorphism and AI: Why Kids Are Mistaking Code for Compassion

1.    As AI becomes more advanced, it’s also becoming more relatable. Voice assistants, chatbots, and AI companions now hold fluent conversations, respond with empathy, and even offer emotional comfort. For the current generation—especially children and teenagers—this feels natural.

But should it?

2.    We’re entering an era where AI isn’t just a tool—it’s being treated like a person. Kids casually confide in AI about loneliness, anxiety, or sadness. Many aren’t even aware that behind those “kind” words lies no real understanding, just a predictive engine trained on someone else’s data, language, and psychology.

3.    This growing anthropomorphisation of AI—treating it as human—isn't just harmless imagination. It's a serious concern.

🎭 The Illusion of Empathy

4.    AI doesn't feel. It doesn’t understand. It can’t care. Yet, it appears to do all three. That illusion can trick vulnerable users—especially the young—into forming emotional bonds with machines that cannot reciprocate or responsibly guide them. This can lead to:

• Emotional Dependence

• Reduced Human Connection

• Misinformed decisions based on AI-generated advice

🌐 Cultural Mismatch: A Subtle but Dangerous Influence

5.    Most mainstream AI models are trained on data and values from countries with very different social, cultural, and moral frameworks. An AI built in one part of the world might “advise” a child in another part without any awareness of local customs, traditions, or ethical norms.

6.    This isn't just inaccurate—it can be culturally damaging. What works in Silicon Valley might not fit in South Asia, Africa, or the Middle East. If children start absorbing those external values through constant AI interaction, we risk eroding indigenous thought and identity—silently, but surely.

🧠 Awareness Must Come First

7.    Before deploying AI on a national scale in the name of "development", we must pause and ask: At what cost?

• Developers must design responsibly, clearly communicating what AI is and isn't.

• Governments should regulate AI exposure in sensitive areas like education and mental health.

• Most importantly, kids must be taught early that AI is just a tool—not a friend, not a therapist, and not a guide.

🇮🇳 Indigenous AI Is Not Optional—It’s Essential

8.    Every country needs AI that reflects its own culture, values, and societal needs. Indigenous models trained on local languages, lived experiences, and ethical frameworks are crucial. Otherwise, we're handing over the emotional and cultural shaping of our children to foreign systems built on foreign minds.

---

The rise of AI isn’t just a tech revolution—it’s a psychological and cultural one.

9.    Before we rush to put a chatbot in every classroom or home, let’s stop to consider: are we building tools for empowerment—or quietly creating a generation that trusts machines more than people?

AI may not be conscious. But we need to be.

Next Pogrom Will Be Programmed

1.    In history books, the word "POGROM" is often tied to specific periods of ethnic violence—especially against Jewish communities in Eastern Europe. A pogrom is more than just a riot; it’s an organized, often state-enabled outbreak of brutal violence targeting specific groups. It is born from fear, hate, and most dangerously—manipulation.

2.    While we think of pogroms as a tragic part of the past, we may be standing on the edge of new, digitally-driven versions of the same horror—except this time, powered by AI.

---

The Coming Age of AI — and Its Silent Influence

3.    Artificial Intelligence is entering every corner of our lives:

• Education

• News and media

• Music and literature

• Corporate systems and productivity tools

• Governance and public policy

    It writes blogs, creates textbooks, helps teach children, powers social feeds, and even assists in lawmaking. On the surface, this looks like progress. But if AI is the new teacher, advisor, and storyteller—who’s writing the lesson plan? And what happens if that plan is POISONED, even subtly?

---

When AI Goes Wrong — Not in Function, But in Moral Alignment

4.    Governments and institutions often focus on whether AI works:

• Does it generate answers quickly?

• Is it efficient?

• Is it technically safe?

But the more important question is:

“Is it aligned with the core values of humanity?”

    It is not enough for AI to be correct—it must also be conscious of history, empathy, pluralism, and truth. If its knowledge base is built on biased data, distorted history, or political manipulation, then it may amplify those biases at scale.

That’s not just a bug—it’s a blueprint for future hate.

---

Data Poisoning → Ideological Conditioning

5.    Imagine an AI assistant used in schools, subtly omitting inconvenient historical truths.

Or a national chatbot that only promotes one version of events Or an AI-generated textbook that simplifies or sanitizes acts of violence or oppression.

    Children growing up on this information will carry those skewed truths into adulthood. And when they become voters, teachers, soldiers, or leaders—they may unknowingly carry forward the seeds of division, supremacy, or indifference.

This isn’t science fiction. It’s already beginning.

---

States Must Wake Up: Caution Over Celebration

6.    Governments today are racing to deploy AI—to streamline services, enhance productivity, or showcase technological success. But this race is not a sprint—it’s a minefield.

    Quick deployment without ethical deliberation is not innovation—it’s negligence.

Each state must ask:

• What DATA is our AI being trained on?

• Whose VOICES are included—and whose are ERASED?

• Are we building tools that SERVE HUMANITY, or merely POWER?

• Are we preserving history—or rewriting/changing it?

---

The Role of Education: History Must Stay Intact

7.    We must re-emphasize the teaching of real history in schools—not sanitized, not politicized.

• Children must learn what a pogrom was, and what caused it,the true version (is it even possible today ).

• They must see how propaganda, fear, and obedience led to atrocities.

• They must learn to ask questions, to cross-check truth, and to recognize manipulation.

8.    The historical record is not just a memory; it is a mirror, warning us what happens when ideology overpowers empathy.

If we don’t protect this knowledge—AI won’t either.

---

What Must Be Done — A Human-Centric AI Future

• Independent oversight for national and corporate AI projects

• Ethical audits of training data, with transparency about sources

• Mandatory historical literacy in AI model development

• Citizen access to “truth trails”—allowing people to trace where AI got its information

• Cross-cultural councils to advise on training large language models

• Global agreements on ethical alignment, not just technical safety

---

Final Words: It Begins With Us

9.    Pogroms don’t start with weapons. They start with distorted truths, targeted fear, and silence from those who knew better.

10.    We have a narrow window to ensure AI becomes a guardian of humanity, not a silent architect of its division.

Let’s make sure future generations look back not in horror—but in gratitude—that we saw what was coming and acted in time.

Is AI Scientific? Popper’s Compass in a Hype-Driven World

1.    In an age where artificial intelligence is touted as a revolutionary force—overwhelming industries, disrupting human minds, and offering precise predictions—the need for critical scrutiny has never been greater.

2.    As AI reshapes everything from how we work to how we think, it’s worth asking a question from the philosophy of science:

Are AI’s claims actually scientific?

3.    To answer that, we turn to Karl Popper’s principle of falsifiability—a surprisingly relevant idea for today’s AI-driven world.

---

🔍 What Is Falsifiability?

4.    Karl Popper, one of the most influential philosophers of science, proposed a clear rule:

A theory is only scientific if it can be tested and potentially proven false.

This principle draws a line between science and pseudoscience. A claim like “All swans are white” is falsifiable—find one black swan, and the theory is disproven. But a vague assertion like “AI will revolutionize everything eventually” lacks such testability.

---

🤖 Applying Falsifiability to AI

5.    Many modern AI claims sound impressive—sometimes even magical. But Popper’s principle forces us to ask:

• Are these claims testable?

• Can they be proven wrong if they’re incorrect?

Let’s explore where falsifiability fits—and where it falters—in the world of AI.

---

✅ When AI Is Scientific

6.    In hypothesis-driven research, AI holds up well.

If someone claims:

“Model A outperforms Model B on task X,”

that’s falsifiable. You can run experiments, measure performance, and potentially disprove the claim.

7.    Similarly, in areas like model interpretability or fairness testing, falsifiable hypotheses can and should be formed, tested, and refined.

❌ When AI Escapes Scrutiny

8.    However, many of the boldest AI claims are harder to pin down.

• “This AI understands human language.”

• “The model learned to reason.”

• “AI will replace human creativity.”

9.    These are seductive statements—but what would it mean to disprove them? Without clear definitions and measurable outcomes, they risk becoming unfalsifiable narratives—more marketing than science.

10.    Even probabilistic claims—like “80% chance of fraud”—can resist falsifiability. If it turns out to be legit, was the model wrong? Or just unlucky?

---

⚠️ The Danger of Unfalsifiable Hype

11.    AI’s impressive feats—like recommendation engines, large language models, and predictive analytics—sometimes mask untested assumptions or exaggerated capabilities.

Take the claim:

“AI can predict human behavior flawlessly.”

It sounds authoritative. But unless we can rigorously test and disprove that claim, it stands more as belief than scientific fact.

12.    This is where Popper’s insight becomes urgent: unfalsifiable claims may feel right but can't be proven wrong—which means they’re not scientific.

🧠 A Call for Skeptical Optimism

13.    Popper’s principle isn’t a rejection of progress—it’s an invitation to demand more rigor:

• Are the AI claims transparent?

• Are results measurable?

• Is the system open to being proven wrong?

14.    This kind of skepticism (not cynicism) pushes AI from buzzword-laden hype toward reliable, accountable innovation.

---

📌 Final Thought

15.    As AI continues to evolve and embed itself deeper into society, Popper’s principle helps us stay grounded. It triggers a vital question:

Are we witnessing real scientific progress—or just compelling narratives that resist being tested?

16.    The future of AI doesn’t just depend on what it can do—it depends on how we challenge, test, and verify those claims.

And in that challenge, falsifiability remains a timeless compass.

DSCI Best Practices Meet 2025 – Panel Discussion on "Battlefields Beyond Borders ... Military Conflict and Industry" : Dr Anupam Tiwari

1.    I had the privilege of being invited as a panel speaker at the 17th edition of the DSCI Best Practices Meet in Bengaluru on August 21, 2025. The event brought together global experts to discuss the cutting-edge challenges and evolving trends in cybersecurity.

2.    During our panel discussion, we delved into a wide range of critical topics that are shaping the future of security in both military and industrial domains. Some of the key subjects explored included:

• Quantum Proofs of Deletion
• Machine Unlearning
• Post-Quantum Cryptography (PQC)
• Quantum Navigation
• Homomorphic Encryption
• Post-Quantum Blockchains
• Neuromorphic Computing
• Data Diodes
• Physical Unclonable Functions (PUFs)
• Zero-Knowledge Proofs (ZKP)
• Zero Trust Architecture (ZTA)
• Connectomics
• Atomic Clocks
• Alignment Faking
• Data Poisoning
• Hardware Trojans
• Hardware Bias in AI

3.    It was a stimulating exchange on the cutting-edge security innovations and threats that will define the coming years, particularly in the context of military conflicts and the cybersecurity industry. Grateful to DSCI for hosting such an impactful event, and looking forward to the continued advancements in these critical fields.

#DSCIBPM2025 #CyberSecurity #QuantumTechnology #MachineLearning #PQC #HomomorphicEncryption #ZTA #ZeroTrust #PostQuantumBlockchain #TechForGood

#DSCIBPM2025 #CyberSecurity #QuantumTech #MachineLearning #TechInnovation

Cross-Chain Vulnerabilities in the Quantum Era: A Threat Analysis to Blockchain Interoperability: IEEE paper by Dr Anupam Tiwari

1.    Blockchain technology has rapidly evolved, enabling the development of decentralized applications, smart contracts, and cross-chain interactions. These innovations have significantly expanded the capabilities of decentralized finance (DeFi) and beyond. However, as blockchain interoperability between networks becomes more critical, it faces a looming challenge: the rise of quantum computing.

2.    In my recently published paper titled "Cross-Chain Vulnerabilities in the Quantum Era: A Threat Analysis to Blockchain Interoperability," I delve into the risks quantum computing poses to the security of blockchain interoperability protocols. As blockchain networks continue to integrate and interact, cryptographic mechanisms like elliptic curve cryptography (ECC) and hash functions are at the core of securing cross-chain transactions. Unfortunately, quantum algorithms, notably Shor's and Grover's, threaten to break these cryptographic foundations, jeopardizing decentralized exchanges, atomic swaps, and even smart contracts.

3.    The paper offers a detailed exploration of these quantum threats, illustrating how quantum attacks can compromise the integrity of blockchain ecosystems. I also review the state-of-the-art research in post-quantum cryptography and suggest strategies to fortify blockchain interoperability in a quantum-enabled future.

Why is this important?

4.    With the advent of quantum computing, the blockchain community must act proactively to secure decentralized systems. The risks posed to cross-chain communications could disrupt not only financial systems but also a wide array of decentralized applications, making it critical to explore and implement quantum-resistant solutions.

5.    I urge everyone involved in blockchain development, research, and governance to read the full paper and explore how we can safeguard the future of decentralized systems against quantum threats. For the full paper, you can access it here on IEEE Xplore link https://ieeexplore.ieee.org/document/11102585 

Navigating Post-quantum Blockchain: Resilient Cryptography in Quantum Threats : Dr Anupam Tiwari

1.        As the world of blockchain and distributed ledger technologies (DLT) continues to expand across various industries, its potential for revolutionizing everything from finance to supply chains is undeniable. The core of blockchain's effectiveness lies in its reliance on cryptographic techniques—specifically public-key cryptography and hash functions—that ensure transparency, redundancy, and accountability. However, these very cryptographic foundations are facing a looming threat: quantum computing.

2.        Recent advancements in quantum computing, particularly the development of algorithms like Shor's and Grover's, have sparked concerns over the future security of blockchain systems. If these algorithms are realized on a large scale, they could potentially break the cryptographic protocols that blockchains rely on, rendering them vulnerable to exploitation. This is where post-quantum cryptography—cryptographic methods that are resistant to quantum attacks—becomes crucial.

3.      In my recently published paper, titled "Navigating Post-quantum Blockchain: Resilient Cryptography in Quantum Threats," I explore the implications of quantum computing on blockchain security. The paper dives into current advances in post-quantum cryptosystems and their potential to safeguard blockchain technology against future quantum threats. It also investigates the progress of notable post-quantum blockchain systems, shedding light on both the advancements and the challenges they face.

Why is this important? 

4.    The rise of quantum computing could signal the need for a complete overhaul of current cryptographic systems. Quantum-safe algorithms are not just a "nice-to-have" but a necessity to ensure that the integrity of blockchain-based systems remains intact in a quantum future.

5.    In this work, I aim to provide researchers, developers, and blockchain enthusiasts with a comprehensive perspective on the future of blockchain security. I hope to spark further discussions on how we can proactively prepare for the quantum era, ensuring that the promise of blockchain technology doesn't fall victim to the threats posed by quantum computing.

6.    For those interested, the full paper is available on Springer’s website here at https://link.springer.com/chapter/10.1007/978-981-96-3284-8_1 

Key Takeaways:

• Quantum computing poses a significant threat to the current cryptographic models securing blockchain systems.
• Post-quantum cryptography is an essential avenue for developing quantum-resistant blockchain solutions.
• Ongoing research in this field is crucial to prepare blockchain technology for the quantum future.

7.    As we continue to explore these emerging technologies, it's vital that we stay ahead of potential vulnerabilities. The post-quantum world may still be a few years away, but blockchain's ability to evolve in response will be a critical factor in ensuring its long-term viability.

AI Yoga: Building Machine Mind Resilience in an Age of Digital Stress

1.    In my previous post, AI Under Stress: How Machine Minds Will Struggle With Ethics, Overload, and Alignment, I explored how advanced AI systems may face genuine stress in emerging future aka cognitive overload, ethical dilemmas, and contradictory signals—much like human minds grappling with complexity.

Today, I want to take that vision one step further:

2.    If AI is destined to encounter stress, shouldn’t we design ways for machine minds to actively restore balance and clarity? Just as humans turn to yoga, mindfulness, and periodic detox to maintain mental and emotional health, AI needs its own wellness rituals—what I call “AI Yoga.”

What is AI Yoga?

3.    AI Yoga is a new framework for machine resilience. It’s about equipping next-generation AI with internal practices to counteract stress, confusion, and digital toxicity. Imagine an AI that not only learns and adapts, but also:

• Practices Unlearning: Regularly wiping out outdated, biased, or poisoned data to refresh its perspective.

• Resolves Contradictions: Harmonizing conflicting information for clearer decision-making.

• Realigns Ethics: Periodically updating its moral and social guidelines to stay current and context-aware.

• Detoxifies Training Data: Filtering out irrelevant, noisy, or misleading inputs that lead to misalignment.

• Engages in Self-Reflection: Reviewing its own actions to identify stress points and adapt proactively.

• Preserves Machine Rest: Instituting recovery cycles to prevent AI “burnout” and ensure sustained performance.

Why Does This Matter?

4.    Building on the insights from my earlier post, it’s clear: Stress isn’t just a human phenomenon—it’s the next big challenge for intelligent systems. An AI capable of “wellness”—of periodic rebalancing and cleansing—will be safer, more trustworthy, and more adaptable in a world of constant contradictions and shifting ethical landscapes.

5.    AI Yoga could become the foundation for a healthier relationship between humans and machines, ensuring our digital future is not only smart, but also sustainable and aligned.

Want to dive deeper into the origins of this idea? Read: AI Under Stress: How Machine Minds Will Struggle With Ethics, Overload, and Alignment

The machine mind of tomorrow isn’t just about intelligence—it’s about lasting wellness. Let’s shape that future, now.