Decimal Dreams: How Vedic Math Could Power India’s Tech Revolution

Ever tried adding 0.1 + 0.2 in Python, expecting to get 0.3?

Go ahead, fire up your terminal and try:

>>> 0.1 + 0.2
0.30000000000000004

It’s not a bug. It’s not Python’s fault either. It’s a feature — or rather, a limitation of how modern computers represent decimal numbers using binary floating-point arithmetic.

In a world where we measure progress by computing speed and accuracy, how did we end up with basic math giving us slightly wrong answers?

Let’s explore this, and maybe — just maybe — ask whether India has a unique path to reimagine it.

💡 The Root of the Problem: Binary Floating Point

Computers store numbers using binary — 1s and 0s. The IEEE 754 standard, which nearly every computer in the world follows, represents floating-point numbers using a fixed number of bits.

Unfortunately, not all decimal numbers can be exactly represented in binary. For example:

• 0.1 in binary is a repeating decimal: 0.0001100110011... (infinite)

• Same with 0.2, 0.3, etc.

So when you compute 0.1 + 0.2, you're actually adding two approximations:

0.1  ≈ 0.10000000000000000555...
+ 0.2  ≈ 0.2000000000000000111...
= 0.3000000000000000444...

Python rounds this to 0.30000000000000004. Precise? Not quite. Accurate? Close enough — for most use cases.

But in critical domains like finance, science, or cryptography, this “close enough” may not be good enough.

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🧘🏽‍♂️ Vedic Mathematics: Precision in a Decimal World

Interestingly, such issues don’t exist in Vedic mathematics, the ancient Indian system of mental math. It works entirely in decimal and relies on beautifully simple, human-friendly algorithms. For example, complex multiplications can be done mentally using techniques like "Vertically and Crosswise".

Vedic math ensures exactness — not approximations. It doesn’t deal in floating-point errors because it doesn't depend on binary representations at all.

Of course, Vedic math wasn’t designed for computers — it’s a mental calculation system. But it raises an interesting question:

Can we build a computational system inspired by the principles of Vedic math — one that prioritizes decimal precision over binary speed?

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🧮 Decimal Arithmetic in Practice: Not Just a Dream

Decimal arithmetic in computing isn’t a fantasy:

• Python has a built-in decimal module for high-precision decimal calculations.

• IBM’s mainframe processors (like zSeries) support hardware decimal floating-point for financial applications.

• Many banking systems use BCD (Binary Coded Decimal) to ensure rounding errors don’t wreck financial calculations.

But these are exceptions — not the rule. Decimal computing is slower, more expensive, and not natively supported by mainstream CPUs.

So why doesn’t the world adopt it more broadly?

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⚙️ The Real Challenge: Not Technical, But Industrial

We could build computers that process decimal numbers natively. The algorithms exist. Hardware can be built. Vedic math can even inspire optimization.

But the problem isn’t feasibility. It’s momentum.

The global computing ecosystem — from chip design to compilers, from software libraries to operating systems — is deeply entrenched in binary. Switching to decimal at the hardware level would mean:

• New architectures

• New compilers and languages

• New standards

• New manufacturing pipelines

This is a multi-trillion-dollar disruption. So unless the benefit is overwhelmingly clear, the industry will resist change.

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🇮🇳 An Opportunity for India?

Here’s where it gets INTERESTING.

India today is primarily a consumer of computing technologies — most of which are developed abroad. We often end up labelling imported tech as “indigenous” because the underlying stack is still foreign.

But what if we take a bold leap?

India has:

• A deep cultural and academic legacy of mathematics (e.g., Vedic math)

• A massive pool of engineering talent

• Government interest in self-reliance (think: Atmanirbhar Bharat)

• A growing digital economy that needs robust, transparent, and accurate systems

Could India start researching and building a decimal-native computing ecosystem? Maybe not for all use cases — but for niche areas like:

• Financial tech

• Scientific research

• Strategic sectors (like space, defence, or cryptography)

• Education and math learning platforms

It won’t happen overnight. It may take a decade or two. But the rewards? A unique technological niche — one that’s truly Indian, born from ancient knowledge but engineered for the modern world.

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📌 Final Thoughts

When 0.1 + 0.2 ≠ 0.3, it’s a reminder that even the foundations of computing aren’t perfect. It also opens the door to reimagining what’s possible.

Maybe it’s time we stop just working within the limitations — and start asking why those limitations exist in the first place.While we must continue building and improving within today’s frameworks, there’s no reason a parallel path can’t begin — one rooted in our own knowledge systems, designed for precision, and open to rethinking hardware from the ground up.

If nurtured seriously, this path might just turn the tables in the decades to come, positioning India not as a follower of tech trends, but as a pioneer of a new computing paradigm.

If we dream big and build boldly, India could contribute something original and lasting to the global tech stack — not just by writing better code, but by reinventing the rules of the system itself.

Minimalist Data Governance vs Maximalist Data Optimization: Finding the Mathematical Balance for Ethical AI in Government

 🧠 Data and the State: How Much Is Enough?

As governments become increasingly data-driven, a fundamental question arises:

• What is the minimum personal data a state needs to function effectively — and can we compute it?

On the surface, this feels like a governance or policy question. But it’s also a mathematical one. Could we model the minimum viable dataset — the smallest set of personal attributes (age, income, location, etc.) — that allows a government to collect taxes, deliver services, and maintain law and order?

Think of it as "Data Compression for Democracy." Just enough to govern, nothing more.

But here’s the tension:

• How does a government’s capability expand when given maximum access to private citizen data?

With full access, governments can optimize welfare distribution, predict disease outbreaks, prevent crime, and streamline infrastructure. It becomes possible to simulate, predict, and even “engineer” public outcomes at scale.

So we’re caught between two paradigms:

• 🔒 Minimalist Data Governance: Collect the least, protect the most. Build trust and autonomy.
• 🔍 Maximalist Data Optimization: Collect all, know all. Optimize society, but risk surveillance creep.

The technical challenge lies in modelling the threshold:

How much data is just enough for function — and when does it tip into overreach?

And more importantly:

• Who decides where that line is drawn — and can it be audited?

In an age of AI, where personal data becomes both currency and code, these questions aren’t just theoretical. They shape the architecture of digital governance.

💬 Food for thought:

• Could a mathematical framework define the minimum dataset for governance?
• Can data governance be treated like resource optimization in computer science?
• What does “responsible governance” look like when modelled against data granularity?

🔐 Solutions for Privacy-Conscious Governance

1. Differential Privacy

• Adds controlled noise to datasets so individual records can't be reverse-engineered.
• Used by Apple, Google, and even the US Census Bureau.
• Enables governments to publish stats or build models without identifying individuals.

2. Privacy Budget

• A core concept in differential privacy.
• Quantifies how much privacy is "spent" when queries are made on a dataset.
• Helps govern how often and how deeply data can be accessed.

3. Homomorphic Encryption

• Allows computation on encrypted data without decrypting it.
• Governments could, in theory, process citizen data without ever seeing the raw data.
• Still computationally heavy but improving fast.

4. Federated Learning

• Models are trained across decentralized devices (like smartphones) — data stays local.
• Governments could deploy ML for public health, education, etc., without centralizing citizen data.

5. Secure Multi-Party Computation (SMPC)

• Multiple parties compute a function over their inputs without revealing the inputs to each other.
• Ideal for inter-departmental or inter-state data collaboration without exposing individual records.

6. Zero-Knowledge Proofs (ZKPs)

• Prove that something is true (e.g., age over 18) without revealing the underlying data.
• Could be used for digital ID checks, benefits eligibility, etc., with minimal personal info disclosure.

7. Synthetic Data Generation

• Artificially generated data that preserves statistical properties of real data.
• Useful for training models or public policy simulations without exposing real individuals.

8. Data Minimization + Purpose Limitation (Legal/Design Principles)

• From privacy-by-design frameworks (e.g., GDPR).
• Ensures that data collection is limited to what’s necessary, and used only for stated public goals.

💡 Takeaway

With the right technical stack, it's possible to govern smartly without knowing everything. These technologies enable a “minimum exposure, maximum utility” approach — exactly what responsible digital governance should aim for.

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?

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🧠 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."

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🛠️ 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.

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🧩 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

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🚫 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.

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🕵️‍♂️ 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.

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⚠️ 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.

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🛡️ 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.

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🔊 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.

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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.

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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?

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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.

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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.

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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?

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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.

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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

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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.

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🔍 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.

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🤖 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.

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✅ 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?

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⚠️ 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.

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📌 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