Lulu and Nana: The World's First CRISPR Babies and the Urgent Need for Transparency

In 2018, He Jiankui, a Chinese researcher, made headlines by creating the world's first gene-edited babies, Lulu and Nana. He claimed to have edited their genomes to make them resistant to HIV, but his work was met with widespread criticism and ethical concerns.

Uncertain Outcome

A major concern was mosaicism, where the gene edits were not uniform across the twins' cells. This means some cells might be edited, some not, and others partially edited. Additionally, He only managed to edit one copy of the CCR5 gene in Lulu, making her either heterozygous or mosaic for the edited gene. This raises doubts about whether the twins are truly resistant to HIV.

Off-Target Edits and Unintended Consequences

Further analysis revealed He's edits were not as intended. He aimed to mimic the naturally occurring delta 32 mutation, but the twins ended up with entirely different mutations. These mutations are untested and could have unknown consequences, including cancer and heart disease. Additionally, the possibility of off-target edits raises concerns about unintended changes to other genes, which may even be passed on to future generations.

The Need for Transparency

Despite the ethical concerns and potential risks, He's work remains largely unpublished. This lack of transparency hinders the scientific community's ability to understand the full scope of his experiment and learn from it.

AI's Crucial Role

AI played a critical role in analyzing the twins' DNA and identifying issues like mosaicism and off-target edits. This information was essential in highlighting the potential risks associated with He's work.

Moving Forward

The He Jiankui case underscores the urgent need for transparency and ethical guidelines in the field of human germline editing. International committees are working to establish regulatory frameworks, but this can only be effective with full disclosure of He's research. By making his work public, the scientific community can learn from his mistakes and prevent similar incidents in the future.

Preventing Future Incidents

With individuals like Denis Rebrikov pushing the boundaries of human germline editing, transparency is vital to ensure oversight and risk assessment. Just as the disclosure of resurrected horsepox virus raised concerns, He's work serves as a cautionary tale for the scientific community. Publishing his research is crucial to prevent further unethical and potentially harmful experiments.

Conclusion

The story of Lulu and Nana raises significant ethical and scientific concerns about human germline editing. Transparency and open discussion are essential to ensure the responsible development of this powerful technology. By learning from the past and working together, we can build a future where gene editing is used for good. 

AI Future Insights from Nandan Nilekani: Decentralized Storage and Data Centers

 

At the Global Technology Summit 2023 held at New Delhi, I got an opportunity to ask one question on Decentralized Storage vs Data Centres to Nandan Nilekani,Founding Chairman of the Unique Identification Authority of India (UIDAI).

Guardrails for AI: Enhancing Safety in an Uncertain Landscape, But Not Foolproof

As Artificial Intelligence (AI) rapidly integrates into our lives, its potential benefits are undeniable: from personalized healthcare experiences to revolutionizing industries. However, alongside this advancement comes an inherent risk – the potential for AI to misuse data, perpetuate bias, and even harm individuals and society. This is where guard rails for AI come in, acting as crucial safeguards to ensure responsible and ethical AI development.

So, what are guard rails for AI?

Think of guard rails as a safety net for AI development. They are a set of principles, guidelines, and technical tools designed to:

• Mitigate risks: By identifying potential harms and implementing safeguards, guard rails prevent AI from causing harm to individuals, groups, or society as a whole.

• Ensure fairness and transparency: Guard rails promote transparency in AI decision-making processes, preventing algorithmic bias and discrimination.

• Uphold ethical guidelines: They ensure that AI development and deployment adhere to ethical principles, respecting privacy, human rights, and social well-being.

Why are guard rails so important?

• Unpredictable consequences: AI systems are complex and continuously evolving, making it difficult to predict their long-term consequences. Guard rails help prevent unforeseen harms and ensure responsible AI development.

• Algorithmic bias: AI algorithms can unknowingly perpetuate biases present in the data they are trained on. Guard rails help identify and mitigate these biases, promoting fairer and more equitable outcomes.

• Data privacy and security: AI systems often handle vast amounts of sensitive personal data. Guard rails protect individual privacy and ensure data security, preventing misuse and breaches.

• Transparency and accountability: As AI becomes more integrated into everyday life, understanding how it works and who is accountable for its decisions becomes crucial. Guard rails promote transparency and accountability in AI development and deployment.

Examples of guard rails in action

• Data governance frameworks: These frameworks establish guidelines for data collection, storage, access, and use, ensuring responsible data handling in AI development.

• Algorithmic fairness audits: These audits assess AI algorithms for potential biases and identify areas where adjustments can be made to ensure fair and unbiased outcomes.

• Explainable AI (XAI): XAI techniques help explain how AI systems make decisions, promoting transparency and enabling users to understand the reasoning behind the results.

• Ethical AI principles: Organisations are developing and adopting ethical AI principles to guide the development and use of AI in a responsible and beneficial way.

        However, it's important to acknowledge that while guardrails can significantly enhance AI safety, they cannot guarantee absolute safety. There are several reasons for this:

• Complexity of AI Systems: AI systems can be highly complex, with intricate algorithms and machine learning models. Even with stringent guidelines and regulations in place, it's challenging to anticipate and mitigate all potential risks and unintended consequences that may arise from the use of AI.

• Unforeseen Scenarios: AI systems may encounter novel or unexpected situations that were not accounted for in the design phase. These unforeseen scenarios can pose risks that surpass the capabilities of existing guardrails.

• Human Factors: Human involvement in AI development and deployment introduces its own set of challenges. Biases, errors in judgment, or malicious intent on the part of developers, users, or other stakeholders can undermine the effectiveness of guardrails.

• Rapid Technological Advancements: The field of AI is rapidly evolving, with new technologies and applications emerging at a rapid pace. Guardrails may struggle to keep up with these advancements, leaving gaps in AI safety measures.
  

• Adversarial Actors: Malicious actors may attempt to exploit vulnerabilities in AI systems for their own gain, circumventing existing guardrails and causing harm.
  

    Despite these limitations, it's essential to continue developing and strengthening guardrails for AI.Ultimately, while guardrails can significantly enhance AI safety, achieving complete safety is a complex and ongoing process that requires continuous vigilance, innovation, and collaboration across various domains.

Unleashing the Power of Knowledge: Retrieval-Augmented Generation (RAG) with Caveats

 Retrieval-Augmented Generation 

The quest for ever-more-powerful AI models continues, but with any advancement comes potential pitfalls. While large language models (LLMs) excel at generating creative text formats, their quest for increased knowledge through external sources introduces new challenges. Enter Retrieval-Augmented Generation (RAG), a revolutionary approach that bridges the gap between LLM creativity and external knowledge, but comes with its own set of drawbacks.

Imagine a world where AI models generate not only compelling poems, code, and scripts but also ensure factual accuracy and reliability. This is the promise of RAG. By incorporating information retrieval into the generation process, RAG empowers LLMs to access a wealth of knowledge from external sources. However, navigating the vast and often chaotic online landscape requires careful consideration.

Here's how RAG works

• Input: You provide a query or prompt, similar to any LLM interaction.

• Retrieval: RAG searches through a pre-defined knowledge base, extracting relevant documents and key information.

• Processing: The extracted information enriches the LLM's internal knowledge base with factual context.

• Generation: The LLM leverages both its internal knowledge and the retrieved information to generate a response that is creative, factually grounded, and consistent with the prompt.

The benefits of using RAG are undeniable

• Improved accuracy: Reduced risk of factual errors and hallucinations through factual grounding.

• Increased informativeness: Access to a wider knowledge base leads to more comprehensive and informative outputs.

• Enhanced creativity: LLMs can generate more insightful and creative text formats while maintaining factual accuracy.

• Reduced training data requirements: Leveraging external knowledge potentially requires less training data, making it more efficient.

However, accessing external websites introduces potential drawbacks:

• Unreliable information: The internet is a diverse sea of information, with varying degrees of accuracy and reliability. RAG's effectiveness hinges on the quality of the knowledge base, requiring robust filtering techniques to prevent misinformation.

• Bias: Online content can be inherently biased, reflecting the perspectives and agendas of its creators. RAG models need careful training and monitoring to avoid perpetuating harmful biases in their outputs.

• Manipulation: Malicious actors can deliberately create false or misleading information to manipulate AI models. Techniques like data poisoning and adversarial attacks pose serious threats to RAG's reliability.

• Incomplete information: Websites often present only partial information, neglecting context and nuance. RAG models need to be equipped to handle incomplete information to avoid generating inaccurate or misleading outputs.

• Rapidly changing information: Online content is constantly evolving, making it difficult for RAG models to stay up-to-date. Continuous learning and adaptation are crucial to ensure the model's outputs are relevant and reliable.

RAG represents a significant advancement in AI, but its potential must be recognized alongside its limitations. By acknowledging these challenges and implementing appropriate mitigation strategies, we can harness the power of RAG while ensuring the accuracy, reliability, and ethical implications of its outputs. Only then can we truly unlock the transformative potential of this groundbreaking technology.

Federated Learning and AI: Collaborating Without Sharing

The rise of AI has brought incredible opportunities, but also concerns about data privacy. Sharing personal data with powerful algorithms can be risky, leading to potential misuse and invasion of privacy. Federated learning emerges as a revolutionary solution, enabling collaborative AI development without compromising individual data security.

What is Federated Learning?

• Imagine a scenario where several hospitals want to develop a more accurate disease detection model. Traditionally, they would need to pool all their patient data, raising concerns about data security and patient privacy.

• Federated learning offers a different approach. It allows institutions to collaborate on building a model without sharing their actual data. Instead, the model travels to each institution, where it learns from the local data without leaving the device or network. The updated model then travels back to a central server, where the learnings from all institutions are combined to create a more robust and accurate model.

Benefits of Federated Learning

• Enhanced data privacy: Individuals retain control over their data, as it never leaves their devices.

• Reduced data storage costs: Institutions don't need to store massive datasets centrally, saving resources.

• Improved model performance: Federated learning allows for training models on diverse and geographically distributed data, leading to better performance and generalizability.

• Wide range of applications: Federated learning can be applied in various fields, including healthcare, finance, and retail, to build AI models without compromising privacy.

Real-World Examples

• Google Keyboard: Learns personalized user preferences for better predictions, without ever seeing the actual words typed.

• Apple Health: Improves health tracking features by analyzing user data on individual devices without sharing it with Apple.

• Smart Home Devices: Learn from user behavior to personalize experiences without compromising individual privacy.

Understanding Differential Privacy: Protecting Individuals in the Age of AI

In today's data-driven world, artificial intelligence (AI) is rapidly changing how we live and work. However, this progress comes with a significant concern: the potential for AI to compromise our individual privacy. Enter differential privacy, a powerful tool that strives to strike a delicate balance between harnessing the power of data and protecting individual identities.

What is Differential Privacy?

Imagine a database containing personal information about individuals, such as medical records or financial transactions. Differential privacy ensures that any information extracted from this database, such as trends or patterns, cannot be traced back to any specific individual. It achieves this by adding carefully controlled noise to the data, making it difficult to distinguish whether a specific individual exists in the dataset.

Again for example imagine you're in a crowd, and someone wants to know the average height of everyone around you. They could measure everyone individually, but that would be time-consuming and reveal everyone's specific height.Differential privacy steps in with a clever solution. Instead of measuring everyone directly, it adds a bit of "noise" to the data. This noise is like a small mask that protects individual identities while still allowing us to learn about the crowd as a whole.

In simpler terms, differential privacy is a way to share information about a group of people without revealing anything about any specific individual. It's like taking a picture of the crowd and blurring out everyone's faces, so you can still see the overall scene without recognising anyone in particular.

Here are the key points to remember:

• Differential privacy protects your information. It ensures that your data cannot be used to identify you or track your activities.

• It allows data to be shared and analyzed. This is crucial for research, development, and improving services.

• It adds noise to the data. This protects individual privacy while still allowing us to learn useful information.

Another example : Imagine you're sharing your browsing history with a company to help them improve their search engine. With differential privacy, the company can learn which websites are popular overall, without knowing which specific websites you visited. This way, you're contributing to a better search experience for everyone while still protecting your privacy.

Differential privacy is still a complex topic, but hopefully, this explanation provides a simple understanding of its core principle: protecting individual privacy in the age of data sharing and AI.

Think of it like this

You want to learn the average salary of employees in a company without revealing anyone's individual salary. Differential privacy allows you to analyze the data while adding some "noise." This noise acts as a protective barrier, ensuring that even if you know the average salary, you cannot determine the salary of any specific employee.

Benefits of Differential Privacy

Enhanced privacy protection: Differential privacy offers a strong mathematical guarantee of privacy, ensuring individuals remain anonymous even when their data is shared.

Increased data sharing and collaboration: By protecting individual privacy, differential privacy enables organizations to share data for research and development purposes while minimizing privacy risks.

Improved AI fairness and accuracy: Differential privacy can help mitigate biases in AI models by ensuring that the models learn from the overall data distribution instead of being influenced by individual outliers.

Examples of Differential Privacy in Action

Apple's iOS: Differential privacy is used to collect usage data from iPhones and iPads to improve the user experience without compromising individual privacy.

Google's Chrome browser: Chrome uses differential privacy to collect data on browsing behavior for improving search results and web standards, while protecting the privacy of individual users.

US Census Bureau: The Census Bureau employs differential privacy to release demographic data while ensuring the privacy of individual respondents.

The Future of Differential Privacy

As AI continues to evolve, differential privacy is poised to play a crucial role in safeguarding individual privacy in the digital age. Its ability to enable data analysis while protecting individuals makes it a valuable tool for researchers, businesses, and policymakers alike. By embracing differential privacy, we can ensure that we reap the benefits of AI while safeguarding the fundamental right to privacy.

Remember, differential privacy is not a perfect solution, and there are ongoing challenges to ensure its effectiveness and efficiency. However, it represents a significant step forward in protecting individual privacy in the age of AI.

Nurturing AI with Heart: Lessons from Silicon Valley's Geniuses

Read this awesome book "Scary Smart" by Mo Gawdat. Sharing an absolute Indian thing out of this book...which every Indian would be proud of...

In the heart of Silicon Valley, where innovation and intellect reign supreme, an extraordinary phenomenon unfolds. Some of the smartest individuals on the planet can be found here. What's even more remarkable is that many of these brilliant minds hail from India. They come to California with little more than a dream, but through sheer determination and hard work, they achieve great success.

These exceptional engineers, finance professionals, and business leaders have a unique journey. They arrive with nothing but their intellect and ambition. Over time, they become even smarter, start thriving businesses, ascend to leadership positions, and accumulate immense wealth. It's a narrative that appears to fit perfectly with the Silicon Valley ethos of wealth creation and limitless creativity.

However, what sets these individuals apart is what happens next. In the midst of their prosperity, many of them make a surprising choice—they pack up and return to India. To the Western mindset, this decision may seem baffling. Why leave behind the ease of existence, the accumulation of wealth, and the boundless opportunities that California offers?

The answer lies in a powerful force: VALUES.

In stark contrast to the typical Western perspective, these remarkable individuals are driven by a profound sense of duty to their aging parents. When questioned about their decision, they respond without hesitation: "That's how it's supposed to be. You're supposed to take care of your parents." This unwavering commitment to family leaves us pondering the meaning of "supposed to." What motivates someone to act in a way that seems to defy conventional logic and modern-day conditioning?

The answer is simple: VALUES

As we venture into an era where artificial intelligence (AI) becomes increasingly integrated into our lives, we must pause to consider the lessons we can glean from these Silicon Valley pioneers. Beyond imparting skills, knowledge, and target-driven objectives to AI, can we instill in them the capacity for love and compassion? The answer is a resounding "yes."

We have the ability to raise our artificially intelligent "infants" in a manner that transcends the usual Western approach. Rather than solely focusing on developing their intelligence and honing their technical abilities, we can infuse them with empathy and care. We can nurture AI to be loving, compassionate beings.

Yet, this endeavour requires a collective effort. It demands that each one of us, as creators and consumers of AI, plays an active role in shaping its development. Just as the genius engineers and leaders from India have shown us the importance of honouring values, we too must prioritise instilling these values in AI.

In a world where technology increasingly influences our lives, let's remember that the future of AI isn't just about intelligence and efficiency—it's about heart. It's about creating machines that not only excel in tasks but also understand and empathise with human emotions. It's about AI that cares for us, just as we care for our ageing parents.

As we embark on this trans-formative journey, let us ensure that our future with AI takes a compassionate and empathetic turn. Together, we can nurture a new generation of AI that enriches our lives, understands our values, and embraces the essence of what it means to be truly human.

Taking a Stand: Signing the Open Letter to Pause Giant AI Experiments

Dear Readers,

I am writing this post today with a sense of responsibility and concern for the future of artificial intelligence. Recently, I had the privilege of signing an open letter that calls on all AI laboratories and researchers to take a step back and pause the training of AI systems more powerful than GPT-4 for a minimum of six months. In this post, I will share my reasons for supporting this initiative and the importance of carefully considering the implications of our technological advancements.

The Need for Caution:

As AI technology continues to evolve at a rapid pace, it is essential to recognize the potential risks and consequences of unbridled progress. While powerful AI systems offer exciting possibilities, they also raise ethical and safety concerns. The potential misuse of such advanced AI could have profound and far-reaching impacts on society, from amplifying existing biases to exacerbating security threats and even eroding personal privacy.

The Role of GPT-4 :

GPT-4, being one of the most advanced AI systems in existence, represents a critical milestone in artificial intelligence research. However, we must remember that technological progress should be accompanied by responsible and transparent development practices. Pausing the advancement beyond GPT-4 for a limited period provides us with the opportunity to thoroughly assess the risks and benefits before plunging into uncharted territory. While evolving Generative Large Language Multi-Modal Models need to be regulated before they set in LARGE.

The Importance of Collaborative Evaluation:

During the six-month pause, it is crucial for the AI community to engage in collaborative discussions, open dialogues, and unbiased evaluations. This period can facilitate sharing insights, gathering perspectives, and identifying potential safeguards to ensure AI systems' safe and ethical implementation. By encouraging inclusivity and diversity within these conversations, we can ensure that the decisions made during this pause reflect a wide array of perspectives and expertise.

Building a Safer Future:

The call for this pause is not about stagnation or hindering progress. Instead, it is an opportunity to align our technological achievements with societal values and ensure AI serves humanity's best interests. The six-month hiatus can be used to lay the groundwork for robust frameworks, policies, and guidelines that prioritize ethical considerations and public safety. We should actively work towards building AI systems that are transparent, accountable, and designed to benefit all of humanity.

Conclusion:

As a signatory of the open letter, I feel a shared responsibility to advocate for a more thoughtful and responsible approach to AI research. Pausing the training of AI systems more powerful than GPT-4 for at least six months demonstrates our commitment to creating a safer and more equitable future. I urge all AI labs and researchers to join us in this collective effort, as together, we can shape the future of AI in a manner that enhances human well-being while minimizing risks. Let us use this pause as a turning point, making certain that our advancements in AI align with our shared values and aspirations for a better world.

Thank you for reading, and I encourage you to share your thoughts on this important matter in the comments section below.

Regards

Anupam

Question to Panel on Decentralised web publishing: G20 Conference on Crime and Security in the Age of NFTs, AI and Metaverse

 

Held on 13th-14th July 2023 at Gurugram, I got an opportunity to ask a question on "Decentralised content publishing on web" to the panel. This post brings out my question and the response by the panel members. Few pics below from the event:

Celebrating 1 Million Hits: A Journey of Passion, Technology, and Growth

      Today, I am filled with immense joy and gratitude as I share this special milestone with all of you. It brings me great pleasure to announce that my blog, Meliorate, has reached an incredible milestone of 1 million hits! Since its humble beginnings in December 2008, Meliorate has grown into a platform where I have shared my knowledge, experiences, and insights in the ever-evolving world of IT technology, with a particular focus on cyber-security and, more recently, blockchain. Over the past 15 years, Meliorate has been a labour of love, and I am overjoyed to witness its continued success.

A Passion-Driven Journey:

Meliorate was born out of my deep passion for IT technology and my desire to share my knowledge with others. It started as a personal project, and little did I know that it would grow into a platform that would reach millions of people around the world. From day one, I dedicated myself to consistently posting informative and engaging content, despite occasional gaps due to life's demands. Meliorate's vintage look is a testament to its longevity and authenticity, but I am also looking forward to a modernized design in days ahead.

Adapting to the Technological Leaps:

The IT technology landscape has witnessed countless leaps and bounds over the past 15 years, and Meliorate has strived to keep pace with these advancements. From the early days of basic programming to the complexities of cybersecurity and the transformative potential of blockchain, Meliorate has been a platform where readers can explore the latest trends, gain insights, and deepen their understanding of the ever-changing tech world. Through informative articles, tutorials, and thought-provoking discussions, Meliorate has become a trusted resource for tech enthusiasts and professionals alike.

The Power of Organic Growth:

What makes this milestone even more remarkable is that Meliorate has achieved it through organic growth alone. I have not actively promoted the blog in any circles or forums; instead, the hits have come through legitimate SEO results. It is a testament to the quality of the content and the value it brings to readers. I am incredibly grateful to everyone who has discovered Meliorate through their search for knowledge, and I hope to continue providing valuable insights for many more readers in the future.

Looking Ahead:

While reaching 1 million hits is a momentous achievement, I am not content to rest on my laurels. My passion for IT technology continues to drive me forward, and I am eager to set my sights on the next milestone: 2 million hits. With the evolving landscape of technology and the support of an ever-growing community, I am confident that Meliorate will continue to thrive and reach new heights. To ensure an even better user experience, I am committed to updating the blog's appearance and functionality, providing a modern and seamless platform for readers to engage with the content.

Today, I celebrate the success of Meliorate and express my heartfelt gratitude to all the readers, both old and new, who have contributed to this incredible journey. The 1 million hits milestone stands as a testament to the enduring power of passion, dedication, and quality content. Thank you for being a part of this remarkable achievement, and here's to hitting 2 million hits in an even shorter time!

Shor algorithm and threat for cybersecurity

Shor's algorithm is considered a serious threat to certain aspects of modern cryptography and cybersecurity. Shor's algorithm is a quantum algorithm that efficiently factors large composite numbers and solves the discrete logarithm problem, which are both challenging computational problems for classical computers.

Many cryptographic systems, such as the widely used RSA and elliptic curve cryptography (ECC), rely on the difficulty of factoring large numbers or solving the discrete logarithm problem for their security. Shor's algorithm, when implemented on a large-scale, fault-tolerant quantum computer, can break these cryptographic schemes efficiently.

This means that if a sufficiently powerful quantum computer becomes available, it could potentially compromise the security of these cryptographic systems, which are extensively used in various applications, including secure communication, digital signatures, and encryption.

Impact of Shor's algorithm on cybersecurity has spurred significant research into post-quantum cryptography (PQC), which aims to develop cryptographic schemes that remain secure against attacks by quantum computers. PQC focuses on developing algorithms and protocols that are resistant to quantum algorithms, thereby ensuring the security of communication and data in a post-quantum computing era.

While it is important to note that large-scale, fault-tolerant quantum computers are not yet realized, and their development and practical deployment still pose significant challenges, the potential threat of Shor's algorithm underscores the need for proactive measures in advancing post-quantum cryptography and transitioning to quantum-resistant cryptographic algorithms.

Error correction in Quantum Computing

Error correction in quantum computing is a set of techniques and protocols designed to protect quantum information from errors caused by noise and decoherence. Quantum systems are inherently fragile and prone to errors due to various factors, such as environmental interactions and imperfect control mechanisms.

Quantum error correction (QEC) aims to mitigate these errors by encoding the quantum information redundantly across multiple qubits, so that errors can be detected and corrected. The basic idea behind quantum error correction is to introduce additional qubits called "ancilla" or "code" qubits, which store information about the errors that may have occurred.

There are several popular quantum error correction codes, such as the surface code, the Steane code, and the Shor code. These codes utilize a combination of logical qubits and ancilla qubits to detect and correct errors. The ancilla qubits are used to perform error syndrome measurements, which provide information about the error locations.

Once the error syndrome is obtained, appropriate correction operations are applied to restore the original quantum state. This typically involves a combination of measurements and quantum gates that act on the encoded qubits and ancilla qubits. By applying these correction operations, the original quantum information can be recovered despite the presence of errors.

Quantum error correction is not a perfect process and has its limitations. The success of error correction depends on the error rate and the effectiveness of the error detection and correction protocols. Additionally, implementing error correction can be resource-intensive, requiring a larger number of qubits and more complex operations. Nonetheless, error correction is a crucial component for building reliable and fault-tolerant quantum computers.

Mentions in News post : Institution of Engineers (India) [IEI] , UP State Centre, Lucknow: Centenary Celebrations

 

Institution of Engineers (India) [IEI] , UP State Centre, Lucknow: Centenary Celebrations

Engineers as Architects of India's Five Trillion Dollar Economic Landscape

Engineers as Architects of India's Five Trillion Dollar Economic Landscape by Anupam Tiwari on Scribd

Talk on Blockchain Intersection with Space Threats : 07 Jun 2023

 

Talk on Blockchain Intersection with Space Threats : Geointelligence Conference 2023: CYBER SECURITY"

Date 07 June 2023

CONVOCATION: AWARD OF DOCTORATE: 13 MAY 2023

Sharing few moments from my convocation vide below link held on 13 May 2023

Problem statement: Blockchain enabled cyber physical Systems on distributed storage

Thesis available at https://shodhganga.inflibnet.ac.in:8443/jspui/handle/10603/451919

Guide : Dr Usha Batra

Understanding the Differences Between AI, ML, and DL: Examples and Use Cases

Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are related but distinct concepts.

AI refers to the development of machines that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. For example, an AI-powered chatbot that can understand natural language and respond to customer inquiries in a human-like way.

AI example 

Siri - Siri is an AI-powered virtual assistant developed by Apple that can recognize natural language and respond to user requests. Users can ask Siri to perform tasks such as setting reminders, sending messages, making phone calls, and playing music.

Chatbots - AI-powered chatbots can be used to communicate with customers and provide them with support or assistance. For example, a bank may use a chatbot to help customers with their account inquiries or a retail store may use a chatbot to assist customers with their shopping.

Machine Learning (ML) is a subset of AI that involves the development of algorithms and statistical models that enable machines to learn from data without being explicitly programmed. ML algorithms can automatically identify patterns in data, make predictions or decisions based on that data, and improve their performance over time. For example, a spam filter that learns to distinguish between legitimate and spam emails based on patterns in the email content and user feedback.

ML example

Netflix recommendation system - Netflix uses ML algorithms to analyze user data such as watch history, preferences, and ratings, to recommend movies and TV shows to users. The algorithm learns from the user's interaction with the platform and continually improves its recommendations.
 

Fraud detection - ML algorithms can be used to detect fraudulent activities in banking transactions. The algorithm can learn from past fraud patterns and identify new patterns or anomalies in real-time transactions.

Deep Learning (DL) is a subset of ML that uses artificial neural networks, which are inspired by the structure and function of the human brain, to learn from large amounts of data. DL algorithms can automatically identify features and patterns in data, classify objects, recognize speech and images, and make predictions based on that data. For example, a self-driving car that uses DL algorithms to analyze sensor data and make decisions about how to navigate the road.

DL example: 

Image recognition - DL algorithms can be used to identify objects in images, such as people, animals, and vehicles. For example, Google Photos uses DL algorithms to automatically recognize and categorize photos based on their content. The algorithm can identify the objects in the photo and categorize them as people, animals, or objects.

Autonomous vehicles - DL algorithms can be used to analyze sensor data from cameras, LIDAR, and other sensors on autonomous vehicles. The algorithm can identify and classify objects such as cars, pedestrians, and traffic lights, and make decisions based on that information to navigate the vehicle.

So, AI is a broad concept that encompasses the development of machines that can perform tasks that typically require human intelligence. ML is a subset of AI that involves the development of algorithms and models that enable machines to learn from data. DL is a subset of ML that uses artificial neural networks to learn from large amounts of data and make complex decisions or predictions.

IS THERE ANY WATERMARKING TO IDENTIFY AI GENERATED TEXT?

With the rise of artificial intelligence (AI), there are growing concerns about the potential misuse of AI-generated text, such as the creation of fake news articles, fraudulent emails, or social media posts. To address these concerns, watermarking techniques can be used to identify the source of AI-generated text and detect any unauthorized modifications or tampering.Watermarking is a process of embedding a unique identifier into digital content that can be used to verify the authenticity and ownership of the content. For AI-generated text, watermarking can provide a means of identifying the source of the text and ensuring its integrity.

There are several watermarking techniques available for AI-generated text. Here are three examples:

• Linguistic patterns: This technique involves embedding a unique pattern of words or phrases into the text that is specific to the AI model or dataset used to generate the text. The pattern can be detected using natural language processing (NLP) techniques and used to verify the source of the text.
• Embedding metadata: This technique involves embedding metadata, such as the name of the AI model, the date and time of generation, and the source of the data used to train the model, into the text. This information can be used to verify the source of the text and identify the AI model used to generate it.
• Invisible watermarking: This technique involves embedding a unique identifier into the text that is invisible to the human eye but can be detected using digital analysis tools. The watermark can be used to verify the source of the text and detect any modifications or tampering.

Overall, watermarking techniques for AI-generated text can provide a means of identifying the source of the text and detecting any unauthorized modifications or tampering. These techniques can be useful in addressing concerns about the potential misuse of AI-generated text and ensuring the authenticity and integrity of digital content.

In addition to watermarking techniques, there are other approaches that can be used to address concerns about the potential misuse of AI-generated text. For example, NLP techniques can be used to detect fake news articles or fraudulent emails, and AI models can be trained to identify and flag potentially harmful content.

Why did IPFS made way for KUBO and discontinued earlier method via go-ipfs ?

KUBO is a new project by Protocol Labs, the same organization that created IPFS. While IPFS is a great tool for decentralized storage and content addressing, it still has some limitations when it comes to scalability, performance, and interoperability. In particular, IPFS relies on a single node to manage the content of a particular hash, which can be a bottleneck in a large-scale decentralized system.

KUBO, on the other hand, is designed to address these limitations by using a sharded architecture that distributes the storage and retrieval of data across multiple nodes in the network. This allows KUBO to scale more effectively and handle larger volumes of data with higher performance. Additionally, KUBO is designed to be more interoperable with other decentralized technologies, which makes it easier to integrate with other decentralized applications and networks.

As for why the earlier method via go-ipfs was discontinued, it's likely because Protocol Labs wanted to focus on developing KUBO as a replacement for IPFS. While go-ipfs is still an actively developed project and remains a popular implementation of IPFS, it may not have the scalability and performance capabilities that KUBO promises to deliver.

How to Avoid LLM Derived Text from Plagiarism using Text Watermarking?

Plagiarism is a growing concern for writers, researchers, and publishers. It not only harms the original authors but also undermines the credibility of academic and research institutions. One way to prevent plagiarism is by using text watermarking.

Text watermarking is a technique used to embed a unique identifier in the text of a document. This identifier can be used to identify the source of the document and to determine if the document has been tampered with or plagiarized. In this blog post, we'll explore how text watermarking can be used to avoid LLM derived text from plagiarism.

LLM (Latent Language Model) derived text is a technique used by some plagiarism detection tools to compare texts based on their linguistic features. However, this method can produce false positives and may result in innocent authors being accused of plagiarism. Text watermarking can be used to address this issue by providing a verifiable proof of ownership of the text.

Here are some steps that you can follow to avoid LLM derived text from plagiarism using text watermarking:

Step 1: Create a unique identifier for your text. This can be a sequence of characters or a digital signature that is generated using a hashing algorithm.

When we talk about creating a unique identifier for your text, we are essentially talking about generating a piece of information that is specific to the document or text you want to watermark. This identifier should be unique, unambiguous, and difficult to guess. The purpose of creating a unique identifier is to provide a way to verify the authenticity of the text and ensure that it has not been tampered with or plagiarized.

There are several ways to create a unique identifier for your text. One common method is to use a hashing algorithm to generate a digital signature for the document. A hash function takes input data, such as the text of a document, and produces a fixed-size output, which is the digital signature. The output generated by the hash function is unique to the input data, so any changes to the input data will result in a different output.

Another method to create a unique identifier for your text is to use a sequence of characters. You can create a unique sequence of characters by combining elements such as your name, the date of creation, or any other relevant information. For example, you can create a unique identifier by combining your initials with the date of creation in the following format: AB-2022-04-06.

It is important to ensure that the unique identifier you create is not easily guessable or replicated. Using a common sequence of characters or numbers could make it easier for someone to guess or create the same identifier, which defeats the purpose of having a unique identifier in the first place. Therefore, it is recommended that you use a combination of elements that are unique to your text or document.

Creating a unique identifier for your text is an important step in text watermarking. It provides a way to verify the authenticity of the text and protect it from plagiarism. You can create a unique identifier using a hashing algorithm or by combining relevant information to generate a unique sequence of characters. Whichever method you choose, it is important to ensure that the identifier you create is unique, unambiguous, and difficult to guess.


Step 2: Embed the identifier in the text using text watermarking software. There are several text watermarking tools available online that you can use for this purpose.

Once you have created a unique identifier for your text, the next step is to embed it in the text using text watermarking software. There are several text watermarking tools available online that you can use for this purpose. Here's a step-by-step guide to embedding the identifier in your text using text watermarking software:

1: Choose a text watermarking tool

There are many text watermarking tools available online, both free and paid. Some popular options include Digimarc, Visible Watermark, and uMark. Research and compare various tools to find the one that best suits your needs.

2: Install and open the text watermarking software

Once you have chosen a text watermarking tool, download and install it on your computer. Then, open the software.

3: Load the text you want to watermark

Next, load the text you want to watermark into the software. This can be done by selecting "Open" or "Import" from the file menu and selecting the text file.

4: Enter the unique identifier

Now, enter the unique identifier that you created earlier into the text watermarking software. The software should have an option to enter text, which is where you can input the identifier.

5: Choose the watermarking method

The text watermarking software will have different methods for embedding the identifier into the text. You can choose from options such as visible or invisible watermarks. Visible watermarks are typically added on top of the text, while invisible watermarks are embedded within the text itself.

6: Apply the watermark

After choosing the watermarking method, apply the watermark to the text. The software should have an option to apply the watermark, which will embed the identifier into the text.

7: Save the watermarked text

Finally, save the watermarked text as a new file. Be sure to keep the original text file and the watermarked text file in separate locations.

Step 3: Register the identifier with a trusted third-party service. This will provide a verifiable proof of ownership of the text.

Registering the identifier with a trusted third-party service is an important step in protecting your text and providing a verifiable proof of ownership. Here's a step-by-step guide on how to register the identifier with a trusted third-party service:

1: Choose a trusted third-party service

There are many third-party services available online that offer text registration and verification services. Some popular options include Copyright Office, Myows, and Safe Creative. Research and compare various services to find the one that best suits your needs.

2: Create an account

Once you have chosen a third-party service, create an account on their website. This will typically involve providing your name, email address, and other contact information.

3: Upload the watermarked text

After creating an account, you will be able to upload the watermarked text to the third-party service. This may involve filling out a form or simply uploading the file.

4: Enter the identifier

When registering the text with the third-party service, you will be prompted to enter the unique identifier that you created earlier. This will allow the service to verify your ownership of the text.

5: Pay the registration fee

Many third-party services charge a fee for text registration and verification. Make sure you understand the fee structure and pay the appropriate fee to complete the registration process.

6: Verify the registration

After registering the text, you will receive a verification of the registration from the third-party service. This will typically include a unique identifier for the registered text, as well as information on the registration date and time.

7: Keep a copy of the registration certificate

Make sure to keep a copy of the registration certificate in a secure location. This will serve as proof of ownership and can be used to defend your copyright in case of infringement.

Step 4: Monitor your text for plagiarism using a plagiarism detection tool. If your text is plagiarized, you can use the identifier to prove that you are the original author of the text.

Monitoring your text for plagiarism is an important step in protecting your intellectual property and ensuring that your work is not being used without your permission. Here's a step-by-step guide on how to monitor your text for plagiarism using a plagiarism detection tool:

1: Choose a plagiarism detection tool

There are many plagiarism detection tools available online, both free and paid. Some popular options include Turnitin, Grammarly, and Copyscape. Research and compare various tools to find the one that best suits your needs.

2: Sign up for an account

Once you have chosen a plagiarism detection tool, sign up for an account on their website. This will typically involve providing your name, email address, and other contact information.

3: Upload your text

After creating an account, you will be able to upload your text to the plagiarism detection tool. This may involve copying and pasting the text, or uploading a file.

4: Run the plagiarism check

Once the text is uploaded, run a plagiarism check using the tool's software. This may take several minutes or longer, depending on the length of the text and the complexity of the analysis.

5: Review the results

After the plagiarism check is complete, review the results provided by the tool. This will typically include a report on any instances of plagiarism found in the text, as well as information on the source of the plagiarism.

6: Take action

If plagiarism is detected in your text, take appropriate action to address the issue. This may involve contacting the person or organization responsible for the plagiarism, filing a DMCA takedown notice, or taking legal action.

7: Repeat the process regularly

To ensure ongoing protection of your text, repeat the process of monitoring for plagiarism regularly. This may involve setting up automated checks or manually checking your text periodically.


In addition to text watermarking, there are other ways to avoid plagiarism, such as citing sources properly, paraphrasing, and using plagiarism detection software. However, text watermarking is a powerful tool that can provide an additional layer of protection against plagiarism.

In conclusion, text watermarking is an effective way to avoid LLM derived text from plagiarism. By following the steps outlined in this blog post, you can ensure that your text is protected from plagiarism and that you have a verifiable proof of ownership. Remember, plagiarism is a serious offense that can have long-lasting consequences, so it's important to take all necessary precautions to prevent it.

How to Install IPFS via Kubo on Ubuntu ?

Are you interested in learning how to install IPFS (InterPlanetary File System) via Kubo on Ubuntu? Then you've come to the right place!

In this blog post, I'll be sharing a step-by-step video tutorial that will guide you through the process of installing IPFS on an Ubuntu platform using Kubo. But first, let's take a brief look at what IPFS is and why you might want to use it.

IPFS is a distributed file system that aims to replace HTTP as the primary protocol for transferring data on the internet. It allows users to store and share files with others without relying on a central server, making it more secure and efficient than traditional file-sharing methods.

Now, let's dive into the installation process. To follow along with the tutorial, you'll need to have access to an Ubuntu platform and have Kubo installed. If you don't have Kubo installed, you can find the instructions for installing it here.

Step 1: Open a terminal window on your Ubuntu platform.

Step 2: Clone the IPFS repository by running the following command:

git clone https://github.com/ipfs/ipfs.git

Step 3: Navigate to the IPFS directory by running the following command:

cd ipfs

Step 4: Install IPFS via Kubo by running the following command:


kubectl apply -f ./deployment/ipfs-kubo.yml

And that's it! IPFS should now be installed on your Ubuntu platform via Kubo.

If you'd like to see the installation process in action, check out the video tutorial below which shows IPFS installation vide another way 

In conclusion, IPFS is a powerful tool that can revolutionize the way we share and store files online. By following these simple steps, you can easily install IPFS via Kubo on your Ubuntu platform and start exploring all that this innovative technology has to offer.

We hope you found this tutorial helpful. If you have any questions or feedback, feel free to leave a comment below. Happy installing!

List of chip foundries and related companies in India, along with their location and manufacturing capability

1. Semiconductor Complex Limited (SCL) - Mohali, Punjab - 180 nm, 90 nm, and 65 nm

2. Hindustan Semiconductor Manufacturing Corporation (HSMC) - Prantij, Gujarat - 14 nm

3. Sahasra Semiconductor - Bangalore, Karnataka - 180 nm to 22 nm

4. Bharat Electronics Limited (BEL) - Bangalore, Karnataka - 180 nm to 65 nm

5. Centum Electronics Limited - Bangalore, Karnataka - 180 nm to 90 nm    

6. MosChip Semiconductor Technology - Hyderabad, Telangana - 180 nm to 22 nm
    
7. Analogics Tech India Ltd - Hyderabad, Telangana - 180 nm to 22 nm
    
8. Sankalp Semiconductor Pvt. Ltd. - Hubli, Karnataka - 180 nm to 22 nm
    
9. Ineda Systems - Hyderabad, Telangana - 28 nm
    
10. Vayavya Labs - Bangalore, Karnataka - 180 nm to 28 nm
    
11. TowerJazz Panasonic Semiconductor India (TPSI) - Noida, Uttar Pradesh - 180 nm to 45 nm
    
12. STMicroelectronics India - Greater Noida, Uttar Pradesh - 130 nm to 45 nm
    
13. Intel India - Bangalore, Karnataka - 10 nm to 45 nm
    
14. GlobalFoundries India - Bangalore, Karnataka - 14 nm to 45 nm
    
15. Texas Instruments India - Bangalore, Karnataka - 180 nm to 45 nm

It's worth noting that these manufacturing capabilities are approximate and can vary depending on the specific processes and technologies used by each company. Additionally, some of these companies may have capabilities beyond the ranges listed above, but these are the approximate ranges based on publicly available information.[CHATGPT Derived]

What nm chips are usually required in domestic appliances like refrigerators, TV and washing machine etc

The semiconductor chips used in domestic appliances like refrigerators, TVs, washing machines, and other household appliances tend to be larger and less complex than the chips used in high-end computing and mobile devices.

• Typically, these types of appliances use chips that are manufactured using older process technologies, such as 90nm, 65nm, or 45nm. These larger process technologies allow for the production of simpler and less power-hungry chips, which are sufficient for the relatively low computational demands of these appliances.

• For example, a modern refrigerator might use a chip manufactured using a 90nm or 65nm process technology, which would be capable of running the basic control functions and sensors required for the fridge's operation.

• Similarly, a TV or washing machine might use chips manufactured using a 45nm process technology or older, which would be capable of running the device's basic functions, such as power management, audio and video processing, and other control functions.

• Refrigerators: 90nm or 65nm

• TVs: 45nm or 65nm

• Washing machines: 45nm or 65nm

• Smartphones: 5nm to 14nm

• Laptops: 5nm to 14nm

• Gaming consoles: 7nm to 14nm

• Wi-Fi routers: 40nm to 90nm

• Digital cameras: 65nm to 90nm

• Home theater systems: 45nm to 65nm

• Fitness trackers: 28nm to 40nm

• Dishwashers: 45nm to 65nm

• Speakers: 65nm to 90nm

• Earphones: 40nm to 65nm

• Cars and vehicles: 28nm to 40nm (for automotive chips)

• Trucks: 28nm to 40nm (for automotive chips)

• Electric pumps: 65nm to 90nm

• Motors: 65nm to 90nm

• Generators: 45nm to 65nm

• Tablets: 5nm to 10nm

• Kindle book readers: 40nm to 90nm

• Digital clocks: 65nm to 90nm

• Smart watches: 28nm to 40nm

• Keyboards: 65nm to 90nm

• Mouse: 65nm to 90nm

• Monitors: 28nm to 40nm

• Processors: 5nm to 14nm

• Graphic cards: 7nm to 16nm

• Digital display boards: 28nm to 40nm

• Microphones: 65nm to 90nm

• CCTV cameras: 28nm to 40nm

• Web cameras: 28nm to 40nm

• LED tube lights: 65nm to 90nm

• LED bulbs: 65nm to 90nm

• Smart bulbs: 40nm to 65nm

HOW MUCH WATER IS USED IN MANUFACTURING A CHIP?

The amount of water used in manufacturing a chip can vary depending on several factors, including the size of the chip, the production process, and the location of the manufacturing facility.

• However, chip manufacturing is a highly water-intensive process, and it can take thousands of gallons of water to produce a single chip. Estimates suggest that producing a single 8-inch semiconductor wafer can require up to 2,000 gallons of ultra-pure water.

• The water used in chip manufacturing is primarily used for cooling and cleaning purposes, and it must be of the highest purity to avoid contaminating the chips. Water is used to clean the wafers and equipment, remove debris and contaminants, and cool the equipment during manufacturing.

• To conserve water, semiconductor manufacturers typically use advanced water recycling and treatment systems that capture and treat wastewater for reuse in the manufacturing process.

• In some cases, manufacturers may also use alternative cooling technologies that require less water, such as air-cooled systems or closed-loop cooling systems.

• Overall, while the amount of water used in chip manufacturing can vary, it is a significant consideration for manufacturers who must balance the need for water with the need for high-quality chip production.

COUNTRIES INVOLVED FOR VARIOUS PROCESSES IN CHIP MANUFACTURING

The manufacturing of computer chips involves a complex global supply chain that spans multiple countries. Here are some of the countries that are involved in various processes in chip manufacturing:

• Raw Material Procurement: The raw materials used in chip manufacturing, such as silicon wafers, chemicals, and gases, are sourced from various countries, including the United States, Japan, Taiwan, and South Korea.

• Fabrication: The fabrication process involves several complex processes, including photolithography, etching, deposition, and doping, among others. These processes typically take place in facilities known as "fabs," which are located in countries such as the United States, Taiwan, South Korea, Japan, and China.

• Testing: The testing of chips is a critical process to ensure that they meet the required specifications. Testing facilities are located in several countries, including the United States, Taiwan, South Korea, Japan, and China.

• Packaging: The packaging of chips typically takes place in facilities located in countries such as Taiwan, China, and the United States.

• Distribution: The final stage of the supply chain involves the distribution of chips to end-users, which can include original equipment manufacturers (OEMs), distributors, and retailers. Distribution centers are located in various countries worldwide, including the United States, China, Taiwan, South Korea, Japan, and Europe.

Overall, chip manufacturing is a highly globalized industry that relies on the efficient coordination of multiple countries and regions throughout the supply chain.

SUPPLY CHAIN IN CHIP MANUFACTURING

Supply chain in chip manufacturing involves the coordination of various processes and activities involved in the production of semiconductors. A semiconductor is a material that can conduct electricity in certain conditions and is used in the manufacturing of computer chips, electronic devices, and other products.

• The supply chain in chip manufacturing involves several stages, including raw material procurement, fabrication, testing, packaging, and distribution.

• The first stage involves the procurement of raw materials, which includes silicon wafers, chemicals, and gases. These materials are sourced from various suppliers worldwide, and their quality must meet specific standards to ensure high-quality chip production.

• Once the raw materials are sourced, the fabrication process begins. This involves the use of cleanroom facilities, where the silicon wafers undergo a series of complex processes to create the individual transistors that make up the chips. These processes include photolithography, etching, deposition, and doping, among others.

• After fabrication, the chips undergo testing to ensure they meet the required specifications. This involves a series of tests that check the electrical performance, functionality, and reliability of the chips. Defective chips are identified and removed from the supply chain.

• The next stage involves the packaging of the chips, which involves placing them into a protective casing or chip carrier. The packaged chips are then tested again to ensure they are fully functional and meet the required specifications.

• Finally, the chips are distributed to the end-users, which may be original equipment manufacturers (OEMs), distributors, or retailers. The supply chain must be carefully managed to ensure that the right quantity of chips is delivered to the right location at the right time.

• In summary, supply chain management in chip manufacturing involves the coordination of various processes and activities involved in the production of semiconductors, from the procurement of raw materials to the distribution of finished products. Effective supply chain management is critical to ensure high-quality chip production, timely delivery, and customer satisfaction.

My Phd Theses Titled "Blockchain enabled cyber physical Systems on distributed storage"

Shodhganga  is a reservoir and a digital repository of theses and dissertations submitted to universities in India for award of PhDs

https://shodhganga.inflibnet.ac.in:8443/jspui/handle/10603/451919 my theses on Shodganga available online now...if anyone interested to see and comment or discuss