Artificial Intelligence

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Little AI Explorer: Creative and Ethical AI Learning for Kids – NEW BOOK ON GENERATIVE AI FOR KIDS

As an established technology author and parent of a 10-year-old, I found myself inspired to create a story that would educate children about AI’s potential, ethical considerations, and responsible use.  My goal was to ensure that children like my daughter would be well-equipped with a solid foundation in understanding AI’s role in society and the importance of moral and ethical values when using this powerful technology.

I believe introducing young readers to the fascinating world of AI in a fun, imaginative, and accessible way can spark curiosity, creativity, and a sense of responsibility. By empowering the next generation with knowledge and understanding, we can enable them to harness AI’s potential for the greater good, creating a future where technology and humanity can thrive together.

I hope this book will serve as a valuable resource for parents, educators, and children alike, inspiring young minds to explore the limitless possibilities of AI while remaining grounded in the ethical principles that guide our actions and decisions.

Buy the book at Amazon –

Dream Big & Stay Curious – Navveen Balani

More about the book –

Artificial Intelligence (AI) is rapidly transforming our world, and as parents, educators, and responsible citizens, it is our duty to prepare the next generation for this inevitable change. In this enchanting and educational journey, “Little AI Explorer: Creative and Ethical AI Learning for Kids,” we aim to introduce children to the fascinating world of AI while emphasizing the importance of ethics, creativity, and responsibility.

AI is here to stay, and we, as parents, must embrace this technology and equip our children with the knowledge, skills, and values required to thrive in an AI-driven future. As the lines between the digital and physical worlds blur, it becomes even more crucial to foster an emotional connection with our children and provide a nurturing environment that balances human warmth and technological innovation.

This book has been carefully crafted to create a unique and impactful learning experience, taking young readers on a magical adventure through the fictional world of Generative Island. Through captivating stories, engaging quizzes, and meaningful lessons, children will explore the boundless possibilities of AI while understanding the ethical considerations that come with its power.

As the young explorers traverse the island, they will discover AI’s potential in art, music, storytelling, gaming, and daily life and learn about the importance of fairness, empathy, and responsibility in the AI ecosystem. Alongside the wonders of AI, we emphasize the value of human connections and the need to strike a balance between technology and real-life experiences.

Our aim is to spark curiosity and creativity in young minds, empowering them to be both the architects and the guardians of a future where technology and humanity coexist in harmony. By instilling a sense of responsibility, we hope to inspire the next generation of innovators, thinkers, and leaders who will harness AI’s potential for the greater good.

“Little AI Explorer: Creative and Ethical AI Learning for Kids” is more than just a book – it’s a call to action for parents and children alike to embrace the opportunities and challenges presented by AI, and, together, forge a path towards a brighter, more inclusive, and compassionate future.

Embark on this captivating adventure with your little ones and witness the magic of AI unfold as they uncover its secrets, embrace its creative potential, and learn to use it responsibly. Let’s prepare our children for a world where technology and ethics walk hand in hand, and let’s do it together.

Join us on this unforgettable journey and be a part of the Little AI Explorer family. Let’s inspire, educate, and empower the next generation, shaping a future we can all be proud of

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From Metaverse to Generative AI: A Journey of Hype, Reality, and Future Prospects

A not long time ago, the tech world was abuzz with a futuristic concept known as the Metaverse. This interconnected universe of virtual reality spaces, where individuals could interact in a simulated environment, was hailed as the future of technology. Fast forward to the present, the hype around the Metaverse has fizzled out considerably. The technological focus has now shifted towards Generative AI, with the spotlight on Large Language Models (LLMs) like GPT-4 and Google’s Bard. But why has this shift happened, and what does it mean for the future of technology?

The Metaverse Hype and Its Fade

The Metaverse, inspired by science fiction, promised a future where people could virtually live, work, and play in a digitally created universe. The possibilities seemed endless: avatars interacting in virtual spaces, immersive gaming experiences, and a revolution in remote work and social interaction.

However, the Metaverse hype began to fade due to several challenges. Firstly, the technological infrastructure required to create a fully immersive, interconnected virtual universe was found to be more complex than initially anticipated. From achieving high-quality, real-time 3D graphics to creating an inclusive and universal user interface, the hurdles were numerous and steep.

Secondly, the economic and business models of the Metaverse remained elusive. Monetization strategies that could support the massive infrastructure while providing value to users were hard to identify and implement. Moreover, the question of who would control and govern the Metaverse raised issues of centralization versus decentralization, leading to further complications.

Finally, the sheer scale of the Metaverse presented unique challenges. Coordinating multiple platforms and technologies to work seamlessly was a considerable task. It required not just advanced technology but also extensive collaboration and standardization across industries and platforms – a feat easier said than done.

The Rise of Generative AI and LLMs

As the Metaverse hype faded, attention turned towards another transformative technology: Generative AI, specifically Large Language Models (LLMs) like GPT-4 and Google Bard. These AI models are capable of understanding and generating human-like text, making them powerful tools for a multitude of applications.

The hype around LLMs is not without reason. They can generate high-quality text for a variety of uses, from creative writing and customer service to programming and academic research. They can also help democratize access to information and educational resources, providing personalized tutoring and making knowledge more accessible.

Moreover, LLMs like GPT-4 and Google Bard have shown remarkable advancements in understanding context and generating nuanced responses, bringing us closer to the goal of creating AI that can truly understand and mimic human communication.

Challenges and Future Prospects of Generative AI

However, as with any transformative technology, Generative AI and LLMs face their own set of challenges. Ethical concerns are at the forefront. The potential for misuse of these models to spread misinformation, generate deep fake content, or automate malicious activities is a significant worry.

Further, while these models are impressive, they don’t truly understand the content they generate. They are statistical models that generate text based on patterns in the data they were trained on. This leads to potential biases in the output, reflecting the biases present in the training data.

In spite of these challenges, the hype around Generative AI and LLMs seems to be more justified compared to the Metaverse and made it accessible to the public. The technology has already shown its value in numerous applications, and with the right guidelines and ethical considerations in place, its potential benefits far outweigh the risks.


While the Metaverse represented an exciting vision of a virtual future, its realization proved to be more complex and fraught with issues than initially anticipated. Conversely, the rise of Generative AI and LLMs appears to be more grounded in reality, with tangible benefits and applications already visible.

However, it’s crucial not to let the hype overshadow the potential risks and challenges associated with Generative AI and LLMs. Robust regulation, ethical guidelines, and transparency in how these models are trained and used are crucial to prevent misuse and mitigate any harmful impact.

In the end, the hype around technological advancements like the Metaverse, Generative AI, and LLMs provides valuable lessons and guides us closer to our goal of leveraging technology for the betterment of humanity. It’s not the hype that determines the success of a technology, but its impact, its ability to address real-world problems, and the safeguards in place to prevent its misuse.

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Future of Software Development: Generative AI Augmenting Roles & Unlocking Co-Innovation

Generative AI is transforming software development by automating tasks, enhancing collaboration, and accelerating innovation. This cutting-edge technology is poised to augment various software roles, creating diverse perspectives and opportunities for co-innovation. In this article, I will delve into the future of Generative AI in software development, discuss the ethical considerations, and summarize the potential impact on the industry.

Developers: AI-Powered Code Generation & Collaboration

Generative AI will enable developers to focus on more complex, creative tasks by automating mundane coding activities. AI-powered code generation will help developers solve intricate problems more efficiently and accurately. In addition, Generative AI will enhance collaboration among team members by suggesting code snippets or assisting with debugging, making it easier for developers to work together on large-scale projects. While AI-generated code promises increased productivity, developers must remain vigilant in reviewing and verifying its quality, ensuring the adherence to best practices, and addressing potential biases or security vulnerabilities.

QA Engineers: Intelligent Test Case Generation & Failure Prediction

Quality Assurance Engineers will witness a significant shift in their role with the advent of Generative AI. AI-generated test cases, edge scenario identification, and failure prediction will allow QA engineers to focus on improving software quality, reliability, and security. The integration of Generative AI into QA processes will make testing more comprehensive and efficient, reducing human error and enhancing the overall user experience. QA engineers must ensure fairness in the AI-generated test results, mitigate biases, and maintain the integrity of the software.

UI/UX Designers: AI-Enhanced Creativity & Inclusivity

Generative AI will play a crucial role in augmenting UI/UX designers’ creativity by providing design suggestions, generating UI components, and recommending user flow. This technology will enable designers to create more intuitive, visually appealing interfaces that cater to the needs and preferences of diverse user groups. AI-generated design elements can help designers experiment with various styles and layouts, fostering a more inclusive and accessible user experience. It’s essential for designers to maintain a human-centric approach, address potential biases, and prioritize user well-being.

Technical Writers: Streamlined Documentation & Code Examples

Generative AI will simplify the lives of technical writers by assisting in drafting documentation, creating code examples, and keeping information up-to-date. With AI-generated content, technical writers can produce clear, concise, and comprehensive materials more efficiently, ensuring that both team members and users have access to accurate, relevant information. Technical writers must remain accountable for the content’s quality, respect user privacy, and protect sensitive information.

Project Managers: Data-Driven Decision-Making & Planning

AI-generated insights for resource allocation, risk assessment, and project planning will enable project managers to make better data-driven decisions, keeping projects on track and under budget. Generative AI can help project managers monitor progress and adjust plans in real-time, considering various factors like team dynamics, changing priorities, and unforeseen challenges. However, project managers should remain responsible for the final decisions, ensuring AI-generated insights align with ethical principles and account for human factors.

DevOps Engineers: Streamlined CI/CD Pipelines & Performance Monitoring

Generative AI will streamline CI/CD pipelines, monitor system performance, and automate deployments for DevOps engineers. AI-generated optimizations will help DevOps engineers identify bottlenecks, proactively address potential issues, and maintain system stability. DevOps engineers must implement robust security measures in AI-augmented pipelines and ensure that AI-generated solutions adhere to best practices and organizational standards.

Architects: Optimal System Design & Scalability

Generative AI will provide architects with insights for optimal system design, technology selection, and scalability. AI-generated architectural recommendations will help architects make informed decisions, ensuring that systems are robust, flexible, and scalable to meet future demands. Architects should consider the long-term implications of AI-generated suggestions and choose AI solutions that uphold ethical standards and align with organizational values.

Opportunities for Co-Innovation

Generative AI promises to unlock numerous co-innovation opportunities across the software development landscape. By augmenting human intelligence and creativity, Generative AI can facilitate the exploration of new ideas, techniques, and approaches that were previously unattainable or time-consuming. Collaboration between AI systems and human experts can lead to the development of groundbreaking solutions, enabling organizations to stay ahead of the competition and drive industry transformation.

Ethical Considerations

As Generative AI continues to permeate software development, ethical considerations become increasingly important. Ensuring transparency, explainability, fairness, and accountability is vital in fostering trust, creating equitable solutions, and promoting responsible AI adoption. Software professionals must be aware of potential biases, privacy concerns, and other ethical issues that may arise when integrating AI into their work and proactively address them.


The future of software development will see Generative AI augmenting various roles, streamlining processes, enhancing collaboration, and unlocking new avenues for co-innovation. As AI technology continues to advance, software professionals must adapt to these changes and embrace the opportunities they offer. By integrating Generative AI responsibly and upholding ethical principles, the software industry can harness the full potential of this transformative technology to elevate the entire development ecosystem and create a more sustainable, efficient, and innovative future.

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Ethical Prompt Engineering: A Pathway to Responsible AI Usage

Artificial intelligence (AI) is transforming our world at an unprecedented pace. As AI becomes more ingrained in our daily lives, concerns about bias and fairness in AI models continue to grow. In response to these issues, the field of ethical prompt engineering has emerged as a vital tool in ensuring AI applications are transparent, fair, and trustworthy. This blog post will explore ethical prompt engineering, discussing its role in mitigating AI bias and providing real-world examples to showcase its importance.

Ethical Prompt Engineering: The Basics

Ethical prompt engineering is the process of crafting input queries or prompts for AI models in a way that minimizes biases and promotes fairness. This method acknowledges that AI models may inherently have biases due to the data they were trained on, but it aims to mitigate those biases by carefully designing the questions asked of the AI. Essentially, ethical prompt engineering helps to ensure that AI output aligns with human values and moral principles.

The Importance of Ethical Prompt Engineering

AI models have the potential to perpetuate harmful biases if their responses are not carefully examined and managed. Real-world examples of AI bias include the unfair treatment of individuals in facial recognition systems, biased hiring algorithms, and skewed newsfeed content. Ethical prompt engineering can be an effective way to address these issues and ensure that AI systems are developed and deployed responsibly.

Real-World Examples of AI Bias

  1. Insurance quotes: AI models used in the insurance industry may inadvertently provide discriminatory quotes based on factors such as age, gender, or race. These biases can result in unfair pricing and reduced access to insurance coverage for certain groups.
  2. Job recruitment: AI-powered recruitment tools may generate biased candidate shortlists by disproportionately favoring individuals based on factors such as gender, ethnicity, or educational background, rather than purely considering their skills, experience, and qualifications.
  3. Newsfeed content: AI algorithms used to curate personalized newsfeeds can contribute to the creation of echo chambers by prioritizing content that reinforces users’ existing beliefs and biases, thereby limiting exposure to diverse perspectives.
  4. Customer service: AI chatbots and virtual assistants may inadvertently treat customers differently based on their names, speech patterns, or other factors, leading to unequal service experiences for certain groups.
  5. Loan approvals: AI models used in credit scoring and loan decision-making may discriminate against minority borrowers due to historical biases in the data used to train these models, resulting in unfair lending practices.

Various Approaches to Ethical AI Development

Several approaches can be employed to ensure fairness and minimize bias in AI models:

  1. Data collection: Ensuring diverse and representative data sets are used during the training process can help reduce biases. By collecting data from various sources and demographics, AI models can learn to be more inclusive and fair.
  2. Training with different perspectives: Encouraging interdisciplinary collaboration during AI development can provide valuable insights to identify and address potential biases. By including experts from different fields, AI models can benefit from a broader understanding of potential issues and ethical concerns.
  3. Regular audits and evaluations: Continuously assessing AI models for biases and ethical concerns can help identify issues early on. By conducting regular evaluations and adapting the models accordingly, developers can work to reduce biases in AI applications.

Ethical Prompt Engineering in Practice

Assuming an AI model has ethical biases, prompt engineering can still be utilized to minimize the impact of these biases. By carefully crafting prompts that guide the AI model to provide responses that align with ethical guidelines, developers can ensure that AI systems are more responsible and unbiased. Following are some of the examples of ethical prompts

  1. AI recruitment tool: Instead of asking the AI model to filter candidates based on the applicants’ names, an ethical prompt could be, “Please rank the candidates based on their relevant skills, experience, and qualifications for the job.”
  2. AI insurance quoting system: Rather than allowing the AI model to consider factors such as age, gender, or race, an ethical prompt could be, “Please provide an insurance quote based on the applicant’s driving history, location, and vehicle type.”
  3. AI newsfeed curation: To avoid creating echo chambers, an ethical prompt could be, “Please recommend a balanced selection of articles that provide diverse perspectives on the topic.”

By using these and similar ethical prompts, developers can create AI applications that are more aligned with societal needs and expectations.

Introducing “Prompt Engineering: Unlocking Generative AI: Ethical Creative AI for All”

If you are interested in learning more about designing and implementing ethical prompts, consider exploring my book, “Prompt Engineering: Unlocking Generative AI: Ethical Creative AI for All.” This comprehensive resource delves into the principles and practices of ethical prompt engineering, providing readers with practical guidance on how to develop and deploy AI systems that are both innovative and responsible.

In conclusion, ethical prompt engineering is a critical component of responsible AI development. By carefully crafting the questions we ask AI systems, we can create more fair, transparent, and ethical AI applications. As the field of ethical prompt engineering continues to evolve, it’s essential for AI practitioners, researchers, and users to prioritize ethical considerations and work together to harness the power of AI responsibly.

Direct Link to the Book –

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Prompt Engineering: Unlocking Generative AI: Ethical Creative AI for All

In recent years, artificial intelligence (AI) and machine learning have transformed countless industries, revolutionizing how we work, learn, and communicate. One of the most significant advances in this field has been the development of large-scale language models (LLMs), such as OpenAI GPT-4 and Google Bard, capable of understanding and generating human-like text. The potential applications of these models are vast, from writing assistance and content generation to information retrieval and natural language interfaces. Generative AI, a subset of AI that focuses on creating new content or data, has emerged as a key player in this landscape.

As the capabilities of language models have grown, so too has the importance of understanding how to effectively communicate with them. Enter the field of prompt engineering—the art and science of crafting the perfect input to achieve the desired output from a language model. This book, ‘Prompt Engineering: Unlocking Generative AI,’ is designed to provide a comprehensive yet accessible guide to the fascinating and rapidly evolving disciplines of generative AI and prompt engineering.

Whether you’re an AI enthusiast, a software developer, a content creator, or simply someone interested in harnessing the power of AI for personal or professional use, this book aims to equip you with the knowledge and tools you need to become a proficient, prompt engineer.
Through clear explanations, practical examples, and use cases, you’ll learn the foundations of language models, the principles of effective, prompt design, and the techniques and strategies that will enable you to unlock the full potential of these remarkable AI systems.

Along the way, we’ll also delve into the ethical considerations surrounding prompt engineering, examining issues such as bias, fairness, privacy, and security. As AI continues to reshape the world around us, we must use this technology responsibly and thoughtfully, and this book aims to empower you to do just that.

Finally, we’ll explore the future of prompt engineering and the exciting opportunities and challenges that lie ahead. The field is still in its infancy, with much to discover and invent. By the time you finish reading this book, you’ll be well-equipped to contribute to this dynamic and rapidly growing area of AI research and application. Together, let us embark on this journey to unlock the true power of AI language models and transform how we communicate with technology.

We hope that “Prompt Engineering: Unlocking Generative AI” will serve as a valuable resource and a source of inspiration as you harness the power of AI to achieve your goals and shape the future. Happy prompting!

Click here to buy the book

Here is a captivating TOC for the book – 
Chapter 1. Introduction to Prompt Engineering

  • Emergence of Generative AI and AI Creativity
  • What is Prompt Engineering
  • From Programming to Prompting: A Paradigm Shift
  • How is Prompt Engineering different from Search
  • Skills Required for Prompt Engineering
  • Key Concepts and Terminology
  • The Importance of Prompt Engineering
  • Your first hello world creative prompt
  • Summary

Chapter 2. Foundations of Language Models

  • What are Language Models?
  • Types of Language Models
  • Evolution of GPT and Technology Advancements
  • How Language Models like GPT-4 Work
  • Limitations of Language Models
  • Summary

Chapter 3. Art and Science of Prompt Engineering

  • The Process for crafting effective prompts
  • Developing a Clear Objective and Goals
  • Crafting Clear Objectives and Goals in Action
  • Design Principles for Effective Prompts
  • Enhancing Prompt Design: From Poor to Better Prompts in Action
  • Eliciting Creativity and Originality
  • Eliciting Creativity and Originality in Action
  • Prompt Optimization
  • Techniques for Prompt Optimization in Action
  • Testing, Monitoring, and Evaluation
  • Techniques and Strategy for Testing, Monitoring, and Evaluation
  • Crafting End-to-End Prompt Solutions: Goal, Design, Innovate, Optimize, and Testing
  • Summary

Chapter 4. Crafting Prompt Types

  • Understanding Prompt Types
  • Cross-Functional Prompt Types
  • 25+ Ingenious Cross-Functional Starter Prompts for Every Occasion
  • 30+ Industry-Specific Prompt Types
  • Summary

Chapter 5. Advanced Prompt Engineering

  • Chaining Prompts for Multi-Step Tasks
  • Iterative Prompting for Ambiguity Resolution
  • Context Manipulation Strategies
  • Dynamic and Conditional Prompts
  • Adversarial Prompts for Model Robustness
  • Mitigating Prompt Bias and Improving Fairness
  • Limitations And Pitfalls
  • Addressing Limitations and Potential Pitfalls
  • Summary

Chapter 6. Ethical Considerations in Prompt Engineering

  • Ethical Concerns in AI Creativity and Prompt Engineering
  • Ethical Principles and Best Practices for Prompt Engineering
  • Ethical Prompts in Action
  • Case Studies: Ethical Prompt Engineering in Practice
  • Industry Initiatives and Regulatory Frameworks
  • Future Directions and Challenges
  • Summary

Chapter 7. Use Cases for Real-World Prompt Engineering

  • Launch of Global Credit Card
  • The Perfect Interview
  • Future of Mobility
  • Social Media Optimization
  • Future of Work
  • Designing a Future-Ready Autonomous Vehicle
  • The Next BlockBuster Movie
  • New Clothing Line for Corporate Work from Home
  • Enhancing Employee Engagement in Workplaces
  • Reimagining Risk Management
  • Metaverse-Ready Shopping Experience
  • Smart Cities and Sustainable Infrastructure
  • Manufacturing Excellence: Supply Chain Optimization
  • Software Architecture Decisions and Code Generation
  • Iterative Personalized Family Travel Itinerary Creation
  • Summary

Chapter 8. The Future of Prompt Engineering

  • A Multi-Modal, Interconnected, and Ethical AI Landscape
  • Summary
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Move towards genApps or Generative AI Apps

Web Apps, Mobile Apps, and now Gen Apps. Gen Apps or Generative AI Applications are applications that can generate new content based on user input. The application can converse and generate text, images, code, videos, audio, and more from simple natural language prompts. The possibilities are endless!

Building Gen Apps requires a new set of integrated tools.  The recent announcement from Google Cloud talks about a Generative AI App Builder allowing developers to quickly ship new experiences, including bots, chat interfaces, custom search engines, digital assistants, and more.  More details at –

Looking forward to trying out Generative AI App Builder.  

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Happy to share that the second edition of my book -“Real and Generative AI,” is now available digitally through Amazon. 

The second edition covers the latest buzz around Generative AI, ChatGPT, the current landscape and challenges, and what it would take for enterprises to adopt Generative AI Chatbots like ChatGPT.

The first edition was released 4+ years back. It was good to go back and validate the predictions that were made.

Is this current hype real, or have we just started scratching the surface of intelligence?  Read the book for more details.

Order your copy at –

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Responsible AI, Ethical AI and CHATGPT

Responsible AI, in simple words, is about developing explainable, ethical, and unbiased models. For instance, Google has published its AI principles –, which discusses this subject in detail. Similarly, Microsoft has published its AI principles at These key AI principles should be included as part of the design and development of large language models, as millions of users would view the output out of the AI systems. However, with ChatGPT, many instances fall shorts of these AI principles. 

  • Lack of Trust – Responses can sound plausible even if the output is false (Reference – You can’t rely on the output and need to verify it eventually.
  • Lack of Explainability on how the responses are derived. For instance, if the responses are created from multiple sources, list the source, and give attributions. There might be an inherent bias in the content and how this would be removed before training or filtered from the response. The response can be generated from multiple sources, and was there any priority source that was preferred to generate the response. Currently, ChatGPT doesn’t provide any explainability on the answers.
  • Ethical aspects – One of the examples is around code generation. As part of the generated code, there are no attributions to the original code, author, or license details. For instance, Open source has many licenses (; some might be restrictive. Also, were there any priority open-source repositories preferred during training (or filtering outputs) over others. Questions about the code’s security, vulnerability, and scalability must also be addressed. It is ultimately the accountability and responsibility of the developer to ensure that the code is reviewed, tested, secure, and follows their organization’s guidelines. All the above details should be transparent and addressed. For instance, if customers ask for Certification of Originality for their software application (or if there is a law in the future), this might be a challenge with AI-generated code unless the above is considered.
  • Fairness in responses – An excellent set of principles and an AI Fairness Checklist are outlined in the Microsoft paper – Given that ChatGPT is trained using internet content (a massive 570 GB of data sourced from Wikipedia, research articles, websites, books, etc.), it would be interesting to see how many items from the AI Fairness Checklist are followed. For instance, the content might be biased and have limitations. The human feedback used for data labeling might not represent the wider world (Reference – These are some of the reported instances, but many might be undiscovered. Large-scale language models should be designed fairly and inclusively before being released. We should not say that the underlying content was biased; hence, the trained mode inherited the bias. We now have an opportunity to remove those biases from the content itself as we train the models.  
  • Confidentiality and privacy issues – As ChatGPT learns from interactions, there are no clear guidelines on what information would be used for training. If you interact with ChatGPT and end by sharing confidential data or a customer code for debugging, would that be used for training ?. Can we claim ChatGPT is GDPR compliant?

I have listed only some highlights above to raise awareness around this important topic.

We are at a junction where will see many AI advancements like ChatGPT, which millions of users would use. As these large-scale AI models are released, we must embed Responsible AI principles while releasing the models for a safer and trusted environment.

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ChatGPT and Generative AI Similarity syndrome

What is Similarity Syndrome?

Similarity Syndrome is a perception that might be created by large language models like ChatGPT, where any unique work would be ultimately treated as common knowledge or work similarly to others.

For instance,

  • You write a unique quote for your book, and ChaTGPT may end up saying it’s common knowledge and similar to many other quotes instead of attributing it to you.
  • You create an algorithm, and Codex uses your code with other algorithms to create something similar. It will reuse your code and sell it back to you.
  • You create digital art and unique images, and DALL·E 2 uses your image and other images to create similar images. It will sell this as an image library, and you buy it without releasing your image contributed to it.

In general, any piece of content can be used to create something similar. There is no explainability and attribution to the original authors/content creators of how the dynamic response was created. There should be strict laws for copyrights and accountability in AI.

I did an experiment to ask ChatGPT about the quote I wrote in my book. Given below is the interaction

Many websites, through google search, attributed the quote to me (like

This is not about quotes but probably any generated content, be it digital arts, music, software code, or marketing content. AI would use your data, generate something similar, not attribute it to you, call it common knowledge, and even sell it to you. We will end up living in an AI world where everything is similar : )

To conclude, AI models should be transparent, explainable, auditable, ethical, and, most importantly, credit the original work created by the authors.

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ChatGPT Even Predicts the Future

ChatGPT seems good at converting facts to fiction. I asked ChatGPT about myself; around 70% of the information is cooked up (see image below).  On a lighter note, maybe it’s predicting the future, which I am unaware of : ). But what about past details? I need a time machine to go back and change it. Hopefully, I will write a post on the Time machine someday.

With Google search, all the correct details and my website shows up. 

With ChatGPT, I expected that at least this information should be correct as its readily available. The sources can be from Linkedin, my website, or other credible sources. There is no logic and no complex deep learning network to apply. The biggest problem with the below response is that there is no explainability and no details on the sources that were used to construct the response. How one can verify the correctness of the response ?. The responses are created dynamically even when not required. Unless you design with explainability in mind, the issues around trust and transparency will not be resolved. There are other issues around bias, ethics, and confidentiality, which I will talk about in future posts.

Unless you know the right answer, it would be difficult to know from the above response as the answers are grammatically right and may sound right. This is a very simple scenario. Moreover, moving from general intelligence to specific verticals like healthcare (for instance, diagnosis), increases the complexity of large language models and poses significant challenges. I have discussed this in one of my previous blogs, discussing the Lack of Domain intelligence for ChatGPT (and other conversational engines).  

I can’t predict the future, but I believe Google Search might be better positioned to take Generative AI and integrate it with its search engine. Hopefully, they follow their 7 AI principles – before the release to make AI applications transparent, explainable, and ethical. “Slow but steady wins the race” might come true for Google.

With the AI-powered Bing search engine, some early feedback and interesting facts about long chat sessions going wrong are documented on their website –

In my next post, I will talk about an interesting subject -“Similarity syndrome in ChatGPT

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