Around nine years ago, around the PI day, I wrote a blog post about an ancient method of remembering digits of pi. Here is the link to blog post.
Here is the operative part of the blog post.
The code is as follows.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| क | ख | ग | घ | ङ | च | छ | ज | झ |
| ट | ठ | ड | ढ | ण | त | थ | द | ध |
| प | फ | ब | भ | म | ||||
| य | र | ल | व | श | ष | स | ह | क्ष |
With the above key in place, Sri Bharathi Krishna Tirtha in his Vedic Mathematics gives following verse.
गोपी भाग्य मधुव्रात श्रुङ्गिशो दधिसन्धिग |
खलजीवित खाताव गलहालारसंधार |
If we replace the code from the above table in the above verse, here is what we get.
31 41 5926 535 89793
23846 264 33832792
That gives us
Today, I asked ChatGPT this question.
If you look at the details explanation provided by ChatGPT, it is completely wrong. What I picked up is from a book on the topic. Once more and more such content floods internet, it will become extremely hard to decipher what is right and what is wrong. Furthermore even academic text being written in future would consist of ChatGPT created stuff.
Unles GenAI can provide traceability to the source material, I don't think it is worth giving the attention that it is being given.
The article rightly emphasizes that transparency and verifiable sources are essential for the responsible use of AI-generated content. As large language models continue to power chatbots, content creation tools, and intelligent assistants, ensuring factual accuracy and traceability will become increasingly important. Researchers and students exploring these emerging technologies can gain deeper insights through Generative AI Projects for Final Year, which focus on LLMs, conversational AI, and AI-driven content generation.
ReplyDeleteWhile Generative AI offers remarkable capabilities in text generation and knowledge assistance, the risks of hallucinations and misinformation cannot be ignored. Developing more reliable models with improved reasoning, source attribution, and factual grounding remains an active area of research. Students interested in understanding the underlying architectures and training methodologies behind these intelligent systems can explore Deep Learning Projects for Final Year, which form the foundation of modern generative AI technologies.
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