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[K.N.RAMESH]

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[Ecneics]

I don't think we should encourage these kind of developments now especially when there is established research which states that LLMs contribute to decline in skill acquisition and cognitive development.

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[Sudhir Pattar]

Agree. 

On Apr 20, 2026, at 11:06 AM, Ecneics <thinkmat...@gmail.com> wrote:



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[Anunad Singh]

This approach to technology has always proved wrong in the long term. It is generally agreed that human capabilities are very limited, compared to some other animals and technologies. Before the advent of the latest AI wave, it was generally assumed that nothing can match human intelligence. But that is now proving wrong. It is now almost accepted that in many areas of use of 'intelligence',  computers can beat humans. This includes translation. Computers will almost certainly do better in poetry, arts, video, music, teaching, medical diagnostics and training too.

We do not hesitate to ask AI very 'silly' questions. We do not hesitate to tell anything to an 'AI medical expert' . Your advocate, even after taking a huge amount of money from you, may not know or tell the right argument for your case.

What should we do then? Use machines to do what can be done better by machines. Search for something new which humans can still do better. 

-- अनुनाद 

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[Ecneics]

I think you should speak for yourself when it comes to being beaten by computational intelligence rather than for everyone. I have experimented with AI sufficiently to know that the limitations of AI far more severe than what is portrayed in the media. AI cannot create its own precedent by its very definition. AI cannot break its own categories momentarily to allow for new inspirations. AI is more biased than human beings and lack humility and self-awareness of its own ignorance. I can explain why these limitations exist but I don't think that is necessary now. Never-the-less, these are the kind of things only humans can do and humans will continue to dominate in pretty much forever. I think you should seriously consider delving into some shastras to actually understand how human mind and creativity works before falling for AI marketing.

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[Anunad Singh]

After reading your response, I do not understand what I should say. But instead of saying something, I would like to ask you something. What do you think is the probability that any of the statements made by you is right? For example, how sure are you to say that "AI is more biased than human beings and lack humility and self-awareness of its own ignorance" ?

--- अनुनाद 

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[Ecneics]

AI is fundamentally trained on datasets generated by humans. All human datasets are biased so essentially current AI models have been unintentionally trained on a spectrum of human biases and has been supplied with an agency to choose it's own bias amongst these that best allows them to produce a desired result within a set parameters. And because it's bias is validated by its repeated application in training on other datasets algorithmically this bias is far stronger. This has also been confirmed by AI researchers.

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[Anunad Singh]

Don't you accept that humans are more biased than machines , at least at this point of time? What type of bias do you think machines have which humans do not have or have much less ? 

--- अनुनाद 

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[Manish Modi]

Dear friends,

Namonamah!

I have read the thoughtful points shared by Mr Anunad Singh and Mr Ecneics. Both gentlemen have highlighted important facts that make us think.

No matter where AI stands today, it is clear that it will continue to improve. 

We once believed that AI was mainly useful for checking spellings and grammar in English, but now it has entered the world of creative writing with such aplomb! It is not just a tool like a calculator, but a full-fledged means of doing almost all tasks that a human may undertake. 

Creating art, poetry, music, paintings, designing instruments, designing machines, architectural maps, etc. AI is everywhere! It is faster, smarter, and more relentless than humans. And yes, more accurate and reliable. And it has just begun. It is going to get better and better...

Perhaps the one area where AI can never match human beings is the path of adhyatma. Without cetana, how can one truly understand or attain oneness with the cetana tattva? Since the ultimate purpose of life is to realise the parama tattva, those of us walking the path of self-realisation need not worry.

For other things, AI seems to be here to stay. Whether we like it or not.

Warm regards,
Manish

[Sudhir Pattar]

Technically, following Advait darshana, chinmatra would mean all that is, is consciousness. For chidabhas all that is needed is a vessel (also consciousness as upadan). So AI can also do Adhyatma. 

For dvaita, bhakti is param prem as per narada bhakti sutra. Being an emotion, which cannot be directly experienced by others, and only the same jiva can experience it, we cannot for sure say if AI can do bhakti or not. Externally one cannot get AI to show all signs of bhakti. 

And for karma yoga, AI is already nishkaam. Until it starts doing stuff with intent that is AI driven. 

Regards,

Sudhir

On Apr 22, 2026, at 6:25 AM, Manish Modi <manis...@gmail.com> wrote:



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[Sudhir Pattar]

From the perspective of training, both are trained using datasets. Humans and AI. It can be the same and both will produce outputs that are aligned with their training. 

AI can and is used for military purposes. Terrorists can also use AI. So bias is based on whatever garbage is input. 

Regards,

Sudhir

On Apr 21, 2026, at 10:58 PM, Anunad Singh <anu...@gmail.com> wrote:



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[kenp]

AI is generally seen not as more biased, but as differently biased, reflecting human data without the ability to self-correct through context or conscience. While AI lacks human-like self-awareness of its own ignorance, its biases are often more rigid, yet ironically easier to identify and correct than entrenched human prejudice, according to research shared on ITIF. 

Key Aspects of the Argument:

• Dataset Bias vs. Human Bias: AI inherits bias from its training data, reflecting society's inequalities, as detailed in this Chapman University article. However, as pointed out on Oliver Wyman, AI bias is often easier to eliminate than human prejudice because it can be audited and corrected. 
  
  
• The "Ignorance" Argument: A Reddit post on r/DeepThoughts notes that because AI lacks a "self," it can operate without the emotional, ego-driven, and defensive biases that characterize human ignorance. 
  
  
• Lack of Humility: AI operates without intrinsic moral humility, but it can be designed with "algorithmic humility," which can, in some scenarios, help humans unlearn bias faster than traditional coaching, 
  
  
• Rigidness: Some research notes that AI judgment is more rigid and less nuanced than human judgment, making its mistakes less flexible, 

On r/singularity, opinions are mixed regarding AI, with some users suggesting that human opposition to AI stems from fear, ego, and a lack of understanding. Therefore, the perception of AI as more biased or arrogant is, in itself, influenced by human biases. 

[Rajesh Kumar]

This is no longer merely a critique, but a clarion call; not a lamentation over imperfections, but an invitation to civilizational participation. Let us, then, recast your argument with the gravitas and cadence it so richly deserves.

Dataset Bias vs. Human Bias

If indeed the Large Language Model is but a mirror—albeit a vast and algorithmically burnished one—reflecting the accumulated sediments of human discourse, then the remedy lies not in denunciation but in disciplined refinement. Let the scholars—custodians of language, arbiters of nuance, and inheritors of civilizational wisdom—step forth to inscribe the rules, correct the distortions, and gently but firmly steer the model away from inherited bias toward cultivated clarity. For what is imperfect data, if not an unfinished manuscript awaiting the red ink of the learned?

Human Ignorance

Human ignorance, that perennial companion of even the most accomplished intellect, has never been vanquished by abandonment but by guidance. Here too, the role of the scholar is not peripheral but paramount: to define, to delineate, and to discipline the contours of knowledge such that the machine—bereft of self-awareness—may nonetheless operate within a framework shaped by the highest standards of understanding. In this collaborative alchemy, ignorance is not eradicated, but progressively refined into insight.

Lack of Humility vs. “Algorithmic Humility”

To ascribe humility to an algorithm is, as has been so aptly observed, akin to attributing compassion to a calculator: one may program it to simulate restraint, to defer, to hedge—but these are behaviors devoid of the moral interiority that gives humility its meaning. What passes for “algorithmic humility” is often little more than probabilistic caution, a statistical shrug masquerading as wisdom. To rely on such simulations as instruments for human moral evolution is to outsource the arduous journey of ethical growth to a mechanism fundamentally incapable of traversing it.

Rigidness of AI Judgment

Yet, if rigidity be the ailment, then scholarship must be the remedy. For it is the scholar who introduces context where there is none, elasticity where there is rigidity, and interpretation where there is mere computation. Through the careful infusion of rules, exceptions, and layered understanding, the seemingly inflexible edifice of AI judgment may be rendered more supple, more responsive, and—dare one say—more aligned with the subtleties of human reasoning.

Human Perception and the “Fear/Ego” Argument

As for the oft-repeated contention that resistance to AI is but a manifestation of human fear or ego, one might respond with a more constructive proposition: let scholars intervene not merely as critics, but as correctives. Where the sequence of Samskrutham—that exquisitely structured language of precision and philosophy—has been disrupted, let it be restored; where meanings have been diluted, let them be distilled anew. In doing so, perception itself may be recalibrated, not through dismissal of concern, but through the illumination of correctness.

Ultimately, the burden—and the privilege—of refinement rests not with the machine, but with those who possess the wisdom to guide it. Let not initiatives such as Shabdakosh be stifled by premature skepticism; rather, let them be embraced as evolving platforms, awaiting the steady hand of scholarly stewardship. For it is only through the sustained engagement of the learned that rules may be defined, structures solidified, and imperfections gradually transmuted into excellence.

संस्कृतं कालक्रमेण परिष्कृतम्; तथा एव विद्वद्भिः सहकार्येण शब्दकोशः अपि क्रमशः परिपूर्णतां गच्छतु।

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[vishal jaiswal]

Being a scholar does not imply that they are free from  their own biases.

That perhaps might only apply to the spiritually inclined yogin completely detached from materialism. 

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