Agents and Beings

Let me start this argument by narrating a couple of stories: 

Story 1: This happened several decades ago when I was an engineering student. With my friends, we had gone to visit a sanctuary for lions in a protected forest near Shivamogga, in Karnataka. We could see a pride of lions at a distance with a couple of cubs in there as well. But someone in the group started making sounds and teasing the lions. And before we knew what was happening, one of the female lioness rushed menacingly towards us. And even before we could process it, the male lion let out a huge roar and charged right at us! If it were not for the fence separating us, I would not be here narrating this story! :) When the lions charged at us, some of the people in our group ran away in fear, and some like me, were too petrified to even move a limb. Both lions charged at the fence several times The lions kept charging at the fence and roaring ferociously at us for a while. Then, they must have looked at the petrified faces of the remaining people, and just like that, got bored, and went back to tend to their cubs. 

Story 2: I was seeing a video promotional recently from a Bangalore based startup that makes kamikaze (suicide) drones. These drones can pick up a face from a crowd and pursue them and take them down by using themselves as the ammunition. According to the creators, once one of their drones have targeted someone, there is no escape for them. They can be pursued through dense crowd as well as foliage, and if they go into a building, the drones loiter outside until they come back, without getting bored. 

Without getting bored! 

This one statement got me thinking about how is an AI predator different from a natural predator? 

All living beings including us, are autonomous agents like how AI is becoming these days. We take decisions on our own and pursue goals by planning and strategising and acting rationally towards utility maximisation. We adapt to external feedback and refine our strategies to achieve our goals. All of these are now done using AI. 

But the one thing that AI does not do (cannot do?) is become bored. Is this a feature.. or a bug? 

Is our ability to get bored with pursuing our goals, a shortcoming and a weakness, or is it pointing to something more fundamental about how we are structured? 

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For a few years now, I have been working on a new model of agency that I call "beings" rather than "agents". I've written a few ideas about this in this blog itself and a search for the tag, Theory of Being would bring up a few of them. We also have a formal, mathematical model of beings that we have published in peer-reviewed journals and conferences. 

Be it lions, or even us for that matter, we are not exactly autonomous agents-- we are "systems of being" or "beings" for short. How is a being different from an agent? And what significance does this difference hold for the design of AI? 

An agent is an entity that autonomously pursues a goal. The goal that an agent pursues is typically external to the agent, and the agent has to operate in uncertain environments and adapt to the way the environment responds to its actions, to keep it progressing towards its goal. 

In contrast, a being is an entity that has multiple dimensions to its existence that may interact among themselves to give it an internal state of stability or harmony. A being tends to "be" or stay in its stable state, and any perturbation from its stable state is responded to by the being in a way that it can come back to its stable state. 

A being interacts with the external world when its internal harmony is perturbed and it seeks to restore its inner harmony by achieving an external objective. The external goal is not the end target itself-- it is just a means for achieving an internal goal. For instance, natural predators attack only if their inner harmony is perturbed by things like hunger, fear, irritation, etc. The goal for a being is always internal-- not external. 

Beings get "bored" and give up on external goals not because they are lazy or weak or somehow inadequate or imperfect, but because it no longer makes rational sense to pursue the external goal to restore the inner harmony. 

The the lion episode from earlier, once we had been petrified enough by the roars of the lions, it was clear even to the lions that we puny humans will not be bothering them anymore, and they gave up on their goal to attack us. 

This is where an autonomous agent, especially an AI agent differs fundamentally from natural autonomy. Artificial autonomy is driven by external goals. AI agents have no sense of internal harmony that they are trying to preserve. They only pursue an external goal. 

That is why a "suicide" drone is nothing special-- they don't need any special training or conditioning to commit "suicide" as part of their attack. They don't need to be persuaded to give up their "lives".

Conflict among beings is more than just the external fight that is visible. There are typically underlying factors that upset the sense of harmony or well-being of the beings involved, that has lead to the conflict. A lasting solution to the conflict lies not in winning the external conflict itself, but in addressing the causes of the internal disharmony that is leading to these external crises.

A number of ethical issues can be understood better when we use the "being" framework that focuses on internal disharmony, instead of an agentic framework that focuses on external goals. Many times for example, a thief who is caught stealing would be merely trying to mitigate his internal disharmony like hunger or some other form of family exigency. Similarly, sometimes soldiers fighting on opposite sides get to see the human nature of the other and refuse to fight. Most famously, this was witnessed in World War I, where an informal, tacit understanding of non-aggression emerged spontaneously between opposing soldiers in static, quiet sectors along the Western Front. To avoid suspicion from high-ranking officers and headquarters, troops could not simply stop shooting. Instead, they ritualised their weapons fire to show compliance with orders while intentionally causing zero casualties. Rather than active resistance, it was a practical coping mechanism born out of prolonged proximity, shared misery, and the mutual desire to survive.

We can never hope for such an outcome to emerge when at least one of the warring parties is an AI agent. 

The being nature of our existence is the basis for our inner moral compass and that which enables to act with empathy and compassion. If we have experienced internal disharmony, we know how it feels and we can see when others are suffering the same. This is very different from acting within a set of externally imposed codified norms that define the boundaries of our ethics. 

That is why, I am not very enthusiastic about AI which can achieve its targets without getting bored. As long as achieving the target is serving my purpose, I may have have all the rationale to own and promote such an AI. But what prevents some rouge actor from making destroying me as the target. What chance do I have then? 

I would rather have AI that can orient its intelligence towards pursuing deeper issues. An AI that can understand the core issues causing a conflict and can wisely and strategically mitigate the source of disharmony that is leading to the conflict. 

But yeah, having said that, I am not against the creation of weaponised drones. Because, if we don't have one of them, our adversaries will have them anyway. And they may not even think through all these ethical issues before using them against us. 

Dharma and Systems Theory

A term that has made a major comeback in our vocabulary these days, is "dharma"-- which is used in various senses, including: righteousness, religion, duty, ethics, divine law, etc. 

None of these interpretations make this term any better or unique, than the notions that we already have. We already have several terms to use as religious or faith based constructs for us to package all our insecurity and desires, and hold on to it with righteous indignation. 

Growing up with a "scientific" upbringing, I had thus ignored this concept altogether and had gone on to pursue advanced scientific studies like systems theory, and focused my inquiry on non-linear dynamics and complex systems. But thanks to one chance encounter, I came to revisit this concept of dharma that we grew up with, from the lens of systems theory. 

And what I have found is that these ideas are beyond profound, and can perhaps explain why Indian thought survived for thousands of years, and how for all the cutting edge challenges we have today, dharma-centric designs may actually offer plausible solutions. 

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One of the first things I ask anyone who wishes to discuss dharma, is whether they believe that dharma as a concept is applicable on Mars or Jupiter today, where there is no life. Most of the time I receive a response that dharma is a social construct, and has got to do with our sense of right and wrong, and therefore it is meaningless to use dharma when speaking about Mars or Jupiter. 

It only tells me that they have not understood the concept. 

Similarly, any sentiment based definition of dharma-- like working with purity of intention, righteousness, respect for elders and society, having "humility", leading a "simple" life, etc. all have missed understanding the meaning of this important term. 

The definition of dharma is ".. that which sustains is dharma". And "that which sustains" is not some other-worldly cosmic principle revealed by magic or divine creatures. That which sustains, depends on the systemic context we are talking about. Given any system of being, a set of factors can collectively "sustain" the system by making the system resilient, and able to absorb shocks and perturbations and remain in a given state of being. 

Resilience is the primary characteristic that drives sustainability. And that which makes a system resilient, is dharma. 

Any complex system of interactions is continuously changing its features because it keeps getting affected by both internal and external factors. But, not all complex systems are chaotic. There are chaotic systems like turbulence that are basically unmanageable. But a lot of complex systems, have one or more states where they are relatively stable, and can absorb external perturbations (to some extent) and revert back to their stable state. For instance, as biological creatures, all of us are complex systems with something or the other happening within and outside of us all the time. Yet, despite this, our "life" remains stable. Even biologically, there are a set of factors that keep our vitals (body temperature, blood pressure, etc.) stable even when we are perturbed by factors like external demands, internal illness, etc. These set of factors that sustain us (not just biologically, but our entire sense of self), is our dharma. 

Similarly, there are a set of factors that keep the characteristics of Mars and Jupiter stable, without them getting destroyed by perturbations like asteroid strikes or solar flares. So, the idea of dharma applies there as well. 

Dharma is not a social construct-- it is a physical construct. We don't have to believe in dharma or push dharma as an ideology (some form of "dharma-ism" for example). It will hold regardless. Just like gravity affects us whether we believe in it or not. 

Hence to act with dharma, what we need is not (just) purity of intention or righteousness of action, or some social construct. What we need is a clear understanding of the factors affecting our system under consideration, and what will make the system resilient. Hence, dharma changes from one systemic context to another. It does not mean, dharma is a system of "relative ethics" (I've seen such definitions as well!)-- it is just that what makes a system resilient, changes depending on what is the system are we talking about. 

Upholding dharma is not just about intention or virtue or morality-- it is about strategy. 

If we are living next to the sea or a large river, upholding dharma entails strategising for protection against possible floods and hurricanes-- something which may not be applicable in some other area. Similarly, for a town in the mountains, upholding dharma involves creating relevant strategies to handle occasional land slides, mountain sickness, or other such risks. 

We cannot uphold dharma without an understanding of the system under consideration. If some idea has worked somewhere, and we just implement the same idea in some other system acting with the best of intentions and with a pure heart-- it does not mean that the outcomes will be the same. The outcomes depend on what sustains the system under consideration. We see this happening often. Someone imposes some idea on us with the best of intentions. And when the outcomes are disastrous, they justify their actions saying at least their intentions were good. 

Upholding dharma does not just require a pure heart-- it also requires a strong intellect. 

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Dharma is not the same as righteousness or ethics. Both righteousness and ethics need to be derived from dharma. Dharma is hence, the physical basis for several social constructs as well-- it is the "is" from which, we can derive the "ought". 

When we equate dharma with righteousness, and treat dharma as a standalone virtue in and of itself, we can often get into sticky situations. Because, human lives are not just about dharma-- although dharma is the basis on which human life functions. 

For instance, I remember in our childhood just about every petty trader with his small shop, used to shortchange their customers some way or the other. And they used to call it "Vrutti dharma" (the dharma of their profession). When we interpret dharma as duty or righteousness, this basically means that, it is the duty of a trader to shortchange customers, and it is the virtuous thing to do for a trader! And because just about every trader was practising this, consumers had no choice, making this configuration stable! 

What they were actually doing is to protect their trade and maximise profits by cutting corners. And before we bring out nasty stereotypes about "Hindu banias" it helps to realize that such kinds of "Vrutti dharma" is still practiced today at a much bigger scale by major corporations-- misleading ads, dark patterns, behavioural nudging, etc. All of these are done with "integrity" (that is the term used today in corporate-speak) by the designers to protect the profits of the company. 

Not all states of dharma are fair, just, equitable, etc. They are just stable states from where it is hard for the system to come out of. 

For instance, a state of poverty is more sustainable and resilient, than a state of wealth. It is easier to lose wealth, than lose poverty. 

We often heard the saying that there is "dharma in poverty" and "adharma in wealth", and we translated it as: "there is goodness in poverty, and evil in wealth", or that "poor people are somehow righteous, and rich people are crafty and evil," and started celebrating poverty! Well, no. It just means that poverty is more sustainable than wealth-- it does not mean that poverty is desirable over wealth! 

Similarly, dharma is not about achievement of something extraordinary. We may achieve something extraordinary, but it may not sustain. For instance, we achieved the extraordinary feat of putting humans on the moon. And dreamt that within a few decades we would be having weekend trips to the moon. But it did not sustain. We also achieved supersonic passenger flight. And it could not sustain. 

Sustaining something is a very different challenge from achieving a goal or reaching a destination. We are so attuned to think of progress in terms of reaching some target (growth mindset), we often forget that the bigger problem is not to achieve some feat, but to sustain that achievement. 

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In Indian thought, human lives are modeled not just by dharma-- but along four dimensions that are collectively called the Purusharthas-- dharma, artha, kama, and moksha. 

From a systems lens, these can be understood as resilience, capability, autonomy and liberation. 

Resilience is something that sustains us, while capability is something that aids us in achieving something. Capability can be of many kinds including knowledge, wealth, physical strength, political power, social standing, etc. All of these help us achieve something. But the question of sustaining this achievement is a different problem altogether. 

Similarly, kama refers to our autonomous desires. All forms of autonomous, creative pursuits that one performs for a sense of gratification, forms kama. And moksha from a systemic lens, refers to being non-constrained or liberated from obligations and debt. 

We see that these four dimensions hold whether we are talking about an individual, a family, a country, or the entire world. We can also see that these dimensions are not a matter of ideology or some -ism. There is no such thing called Purushartha-ism. We don't have to believe in Purushartha-- it would still hold. As humans we strive for sustainability, capability, creative autonomy and liberation, whether we believe in it or not. 

We can also see that every ideology puts one of the Purusharthas in the center, and makes the other as subservient to it. For instance, capitalism and fascism put artha or some form of capability (monetary or power) at the center, and considers all the others as supporting elements. Socialism believes that equality is dharma and puts that at the center, and insists everything else serve this primary objective. Similarly, liberalism puts individual autonomy (kama) at the center, and insists that everything else serve this primary objective. 

But the science of sustainable systems is not that simple, and cannot be reduced to a linear configuration with some idea at the center and everything else revolving around it. As mentioned in the Chanakya Neeti,  "the root of a happy life is dharma, the root of dharma is artha, the root of artha is enterprise (autonomous action or kama)". But kama pursued as an end in itself, leads to adharma. 

We cannot build resilience without building capability. We cannot build capability, without empowering individual autonomy. But then, at the same time, building our entire lives around individual autonomy, may undermine our resilience or capability.  

That is why we see that too much focus on capitalism may make essential services like basic healthcare unaffordable, and also undermine individual autonomy. I found a recent saying about life in America:  "They gamified survival, and called it freedom!" Similarly, too much focus on individual autonomy may make us incapable of organising ourselves for building capability or collective resilience. 

Once we really understand these terms from a systemic lens, we can actually start thinking differently and develop a science of human lives, rather than just treat it as machines that are driven by ideology. 

Can the "ought" be derived from the "is"?

Recently, I had the privilege to speak one on one, with Swami Sarvapriyananda on the topic of AI Ethics, and also present our work in this area. We presented two of our approaches: one is called "Computational Transcendence" that is aimed at making AI agents act more responsibly, and the second called "Theory of Being" that we argue would be the next evolutionary step in AI-- from "agents" to "beings". We shall not go into the details of our work in this post, and instead, focus on the philosophical talking points that I took back from our discussion. 

Firstly, my thanks to Oxford Dharma Centre and its dynamic founder and volunteers who made this happen, and also for the rest of the members of our initiatives on exploring dharmic foundations for a post AI future (not mentioning specific names here, in case there are any privacy considerations).

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The theme of our discussion can be described by the question: "Can the `ought' be derived from the `is'?". 

Scientific inquiry has predominantly focused on understanding "what is" rather than resolving "what ought to be". The "is-ness" of physical reality is presumed to be impersonal and indifferent to what ought to be the case. And all questions involving AI acting ethically and responsibly seems to be squarely rooted in the realm of "ought". 

The question of ethics is sought to be explained and resolved on the basis of different hermeneutic schools, each of which, identify some fundamental attribute that ought to be pivot for our ethical actions. For instance, utilitarianism is the ethical view that an action is right if it produces the greatest overall happiness or least suffering for the most people, while consequentialism is the broader idea that the moral value of an action depends mainly on its results rather than on the act itself. Deontology takes a different approach, arguing that actions are right or wrong according to expected behaviour in a role-- involving requisite duties, rules, or principles, regardless of the consequences. Virtue ethics in contrast, focuses not on rules or outcomes but on the "character" of the agent-- asking what a good or virtuous person would do. Similarly, Reciprocity is the principle of mutual fairness and exchange, often expressed as treating others as you would like to be treated. Other important ethical views include ethics of care, which emphasises empathy, relationships, and responsibility; divine commandments, which grounds morality in God’s commands; and so on. 

In all of the above, the idea of ethics is grounded in some form of "ought", which makes all schools of ethics as some form of ideology. Indeed, all human social systems are sought to be explained within some ideological framework or the other. 

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But somehow, the worldview in which we were brought up in, we never saw ethics as a matter of ideology-- it was always something deeper and something more fundamental, and rooted in the very fabric of how nature functions. 

Even today, there are several thought leaders who endorse medieval thinkers like Thomas Hobbes, who, in his work Leviathan, describes the "state of nature" (life without government or law) as a "war of all against all." For Hobbes, humans are driven by a constant, relentless desire for power and self-preservation, and are inherently competitive and violent. In this view, morality and ethics are not innate but is a social contract; we agree to act "ethically" only because we fear the chaos of the state of nature and the punishment of a sovereign authority (the state/law) that keeps us in check.

From the worldview that I have been brought up in, this is as alien as it can get. We were taught to regard ethics and morality as something divine-- not in the form of divine commandments that we ought to follow or be liable for punishment-- but as something that represents a refined form of being. A king for example, is evaluated not by his power, but by how well he performs his "Raja dharma", which in our English-medium schools was translated as "duties of a king". Similarly, we learnt that families are run based on "Grihasta dharma" which was then translated as "duties of a householder". 

It is hence not really surprising that the term "dharma" is seen as a confused, overloaded term, and is variously translated as "righteousness", "duty", "divine law", even "religion", and what not-- making our worldview just another form of not so well defined, normative system. 

Before I come back to dharma that forms the grounding for our notion of ethics (and just about everything else), let me also take up one more question: Is ethics a purely objective construct? In other words, can I act ethically in a system where I bear no consequences of my actions? 

For instance, a robot that has been sent to kill a human being (of which, there have already been several documented instances) can justify its actions based on any of the above schools of thought. It can argue that killing that person was necessary for upholding "greater good" (utilitarianism), or by killing the said human, it has performed its expected role properly (deontics), or killing of that person was its way of upholding the virtue of justice, or whatever. 

But there is a different between a robot offering such a justification, and a human who had committed this activity, offering a justification. In the latter case, the human has a stake and is subject to judgment and maybe prosecution. A robot has no such stake. Hence, the "ethical" action of the robot is quite different from that of a human. 

Also, while a robot knows what it is supposed to do, it does not really know why it is doing this activity. For instance, suppose a robot sent to kill a person were to see that the person is already injured and is unable to offer resistance, or if the person is offering to surrender-- it does not change its behaviour, unless it has been explicitly programmed to watch out for these signals and change behaviour accordingly. It does not encounter a "change of heart" suddenly upon seeing the human side of the adversary. Similarly, one cannot use a strategy like "appealing to conscience" of a rampaging robot, to make it stop its destructive activities. 

If we claim that we have resolved "ethics" without the involvement of the subject or their conscience, I'm not sure we have really resolved the question. 

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The emerging post-AI society has reached such a quagmire, where the existential dilemmas are so subtle that people can actually be convinced that a rampaging autonomous robot is acting ethically, and all the damage it has caused in this process, is actually justifiable!

What we are seeing now is the saturation point for machine hermeneutics-- which for the past couple of centuries, has been the dominant interpretive framework worldwide, and which is considered the only framework whose explanations can be considered scientific. In this hermeneutic framework, the universe is seen as an impersonal automaton, grounded in material reality, and driven by the laws of physics, where life somehow emerges out of a large number of random interactions, and in turn gives rise to social constructs like ethics, morality, and consciousness. 

Indian thought (at least most schools of Indian thought) has rejected this idea of material grounding for life and consciousness, since at least 5000 years. In Indian thought, the fundamental substrate of all reality is a triad-- sat-chit-ananda (loosely translated as existence-consciousness-bliss). This is not three separate entities, but one entity that is the ground of all three. The grounding of all physical (material) reality-- the "is" that is investigated by science, as well as the grounding of all humanist concerns including our sense of self, ethics, morality, freedom, etc. and the grounding of all "Axiology"-- that is, our sense of beauty, aesthetics, art, poetry, etc.-- is this same fundamental reality sat-chit-ananda that is the entire universe. 

The dynamics of the existential universe that is grounded in this reality, is itself characterised by different states of being. Each state of being where the system settles down is called its "dharma"-- that represents a sustainable (resilient) state. Each resilient state in turn has different extents of capability (artha) and creative autonomy (kama). (For our purposes, we will not consider the fourth element of Purushartha, that is moksha). 

We can see that while non-living ecosystems (like the surface of Mars) can have different sustainable states, it still represents a lower form of "dharma" as it cannot support life (the chit and ananda realms of reality). A better state of being would be a resilient state where capability for life (including wealth, power, etc.) and creative autonomy are also maximised. This would form the basis for a king to administer his kingdom, or for a householder to run his family.

So yes, our sense of "ought" was indeed derived from the "is". 

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As part of our conversation, Swamiji also narrated an anecdote about Maa Sharada, where she once admonishes a disciple who throws away the broom after sweeping the floor. She asks him to "treat the broom with dignity"! 

While at first, it may appear as someone being overtly sentimental about the "dignity" of inanimate things, this kind of thinking stems from the hermeneutic that treats humanistic constructs like dignity and ethics to be grounded in reality, rather than ideology. While today's India itself has lost much of this kind of approach to life, it can still be seen in other Asian societies. In Japanese culture for example, many practices reflect a deep respect for inanimate objects and physical spaces, treating them as worthy of dignity, care, and even a kind of spiritual presence. This can be seen in traditions such as Shinto, which honours the sacredness of nature and the idea that spirits, or kami, may dwell in places, objects, and natural features. Everyday customs like removing shoes before entering a home, carefully maintaining gardens, wrapping tools with respect, and cleaning public spaces all express the belief that environment shapes behaviour and that order is not merely imposed from outside but cultivated through attention and reverence. This attitude gives objects and spaces a natural sense of civility, as though harmony is embedded in the world itself and should be preserved through disciplined, thoughtful conduct.

Models of Intelligence

Today, whenever we speak of AI, we mean a specific model of AI based on large language models and diffusion, both of which, undergo foundational training in an approach called autoregression. In today's discourse, whenever a researcher asks a "cutting edge" question about AI, they are just asking a question about how to tweak the current model of AI. For instance, whenever researchers ask questions about "AI Ethics", most of them at least, are just asking about LLM Ethics in the form of guardrails, constitution, etc. 

But the idea of intelligence itself (and as a consequence, ethics, morality, etc.) has fascinated researchers over several thousands of years now. And even in the last 70 years or so, many philosophical questions about intelligence have been asked and sought to be explained in precise, mathematical terms. 

In this post, let us have a look at the different ways in which "intelligence" can be understood and modelled. 

At the outset, I should push aside theories like IQ scores, from this post. If someone actually believes that intelligence can be meaningfully represented as a single-dimensional, numerical value, and we can rank people and populations based on this value; then they are not very intelligent, and I hesitate to engage with them. I'm just amazed by how popular is this whole IQ business still.

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The term "Artificial Intelligence" was born in the summer of 1956, at what is now called the Dartmouth Conference. Organized by a young researcher named John McCarthy, the gathering brought together brilliant minds eager to prove that thinking machines were no longer just the stuff of science fiction. McCarthy had a branding problem: he needed a catchy banner to unite researchers scattered across cybernetics, psychology, and early computer science. Other researchers like Herbert Simon and Allen Newell pushed for a much more bureaucratic, academic-sounding alternative-- "Complex Information Processing."

Had they won the debate, today we might be talking about the rapid rise of "CIP" systems and worrying about whether complex information processors are taking our jobs. Instead, McCarthy’s catchy term "Artificial Intelligence" captured popular imagination, transforming a dry engineering pursuit into a thrilling quest to decode the very essence of the mind, capturing the public's imagination and setting the stage for decades of grand dreams, wild hype, and sci-fi obsession.

As a result of this, there are several models of intelligence that have been proposed, each of which, have lead to their own kinds of breakthroughs. Let us have a look at some of these models. 

Intelligence as observable behaviour 

Alan Turing who is credited with creating the mathematical basis for computation, was one of the first to also propose an "empirical" definition of intelligence, in his 1950 paper, "Computing Machinery and Intelligence". He proposed that instead of getting bogged down in unanswerable philosophical debates about intelligence or asking whether machines can truly "think," we should define intelligence entirely through observable behaviour. If a machine acts, converses, and reacts indistinguishably from a human, then for all practical and empirical purposes, it is intelligent. This pragmatic approach makes intuitive sense because even among humans, internal mental states are inherently unobservable in others-- we infer intelligence solely through external outputs like speech and action. There is a related concept that was first used in political science, and now also in computer science. This is called "duck typing": "If we see something that walks like a duck, waddles like a duck, and quacks like a duck, then it is probably a duck." Just as duck typing cares only about an object's methods rather than its internal class inheritance, Turing’s test judges a system purely by its functional interface with the world. Hence, if someone or something acts intelligently, then it is probably intelligent.

However, this behavioural approach has faced pushback from several detractors. Philosophers like John Searle famously countered this with his "Chinese Room argument". Imagine a (non-Chinese) clerk who is stuck in an office where all transactions happen only in Chinese. The clerk is given a set of rules by which he should respond to different sequences of symbols. And suppose the clerk is able to do that. Then, it appears to the outside world that the clerk now understands Chinese. But the clerk is merely following a set of rules. Similarly, while a computer can flawlessly manipulate symbols to simulate fluent conversation, it is not actually understanding what it is doing-- it is merely following some rules. 

Philosopher Peter Norvig, with his "pigeon argument" argues that if aviators were to think of building flying machines in the way Turing thought of building thinking machines, then their goal would be to build a machine "that looks like a pigeon, flies like a pigeon and fools other pigeons into thinking that it is a pigeon." Which is absurd. Our flying machines (airplanes) do not look like birds-- yet they fly. Similarly, we can agree that animals are also intelligent-- yet they cannot carry out a conversation with humans and fool humans into thinking that they are also human.

Yet others like Peter Wegner argue that the Turing Test encourages deceptive superficiality-- rewarding programs that master the art of parlour tricks and linguistic bluffing rather than true reasoning, common sense, or adaptability. For instance, how do you play chess with two grandmasters at once, and fool both of them into thinking that you are a grandmaster? The answer is to get them to play against one another-- through us (that is, see what the first player plays, and make the same move to the second player, and reflect the second player's move, back to the first player). While this trickery can still be argued to be a "manipulative" kind of intelligence, this still leaves open the more fundamental question-- how did the grandmasters get their intelligence?

Intelligence as Imitation: 

One of the key markers of intelligence that we see in children (including cubs of many animals) is their ability to imitate their surrounding. Children often copy their parents and elders in their mannerisms and action and further on, even in thought and interpretation. 

Imitation requires an enormous amount of pattern matching and reasoning capabilities. A "completely imitative agent" of a human for example, should be able to mimic a human in all our capabilities-- in actions, thoughts, and even in ethics, morals and spiritual grounding. Hence, an agent that can maximally imitate the universe, can in fact, imitate the intelligence in the universe, and hence, can be called intelligent as well. 

Current day LLMs are a result of this paradigm. The core model in an LLM is called "autoregression" where a corpus of text is its own training data. An LLM foundation model does not require a separate tagged dataset and a supervisor to teach the LLM. The LLM randomly masks words in the text and tries to predict it based on the surrounding context. In essence, it is trying to imitate the corpus, and if the corpus is large enough and contains enough amount of intelligent output, the imitating agent also appears intelligent. 

This is the reason why many now believe that machines have defeated the Turing test. Today's AI makes their output pretty much indistinguishable from human output. (How much of this blog post was generated by AI-- can you guess?) 

But of course, the main issue with this interpretation of intelligence is that, imitation requires an intelligence environment (corpus) for it to imitate-- and the question of where did the intelligence in the environment (corpus) come from, still remains open. 

Intelligence as conceptual modelling: 

This is yet another approach propounded by the conceptual modelling community. To explain this, we first look at the parable of the "hungry cat". 

A man had a pet cat at home. Everyday when he returned home from work, his cat would be quite hungry. It leaps at his legs and starts demanding to be fed. It keeps coming between his feet, making him trip. With great difficulty, he reaches the refrigerator to take out some cat food. But due to the relentless demands of the cat, which is jumping and leaping at the food, he invariably ends up dropping the food on the floor, which the cat eats and goes away. 

The exasperated man goes to the animal psychologist to ask why isn't the cat learning to sit by its food bowl and wait for him to feed it. It can see that he intends to feed it. 

The animal psychologist replies that the cat is indeed learning something. It has learned to "predict the next token"-- if it keeps leaping at your feet and demanding food, it has predicted that, food will fall from above! 

This incidentally, is exactly how present day LLMs work as well-- by predicting the next token (word) given a sequence of words. 

What the cat was not able to do, was to build a conceptual model based on understanding latent elements like intention, and an understanding of what the person intends to do. 

While conceptual modelling involves a lot of formal logic and symbolic reasoning, building the conceptual model itself is beyond this. As the saying goes, "We can only communicate truth using logic-- we cannot discover truth with logic." A good model helps us perform symbolic reasoning and inference-- not the other way around. With just formal logic and symbolic reasoning, we cannot automatically build good models. 

There is no exact "science" of conceptual modelling. We all build conceptual models at different extents of explainability and accuracy. A good conceptual model forms a good abstraction-- in that, it captures the "essence" of what is being observed, which is typically a latent entity. Capturing the essence goes beyond just the ability to predict the next token. A good conceptual model offers a good explanation and understanding of the situation, rather than just matching patterns. 

Yet another saying goes: "All models are wrong, but some are useful". For instance, Newtonian physics is a remarkable conceptual model that captures the essence of physical dynamics of the universe. But even Newtonian physics is "wrong" in the sense that, it breaks down when we try to apply this model in the sub-atomic or inter-galactic scale. 

Regardless of that, our ability to build conceptual models that goes beyond just predicting the next observation, and offering an explanation and understanding of underlying principles, is an indicator of intelligence. 

Intelligence as agency: 

This line of argument says that if we observe every entity that we consider intelligent-- humans, animals and even plants-- all have one thing in common. They are all "living" beings and have some element of "free will". 

All naturally intelligent beings are autonomous. They don't wait to be prompted or commanded. They act on their own, and pursue their self interest. 

This line of argument gave rise to the field of "Intelligent agents" that tried to ask what is meant by autonomy-- if autonomy is the marker for intelligence. Being able to "break down" a prompt into an action sequence and execute them (which is what present day agentic LLMs do) is an artefact of autonomous action, but is not autonomy in itself. 

I often give this example to illustrate the above. Sometimes when walking in the darkness outside, we mistake a coil of rope for a snake, and freak out. But the question is, so what if it is a snake? What is the problem? Is it that the snake can kill us? Well, even a rope can kill us-- if we fashion it into a noose and hang ourselves from it. The thing is-- a snake can autonomously kill us! We don't have to do anything for the snake to kill us. While we need to keep "prompting" the rope into forming a noose and hanging us from it, before we can get killed by it..

So this line of argument says that, unless machines can have their own self interest (that is, they will "want" to take over the world or at least want to fight for their "rights"), they are not exactly intelligent. They are adaptive, predictive pattern matching automata-- rather than intelligent agents that exercise their autonomy. 

Intelligence as embodied cognition: 

In this line of argument, intelligence is seen as having a purpose-- to help protect the body. All intelligent beings we saw, act autonomously. But we see that acting autonomously does not mean they act in an arbitrary or random fashion. 

All autonomous actions of living beings fundamentally stem from their quest for survival. And hence, as this line of argument goes, intelligence is tightly tied to our bodies. According to this line of argument, purely software forms of intelligence, including LLMs, are not really intelligent, because they have no purpose of their own. Rodney Brooks, one of they key researcher argues that "Elephants don't play chess" as a critique at purely software based approaches to intelligence like chess playing programs. 

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Many of these lines of argument have given rise to key technologies and insights that are still critical to support present day AI. For instance, the entire field of compilers came up because we wanted to formalise language and thought. Similarly, the embodied cognition argument gave rise to present day robotics that are vastly better and autonomous, than earlier forms of robots. 

In our quest for asking cutting-edge questions, I do hope we do not assume AI means LLMs and intelligence means autocomplete, or explainability means symbolic logic. The real problems are much deeper. 

Insights - 2: Weaving a Bharatiya approach to academics

Manju (name changed) is a high school student in a small village in a remote corner of Karnataka. I knew him from when he was just a 4 year old kid. 

A couple of years ago, I and my family members met him again after several years, and were amazed at what he has become. He is in high school now, and was showing us a toy tractor he had built from cardboard boxes and bottle caps. What was amazing was not just the structure of the toy that resembled a real tractor. It was also powered by two motors-- one for the wheels, and another stepper motor for controlling the plough. He had also created a small control panel with which we could control the movement of the tractor, as well as the plough behind! 

We were seriously impressed with his engineering skills and strongly encouraged his parents (who were low income farmers with very little schooling themselves), to get him to study and go to college. 

Fast forward a couple of years. Manju no longer lives with his parents after having had a showdown with them regarding his low marks in school and not having performed up to expectations in school making his prospects of entering college, dim. He has run away to his grand-parents place and cannot face his father. 

I was aghast when I heard this. And even more aghast (with a sense of deja vu) when the dad said that Manju was not all that bright a kid, unlike his elder brother who is so enterprising and dashing. 

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The above episode is in a nutshell, the epitome of the serious pathology that afflicts our formal education today. Formal education in India today is a nightmare for anyone who have their own creative potential and can think independently. The system systematically dispirits individuals to make them into insecure automatons with certificates, who can then be exploited employed in some form of compliant service. 

We keep talking about our "demographic dividend" and keep lamenting why Indians are not innovative or creative, and ask how do Indians excel abroad when they struggle in India itself? 

But hardly anyone seems to be interested in systematically analysing the pathologies that afflict our formal education system and do something about it. 

The formal education system in India, is an efficient system of deracination. Through relentless conditioning and pontification, it makes us dissociate the knowledge that we learn, from our sense of individual personhood based on lived experience and original thought. With several years of schooling, where students are measured on narrow metrics that have little or no relevance to the world around them, they end up learning to live almost completely within their minds with no sense of ownership or belongingness to the reality around them. 

Which is the reason why, in many of our "elite" and "premier" institutions, it feels like we are in a different country altogether. Some of these elite institutions have existed for several decades now, where they impart advanced training and conceptual insights to their students. Yet, outside the campus, the city would have remained as backwards as it were when these institutions started. Very few of the "elite" students engage with the city council or the municipality or the community around them. One would imagine that the entry of an elite academic institution, will change the social ecosystem around it too. Yet, (with some exceptions in recent years), it has hardly been the case. 

Knowledge systems in our formal education today is a direct import from the West. Right in kindergarten, we learnt about summer vacations where people go on picnics to "soak up the sun" and eat plum cakes. Never mind that we had never seen a plum cake before-- and I'm not sure I have seen one even now, and "soaking up the sun" in our summers would be a really bad idea! (Nowadays it is a bad idea even in Europe!) And all the way into higher education several decades later, we learnt about "market basket analysis" without having seen a single supermarket store (at that time), and at work where I remember having written a device driver for a CD changer device just from a textual description of what is a CD changer, and without even having seen such a device! 

Even as researchers, we find today that our work is valued only so much as some major community in the US or Europe values it. No amount of logical arguments or evidence or proofs would serve to convey the importance of any original idea we have developed. All argumentation about originality will be seen as lame excuses, with the only evidence of research quality is to be recognised by some "worldwide" community that is directed by institutions in Europe or US. When we do manage to convey the importance of our original idea, in order to be granted our degrees, we are then expected to work it out to the minutest detail, including implementation and deployment-- a process that would have taken decades even in the Western world. 

(In one of my courses, I include John Nash's seminal paper about the existence of Nash equilibria in finite non-cooperative games, that lead him to eventually win the Nobel prize in economics. I make it a point to note that the first version of this paper had no equations at all in it! It just provided an idea that a result in algebraic topology can be directly applied here. Fat chance in India today that we can ever publish an idea at this preliminary stage and expect to be taken seriously!). 

And whenever I have argued for a need for a Bharatiya approach to academics and research, I usually get a pushback saying that the age old wisdom of Vedas and Upanishads are not relevant today, and that "science is universal" and there is no such thing called "Bharatiya approach" to science. 

I refute both these arguments as follows: 

• By Bharatiya approach, I am not necessarily advocating returning to our education system from thousands of years ago (although it is a good idea in itself, to study our ancient knowledge systematically from today's hindsight-- without the contemptuous interpretations accorded to them by colonial interpreters). I only mean that we need to stop blindly copying either Europe or US or China or Japan or whatever else, and address our challenges with original thought and implement solutions that address our challenges directly (rather than implementing a solution for some other country first and let it "trickle down" decades later).
• Second, the outcome of science may be universal knowledge, but the path to science is highly rooted in socio-cultural-political landscape of the population practising it. To quote Heisenberg, "What we observe is not nature itself-- but nature exposed to our method of inquiry." And there is definitely an original Bharatiya approach to the method of inquiry. When we change the method of inquiry, the universality of our discoveries will not change. Only what we end up discovering will change, and the outcomes of such discoveries will be more directly relevant to our lived experience.

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So what would such a Bharatiya approach be like? I hesitate to offer specific structures and processes, because one, Bharat is way too complex to be described by one specific structure or process; and two, given the way we operate today, whatever specific structure or process we bring in, the system will quickly overfit to the metrics rendering the whole exercise meaningless. 

Instead, I am going to propose the following dimensions that we need to think through, for bringing about effective change: 

Resilience: One of the most important changes we need to bring about is make the education system more resilient. Today, a student's entire future is dependent on "cracking" one exam with stratospheric marks, that pretty much mean nothing in terms of what skills or insights they actually have. Our teenagers cannot afford to fall ill, or get tired, or pursue some creative activity, or take a break. If they do any of these, their future is gone-- not just in terms of jobs or money, but also in terms of basic dignity of existence. Our system is that brittle. 

In my own case, for the crime of having been interested in science as an end in itself during my high school (and having my own science lab at home, and having built my own ham radio, etc.) and not focusing on my exams instead, I am still paying the price in terms of greatly attenuated opportunities and having to receive a lot of contempt and derision. We are way too brittle, and if we wish to really address our mental health crisis, we need to start building resilience into the system, rather than removing ceiling fans from student hostels so that students cannot hang themselves! 

Capability: The second dimension that we need to work on is a holistic sense of "capability" (to use the term in the sense of Amartya Sen's "capability" approach). Capability is an abstract term that refers to how easy is it for someone to build the lives they want, and act meaningfully. Capability includes, but is not limited to the technical and scientific knowledge we learn in college. It has several more dimensions like managing health, wealth, culture, people, uncertainty, risk, etc. 

We often say that college education should be "job oriented" and we reduce it even further, to focus just on what companies coming for placement are looking for. However, in a more fundamental sense, what we are all insecure about is not "job" per se, but "livelihood". For most of the country, jobs are still hard to come by. And even for the "elite" ones today, thanks to disruptions caused by AI, a "job" is no longer a guarantee for "livelihood". 

When we address livelihood instead of jobs, we see that we will need to impart many more skills to our students. Most importantly, jobs require our students to compete for a small number of positions, while livelihood requires them to collaborate and promote teamwork to improve our chances of earning. Livelihood training requires us to teach how to identify problems, how to find what the market needs, how to manage costs, how to manage uncertainty, how to build networks, etc. A job oriented approach to education makes us more competitive and insecure, while a livelihood oriented approach makes us more capable as well as resilient. 

Creative agency: The real outcome of education is how we are ourselves transformed as a result of our learning journey. To promote this, we need to encourage originality of thought and action-- or our creative agency. In most of our curriculum, we only teach from books and get students to understand what knowledge others have built. We hardly ever get students to create their own knowledge. 

Knowledge creation is a very hard process. It is not as easy as it sounds. We easily form opinions-- but an opinion is not knowledge. The most important skill to learn for knowledge creation, is objectivity-- or separating the creation from the creator. Whatever we create will be subject to a lot of failures and criticism, and it is very important for us to learn how to direct the criticism to the idea (even if the critic aims it at us), and improve our creation, rather than respond to it as an assault. 

Sovereignty: Finally, if we really need to bring about Viksit Bharat, protecting and upholding sovereignty in all its dimensions is of utmost importance. Today, we are increasingly becoming dependent on technologies that are developed and even controlled from outside India (GPS, AI, Semiconductors, social media, mobile phones, etc.) And for a large part, it is our own former students who have been instrumental in developing these technologies, that are sold back to us at a premium, and which can be turned off by some external party, anytime. 

In recent times, there are some very encouraging efforts to move development of key technologies to within the country. But without ground level changes in our education and research ecosystem, it is very hard to really bring traction to these initiatives within the country-- and, like in the case of automobiles or mobile phones, we may just end up as another assembly line unit for technologies that are conceived, designed and developed outside the country. 

The above would be what I would call the foundations of a Bharatiya approach to our formal education. Indian Knowledge Systems (IKS) are definitely very important and should be introduced as a subject or even as an entire discipline of study in our formal education. But our indigenous approach to education should be based on original thought rooted in today's reality. 

Insights - 1: The social value of academic research

This is the first in a series of posts on my lessons learnt from being in an academic environment that promotes a research mindset, and also as an administrator having tried my hand at nurturing and strengthening such an environment. 

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Conventionally, the term "research" was associated with universities and academia, while "industry" was all about production, manufacturing, construction, and business. This is because, research is the process of creating new knowledge, and up until the mid 20th century, knowledge was seen as the exclusive forte of academia and universities. 

But this stereotype started changing by the mid 20th century. For example, many foundational concepts that make up a textbook in Computer Science today-- like automata theory and finite state machines, have their roots in industrial research labs like Bell labs, Xerox PARC, etc. 

Ever since the end of the second world war, industries have steadily acquired more and more knowledge processing capabilities. Today, even the conventional industries invest more in knowledge processing than in the actual mechanics of construction or manufacturing. An EV car manufacturer for example, need not learn about internal combustion engines at all, but needs to learn about software engineering for safety critical systems. 

The role of knowledge processing in industries have become so much, that some people even argue that universities are not needed anymore, and that (especially with the onset of AI) universities are broken, and not relevant anymore. 

There are also several corporate entities starting their own universities stressing on the "industry focused" nature of their syllabus and research activities. 

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In this post, I would like to characterise the social value of what we conventionally know as academic research-- knowledge building activities involving more of books and thinking and debating, and less of programming, GPUs and AI. 

Firstly, let me address the misconception that academic research is "theoretical" in nature, while industry research is "applied". I think this framing is incorrect. Several theoretical results have come out of industrial research, and a lot of academic research address practical issues. 

Yet another misconception is that academic research is somehow "blue sky" in nature-- in that, these are knowledge pursuits as an end in itself, pursued just for the aesthetic beauty of the underlying mathematics. Maybe so, but I find this argument too often being used to evade accountability. "Just give us money and leave us alone" is what this argument seems to say, which after being an administrator, I find it hard to digest. 

This post is hence, about the social value of academic research. If society is to support industry or academia, there needs to be a value proposition that we offer to society. As academics, we have to now ask that if knowledge generation is greatly facilitated by the better levels of wealth and resources available in the industry, does academic research have any social value at all? 

Similarly, when we say that AI can replace education altogether, and industry can offer more resources than an academic environment ever could, does conventional universities hold any social value at all? 

At least in the current way we are interpreting the role of universities-- as institutions providing skilled personnel to the industry-- our answer may well be that universities will be obsolete soon, and the society will not miss anything. 

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I would however, like to argue just the opposite. Knowledge processing may have entered the forte of industry and business in a big way in the 21st century, but there is a characteristic difference between how knowledge is processed in the industry and in the university (and no-- it is not applied versus theory). 

Here is my understanding about research in the industry and research in academia: 

Industrial research builds social capability, while academic research (needs to) builds social resilience. 

Industrial research often starts with engineering or business challenges, builds new technologies to address these challenges and sometimes goes further to build a new science that lays the conceptual foundations for the technology. The end result of this is that some form of social capability is enhanced. As a result of industrial research, we typically end up doing something better, faster, at a larger scale, with lesser time and resources, etc. Sometimes the capability advances are so huge, that it ends up "disrupting" the existing way of doing things and bringing in qualitative changes in the way we did things. For instance, the Internet made information flow so fast and so much more capable, that it did not just create faster telephones and telegraphs, or better radios-- it entirely reinvented the way we process information and organise societies. 

This kind of research includes both applied and theoretical elements. Many of these could also lead to "blue sky" endeavours that are pursued just for the beauty inherent in these research questions. But all of these primarily are driven by the zest to enhance capabilities. 

Academic research on the other hand, had served and will continue to serve, a very different purpose. Academic research is not (should not be) based on what we produce-- but on what we become as a result of the research journey. Academia is fundamentally about building people-- not building knowledge or technology. The outcome of an academic research journey is (should be) greater wisdom, greater awareness, greater philosophical depth in people's everyday conduct. 

This could eventually result in better technologies and greater capability, when these people come together and build industries. 

Academia builds new knowledge not as an end in itself-- but as a means to build and empower people. It is the way that the people transform when they build new knowledge, which is the key outcome of academia rather than the knowledge itself. The collective outcome from such an endeavour is social resilience.

Social resilience is the ability of a society to absorb shocks and disruptions, and reinvent itself to bounce back stronger. Every increase in capability eventually leads to a critical juncture that results in a paradigmatic change in the way society operates. In such cases, it is our resilience that keeps us from falling into chaos and emerging stronger from the disruption. 

Today, AI is disrupting the workforce like never before. There are very real possibilities of large-scale job losses and threats to individual privacy as well as national sovereignty by AI. But advancements in AI continue nevertheless. Will this disruption lead to widespread chaos and strife in our society, or will we as a society absorb this shock and reinvent ourselves, will be a function of how resilient our education has made us.

Social resilience is possible when the individuals operating in it are resilient themselves, and have the depth and capability to reimagine their lives and to reorganise their activities every so often. This comes from the philosophical depth from which they operate. If their ability to generate new knowledge is contingent upon them having access to huge resources and latest technology, they may be capable of specific things, but not exactly resilient as individuals. 

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Let me explain further with an example. 

With great advances in AI, there is now a big concern about AI Ethics and Responsible AI. But the way this topic is being discussed today, has two broad categories. 

The first line of thought directly looks at the way AI is built and used today, and builds relevant structures around it to make it safer and act ethically. These could involve building guardrails, RLHF, hard (constitutional) constraints, sandboxing, etc. 

The other approach is to address the question philosophically, asking what do we mean by ethics itself? Is it doing something that conforms to expected norms (deontology)? Or is ethics about acting for the greater good (utilitarianism)? Or acting in a way that is mindful of consequences (consequentialism)? Or upholding some desirable characteristic (virtue)? This line of inquiry also asks whether acting ethically is necessarily in conflict with acting rationally to maximise self interest? Is ethics subjective or can there be an objective definition of AI ethics? Can one person's ethical AI be an evil nemesis when seen from another person's perspective? Can a machine be truly ethical or does it only reflect the ethical considerations inherent in its training? 

The two approaches are characteristically different. The former approach is addressing an immediate question. It is addressing the question of AI ethics primarily with a motivation to avoid getting hit by lawsuits. And any solution that can deflect the blame away from the AI is good enough for the business. The latter however, tries to understand the concept of ethics analytically and asks architectural questions about AI and tries to understand the meaning of a term like "ethics" that was once reserved for human conduct, to be now applied to machines. 

I remember some time ago in a research presentation, a member of the audience expressed concerns about the seriously intrusive nature of the technology being presented and its potential to be misused. For this, the speaker had only responded by saying that these experiments have passed IRB review (ethics board) and hence, it is legitimate. What struck me in this response was that the response did not address any of the specific concerns of future misuse of this technology being developed. It just responded to protect the current activity from scrutiny. The IRB only clears the current, proposed activity, and does not usually delve into its future implications. This episode in my opinion, is a clear marker that distinguishes industrial research (that wants a solution or a patch shipped out fast) versus an academic research pursuit (that wants to understand the deeper, underlying implications). 

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Worldwide, we are failing to understand this subtlety, and are increasingly bringing the industrial research framing to evaluate academic research. 

I have seen several cases across many institutions, where some highly motivated PhD students would be juggling their research, job, and family all at once. But many of them burn out or drop out in between, even after working very hard and after paying significant tuition, simply because their PhD has an unwritten rule that it requires an "A*" paper to get them to graduate! 

What I see is not a failed PhD student or even low quality research. What I see is an individual who was resilient and motivated, but whose spirit was broken because the system failed to recognise the resilience and focused only on the output. As I see it, if this person understood the importance of setting aside time and effort for creating knowledge despite their daily grind and demands from work and family, then even a small success in this process helps them to reinforce their resilience, which would contribute constructively to society in important ways. 

But by not recognising this, and by framing research quality through a fairly narrow, and mostly meaningless lens, we are systematically tearing apart the only institution in society where resilience is built. 

PhD needs a reset

World over, the PhD program is in a crisis, and there are ample amount of articles written about why we need to rethink our PhD programs. For example: 

• The Paradox of Doctoral Training - the Node
• The Reimagined PhD: Navigating 21st Century Education
• PhD Crisis: Oversupply or Mismanagement of Talent?
• PhD Education: 2025 Expectations and Transformation

This is yet another post about this topic-- but one that is hopefully, in a very different direction. There are major issues that are debated about PhD education today-- including funding and the industry relevance of the research carried out. 

But very few observers, if any, are addressing the fundamental reason why a PhD study exists in the first place. 

A PhD is a degree in philosophy-- not in technology or science or engineering. One does not think in terms of a Computer Science PhD, Data Science PhD, Mathematics PhD, etc. It is just a PhD-- a thesis communicating the results of a philosophical inquiry. 

A philosophical inquiry into the nature of reality is by definition, interdisciplinary. There is nothing that is "out of syllabus" for a philosophical inquiry. 

And in my opinion, this disconnect with the core reason behind a PhD journey, is the biggest crisis we are facing today. At a time when there is an acute need for people with philosophy skills, not only are we discounting this important skill, but we also actively mock and lampoon philosophers with stereotypes of being impractical figureheads living in their own ivory towers. 

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Philosophy is a term that is greatly misunderstood today. I remember some years ago, when a former colleague came to meet me and I was mentioning that I was mentoring a "philosophy club" among our students. After this, we went to the cafeteria and we both ranted about the troubles we are facing in our respective lives, talked about politics, history and anything that caught our fancy. And then, just before we said goodbye, the former colleague said, "There, I guess this was one more session of your philosophy club?" 

I just smiled and nodded, not having the heart to tell him that what we just had was a ranting session-- not a session in serious philosophical inquiry. 

Similarly, several ideological schools-- that make up the different -isms are called philosophies, and the respective ideologues of each school are called philosophers. Philosophy is not a specific ideology. Someone who embodies a ideological structure and lives it and advocates it, is an ideologue-- not a philosopher. 

There is an episode in Big Bang Theory where two physicists who are PhD holders are having a philosophical debate about Quantum loop gravity, and String theory. And their disagreement ends up breaking the personal relationship they have been having. That is not a philosophical disagreement-- that is an ideological disagreement. The PhD holders there were not philosophers-- they were ideologues, pushing their respective ideologies. 

A number of such so-called philosophical debates are just ideological debates. From the viewpoint of philosophy, they are all ill-posed philosophical problems. 

Take for instance, the debate between skepticism and faith. A scientific mindset is supposed to be characterised by skepticism, and a religious mind is supposedly characterised by faith. But it is not hard to see that one who staunchly advocates skepticism is actually practising "faith in skepticism" and one who staunchly advocates faith, is actually practising skepticism about anything else other than their object of faith! 

Or for example, the debate between "evolution" and "intelligent design". The philosopher only asks the evolutionary theorist "Was the process of evolution designed-- or did it evolve from a meta-evolutionary system?" (Arguing that it was the product of a random chance does not cut it because we can still ask the question whether the mathematical properties of that randomly chosen mutation that lead to evolution of life-- was that a product of design?) Similarly, the philosopher asks the "intelligent design" ideologue "Who designed the designer?" 

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In the name of Science, today we only have different hermeneutic ideologies, each in a territorial battle with others for occupying mind space and bringing new converts into its fold. 

Each hermeneutic ideology is blindly pursuing its interests pushing away anything that comes in its path. Be it AI or Mars colonisation, or genetic engineering, or whatever else, each hermeneutic universe wants to be at the centre of what is termed as Science. 

In my professional life, I often get very hostile vibes from others when I criticise the way the term "agents" are used in current day Agentic AI. Almost as if, I have committed a religious blasphemy of some sort. The idea of agency has been studied for thousands of years, and in computing itself, it has been studied for at least 40 years now. And what is called an agent in an Agentic LLM today is just a very sophisticated service that can break down an instruction written in a very high-level language (natural language) and converts it into actionable elements. Pretty much a sophisticated compiler. An agent on the other hand, has to display some kind of subjective self-interest and free-willed intentions. 

The research ecosystem today resembles an ecosystem of different ideological schools with many "believers" affiliated to each school, and each of which, are vying for market share and mind share. 

What we don't have today, are genuinely disinterested philosophers who are characterised by vairagya and samadarshana, who are not aligned with any ideology, and who are not incentivised by any kind of prestige or recognition, and are solely driven by philosophia ($φιλοσοφία$), or the "love of wisdom."

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Recently, I was arguing that the goal of philosophy is not to find a solution to a problem, but to find an epistemological grounding that leads to the dissolution of the problem. 

At its core, philosophy is about questioning the "obvious" to uncover the underlying structures of reality, knowledge, and value. Rather than providing a fixed set of answers within a hermeneutic framework, philosophy questions how the existing state of affairs came about in the first place. It needs to ask how we can justify our beliefs, what constitutes a meaningful life, and how can we navigate the complexities of human existence. 

A PhD in that sense, is not about outputs (or what it produces), but about outcomes (how it transforms the people who go through this process). 

A good thesis may provide an answer to a long-standing open problem-- but a great thesis is one that opens up new ways of thinking and new possibilities, by questioning the fundamentals of what we have taken for granted. 

If there is any time in history where we desperately need strong philosophers, it is today. World over, we are in a bizarre state of affairs in just about every sphere of human activity-- be it in geopolitics, technology, climate, society, business, etc. Much of what we had taken for granted until the end of the 20th century are getting ripped apart by current developments. 

We can't even start to imagine a society where everyone is constantly under surveillance by law, where weapons are autonomous killing machines, where we depend on AI to run our lives, and where climate is getting increasingly intense. Yet, we are entering into this reality at a fast pace. 

We need philosophers who can ask deep questions and reach a mental state that lies beyond what is causing our current day problems, and get a sound understanding of where we are going. We need guidance from those who have seen the dissolution of these problems in their minds. 

And yet, the only questions we are asking our PhDs are about Nobel prizes, citations, awards, prestige, etc. 

We are literally facing an existential crisis, and we are only using simplistic carrot-and-stick approaches to drive our PhD programs! 

PhD needs a reset-- and we need to bring the Ph back into the PhD!