God and Science

Topics on which people hold very strong beliefs … sometimes these beliefs are no more than just those. While on the one hand some of those who believe in God take Scripture literally, and will tell you that indeed the world was made in 6 days, or that there was indeed a time when snakes talked or horses flew. Little does it occur to them that maybe these stories are more allegorical, and one must look into their subliminal meaning which seems to be a consistent characteristic of Scripture to get a true understanding. Scientists on the other hand use these same stories, read at their superficial level to try to prove that religion flies in the face of logic.

There is another aspect of the scientific discourse which tells us that there is no ‘need’ for God since all phenomena can be explained by science, those which can’t be explained today would well be explained at some point. Let’s take an example to see the folly of this logic. Let’s say you have to go from place A to place B. Now, you could do this commute on foot or by bus. Now, since your commute can be explained by walking, there is therefore no ‘need’ for the bus and therefore the bus doesn’t exist.

Another logic which is most prevalent is that there is no proof of God. Now, one could also on the other hand say that there is no proof for the non-existence of God, but then that takes us into a different byway, so for the moment let’s shelve that one. Let’s say, for instance, if you are asked to cook a meal with a pen, crayons, toothpicks, and a screwdriver and wrench. Doesn’t sound logical, does it? Fact is, different tools are meant to be used for different purposes and extending this it’s easy to see why science has not, maybe will not, been able to prove the existence of God … that as a tool science isn’t the right one.  Let’s try to analyze that. Science is essentially a study of this creation, maybe (if we keep the idea of the multiverse aside for a moment) of the universe. This means that science, meant to study nature, isn’t meant to study whence nature came from.

Is that why so many scientists are so antagonistic to the idea of God?

How To Teach Maths?

A recurring question which keeps coming up in discussions is how mathematics should be taught. There is a strong view that given the computing power which is available to us, we should relook the basic maths curriculum. So i went looking, and found this video which i feel looks at the problem in a logical way.

Conrad Wolfram is giving some compelling reasoning for why maths education should change, and gives a description of how it should change, too.

Hand-culcating the mundane way should not be the focus on the curriculum. Rather, the focus should be on building and enforcing the concepts, and leave the calculating to computers. In other words, as he says, students should be taught the procedures which define fundamental concepts, but the implementation of those should be left to computer programs. For example, students should know what a square root it, how it is calculated, but they shouldnt have to calculate, beyond illustrations. And here is the cool part he says … focus on teaching students how to write programs to calculate square roots, rather than having them mechanically execute the procedure for calculating. This will immensely help students clarify their concepts (how can one write a program without understanding the underlying principles to a very large extent?), while at the same time help them become more comfortable with the concept of application of these concepts. In other words, our mathematics curriculum should stress understanding and application (application to real world problems is a very good way of teaching these concepts) rather than stress on the mundane calculations which stress out students as well as parents alike. After all, why should a child lose marks in an exam (thats what happens) if he or she takes the square of 5 to be 10 when all the conceptual aspects of the solution are correct, and the only mistake is a calculation mistake?

Connect this with the post i had written earlier, and a rather innovative picture of mathematics teaching emerges.

Big Data Analytics

For quite a while, I have been thinking that maybe I am the only one who doesnt understand what these words mean. I mean, with the buzz around these concepts (and here I mean the concepts, not the technology), these must be complex concepts to define, but the definitions that I was able to understand were all quite simple.

Big data is just that … BIG! There are essentially 3 things which define it:

1. Theres lots of it! Much more than we had imagined maybe even a couple of years ago.

2. The form of this data is too diverse. There text, images, videos, and what have you. Theres structured data and unstructured data, and data comes with its own context which makes it even more complex to handle.

3. Its being generated at a very fast pace. In fact, writing this blog is adding to this big data, as is your tweet, and those pictures you post on facebook, or those status updates that you like.

I was looking for whether this definition is correct or not, and I came across this video from Ericsson Research, which describes it quite simply with an example. If you want to get past the buzz and get to understand the concept, I would suggest you watch this.

So where does analytics come into the picture? Well, if theres so much of data, theres also the fact that its very difficult to build any coherent picture from this mass of data, and this problem is addressed by the analytics domain. Analytics helps us make sense of big data!

So what does Big Data Analytics need?

1. It requires infrastructure which is able to scale up or down based on the demands of the those who are generating this data, and those who are analyzing it. This means that the infrastructure needs to be flexible, and this can be handled much more easily with cloud solutions, and this is where cloud comes into the picture of big data and analytics.

2. It requires the applications which gather this data. A lot of this data is being generated by automated systems like sensors, and through mobile devices. With the scenario of equipment communicating with other equipment, the concept of the internet of things comes into the picture. Also, with the mobile device explosion, the importance of mobile applications and mobility solutions as an integral part of the picture also becomes apparent.

3. It requires the statistical and technology foundation which will help users or systems to make sense of this data. This is the analytics piece of the picture.

Heres a nice video about an IBM study on analytics.

This is how the picture gets a little clearer, and we can see how the cloud, internet of things, mobility, big data, and analytics are coming together to create a whole new technology paradigm.

Discover, not Invent

Einstein once said:

I want to know God’s thoughts; the rest are details.

Ramanujan once said:

An equation for me has no meaning unless it expresses a thought of God.

Two geniuses of our times, two men recognized the world over for their far-reaching contributions to the realm of science. And yet men who understood something, something i believe to be fashionably unfashionable in the scientific realm of today. A simple thought, yet profound. A thought which one could understand simply if only one asked the right questions, or simply took some of the scientific premises scientists are working on to their logical conclusion. Something these great men probably did.

The conclusion is simple. If we understand one basic truth about the nature of science, that science doesnt invent, science discovers. Beginning with prehistoric man, who didnt invent fire, rather found a way to generate it. Or, if you take the steam engine, the power of steam the engine harnessed always existed. Who gave steam the power? Lets take this question further. Who created the consistent laws of nature which science discovers? Who created the picture science is trying to unravel? Who created gravity that Newton discovered? Who created the infinitesimals which, for example, differential calculus explores? If these be seen as different parts of the same picture, somewhat like a jigsaw, who made sure the pieces all fit together, and that each of the pieces is consistent with all the other pieces?

And this is what i believe these great men understood. That science is trying to understand the picture, not the creator of the picture, because while the picture hints at the existence of the creator, it cannot help understand the creator. And this is where, i believe, human development needs to lead to.

Cause and Effect

Over coffee last month, i was discussing some of the philosophical aspects which emerge from science the way scientific knowledge stands. As you can understand, the topics were revolving around Quantum Physics, and Relativity (not that i understand either, but my friend Sanjay Sethi does, quite a bit). And as you can imagine too, this was a discussion which was very interesting. At least those parts i wasnt talking. Without getting into the discussion we had (for the simple reason that i cant remember one half, and couldnt understand the other half, and there are no more halves to tell), Sanjay recommended reading a book on Relativity by Albert Einstein. I have been looking for the book, but it doesnt seem to be available. Few days back, i had a boys evening out at Landmark. Sonny got a nice Toy Story 2, and a wonderful book about Eagles (birds, not band), and i reluctantly asked if they had this book in stock. They did. But it didnt end there. After that, the guys at the shop had quite a time trying to find where it was.

So why am i writing about this? Simple. This brought to mind a thought i had for some time. What is the relation between a cause and its effect? Is it not that the relation between the cause and its effect is built through the passage of time? I someone doesnt shave, then he has a beard. But this needs to be given time. Now the question is, as Relativity points out, time is not constant, which means that due to time dilation, the passage of time slows at speeds close to the speed of light. Now, does this mean that at the speed of light, the relation between cause and effect doesnt exist? If someone travelling at the speed of light doesnt shave, will they still have a beard? Or can we look beyond the material manifestation of this relationship to another relationship between cause and effect which is unvarying, constant, Ultimate?


I read an interesting book on the life of Ramanujan, titled The Man Who Knew Infinity, by Robert Kanigel. The book gives an interesting idea of the concepts which Ramanujan presented to the world without needing an advanced degree in mathematics to understand. But thats not what i am writing about. There is an interesting aspect which is written about. While it is the logical, rational process to prove a theorem with the tools of mathematics available to the mathematician, what to prove is a less logical thing to find out. What is the theorem which should be proved. How to find out what to prove. And this is what i am writing about. As G. H. Hardy is quoted:

unconscious activity often plays a decisive part in discovery; that periods of ineffective effort are often followed, after intervals of rest or distraction, by moments of sudden illumination; that these flashes of inspiration are explicable only as a result of activities of which the agent has been unaware – the evidence for all this seems overwhelming.

This means that while the mathematical process is logical, there are aspects of mathematical discovery which are not completely rational, which depend on something which is beyond the human mind. I have written about this earlier, where i have asked how it is that scientists decide the questions they seek answers to. Or, how does a mathematician what should be the form of a theorem, which they can then go ahead and prove. Some of this comes from a part which seems to be inexplicable to the world of science. Inspiration, we may call it, or intuition. Or give it another name, but this is something which remains outside the domain of natural enquiry. As David Gurteen had pointed out, a quote from Poincare:

It is by logic that we prove but by intuition that we discover.

What this means is just that logic takes to a particular point, where something else takes over the process of discovery. What this is, i would not name, for we all have different names for this. But this is something which we need to recognize as the source of a number of great scientific discoveries.

About Knowledge

Lot has been written about the DIKW hierarchy, and recently, David Weinberger wrote an interesting one about the problem with the DIKW hierarchy. While i dont really understand the difference between knowledge and wisdom (while i think there is a difference, at one level the difference starts to dissipate, till we reach a point where the difference may not actually be there), it is formal definitions which i am not too sure about. This is because none of them seem to define the essence. But thats not exactly what i am writing about. What i found interesting in the post:

But knowledge is not a result merely of filtering or algorithms. It results from a far more complex process that is social, goal-driven, contextual, and culturally-bound. We get to knowledge — especially “actionable” knowledge — by having desires and curiosity, through plotting and play, by being wrong more often than right, by talking with others and forming social bonds, by applying methods and then backing away from them, by calculation and serendipity, by rationality and intuition, by institutional processes and social roles. Most important in this regard, where the decisions are tough and knowledge is hard to come by, knowledge is not determined by information, for it is the knowing process that first decides which information is relevant, and how it is to be used.

What this means is that the idea of knowledge is more complex than we think it is. Lets look at it in two ways. To begin with, instead of looking at this as a pyramid, what if we were to look at it as a cycle? Where does datd come from, to begin with. Data comes from activities which we do, but where does the need for these activities come from, and how do we know how to do these activities. One would think, at a leve, from knowledge. So, rather than looking at it as a hierarchy, lets look at it as a cycle. And once we do that, then the thought from the blog makes more sense. That there is no linear, well-defined way which describes how information leads to knowledge. Rather than a linear form, this probably becomes a web, and the knowledge creation aspect becomes one part of the web which derives from, and contributes to, other parts of the web. As i have written about the complexity of knowledge sharing, and about scientific discovery, the process of creating and sharing knowledge is not as linear as we think it is.

Determinism, Computability, Observer Effect

I am reading a fascinating book, The Emperor’s New Mind, by Sir Roger Penrose. In the book, he has very forcefully brought out the idea that the deterministic view of science, developed over the past five centuries, taken to an extreme area by those supporting Artificial Intelligence as a viable alternative to Human Intelligence, is largely misplaced, and that while there are certain aspects which are deterministic in nature, there are many others which are not and even others which are not computable even though deterministic.

Whats the difference? To my understanding, deterministic is the property of a system which tells us that there is some logic, or rule, or algorithm by which given the current styate of the systesm (in sufficient detail, lets add for good measure), the stateof the system at another point, in space, time, or both, can be determines. Computability, on the other hand, is the property which defines the possibility of this determinism being carried out. In some scenarios, this may not be possible to compute because there may be operations which are mathematically not possible, for example, division by zero. Thats a basic one (and the one I understand), though I am told there are others.

The question of determinism and computability apart, because not what I think I am writing about (though with topics like these ones, can never be sure), what I am writing about is my own puny interpretation of the observer effect. For those not familiar with that (either definitionally or conceptually), let me juwst mention the scenario has has described:

In a two-slit experiment, one possibility to describe the interference effect could be the photons of light get split (or maybe the vibrations of the wave function do, I am not quite sure which … or shall we say I am uncertain which?) and each part goes through one of the slits. This can be found out using particle detectors, which would be able to observe either full or non particles. However, if a particle detector is placed at one of the slits, so the observer can see which slit the electron or photon went through, the interference pattern disappears. For the interference to happen, there must be a lack of knowledge about which slit the electron went through. In other words, an observer, by his very presence can make the experiment unfeasible.

Interesting, this. Lets look at this in detail. Who is the observer here. Or rather, what is the observer here? The particle detector. And what is the cause behind the particle detector being there? The cause was human. So, shall we say that the observer in this scenario is human? If we were to say that, then we would also need to agree that what this means is that the presence of the human observer impacts the outcome of the experiment.

Why would that be? Are subatomic particles or photons allergic to human beings? I dont think so. If they were, we would all be invisible because photons would stay away from us. And nobody would ever get an electric shock because electrons would avoid the human body. Do you think I am rambling? In a word, I am. But not from here.

If these particles or waves (take your pick) are not allergic to observer presence then there must be a rule of nature which leads to the phenomenon observed. Is this rule only with respect to the scenario described or can it be generalized? If we were to generalize it, then would it be an over-generalization to say that there are some aspects of nature which are not meant to be observed or measured? We cannot say that these phenomena dont occur because we cant observe them, because we can observe their manifestation. But we have to accept that we are not meant to observe the phenomenon itself. Question then is are there other such phenomena? There are phenomena which have been predicted but not yet observed, but this doesnt mean they would never be observed. There could, I believe, more such scenarios which would arise from developments in scientific thought. And this is where, I believe, we need to understand that not everything is meant to be observed or measures at the material level. That there are things which lend themselves more to thought than to observation, things study of the material universe may never be able to define.

Human Knowledge

I wrote recently about some of the messages which came out of KM Asia.  What i find interesting about these messages are two things. One is that the concerns which are raised there seem to be quite common, or at least are more or less similar to the ones being faced by KM practitioners in India, and another is that some of the problems which knowledge managers seem to be facing today are the same as the ones maybe a few years back, which means that basically the appreciation for knowledge-sharing hasnt changed much in organizations even given the enthusiasm that organizations seem to be having for concepts like social computing, and even e2.0 which is something i believe to be something far down the road for an organization to implement. What this means is that while at the organziation level there might be an understanding that the organization needs to evolve, this need doesnt seem to be shared at the individual level.

One of the messages which came out of KM Asia which i think we dont look at enough is:

It is important to focus not just on knowledge stocks but knowledge flows, advised David, since human knowledge is inherently messy (and process people find it hard to deal with this!).

While its true that process folks find it a little difficult to handle this kind of messy knowledge, it is also true that more and more, people are coming to accept this view of human knowledge as being messy, which would mean that managers (including the process folks) would do good to accept this reality. The reason why i say this being reality is the book i am reading these days … Chaos, by James Gleick. Since i am myself trying to understand Chaos Theory, i believe i am not the right person writing about it, but there are some aspects which i think are quite easily linked to the world of organizations, more so to the role of knowledge in the way organizations work. This is because just like chaos theory seems to be about the unpredictable, KM also is, to my mind, in a realm which is similar, where its very difficult to predict not just knowledge, but also how knowledge flows and knowledge-sharing happens. This is because knowledge stocks and knowledge flows both seem to be in a realm where patterns are rich and to a large extent unpredictable. In other words, knowledge stocks and flows dont follow mathematical equations.

What i found quite interesting is:

Unpredictability … generated complexity: richly organized patterns, sometimes stable and sometimes unstable, sometimes finite and sometimes infinite, but always with the fascination of living things.

Read this closely and you see a similarity to the way knowledge is being more and more seen. Knowledge stocks are unknown because it is almost impossible to create their inventory (because we get to know what we know when we need to know it) and knowledge flows are unpredictable. If we take these similarities and try to explore the rest of this then we may come across some things interesting. That unpredictability and, by parallel, knowledge-sharing, create a rich tapestry of complexity and patterns which might not be discernible by themselves but look at them over a period of time and you can get to make some sense out of those.

Can we read more into this similarity? Could this similarity be taken to a point where one could build a connection between the science and the theory? Please do post your thoughts, maybe we could develop something out of this.

Scientific Harmony

The two words arent usually used together, which is probably why writing like this is important. This comes from something interesting Panditji writes in The Discovery of India …

Perhaps science has been too narrow in its approach to life and has ignored many vital aspects of it, and hence it could not provide a suitable basis for a new unity and harmony.

Why i find this interesting is because this to me is the one area where science hasnt been able to create too much understanding. Science is seen as a cold study into the phenomenon of nature, where nature is defined essentially as the external. What i find more restricting in the scientific method is the focus on the things happening around us. Rather, i would look to science and the scientific method to answer questions which humanity has asked since time immemorial. Questions like where have we come from, and where are we going. Are time and space really an illusion? If they are, what would a world without them feel like. What are human beings all about basically, and the relation human beings have with each other and the external world. Questions which arent really scientific the way science is defined today, but questions which are asking for applying the scientific method to find answers. In other words, science needs to be about answering questions which humanity is plagued with, and not just restrict to finding answers to physical or external phenomenon. Only then can science truly look to advance humanity towards development.

As i was writing this, interesting how i came across a post by Sonnie Santos about Spiritual Intelligence. What i am referring to is a comment which Sonnie mentions too. This comment talks about how science, in the current state of development, isnt answering some of the questions which mankind has been asking for a very long time. How to live is the question posted there, but there are other questions which come up too. Like who created us, why, from where do we come, where do we go. As of now, these questions are left to philosophers, but with philosophy being “un-chic”, these questions are probably not getting the kind of thought they more than deserve. As Einstein said:

I want to know God’s thoughts; the rest are details.

And it is this question which i think is not getting the respect it must. Though i think over the last few years, there is more interest in this search. Probably a reaction to maybe a century of going in the other direction? Or just a search for a harmony which can come from a humane approach, rather than a purely rationalistic one?