Wednesday, October 10, 2018


Linux down the Memory Lane


This is the story of not just Linux down my memory lane, but also the story of why I fell for Linux and have remained a devout follower of it ever since. As folks now know, the reasons why Linux is preferred these days are very technical but for me besides being technically a better OS, the reason is also nostalgia.

Early 90s was a fascinating era for me. Let’s begin in 1991, now fondly known as the year of the Internet. The PC-XT (80186) and the higher-end PC-AT (80286) were just about proliferating work places and some homes. Intel 80386 processor based systems weren’t so common still. I had joined for my PhD in computer vision also in 1991.  In IIT Bombay, we had only a central computer centre (with the Cray X/MP super computer) from where there was a 64kbps VSAT link with the rest of the world for the Internet access. My department (electrical engineering) wasn’t even on the local network. In fact none of the departments were, except possible computer science department. I met two like-minded guys in my lab and we all started spending endless hours on improving infrastructure for the joy of doing it. First, we set up Ethernet cable from computer centre to our department, and then setup our department server which would be connected to the computer centre, so that we could login to the super computer by physically being in our department. We learnt about Ethernet, TCP/IP, networking, routing all on the job and without attending any course!

Now, with the “comfort” of accessing the Internet from the luxury of our own lab was achieved, one of my colleagues started looking out for more stuff and he found out about this guy called Linus Torvalds in Helsinki. While studying computer science at University of Helsinki, Linus began a project that later became the Linux OS. His reasons, too, were similar to ours. In those days, a commercial UNIX operating system for Intel 386 PCs was too expensive for private users. So, he wanted to build a free OS which could make the most of 80386 based PCs at the time. He apparently said once that if either the GNU or 386BSD had existed then, he may never have written his own.
Linus developed what he called “Freax” (for free freak unix, which later became Linux). He developed his OS on MINIX system, for which free code existed at that time. MINIX source code was released by Andrew Tannenbaum in his book “Operating systems: design and implementation”. Reason why freax had to be invented was, because Linus argued, that 16-bit design of MINIX was not well adopted to the 32-bit features of 80386 based computer architectures.

First version of Linux was launched on 25th August in 1991 by Linus. Probably the only other installation of Linux 0.0.1 in the world other than that by Linus, was in our lab and I still have the source code of the first ever Linux kernel ! Since the 0.0.1 kernel, I have pretty much used every other version released (especially in earlier days) and continue to remain an avid user of Linux till date. It’s fascinating to see Linux grow as I grew up.

Back in 1991, there was no Ubuntu, or RedHat or any other distribution of Linux available. The closest that came was H J Lu’s boot/root floppies. They were 5.25” 1.2MB diskettes that could be used to boot a system into Linux. One booted from the boot disk, and then, when prompted, one would insert root disk and after a while one would get the prompt. Back in those days, if one wanted to boot from the hard disk, then one had to use a hex editor on the master boot record of the disk and it wasn’t for the faint hearted ! These were the days when we could predict the life of the hard disk just by listening to the sounds it made !

This was all before a real distribution came in existence. The first such thing was the MCC Interim Linux (from Manchester Computing Centre). This was still console only Linux and no X. Shortly after there as a release from Texas A&M University, called TAMU 1.0A. This was the first distribution that let one run X. The first polished distro was Yggdrasil. One could boot from the floppy and run everything else from the CD (the equivalent of today’s Live CD). Folks don’t know this was in the days of 1x and 2x CD-ROM drives. Then, the distributions that followed were SLS Linux, SuSE, Debian and Slackware. Then there was the SCO Linux and after these came the Red Hat and Ubuntu.

In 1992, hearing of success of Linux, Andrew Tannenbaum wrote a Usenet article in the group comp.os.minix with the title “Linux is obsolete”. One should note that while Linus used MINIX for development, the principles of the OS were diametrically opposite to those held by Andrew at the time and also mentioned in the book. Andrew’s reasons why he thought Linux was obsolete, was primarily because kernel was monolithic and old-fashioned. Tannenbaum predicted that Linux would be obsolete soon. Rest is history as we today know where Linux is and where MINIX is or for that matter GNU Hurd, of which Andrew was a great proponent.

Today, the aggregate Linux server market revenues exceed that of the rest of the UNIX market. Google’s Linux based Android claims 75% market share of smart phones. Ubuntu claims 20,000,000+ users and kernel 4.0 is now released.

The free and open philosophy of Linux and the enterprising nature of Linus Torvalds left an indelible mark on me during my graduate days and I continue to respect the open community and hence have hardly used any other OS. My devices of choice today are Ubuntu based laptop and Android based phone.

Monday, October 8, 2018


Deep Learning and Genomics

Deep learning at work can be seen all around us. Facebook finds and tags friends in your photo. Google DeepMind’s AlphaGo beat many champions at the ancient game of Go last year. Skype translates spoken conversations in real time. Behind all these are deep learning algorithms. But to understand the role deep learning can play in ever fascinating umbrella branches of Biology, one has to understand what is deep in learning? I would skip the definition of learning here for the sake of brevity. The “smart” in “smart grid”, “smart home” and other such was equally intriguing initially and eventually turned out to be a damp squib. You will be surprised if “deep” could end up as smart’s ally eventually.

There is nothing ‘deep’ in deep learning in the colloquial sense of the word (well, there will be many who may want to jump on me for saying this and try proving just why deep learning is deep – but hold on). Deep learning is simply a term used to describe learning by a machine in a way similar to how humans learn. Now here is the dichotomy. We are still struggling to fully understand how the brain functions, but we do know how deep learning should model itself after the way brain operates! This reminds me of my problem in my PhD days in the late 90s in computer vision, the branch that deals with making machines see things as humans do. Back then, David Marr of MIT had written a seminal book on Vision popularly known as “Vision by Marr” that spent a whole lot explaining the neuroscience behind vision and how computer models should mimic that behavior. Computer vision seemed a saturated field in 90s though, as just how much maths and algorithms can be invented by looking at 2D array of numbers (pixels in an image)? But recent developments in machine learning and deep learning have brought focus right back to computer vision. And these days, folks don’t write the crazy low level image processing algorithms I used to write back then! They just show the algorithm 10,000 images of dogs and cats and then after ‘learning’ the computer is given another unknown image with a dog or cat and it would tell which is which with incredible accuracy. Doing these tasks of learning and prediction in the assumed model of how brain functions, namely the neural network, led to the development of field of artificial neural network (ANN). So any ANN that thinks like brain (at least as we think so) and produces results that are acceptable to all of us, generally speaking, is called deep learning.

There are two thoughts that I came across at different points in time that have shaped my professional career. One was by Jim Blinn. In his column in IEEE Trans. on computer graphics, vision and image processing in the 80s, he once wrote in the context of maturity of computer graphics at the time, that practical solutions should not necessarily be driven by theory. One should experiment and then use theory to explain why the best result one got, should work anyways. This is the essence of machine learning and deep learning. There is data and more data. If there isn’t enough, we carry out data augmentation and add more data, try multiple splits of training data as training and validation, then use multiple models to find accuracy of that model, whether it over-fits or doesn’t etc and then choose the best model. As a practicing data scientist, I can say there is no single approach at the outset that sets the path for required results. There is exploration and experimenting. Unfortunately, Blinn’s thesis can’t be applied to deep learning here after, for even after one gets the desired results, there is no direct way of applying theory to figure out why it should work anyways. In fact, many researchers have dedicated their lives figuring out why deep learning should work anyway and there is no consensus. Geoff Hindon and a few others perilously kept the branch of machine and deep learning alive during the years when it seemed saturated and while at the same time, scale became possible and now with multi-core CPUs and more importantly powerful GPUs (and now TPUs), artificial neural networks yield surprisingly fast and acceptable results, without anyone quite able to explain why it works anyways. Prof Naftali Tishby and his team have the most credible work to their credit. Called “information bottleneck”, they use concepts from information theory to explain why deep learning models should work. It is a fascinating field and still under development and many including Hindon have agreed that information bottleneck is a real mathematical tool that attempts to explain neural networks in a holistic way. But at the level of a practicing deep learner today, one tries tens of models and chooses the one that gives best results (or chooses an ensemble) and use accuracy or any other metric to crown it as the best among the equals and leave at that, for theory plays no further role.

The second thought is from Prof Eric Lander of the MIT. I had taken his online class on ‘Secret of life 7.00x’ in 2014. He has a PhD in Mathematics (information theory) and he got interested in Biology and became the principal face of the Human Genome project in 2000. In one of the classes, he had said that as a student one should build skills to learn all tools available and then later choose from them to problems at hand, as you never know which one is helpful when. He used his maths training in solving many tasks in the human genome project. He is singularly responsible for revival of my interest in Biology again. His course was a fascinating time travel in the fields of biochemistry, molecular biology and genetics and then an overall view of genomics. Interestingly for me, the timing was correct. 2014 onwards was also the time when machine learning and deep learning was sweeping the technology landscape and with my fresh perspective in Biology, I decided to work on applying deep learning to genomics.

In this article, I don’t intend to either use too much of technical jargon or make it look like a review article, so will skip many details. But I will say how I got involved in using deep learning with genomics. Genomics is a challenging application area of deep learning that entails unique challenges compared to others such as vision, speech, and text processing, since we have limited ability ourselves to interpret the genome information but we would expect from deep learning a super human intelligence to explore beyond our knowledge. There is still much in the works and as yet a watershed revolution has not been round the corner in deep genome. In one of the classes, Prof Lander was explaining the Huntington’s disease. Huntington’s disease is a rare neurological disease (five in 100,000). It is an unusual genetic disease. Most diseases are caused by recessive alleles, and people fall ill only if they get two copies of the disease allele, one from each parent. But Huntington’s disease is different, the allele that causes it is dominant and people only have to receive one copy from either parent to contract it. Most genetic diseases cause illness early in life, whereas Huntington sets in around midlife. Prof Lander went on to explain the works of David Botstein and Gusella where they identified the genetic marker linked to Huntington’s disease on chromosome 4 through a series of laborious experiments.  The idea was to use positional cloning and genetic markers (polymorphisms) to locate a gene that you don’t know where to look for. This work was carried out in 1983 when there was no human genome identified.

This introduction was good enough for me to get initiated in genomics. After all, we are looking for the unknown most of the time, and for a change we have a human genome now. So the thought is can we use markers to identify and locate specific genetic condition? Deep learning is good at doing boring tasks with incredible accuracy and bringing insight that may be humanly impossible. With computational speed available at hand, doing searches in blind alleys using deep learning is incredibly powerful and may hitherto lead to insights not intended for in the beginning.
Genomic research targets study of genomes of different species. It studies roles assumed by multiple genetic factors and the way they interact with surrounding environment under different conditions. A study of Homo sapiens involves searching through approximately 3 billion base pairs of DNA, containing protein coding genes, RNA genes, cis-regulatory elements, long range regulatory elements and transposable elements. Where this field intersects deep learning has far reaching impact in medicine, pharmacy, agriculture etc. Deep learning can be very useful in exploring gene expression, including its prediction, in regulatory genomics (i.e. finding promoters and enhancers), splicing, transcription factors and RNA-binding proteins, mutations/ polymorphisms and genetic variants among others. The field is nascent though. The predictive performances in most problems have not reached the expectation for real-world applications; neither the interpretations of these abstract models seem to elucidate insightful knowledge.

As the “neural” part in Artificial Neural Network (ANN) suggests, the ANNs are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer(s) consisting of units that transform the input into something that the output layer can use. Deep learning tools, inspired by real neural networks hence, are those algorithms that use a cascade of multiple layers neurons each serving a specific task. Each successive layer uses the output from the previous layer as input. While at the outset, I did say that there is nothing ‘deep’ about deep learning, technically one can say that just how deep a network is depends on the number of hidden layers deployed. The more the layers, the deeper is the network. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. While neural networks existed since 1940s as perceptrons, they have become a serious tool for use only after 80s due to a technique called backpropagation, which allows networks to adjust their hidden layers of neurons in situations where outcome does match the expected. There are many types of neural networks. The most basic type is the feedforward type, the more popular is recurrent type and then there are convolutional neural networks, Boltzmann machines, Hopfield networks amongst others. Picking the right network depends on the data one has to train it with and the specific application in mind.

Hopefully, some day, we would be able to place all jigsaw pieces of the puzzle together. We would then be able to not only get good results, but have information bottleneck or any other tool explain why it should work anyways. And hopefully, that substantial, deep learning could pave way to provide deeper insights (no pun intended) on just how the brain works.

Sunday, April 24, 2011

Not so foolish anymore..

Chaucer’s celebrated ‘Canterbury tales’ published in 1392 makes reference to April 1st and its association as being the April Fool or All-Fools Day. This is that month of the year and the tradition has shown no respite when friends and relatives pull tricks of jokes and make fool of us on that day. When these tricks reach the level where ones’ tricks makes a fool of many, it becomes even more interesting and one of the leaders in this field has been Google. Did you hear about Google Motion? If not, I give a brief below. They published this article on their blog at 12:01 AM on 1st April and was truly their joke on their social network. Google wrote thus:

“In 1874 the QWERTY keyboard was invented. In 1963, the world was introduced to the mouse. Some 50 years later, we’ve seen the advent of microprocessors, high resolution webcams, and spatial tracking technology. But all the while we’ve continued to use outdated technology to interact with devices. Why?

This is a question that we’ve been thinking about a lot at Google, and we’re excited to introduce our first attempts at next generation human computer interaction: Gmail Motion. Gmail Motion allows you to control Gmail — composing and replying to messages — using your body.

To use Gmail Motion, you’ll need a computer with a built-in webcam. Once you enable Gmail Motion from the Settings page, Gmail will enable your webcam when you sign in and automatically recognize any one of the detected movements via a spatial tracking algorithm. We designed the movements to be easy and intuitive to perform and consulted with top experts in kinestetics and body movement in devising them.

We’ve been testing Gmail Motion with Googlers over the last few months and have been really excited about the feedback we’ve been hearing. We’ve also done some internal tests to measure productivity improvements and found an average 14% increase in email composition speed and12% reduction in average time in inbox. With Gmail Motion, Googlers were able to get more done and get in and out of their inboxes more quickly.

To use Gmail Motion, you’ll need the latest version of Google Chrome or Firefox 3.5+ and a built-in webcam. If it’s not already enabled on your account, sit tight — we’ll be making it available to everyone over the next day or so.”

You might ask what is different this year? Google and other technology companies play this prank every 1st of April and what relevance has this to the title ‘not so foolish’ ? They had after all fooled the world through their in-depth video that swinging a first backhand in the air would allow you to reply to the message, swinging two-fists would do a reply-all, and licking your hand and tapping the knee would send the email.

What is not so foolish? You mean, can this not be done? Well here is some development. Inspired by the Google blog, hackers at the University of Southern California Institute for Creative Technologies wanted to make it a reality! Towards this, a group of developers took Microsoft Kinect sensor and some software they had done for previous projects; and tied them together to create a fully working prototype of Google Motion! This was their response to the Google blog:

“This morning, Google introduced Gmail Motion, allowing users to control Gmail using gestures and body movement. However, for whatever reason, their application doesn’t appear to work. So, we demonstrate our solution — the Software Library Optimizing Obligatory Waving (SLOOW) — and show how it can be used with a Microsoft Kinect sensor to control Gmail using the gestures described by Google.”

While this whole episode was funny (or foolish), what it brings out are the technological advances in sensors and image processing – that what we think is fantasy can become real in no time. So, the bar for being creative has been raised significantly, for it to remain a fantasy for a while, otherwise folks watch out, technology will catch up in no time!

Sunday, April 10, 2011

Days of "Altruism"

Last week India was upbeat with Anna Hazare’s social cause. It can be be classically defined as truly altruistic endeavour. But, was it altruism? What is altruism? In a pure sense, it is the selfless concern for welfare of others. It is a traditional virtue in many cultures. There is no expectation of reward. Is pure altruism possible, though? Social evolution is a discipline that is concerned with social behaviours, i.e. that have fitness consequences for individuals other than the actor, does classify altruism as one of the accepted social behaviours. Social behaviours have been categorized by W D Hamilton in 1960s as follows:

  1. Mutually beneficial - a behavior that increases the direct fitness of both the actor and the recipient
  2. Selfish - a behavior that increases the direct fitness of the actor, but the recipient suffers a loss
  3. Altruistic - a behavior that increases the direct fitness of the recipient, but the actor suffers a loss
  4. Spiteful - a behavior that decreases the direct fitness of both the actor and the recipient

Hamilton proposed the above classification saying that Darwin’s natural selection favoured mutually beneficial or selfish behaviours while kin selection could explain altruism and spite. The closed we come to understanding altruism scientifically is by understanding biological altruism. In evolutionary biology, an organism is said to behave altruistically when its behaviour benefits other organisms, at a cost to itself. The costs and benefits are measured in terms of reproductive fitness, or expected number of offspring. So by behaving altruistically, an organism reduces the number of offspring it is likely to produce itself, but boosts the number that other organisms are likely to produce. This biological notion of altruism is not identical to the everyday concept. In everyday parlance, an action would only be called ‘altruistic’ if it was done with the conscious intention of helping another. But in the biological sense there is no such requirement. Indeed, some of the most interesting examples of biological altruism are found among creatures that are (presumably) not capable of conscious thought at all, e.g. insects. For the biologist, it is the consequences of an action for reproductive fitness that determine whether the action counts as altruistic, not the intentions, if any, with which the action is performed.

For decades, selflessness - as exhibited in eusocial (true social) insect colonies where workers sacrifice themselves for the greater good – has been explained in terms of genetic relatedness. Called kin selection, it was a neat solution to the conundrum of selflessness. The dominant evolutionary theory and its influence on human altruism are now under attack.

On the face of it, self-serving humans are nothing like paper wasps, which along with their relatives, ants, bees and termites, are defined as eusocial, creatures that display the highest levels of social organization. Famed Harvard biologist and author Edward O. Wilson, who gave eusociality its first clear meaning, refers to such behaviour as “civilization by instinct”.

The evolutionary theories , in particular kin selection, go a long way towards reconciling the existence of altruism in nature with Darwinian principles. However, some people have felt these theories in a way devalue altruism, and that the behaviours they explain are not ‘really’ altruistic. The grounds for this view are easy to see. Ordinarily we think of altruistic actions as disinterested, done with the interests of the recipient, rather than our own interests, in mind. But kin selection theory explains altruistic behaviour as a clever strategy devised by selfish genes as a way of increasing their representation in the gene-pool, at the expense of other genes. Surely this means that the behaviours in question are only ‘apparently’ altruistic, for they are ultimately the result of genic self-interest? Reciprocal altruism theory also seems to ‘take the altruism out of altruism’. Behaving nicely to someone in order to procure return benefits from them in the future seems in a way the antithesis of ‘real’ altruism — it is just delayed self-interest.

To some extent, the idea that kin-directed altruism is not ‘real’ altruism has been fostered by the use of the ‘selfish gene’ terminology of Dawkins (1976). As we have seen, the gene's-eye perspective is heuristically useful for understanding the evolution of altruistic behaviours, especially those that evolve by kin selection. But talking about ‘selfish’ genes trying to increase their representation in the gene-pool is of course just a metaphor (as Dawkins fully admits); there is no literal sense in which genes ‘try’ to do anything. Any evolutionary explanation of how a phenotypic trait evolves must ultimately show that the trait leads to an increase in frequency of the genes that code for it (presuming the trait is transmitted genetically.) Therefore, a ‘selfish gene’ story can by definition be told about any trait, including a behavioural trait, that evolves by Darwinian natural selection. To say that kin selection interprets altruistic behaviour as a strategy designed by ‘selfish’ genes to aid their propagation is not wrong; but it is just another way of saying that a Darwinian explanation for the evolution of altruism has been found. As Sober and Wilson (1998) note, if one insists on saying that behaviours which evolve by kin selection / donor-recipient correlation are ‘really selfish’, one ends up reserving the word ‘altruistic’ for behaviours which cannot evolve by natural selection at all.

For the past four decades kin selection theory has been the major theoretical attempt to explain the evolution of eusociality,” writes Wilson and Harvard theoretical biologists Martin Nowak and Corina Tarnita in an Aug. 25 Nature 2010 paper. “Here we show the limitations of its approach.”

According to the standard metric of reproductive fitness, insects that altruistically contribute to their community’s welfare but don’t themselves reproduce score a zero. They shouldn’t exist, except as aberrations — but they’re common, and their colonies are fabulously successful. Just 2 percent of insects are eusocial, but they account for two-thirds of all insect biomass.

Kin selection made sense of this by targeting evolution at shared genes, and portraying individuals and groups as mere vessels for those genes. Before long, kin selection was a cornerstone of evolutionary biology. It was invoked to help explain social and cooperative behavior across the animal kingdom, even in humans.

But according to Wilson, Nowak and Tarnita, the great limitation of kin selection is that it simply doesn’t fit the data. Wilson et al claim that looking at a worker ant and asking why it is altruistic is the wrong level of analysis. The important unit is the colony.

Their new theory of eusocialty may be useful in understanding, for example, how single-celled organisms gave rise to multi-celled organisms. Human selflessness and cooperation, involves interation of culture and sentience, not just genes and genetics. As claimed in the paper, ‘there are certain things we can learn from ants. Its easier to think about ants, but people are complicated’.

I am not proposing any scientific evidence for human altruism, indeed if it exists. Most definitely not for the last week’s event that drew me to read more about it. Was it pure altruism, or apparent altruism? Or kin selection? Or plain selfishness?

Sunday, March 27, 2011

Crowd behaviour

Last week, one of my friends pointed out to me that my many recent blog articles have been only on the energy efficiency topic and are not as unpredictable and/or interesting as the earlier ones. So this time am making a conscious attempt at not writing about energy. There is lot of construction activity in Bangalore and the place where I live in. The construction is not just restricted to residences but increased number of residences puts pressure on municipal corporation to provide more water and handle sewage. The road outside where we live, has been dug to lay in water and all kinds of pipes. That means our only road that gets us out of layout is closed and as is common place – a new temporary road has been found out. That road can not handle the pressures of the traffic. I was thinking about this scenario and it gave me an idea of today’s topic. Is there a deterministic (non-random) way of assessing the crowd traffic and its impact on better understanding of crowd behaviour, improved design of the built infrastructure? Crowd is being used in a generic sense and although it is about a group of people, here it is being used in a more generic sense as you would find anywhere in India – in that it is a collection of group of people, herd of cows and goats, a group of auto rickshaws, a grop of water tankers in summer and in general a group of vehicles that move in all possible directions even though the road may be straight ! If you leave in time, what is the probability in a scientific way of reaching your destination in a fixed time? Does crowd monitoring help? Let us explore.

Although crowds are made up of independent individuals or entities (remember not to leave aside the cows and buffalos and even vehicles that are driven by individuals) , each with their own objectives, intelligence and behaviour patterns, the behaviour of crowds is widely understood to have collective characterisitics which can be described in general terms. Since the Roman times, the mob rule or mob mentality is an implication of a crowd that is something other than the sum of its individual parts and that it possesses behaviour patterns which differ from the behaviour expected individually from its participants. If there is any scientific basis for the study of crowd behaviour, it must belong to the realm of social sciences and psychology, and that the mere mortals of physical sciences and engineering have limited or no business in getting involved with such studies. But I came across an article a few years ago that was interesting. It said understanding of field theory and flow dynamics is good enough to get started on getting a solution to crowd monitoring and may offer solutions that are technology based and control the crowd behaviour using developments in image processing and image understanding.

The article I mentioned above was one of IEE publications. Do not recall which one. But the thought process left an impression. It said our knowledge of study of gases can provide us insgihts into the study of understanding crowd behaviour. After all, a gas is made up of individual molecules, moving about more-or-less independently of each other, with differing velocities and directions. The ideal-gas theory provides a reasonably accurate basis of predicting the properties and behaviour of gases over a wide range of conditions, without considering behaviour of individual molecules. This was a major breakthrough and something not possible to conceive if the notion had prevailed that equations of motion for each individual molecule had to be solved in order to predict overall behaviour of a gas in any particular direction. What it also proved was an observation in mob rule, that the overall behaviour is something other than the sum total of its parts.

Now where does this similarity end? Surely the molecules of gas are different from cows and buffaloes and individuals and vehicles. They are far more complex and have a mind of their own. The theory of gases does not attribute intelligence to molecules. The possessed crowd that moves in a particular direction in a mindless pursuit is akin to the behaviour of charged particles under the influence of electric field. When you have a temporary road that is bi-directional, you not only have a crowd moving in one direction but in both and capable of inducing collisions, like particles of opposite charges.

I have known many techniques in recent years in image processing that use those well-established techniques for monitoring and collection of data on crowd behaviour. A key factor in the solutions is the use of techniques where inferences can be drawn by rising above individual pixels or objects – a notion akin to rising above molecules and individuals that make up the spaces.

Whether all of this can lead me to predict fixed time of arrival at destination is anybody’s guess. But it does provide insights into crowd behaviour and probably an interesting application of science that can make your journey to the destination enjoyable.

Sunday, March 6, 2011

The Green Rebound

What is a rebound effect? In traditional sense, it is used in medicine to describe an effect where it shows the tendency of medication, when discontinued, causes a return of symptoms being treated to be more pronounced than before. So what has ‘green’ got to do with the rebound effect? Well, couple of weeks ago, there was an article in Nature News that has rekindled interest in this topic; which has been a point of discussion for many days now, I must confess. The green rebound, as I call it, is the rebound effect as applied to energy conservation. I have been emphasizing through many articles before on the need to be energy-prudent, to be energy conscious and hence do things which conserve energy. But just what happens when you save?

The green rebound, which is application of rebound effect to energy conservation, is a term that describes the effect that the lower costs of energy services, due to increased energy efficiency, has on consumer behavior. It generally indicates either an increase in number of hours of energy use, or increase in quality of energy use thereby creating a situation where you end up using more than you save and hence portraying a kind of a paradox.

For instance, if a 18W compact fluorescent bulb replaces a 75W incandescent bulb, the energy saving should be 76%. However, it seldom is. Consumers, realizing that the lighting now costs less per hour to run, are often less concerned with switching it off; in fact, they may intentionally leave it on all night. Thus, they ‘take back’ some of the energy savings in the form of higher levels of energy service (more hours of light). This is particularly the case where the past level of energy services, such as heating or cooling, was considered inadequate.

What is not debated is whether the effect exists. You may be surprised to know it does. What is being debated is the extent of this rebound? Like all other economic models, this one too is tending to overstate the reality.

1. The actual resource savings are higher than expected – the rebound effect is negative. This is unusual, and can only occur in certain specific situations (e.g. if the government mandates the use of more resource efficient technologies that are also more costly to use).

2. The actual savings are less than expected savings – the rebound effect is between 0% and 100%. This is sometimes known as 'take-back', and is the most common result of empirical studies on individual markets.

3. The actual resource savings are negative – the rebound effect is higher than 100%. This situation is commonly known as the Jevons paradox, and is sometimes referred to as 'back-fire'.

The rebound effect is a phenomenon based on economic theory and long-term historical studies, but as with all economic observations its magnitude is a matter of considerable dispute. Its significance for energy olicy has increased over the last two decades, with the claim by energy analysts in the 1970s, and later by environmentalists in the late 1980s, that increasing energy efficiency would lead to reduced national energy consumption, and hence lower green gas emissions. Whether this claim is feasible depends crucially on the extent of the rebound effect: if it is small (less than 100%) then energy efficiency improvements will lead to lower energy consumption, if it is large (greater than 100%) then energy consumption will be higher. Note the use of the relative terms ‘lower’ and ‘higher’: what exactly they are relative to has often been left unstated and has been a cause of much confusion in energy policy debates. Sometimes it refers to current energy consumption, at other times to a reduction in the future rate of growth in energy onsumption.

The claim that increasing energy efficiency would lead to reduced national energy consumption was first challenged by Len Brookes in 1979, in his review of Leach's pioneering work, A Low Energy Strategy for the UK, when he criticized Leach's approach to estimating national energy savings because of its failure to consider macroeconomic factors. This was followed in the early 1980s by similar criticism by Daniel Khazzoom of the work of Amory Lovins. The criticism of Brookes and Khazzoom was given the name of the Khazzoom-Brookes (KB) postulate by the economist Harry Saunders in 1992. The KB postulate may be described as: those energy efficiency improvements that, on the broadest considerations, are economically justified at the microlevel lead to higher levels of energy consumption at the macrolevel than in the absence of such improvements.

This work provided a theoretical grounding for empirical studies and played an important role in framing the problem of the rebound effect. It also reinforced an emerging ideological divide between energy economists on the extent of the yet to be named effect. The two tightly held positions are:

1. Technological improvements in energy efficiency enable economic growth that was otherwise impossible without the improvement; as such, energy efficiency improvements will usually back-fire in the long term.

2. Technological improvements in energy efficiency may result in a small take-back. However, even in the long term, energy efficiency improvements usually result in large overall energy savings.

Even though many studies have been undertaken in this area, neither position has yet claimed a consensus view in the academic literature. Recent studies have demonstrated that direct rebound effects are significant (about 30% for energy), but that there is not enough information about indirect effects to know whether or how often back-fire occurs. Economists tend to the first position, but most governments, businesses, and environmental groups adhere to the second.

The Nature news mentions a report from the Breakthrough Institute, an advocacy group based in Oakland, California, that is pushing for huge government investment in clean-energy technologies, suggests that various types of rebound effect could negate much, if not all, of the emissions reductions gained through efficiency. Energy efficiency should still be pursued vigorously as a way to bolster the economy and free up money for investments in low-carbon technologies, the institute says, but rosy assumptions about emissions reductions should be treated with caution.

Should there be an alarm due to such reports that you may across? Well no. Every coin has two sides and if anyone assumes that this report makes a non-case of energy efficiency, that is far-fetched. It only means that as we start conserving, we need to be more careful in terms of usage and hence I believe monitoring of your energy resources not just once in a while, but on a continuous basis will ensure the rebound does not take place. So monitoring is like that medicine, which once withdrawn, can have rebound effect.

Sunday, February 20, 2011

Internet of Things

I, many times wonder, just where the management schools and management gurus were before the watershed year 1991. I will most likely research that topic some day and write about it too. But for now let us see what was special about 1991. I call 1991 a watershed year because it was called the ‘Year of the Internet’ – the year when the TCP/IP protocol suite made its way out of ARPANET and MIT/UCLA and started reaching out to the masses at large. This is my conjecture that the great management thought processes and the schools of thought that continuously generate and/or evolve alternative revenue streams (of which we have excess of these days), also germinated in that year.

In a way, I agree with Malcolm Gladwell’s thought process in Outliers – a classy book published couple of years ago – in which he argues that the main secret of success is the advantage (or just luck) of being born at the right time. He says that the many successful men today just were born between 1953 and 1956 and hence were of a right age by the year 1975 to take advantage of the personal computer revolution. He cites many examples including the greats such as Paul Allen (1953), Bill Joy (1954), Scott McNealy (1954), Steve Jobs (1955), Eric Schmidt (1955), Bill Gates (1955), and Steve Ballmer (1956). Be that as it may, I believe in this theory because I was mid-way in my life around the year 1991 and have seen both the worlds – the Internet-free and Internet-infested worlds and have honestly enjoyed both. But the fact is if I was not born at the right time to experiment with Internet at the University, then I would have missed out on a great learning concept.

Coming back to the “Year of the Internet” and birth of management catch-phrases (which were introduced by you-know-who), the juggernaut has rolled along. 1994, like 1991, changed the face of the world being tagged the ‘Year of the Web’ when the then clumsy looking HTTP protocol made its appearance on the world-stage for the first time outside of CERN premises. Since 1994, each year has been tagged year of something or the other. The trivialization, howsoever metaphorical, has led us to 2011 where the year is actually tagged as the ‘Year of Internet of Things’. We have passed through eras of advertising, searching, mobile commerce, gaming where each has been reduced to a commodity thus waiting for a new innovation each time. Just what is ‘Internet of Things’ and why it is interesting is what I will explain. Like a true neutral observer, I will detail in next couple of paragraphs, the benefits it will bring and likewise the challenges it will bring in. I will never forecast the future as since 1991, each and every forecast has faded away in comparison to reality.

Technically speaking, ‘Internet of Things’ describes a world-scenario where trillions of devices will interconnect and communicate. It will integrate ‘things’ such as the ubiquitous communication layer, pervasive computing including cloud computing and ambient intelligence (wondering what it is?). Internet of Things is a vision where ‘things’ such as ‘every day objects’ such as all home appliances are readable, recognizable, addressable, locatable and controllable via the Internet.

If Internet revolution connected billions of people world-wide through computers and mobile phones, Internet of Things would connect trillions of devices billions of people use. Imagine if all the objects in the world had all the information that they needed to function optimally. Buildings would adjust themselves according to the temperature. Ovens would cook things for exactly the right time. The handles of umbrellas would glow when it was about to rain. We long ago inserted "intelligence" into objects in the form of thermostats and the like; the internet of Things will extend this principle exponentially, giving us unprecedented control over the objects that surround us.

Energy monitoring, infrastructure security and transport safety mechanisms are just some of the envisioned applications that will have tremendous boost due to the Internet of Things. It is being enabled because of technology revolution that includes miniaturization of devices, emergence of IPv6 to resolve finite address space issues, mobile phones as data capturing devices and availability of low-power energy neutral devices.

The vision is great but the challenges are plenty. It is just a vision and its roadmap has many hurdles. Its primary acceptance would depend upon the progress of machine-to-machine interfaces and protocols of electronic communication, sensors, RFID, actuators etc.

As I see it today, the challenges would extend to robustness, responsiveness, privacy among other things, which have no clear cut answers today. Why should you know how much my oven takes to bake a cake? But what is the problem if your oven can learn from mine if I baked one a few minutes ago and use that learning to do a perfect bake for you?

As an article on the topic in ‘The Economist’ summarized a month or two ago, it may just turn out to be the ‘Year of Internet of Hype’.