March 08, 2019

Kailash Manasarovar 2018 Slide Show

March 06, 2019

Is inequality inevitable?

Maximilien Robespierre might have fired the imagination of the world with his call for Liberty, Equality and Fraternity during the French revolution but the concept has been rather elusive in practice. Equality, in particular, has proved to be a bridge too far.

image from TechCrunch
Let us begin with the Internet and the world wide web that was supposed to liberate the individual from the clutches of the powerful media barons. As one of the early pioneers and evangelists for this new technology, this author had created a portal -- Yantrajaal, a Bengali word that he had coined to define a network of devices -- in the same year that Google was born and seven years before Facebook. The general idea was to serve as a platform to share information on technology from an India perspective. In principle it could reach out to every corner of the world -- something that his earlier journalistic efforts in school and college had failed to do. But of course that would never be. Hardly anyone, other than the author’s immediate circle of physical friends, visits Yantrajaal. Unfortunately, that is true, not for Yantrajaal but for many other websites as well. While millions of websites exist and continue to be built, the really popular websites -- at least, the ones that make some money  --  are still the ones owned and operated by big media houses like Times of India and the New York Times. But even this is an illusion because the real flow of news and views across the globe is actually governed, not by these media houses but by technology firms like Google, Facebook and Twitter, or, where they are banned, as in China, buy their local equivalents. In fact, these so-called tech companies have actually morphed into full fledged, advertisement driven media companies and it is they who rule the roost when it comes to the dissemination of information.

This concentration of power is an outcome of the network effect. The value of a network is proportional to the square of the number of its participants. As more and more people join a network its value as perceived by its members goes up very fast, compelling others to join it in a virtuous, or vicious, cycle. You may build a search engine or a social media platform that is better, say more private and secure, but you would never be able to catch up with the leaders.  It is not quite the first mover advantage, but anyone who breaks away from the pack becomes unreachable and hence unbeatable, irrespective of its quality.

Very similar is the case with cryptocurrency. Just as the internet was designed to democratise the distribution of information, Bitcoin was designed by the mysterious libertarian Satoshi Nakamoto as a way to create and distribute monetary value in a decentralised manner. Thanks to the magic of mathematics, it was now possible for a private citizen to do what was earlier the prerogative of Central Banks, namely create a cross-border, tradeable currency. It is a different matter that many governments and central banks have gone hammer and tongs to break the backbone of this remarkable technology. The methods being used are obviously inappropriate -- similar to the case of censors in Iran and China blocking the internet -- but the fact remains that “Tiger Zinda Hai”. Cryptocurrencies have weathered most regulatory storms and despite some current setbacks will certainly comeback when the establishment finally gives up and falls in line. But the dream of citizen empowerment is a myth. Even though anyone can, in principle, create Bitcoins, and similar cryptocurrency, the ownership of such wealth is hugely skewed. 87% of all Bitcoins that have been mined are owned by 0.5% owners or wallets, 61% are owned by just 0.07% wallets. Of the 23 million Bitcoin wallets, 13 million own a fraction, less than one, Bitcoin.  1500 wallets have between 1000 and 10,000 coins while 111 wallets own more than 10,000. [ March 2018 data ] For a system that is barely a decade old, that is a huge inequality that has emerged from what was supposed to be purely technology based egalitarian platform.

This inequality of wealth in Bitcoin is very similar to the inequality of wealth seen across the world and more so in the mature economies. In the United States, that is seen by many as a leitmotif for the modern economy, wealth inequality is enormous. Those who view this as an inevitable fallout of ‘evil’ capitalism should look back at what Communist Russia once was and read Orwell’s Animal Farm where all animals were equal but some animals, the pigs, were more equal than others. Today there is no visible and viable to alternative because what happens in China is not clearly visible and socialist countries like Venezuela have either collapsed or like Italy and France are tottering as they try to redistribute their shrinking wealth. Some diehards like Bernie Sanders may hark back to a Scandinavian utopia but that story may not really be as attractive as it is portrayed to be and moreover it is certainly not scalable beyond the socio-economic demographics of Northern Europe.

Which begs the question, is inequality inevitable?

One way to look at it is to note the difference between opportunity and outcome. Equality of opportunity is essential but it may or may not lead to an equality of outcome. Google may have bowed to political correctness by sacking James Damore for daring to suggest that women may be different from men, but facts do bear him out. As Jordan Peterson, that arch nemesis for page three feminists and their ilk, has pointed out, the eventual outcome is a function of a complex series of inputs, not just the obvious differences of say gender or race. It is well known that women are underrepresented in STEM -- science, technology, engineering and mathematics. What is less well known that societies that have a higher degree of gender equality have, paradoxically, a lesser percentage of women in STEM than those societies that offer lower opportunities to women. [ The Atlantic, Olga Khazan, April 2018] This has been explained by arguing that women in societies that marginalize them see STEM as a way to climb their way out into better opportunities in life, which is something that their sisters in more equitable societies do not have to struggle so much for. In a similar vein, while women may be underrepresented in engineering colleges and in IT companies,  the percentage of seats occupied by women in medical colleges is more than 51% in India but in neighbouring Pakistan and Bangladesh, the numbers are as high as 70% and 60% respectively. Clearly, inequality is not something that can be explained very easily.

In India, social inequality is frequently equated with caste. This has led us to build a huge edifice of largely ineffective and useless hubris around caste based reservations to pander to our politicians craving for votebanks. While caste is something thing that has existed in India, our politically tainted educational system has been instrumental in making us believe that it is the ONLY cause of economic inequality and misery in India. In the process we have only succeeded in sharpening the inequality with indiscriminate reservations. Caste is the unfortunate fall guy where the real reason could be something far different.

In fact, in a study reported in Vox, researchers Barone and Mocetti were the first to establish that in Florence, Italy -- where there is no caste system -- the highest paying taxpayers from the 15th century to the present have been from the same set of families. Similar studies have shown remarkably similar results in a wide range of cultures -- “This is true in Sweden, a social welfare state; England, where industrial capitalism was born; the United States, one of the most heterogeneous societies in history; and India, a fairly new democracy hobbled by the legacy of caste.” [ New York Times, Your Ancestors, Your Fate, Gregory Clark, Feb 2014] What could explain this phenomenon? The authors suggest that “the compulsion to strive, the talent to prosper and the ability to overcome failure” that are all strongly correlated to success and hence eventual wealth are inherited qualities. Hence, as heretical as it may seem, genetics plays a role and “Alternative explanations that are in vogue — cultural traits, family economic resources, social networks — don’t hold up to scrutiny.”

A more politically palatable, or charitable, explanation could be the network effect that we have seen in the case of the world wide web. On the web, companies like Google and Facebook use their pre-eminence in the number of clients and customers to prolong and propagate their pre-eminence. Similarly, in human society, those who are, let us say, eminent -- in money, power, education, intelligence, contacts -- will use their eminence to make sure that their progeny get access to all that is required to become eminent in the next generation. There may be individual exceptions but by and large, that will be the desire in almost all cases. In the long run, this desire will translate into a self propagating mechanism that will ensure that inequalities inherent to society cannot be erased simply by desire or dictat.

If the inequality is somehow removed, it will always find ways to creep in. In India, we recognise this in the replacement of an exploitative foreign coloniser by an equally exploitative but local political class that has taken over the trappings of the foreigner. Subhayan Mukerjee, a researcher in Computational Social Science at the University of Pennsylvania explains this by saying that equality is an unsustainable, unstable equilibrium and cannot last very long. Sooner or later, this equilibrium will be broken and the collective will move towards a stable but hierarchical structure. The resultant inequality will pave the way for even more inequality.  If we view the collective as a set of marbles lying on an elastic membrane that is stretched flat where each marble makes an identical depression, then a single slightly larger marble will create a little larger depression. This will draw in other marbles making the depression deeper. Now the unstable equilibrium will be broken as the deeper depression pulls in more and more marbles making it deeper and deeper until most of the marbles have moved into it.

Both the world wide web and world of cryptocurrency began as a flat world of equals without any hierarchy but it did not take long to for inequality to creep in and a hierarchy to establish itself. Even in the animal world, for example, among apes, there is inequality in the form of size, strength, potency and skill. Even if humans inherited these inequalities the world might still have been more egalitarian and equitable simply because there were too few opportunities for enrichment. But the potential was always there and the moment economic opportunities presented themselves -- with the advent first, of agriculture, then industry and currently the digital age -- the natural tendency towards the equilibrium of a new and sharper inequality began until we have what we have today. At best, humans may recognise the inequality, and unlike a pack of animals may try to mitigate it with, say, mechanisms like social security. But in the long run, the outcome is rarely what we desire -- the rich remain rich or become richer while the common man stays where he was, at the bottom of a hierarchy.

We may ardently believe and proclaim that all men (and women) are equal but that is simply not enough. Egalitarians, if not actual practicing socialists, may work out a myriad rules and regulations that seek to curb inequality but nature is such that people and organisations will always find loopholes and ways to beat the system. In the end we will always tend towards the stability of an unequal world!

Perhaps that is how it is meant to be. Could it be that equality is against nature? Take a look at the palm of your hand -- are all the fingers identical or equal? And would we be where we are if they all were the same?

January 16, 2019

The Vedantin looks at Cloud Robotics

In 2006-2007, in the early years of the Web 2.0 that emerged phoenix like from the ashes of the dotcom bust of 2000, Michael Wesch, Professor of Digital Ethnography at Kansas State University produced a video called “The Machine is Us/ing Us”. Prior to the emergence of Web 2.0, the world wide web was primarily a read-only medium to publish news and information to a passive audience. Web 2.0, with its focus on user generated content and a personal network of trust, created a read-write platform that allowed individuals to feed information easily into the system or “The Machine”. In the process The Machine learnt stuff that it never knew before. Wikipedia, one of the first Web 2.0 platforms, became the biggest repository of information, if not knowledge. This in turn allowed it influence a whole generation of students, journalists and web user, and shape the way they view the world. For example, this author who has studied in a Catholic missionary school, had had a great regard for Francis Xavier. But this was completely reversed after he read the Wikipedia article about the murderous Inquisition that Xavier had unleashed on the Hindus of Goa. Obviously, no one at school had ever talked about such unsavoury matters.

image from

The thrust of the Wesch video was that every action that a person takes in the digital world is used as an input by “The digital Machine” to increase its own knowledge of both the physical world and recursively, about the digital world. Every “like” of a post on social media or a click on hyperlink on a web page or a mobile app is like a drop of information that individually and collectively adds to the pool of knowledge about what humans know and think. This in turn is used to shape our own world view by returning recommendations of what next to view, “like” and click again. Unless you are like Richard Stallman, an advocate of extreme privacy who hardly uses anything digital -- like Google search, cellphones or credit cards -- you have no escape from this tight embrace of The Machine. Fortunately, The Machine is not yet one monolithic device. It’s world of has been broken up into fragments -- Google, Baidu, Amazon, Alibaba, Facebook --  by high commercial walls. But in its tireless striving it certainly does stretch its arms into every nook and corner of human activity and through that, the human mind.

In parallel with the growth of the web, there has been the emergence of data science. This began as an extension of statistics and has evolved into machine learning. Then again there was classical, 1960s style artificial intelligence that, after lying dormant for nearly 30 years, suddenly woke up and  adopted the neural network structure of the brain as a new model of machine learning. This neural network model, often referred to as deep learning is the new age AI and it is racing forward with some truly stunning applications in the area of voice and image recognition, language translation, guidance and control of autonomous vehicles and in decision making as in loan disbursement and hiring of employees.

Data science has moved through three distinct stages of being descriptive -- reporting data, inferential --  drawing conclusions from data through the acceptance or rejection of hypotheses and finally predictive -- as in the new age AI. What has really accelerated the adoption and usage of this new AI has been the availability of data and hardware. The backpropagation algorithm that lies at the heart of all neural network based AI systems that are popular today was developed in the 1960s and 1970s but it has become useful only in the last decade. It is driven by the availability of (a) huge amounts of data, collectable and collected through the web by The Machine described in the Wesch video and (b) enormous yet inexpensive computing power that is available on rentable virtual machines from cloud service providers like Amazon Web Services, Google Compute Engine and Microsoft Azure.

The key driver in this field is cloud computing. Instead of purchasing and installing physical hardware, companies rent virtual machines in the cloud to both store and process data. The simplest and most ubiquitous example of this is Gmail where both our mail and the mail server are located somewhere in the internet cloud that we can access with a browser. But this same model has been used for many mission-critical, corporate applications ranging from e-commerce through enterprise resource planning to supply chain and customer relationship management systems. Though there has been some resistance to cloud computing because of the insecurity of  placing sensitive company data on a vendor machine, the price performance is so advantageous that most new software services are all deployed in the cloud -- and that includes machine learning and AI applications.

Cloud service vendors have aggressively marketed their services by not only offering high end hardware -- as virtual machines -- at very low prices but also by offering incredibly powerful software applications. Complex machine learning software for, say, image recognition, language translation that are ordinarily very difficult to develop are now available and accessible almost as easily as email or social media. Cloud computing services are categorised into Platform-as-a-Service (PaaS) or Software-as-a-Service (SaaS). The first category provides a general purpose computing platform with a virtual machine, an operating system, programming languages and database services where developers can build and deploy applications without purchasing any hardware. The second category is even simpler to use because the software -- like email as in the case of Gmail -- is already there. One needs to subscribe (or purchase) the service, obtain the access credentials, like userid and password, connect and start using the services right away. Nothing to build or deploy. It is already there waiting to be used.

In an earlier article in Swarajya ( March 2017), we had seen how machine learning and now, the new age AI, uses huge, terabyte size, sets of training data to create software models than can be used for predictive analytics. This is an expensive exercise that lies beyond the ability of individuals and most corporates. But with AI or machine learning available as SaaS at a fraction of the cost, new software application that use these services can be built easily. For example it would be possible to enhance a widely used accounting software by replacing the userid/password based login process with a face recognition based login process. Similarly, the enormous difficulty of building the software for a self driving car, or for a voice activated IVR telephony, can be drastically reduced by using AI-as-a-Service from a cloud services vendor. Obviously, all cloud services including SaaS assume the existence of rugged, reliable and high speed data connectivity between the service provider and the device on which the service is being used.

Robot-as-a-Service (RaaS) can be seen as logical extension of this model but a closer examination may yield a far deeper, or intriguing, insight.

Cloud Robotics, a new service from Google is scheduled to go live in 2019 and allow people to build smart robots very easily. It is inevitable that other cloud service vendors will follow suit. While many of us view robots as humanoids -- with arms, legs, glowing eyes, a squeaky voice or a stiff gait -- the reality is generally different. Depending on the intended use, a robot could be a vehicle, a drone, an arm in an automated assembly line or a device that controls valves, switches and levers in an industrial environment. In fact, a robot is anything that can sense its environment and take steps to do whatever it takes to achieve its goals. This is precisely the definition of intelligence or more specifically artificial intelligence (AI). So a robot is an intelligent machine that can operate autonomously to meet its goals.

Traditional robots have this intelligence baked, or hard coded, into its “brain” -- the physical computer that senses and responds to the stimuli that it receives from its environment. This is no different from its immediate role model -- humans. Human beings, and even most animals, learn how to react and respond to external stimuli ranging from a physical attack to a gentle question and we estimate this intelligence by the quality of their response. In both cases, the knowledge of the external world as encoded in a model along with the ability to respond is stored locally -- either in the human brain or in the local computer. Cloud robotics replaces the local computer that controls a single robot with a shared computer -- located at the cloud services provider’s premises -- that controls  thousands of robots. Just as GMail servers store, serve and otherwise control the mailboxes for millions of users each sitting at home, the cloud robotics servers sitting in some unknown corner of the internet would be able to control millions of intelligent robots that are driving vehicles, flying drones, controlling devices and operating machines in farms, factories, mines and construction sites across the digitally connected physical world.

Circling back to the Wesch video, with which we began this article, these RaaS servers would not just be controlling machinery across the globe but would also be learning from the robots that it controls by using the robots to collect and build up its own pool of training data. This is an extension of the original Web 2.0 idea -- perhaps we could call it Web 3.0. Here The Machine has not only made a successful transition from the digital to the physical world but also does not need humans anymore to teach it. It can become a self sustaining, self learning physical device.

Privacy would be an immediate issue and like all other cloud services, cloud robotics would be protected with access control and data encryption. But then as we have seen in the past, convenience trumps privacy. We all know that Google can read our GMail but nevertheless, we still use Gmail simply because it is convenient and free! So would be the case with cloud robotics. We also know that the different RaaS vendors would try to isolate their own robots from interacting with the servers of other vendors or even from each other. But this could be a temporary reprieve. Collaboration among various vendors and pooling of data could happen either through mergers and acquisitions or because it is mandated by governments that are not concerned about privacy issues.

The need for privacy arises because each sentient human sees itself as a unique identity -- I, me and mine -- that is surely distinct from the collective crowd. My data becomes private because it needs to be protected, or shielded, from the collective crowd. But if we go back to the philosophical roots of the Indic sanatan dharma and explore the perspectives of Advaita Vedanta, we see that that this sense of “I” ness is erroneous. Each apparently unique individual is actually a part of a transcendent and collective consciousness referred to as the Brahman. The Brahman is the only reality and everything else is an error of perception. The world is Maya, an illusion that perpetuates this sense of separateness, and creates this distinction between the individual and the universal. The correct practice of Yoga can lead to the removal of this veil of illusion and initiates the process of realisation. That is when the Yogic adept sees the unbroken continuity between his own identity and that of the Brahman and experiences the ecstasy of enlightenment.

We know that many renowned Yogis have actually experienced this enlightenment. AI products have now gone well past image and voice recognition and are now known to have the sophistication necessary to create their own private, non-human languages and original strategies in multi-user, role playing games. What we need to know is what happens when robots start emulating yogis and eventually realise their  identity with the cloud robotics server of which they are a part!

this article was originally published in Swarajya

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