Category: Blog

Around election time, fake news was the new weather — a nice, neutral conversation starter. Having started, we will blah on to blame social media for all our ills (hypochondria included).

Humans have always been too eager to condemn; scapegoat identification should ideally be at the Games. As soon as stories of the American elections hit the stands — with Russia and then Cambridge Analytics playing substantial character roles — we found the culprit, social media. But this is unfair. Let’s look at social media in the news context.

Before social media, news had four roles. To inform people about events happening around them, to educate about the relevance of these events, to provide Governments a platform to reach citizens and of course, to share different perspectives. (And do all objectively.)

And then social media came along and boom! We moved from monologue to discussion. We had a platform for open discussions in the public sphere that could facilitate public opinion. What a marvellous opportunity that was (and I still hold, is).

So what did we do? We converted this platform for open discussion into a polarised, hate-filled, abusive contest about who are right and who wrong. And this definitely is influencing our elections. But not, I would think, in the way we claim it is.

I think we are too far gone to let what we see on social media change our opinion. So, is fake news changing who you would vote for? I think not.

But here’s what fake news really does.

Social media is algorithmically engineered to personalise content for you – which means the machine gets smarter and smarter at showing you more and more of what you like (and are likely to like).

Don’t be surprised to find your opinion repeated wherever you go on social media; your TL, is just a mirror. The more we find our opinions repeated, the more convinced we become that we are right – this is exactly how an echo chamber works.

Now into this charged scenario, along comes fake news. If it confirms what I believe in, it becomes gospel truth to prove my already polarised beliefs. If it does not, I immediately dismiss it as fake news.

For in the process, we have also managed to redefine fake news. Fake news is no more an untruth. Fake news is now something you find unpalatable and unhelpful to your cause. What agrees with you, is never fake news. It is at worst, as Kellyanne Conway put it, an ‘alternative fact.’

Therefore, in my mind, fake news is not changing my views but is confirming my already polarised beliefs. A political party might not be able to employ fake news to get new voters, but it most definitely can make old voters even more committed.

Recently, Facebook announced substantial grants to 60 researchers from premier institutions, to study the impact of social media on elections worldwide. For this, it also provided access to privacy protected data. I don’t know whether I should laugh or cry at this lame attempt. This is like Stalin announcing a 3-minute coffee break in the Gulag.

Sometime toward the end of 2013, the FBI shut down Silk Road, an enterprise run by the very interestingly named Dread Pirate Roberts. It was in this context that a majority heard about a currency called bitcoin. And though it was a beautiful invention, it got bad press.

The Dread Pirate’s efforts to create a marketplace free from authority resulted in exactly the opposite. What Silk Road managed to do was give agencies like the FBI unrestrained rights to snoop under the guise of preventing cybercrime.

Journalist Nathaniel Popper wrote in his book, Digital Gold, that “the unmistakable irony of these wild days was that a technology that had been designed, in no small part, to circumvent government power was now becoming largely driven by and dependent on the attitudes of government officials.”

So how did an internet drug peddler lead us to these discussions on ideal money and freedom?

The idea of money

Many believe that the idea of compensation is an invention as significant as the wheel. Alongside language, it enabled us to solve issues of cooperation that other animals could not.

Money carved out for itself four primary functions: as medium, measure, standard, and store.

For it to perform all these functions, it needed value. So, man came up with coins, where the metal itself was the value. In 1717, Sir Isaac Newton, the then master of the royal mint, put Britain on the gold standard. The gold standard continued in various forms till 1971 when President Nixon nixed it. Since then, the US has been operating on a system of fiat money.

What used to be good, solid, and reassuring metal is now just electrons open to loss, theft, and misappropriation at every stage. In this day and age, shouldn’t there be a more secure, more reliable way to transact business? This is why people have started talking about ideal money—to salvage the idea of money.

The concept of ideal money

Ideal money is a theoretical notion proposed by the mathematician John Nash to stabilize international currencies and evolved as a solution to the Triffin Dilemma. This is the paradox between short-term domestic and long-term international objectives for nations whose currencies served as global reserve currencies.

The nations that owned the global reserve currencies would have to supply the world with an extra supply of currency to meet global demand, thus creating friction between national and global monetary policies.

This led Nash to propose the concept of ideal money that was “intrinsically free of ‘inflationary decadence,’ a true ‘gold standard.’”

This began with the idea of value stabilization in relation to a domestic price index and an international exchange rate that fixed each currency to a standardized basket of commodities (the industrial consumption price index). Ideal money focused on the fluctuations and long-term perceived value of money, with an ideal inflation rate as close to zero as possible.

When you look at it, this accurately describes bitcoin’s disinflationary design: decreasing its inflationary nature by halving new currency issuance at regular intervals.

Which brings us to the bitcoin

In 2008, a certain Satoshi Nakamoto published a paper in which he unveiled bitcoin, a peer-to-peer cryptocurrency and digital payment system. Since it required no intermediary, the concept of the blockchain was used as an administrator or repository, making bitcoin truly decentralized.

No one really knew who Nakamoto was, though. Many believed that Nakamoto was not one man but a group of cipher-altruists who created a universal, decentralized currency that could actually be the way forward with money.

Many also believed that it was Nash’s mind that led to the creation of the bitcoin, as it is his math and his work that lives on in the monetary policies built into the bitcoin protocol. His insight that we should evolve a true form of money that can be used as a true measuring tool that approaches zero inflation is what bitcoin turned out to be.

The bitcoin revolution

The bitcoin was created so that transactions could avoid intermediaries. According to Nakamoto’s own words in his 2008 paper, “I’ve been working on a new electronic cash system that’s fully peer-to-peer, with no trusted third party.” While he made no direct reference to the “third party” being governments or banks, the idea of “trust-less” contains the seeds of anarchic sentiment.

By design, bitcoin is trust-less and borderless and its adoption in its original form can truly destroy the centralized control by governments and banks. This makes it a financial weapon that can take on corrupt, entrenched systems.

Bitcoin was also created in 2008, just after the 2007 financial mess, where extensive banking fraud, fiat inflation, and nasty political machinations were uncovered. The system was broken and needed a fix. But this decentralized “ideal money”—thanks to its open, borderless nature—became ideal for drug peddling, hiring hitmen, and all sorts of monstrosities in the veiled markets of the dark web.

But what was even more worrisome was that governments were getting interested, too. Soon, governments, banks, mainstream investors, and entrepreneurs were looking to cash in on the bitcoin boom.

Bitcoin journalist Sterlin Lujan wrote, “In reality, bitcoin was meant to function as a monetary weapon, as a cryptocurrency poised to undermine authority. Now, it is whitewashed. It is seen as a polite and unassuming technology in order to appease politicians, banksters, and soccer moms.”

But it’s not all dark. There are sunny stories of economic freedom as well. Venezuelans, for example, are using bitcoin to help them bypass high import tariffs and other taxes imposed by the government. And true to the dream of the founding fathers, bitcoin is also making transactions and businesses in Venezuela incorruptible. This is thanks to the blockchain, a unique architecture that can never be tampered with, a solution to the weak institutions we are faced with in many parts of the world.

The true hero of this story: the blockchain

The blockchain is the most disruptive technology of recent times. It could (and will, eventually) completely change how we think about transactions, records, transparency, and the idea of authority. It is a distributed database—dispersed over a system of interconnected computers—that is used to maintain a growing list of records (called blocks).

There’s no centralized record management system, but identical records are spread across everyone connected to a network. They are all updated simultaneously, and transactions only go through when there is a quorum to sign off on them.

Mathematical scrambling is then used to convert an original piece of information into a code (hash). Any attempt to alter the information is immediately apparent because the new hash won’t match the old one.

What Nakamoto’s blockchain did is find a solution to the double-spend problem (also called the Byzantine General’s Problem), a long-standing computer science paradox. Double-spending happens when a digital token is spent more than once. With bitcoin and its timestamp, you can avoid a third party to timestamp transactions. So now, you don’t need a trusted intermediary like a bank to complete a good transaction.

In the bitcoin scenario, the blockchain is public and permission-less, everyone can participate and contribute to the public ledger. Because the blockchain is secure and decentralized consensus can be arrived with it, it becomes ideal for recording events, all kinds of transactions, contracts, identity management, and documenting provenance. This means that all recording could someday become secure, transparent, and decentralized.

The blockchain outside bitcoin

When you think outside the bitcoin, the blockchain is a fantastic secure ledger for any kind of data. It keeps data true, simplifies record keeping, and reduces transaction costs, which makes it ideal for thousands of applications in commerce, finance, and potentially, politics.

In a report, Goldman Sachs outlined five interesting ways in which the blockchain could be used:

• For a network like Airbnb, it could create a secure, tamper-proof system for managing digital credentials and transactions.
• It could be the platform for distributed power generators and individuals to interact and manage transactions.
• It could be the final say in property records and transactions, reducing costs and corruption.
• It could save costs on and reduce errors in securities trading.
• For the finance industry, data stored on a blockchain could help finance firms easily and instantly do a KYC on new customers.

In short, anything that requires maintenance of records and transparency gets a boost. What I envision is a future where entire companies and governments operate in a distributed, automated, and secure fashion.

The brave new world ahead

A brave new world is ahead. But where blockchain will lead us is a guess I’m not willing to make.

In theory, blockchain is potent technology, one that is shared and trusted. It is a transparent ledger which everyone can inspect but no single user controls.

Imagine a currency that is safe from the machinations of government and international politics, giving power back to the people. Imagine being able to trust anyone across the world because the platform or system itself enables trust. Imagine being able to keep an eye on public spending. Imagine politicians whose tax records cannot (and I repeat cannot) be state secrets.

As of writing, there were 307,399 bitcoin transactions today. And there are, as of today, 14,358,642 blockchain wallet users. This information is available out there; it is an open world.

While the hero (or villain) today is bitcoin, the true champion is blockchain. It truly will change us, but hopefully not in the way Dread Pirate Roberts imagined.

As you read this, the world is yet to recover from the US elections. Everyone – journalist, analyst and layman – is still attempting to figure what went wrong (or right.) The first reaction, is to trash experts and these new-fangled analytics algorithms. But as a practitioner of this arcane science, I beg to differ.

I believe this happened because humans prefer to hear what they like; the Democrats, transferred that prejudice to math. So here’s my take on Hillary, Donald and math.

The change – from Cleisthenes to Trump.

I love elections. Not surprising, as I grew up in the Calcutta of the pure whites of Jyoti Basu and the poor reds of communism. We marched our hearts out in those innocent days, because we believed that elections hinged on the motivations of the electorate, on the zeitgeist of the day. But that’s not what elections are, any more.

When Cleisthenes sold direct democracy to the ancient Athenians, he led with an idea and then asked for votes. But what President Obama had, was Narwhal. Narwhal was not a senior Democrat but a data-platform employed by the 2012 Obama campaign – integrating data for functions such as customized emails for fund-raising, identifying likely-voter clusters and using them via social media as influencers. In short, first come the numbers, then personalisation of ideas.

Now meet Hillary’s Ada. Ada (romantically named after Ada, Countess of Lovelace) is the most sophisticated electoral dataplatform, ever. Ada was so precious, it had a dedicated server – not what the rest of the campaign was using (no private server jokes, please).

Ada decided everything. From Hillary’s frequent visits to Pennsylvania to Jay Z and Beyonce. So what went wrong? The data? Or the interpretation?

If you ask me, the humans went wrong.

Man bites math.

Ada was sophisticated, yes. But what we don’t realise is that most data-sets and forecasting models often carry their creator’s predilections. And, they often interpret what they unconsciously want to hear.
Trump, uncomfortable with the numbers his analysts were sharing, kept pushing back (10 days to elections, they were still rewriting sampling methodology). While Hillary, happy with the numbers she saw, kept getting more of the same.

We refuse to challenge results that mimic our opinions; we find comfort in data. Unfortunately, that might just be something we fabricated ourselves.

The politician as analytics-driven marketer.

Today’s campaign analyst uses math and statistics to predict voter behaviour. They are armed with behavioural psychology, social media strategies and the results of randomised experiments. The information we provide on social media helps them analyse public mood – to decide everything from candidate selection, ideological stances, campaign ideas, personalised targeting and predicting voter turnout. And then they use this data for targeted campaigns
– the politician has evolved into a marketer.

Today politicians employ analytics-led customer marketing – like any toothpaste brand manager. They focus on branding, market research, voter segmentation, use of imagery, targeted and personalized communication.

Today’s politician uses the merging of cognitive and behavioural patterns. The citizen in an advanced economy is trained to think as a consumer and this thinking spills into other life situations that require decision-making – like voting.

But is voter, toothpaste?

The marketer offers customers choice – to take an informed, individual decision. What elections offer citizens, is a responsibility that is collective. With micro-segmentation, citizenship could be eroded by selfish, individualistic choices; ‘customer behavior’ could kill ‘citizen behavior’.

Also, with every citizen participating in social media, we could be getting the very idea of representative democracy wrong. We could degenerate from representative democracy to a direct democracy – neither ideal nor practical for complex democracies. (Proof, in one word – Brexit.)

What next?

The future will definitely bring uncomfortable questions. About (manipulative) math and elections. It would help to remember then, that democracy has never been about meritocracy. It’s just about numbers. Apologies, but it’s true.

Democracy has always been about numbers. The difference between the ancient Greeks and us is that with science, we can play numbers better. But, as the Hillary campaign now knows, the numbers should never know of our biases.

I started off life with many heroes. Thanks to my grandparents, I had enough and more from mythology. And then of course, there was Hercules.

Then I grew a little older, became monotheistic and concentrated all my worship on Alan Turing.
The pattern of the monomyth or the ‘hero’s journey’ is always the same – mysterious adventures into supernatural worlds, supernatural encounters, fantastic victories and of course, they all come back with the greater powers to help common folk like you and me.

 And by this definition, Alan Turing is among the greatest heroes this world has ever seen. He ventured into mysterious worlds most men of his time had not even imagined, made some phenomenal discoveries and built for us the foundations of this world that we live in today.

 As an analytics practitioner, it is impossible for me not to acknowledge the founding contributions of Alan Turing. But then when I think about it, there isn’t a discipline of science that Alan Turing has not left an impact on.

 

A tormented existence

Turing’s was an uncommonly tormented life. His childhood was consumed by the pain of living apart from his parents. His adolescence witnessed the pain of an unspoken love (and the agony of losing that soulmate). With adulthood came the ignominy of being hounded as a homosexual and the trauma of forced chemical castration.

On 7th June 1954, Turing was found dead in his room, with a half-eaten, cyanide-laced apple next to him. The coroner believed it to be suicide; but many still believe that he choked on an ignorant world.

 But in this pain, the beauty. In 42 tormented years, this man asked more questions than any of his genius-brethren. Questions on logic, mathematics, computing, theoretical and mathematical biology, metaphysics, the list is endless.

 And the questions he asked, went on to become the foundation for a lot many more, that practitioners in all fields would ask, later.

 How does the mental mind arise out of a physical brain?

How does language relate to thought?
Where does knowledge come from?
How does knowledge lead to action?
What are the formal rules for drawing conclusions?
How do we reason with uncertain information?
How can we build an efficient computer?
How should we make optimal decisions?

 I really do not know if Turing was referring to himself when he said this – “Sometimes it is people no one can imagine anything of, who do the things no one can imagine.” But if he did, it was true then and it is true now.

 

The Turing Machine

Mechanical or ‘formal’ reasoning had been debated and developed by philosophers and mathematicians since antiquity. Aristotle and later Socrates, had developed Syllogism – deductive reasoning to arrive at a conclusion based on two or more propositions assumed to be true.
After that, a lot of tautology followed – debates, propositions and demolitions – till Turing came along. And argued that when human knowledge could be expressed using logic with mathematical notation, it would be possible to create a machine that reasons. That an ideal computing device would be capable of logical reasoning by performing mathematical computations that could be represented as an algorithm.

Turing’s theory of computation suggested that by shuffling symbols as simple as 0 and 1, a machine could simulate any conceivable act of mathematical deduction. This is what we know today as the Turing Machine.

This, lifted machines from simplistic, predictable operations to a level of complexity that could produce insights and innovation. This, is what has led to all the intelligence that we cannot live without today.

And the boxes never stopped opening.

And once we could conceive of a machine that could attempt logical reasoning, the next step was to model the machine on the mind – what we today refer to as weak artificial intelligence (AI) and strong AI.

Turing’s revolutionary ideas and concurrent discoveries in neurology, information theory and cybernetics, were leading researchers to seriously consider the possibilities of an electronic brain – the first step towards AI.

A idea of a machine that modelled itself on the mind was just the beginning. For this led us to delve more into psychology, philosophy, linguistics, neurosciences and biology. The world was waking up – “bliss was it, in that dawn to be alive”.

In a paper titled ‘Computing Machinery and Intelligence’, Turing posed his famous question, ‘can machines think?’

His proposition was that if you were interacting with multiple subjects, and could not distinguish if one of them was a computer, then we had an intelligent machine. A machine deserved to be called intelligent only if it could fool you into believing it was human.

Turing’s path to AI, was to construct a machine with the curiosity of a child – and let intelligence evolve. He was optimistic that machines could possibly in the near future exhibit intelligent behaviour and learn, to the extent of being able to ‘think’ like humans. (He was aware too, of the standard philosophical and scientific objections and rejections of his views.)

A sunset, the scent of a woman, a chilled Chardonnay – while a computer might still not be able to completely comprehend ‘human’, it is capable of a vast range of complicated responses that mimic the human mind. And, it has the ability to learn.

While these thoughts were not even in the realm of science-fiction, Turing was looking at computers as thinking systems.

The mechanical to the metaphysical.

While at school, Turing developed a friendship with another talented student called Christopher Morcom. Christopher’s death, many believe, is what led to Turing’s obsession with deconstructing the nature of consciousness.

He longed to understand what had become of the essential aspect of Christopher – his mind.

This led Turing to delve into every subject that could be of relevance – biology, philosophy, metaphysics, even mathematical logic and quantum mechanics. And unlike philosophers and scientists before him, Turing was not compelled to keep the divine, the physical and the human mind wholly separate from the material universe. He could actively consider a mechanical explanation of the mind. (Fully aware, of the standard philosophical and scientific objections that his propositions could raise.)

And if the man didn’t have enough to think about.…

In an era where natural selection, creationism and intelligent design were still being actively discussed, Turing argued for the chemical basis of morphogenesis – which now serves as the basic model in mathematical biology. (And many say, is the very beginning of chaos theory.)

Having said all this, we still haven’t spoken about Turing The Cryptographer – the man who created the ‘bombe’ that cracked messages enciphered by the German Enigma machine during WWII. An act that helped end the war sooner and saved thousands of lives.

Ralph Waldo Emerson was right. “When nature has work to be done, she creates a genius to do it.”

The two apples

So here was a man, vilified by societal mores who best exemplified Bertrand Russell’s words – ‘Mathematics, rightly viewed, possesses not only truth but supreme beauty, a beauty cold and austere like that of sculpture’. Words that are on the Turing Memorial.

In 2009, the Queen officially pardoned the man convicted and hounded for homosexuality. But more beautiful than this belated pardon, for the man who first visualized this world we live in, is this lovely urban legend about the half-bitten apple next to the dead Turing.

Many believe that the Apple logo, is a Jobs-Wozniak tribute to the man who laid the foundations for the modern-day computer and pioneered research into artificial intelligence. It apparently, is not.

But I would like to believe it is. What better tribute could there be, to a man who had bitten more than his share of the forbidden fruit of knowledge.

14 billion years ago, was heard the first note.
A big bang.
And it’s been playing ever since – the wise say, in B Flat.

70 million years ago came an opposable thumb.
And Neanderthal walked just 250,000 years ago.
Man, erect and proud, took a little longer.

We became hunter gatherers.
We had only the forces of nature around us,
we were in awe.
The sun, the moon, the stars
hum a silent song as they twinkled along.

The ages kept rolling by.
Stone, iron and bronze.
(Heavy Metal came later.)

And faintly, we began hearing the song.
We started keeping the beat of time.
The nature, the pattern, the pass of days.
The sun, moon, the heavens have a rhythm,
the listeners said,
and that’s winter, summer, monsoon, spring.

We started wondering.
Are we alone,
are we created?
Is someone up there in the sky?
The one, with intelligent design?

Must be?
Otherwise, this wonderland , this hugely curious place,
couldn’t have been;
imagined, designed, engineered, crafted.

And we started worshipping,
the first religion, animism
the first gods, everything mysterious
the celestial
the nature
and, the wrath of nature
drought flood tsunami fire.

While much didn’t make sense, much did.
We figured. The dance in the sky,
the changes on the terrestrial are correlated.
We figured.
That it must be the language of the creator,
a twinkly, sign language.

The Babylonians
The Chinese
The Egyptians
The Greeks
The Vedic Indians
The Mayans
The Western civilisation

Each culture developed its own methodology
for making meaning of the sky.
Started keeping account of the stars.
And, juxtaposing the pattern in the sky
to foretell the script to which we were born.

We extrapolated these patterns
to our human lives.
We cleverly divided the sky into 12 parts.
Into familiar forms – bull and sheep and lion.
And now that we had the zodiac,
the rest of the narrative was easily told.

We were born under the influence of one zodiac or the other,
by extension, we had the properties of that animal.
The pattern and the animal foretold our tale.
We were born to a script,
and fatalistically, lived this script tight.

3800 years ago, or so goes the tale,
Abraham wrote the Book of Formation.
Astrology, cosmology, all the secrets of life.

The Jews kept account.
An account of stars, of things, of everyday life.
They noticed eclipses, conjunctions, exaltations.
Celestial harmonies and synchronies.
They noted Jupiter and Saturn,
line up thrice in Pisces.
A once in a millennium occurrence.
They applied geometrical intelligence.

Soon, they were able to see more.
A Star, they said,
will herald the Son of God.
“I see him, but not now;
I behold him, but not near;
A Star shall come out of Jacob;
A Sceptre shall rise out of Israel.”

Sometimes, events did follow the pattern told.
Sometimes, most times, not.
Sceptics called this superstition.
And people’s ability to ignore the inconsistency of prediction,
as conformation bias.

Believers, couldn’t care less.
For him who believed, patterns worked.
The inconsistency with scientific logic,
became the shortcomings of science of that day.

The believers identified.
With the characterisation in the zodiac.
The believers identified.
With the said descriptions of their personality.
Their likes and dislikes. Their mating preferences.
Therein lies,
a good case of collaborative filtering.

Too generalised, psychologists will say.
Too broad-based, this zodiac and our natures.
We all have some cockroach,
some frog, some scorpion,
some goat, some sheep, some fish in us.

But somewhere the data provided by time,
by stars,
by formations, the astrology of things,
had been appropriated by religion.
And that put it beyond the pale,
beyond the rigour of questioning.
The answers of astrology could never
be subject to the questions of science!

There have been honest attempts,
and here and there.
But with sincere and due respect
to all of them,
they were all on a meek scale.
With modest ambition and objectives.

Isn’t it a time then for the search?
For the ‘god’s particle’ of the astrological kind?
Compute the patterns, scientifically?
The stars and their mysteries, astrology,
deserves a large Hadron Collider of its own.

I think so.

As no other time in the history of man,
have we been able to see farther,
look deeper, hear clearer.
We see more stars than Abraham.
More constellations, than Ptolemy.
Make better models than Copernicus.

Mathematics, statistics,
data sciences, astro and quantum physics.
We know all the particles.
This is the age that Kepler and Galileo
fantasised about on Sundays.

With our ability to map every movement in the sky.
With our ability to gather all data of events,
and shape of human life.
With our ability to big data all these inputs.
With our ability to see the causal relationship.
With our ability to detect the absence of it.
With our ability to bring enormous computational power.
With our ability to comprehend patterns.

We have a need to know.
That is what makes us erect and proud.
To know if there is a correlation,
between the great bear and the bear market,
to know if stars are dumb or smart.

I am no Yuri Milner,
I can’t spare no millions. For aliens.
I, though ,
am ready to commit my enthusiasm,
my energy
(alright, some money too)
behind a sincere data science led
scientific enquiry into a mystery
that is as old as civilisation.

Let me know, let me know for once.
Is that just a diamond in the sky?

When he was in the mood, my father would hold forth on why I should not associate ‘things’ with happiness. But when I was not in the mood, my mother would buy me a bar of chocolate and ask me if I was happy. I must confess that my father’s lectures made me feel like a good human being, made me gain a few inches in height. But it was the chocolate that made me happy.

Every time someone posts about Bhutan and Gross National Happiness (GNH), I get exposed to the same old conflict. Why do we have to see GNH and GDP as two opposing forces? Why does the apocryphal king who has to search for a happy man’s shirt, have to figure that the only happy man in the kingdom is shirtless?

While I was contemplating this piece, I looked up quotes on happiness and discovered that I was doomed. The world’s greatest minds agree with my father. And to add to that, I have to practice selfless love. Well, I am only human and I have figured that neither do I have any intentions of becoming a monk. Nor will I sell that Ferrari.

Share models of happiness and you get maximum likes on social media. But whose happiness?

All around, one keeps hearing about pillars and tenets and means to happiness. While I have the greatest respect for people who invest their time in the happiness space, this gets me wondering. Whose happiness?

Happiness, is an intensely personal, contextual emotion. True, there are common pillars and crutches that all of us need. But then, what we have is a generalised version of happiness, not a personal version.

For the single, happiness could be about meeting someone new. For the hitched, it could also be about meeting someone new. But both, are different happiness-es. Again, while a new smartphone can make me happy today, an excellent dessert could mean happiness after a fiery curry.

Happiness is a function of each individual’s temperamental construct, phase in life and context. Which is exactly why a formula for unbroken, personal happiness has remained elusive.

Look at ourselves. When I was 20, a holiday meant a whole lot of action with a whole lot of friends. Today, it could mean a quieter experience with someone dear. This of course, is the reason family holidays always end with people with shorter nails.
Imagine a family of 4. Dad, mum, 16ish daughter and 14-ish son. The father wants to chill with a beer when the mother wants to hit the spa when the son wants to go skiing when the daughter wants to go shopping. A sure recipe for a manicure for all.

So is a one-size fits all model for happiness… a possibility?

The incomplete happiness elephant.

Look, everybody has had a shot at defining the happiness elephant. And at the risk of getting stoned again, I believe that every attempt has viewed us as Nietzsche’s last men – alike, like herd animals, enjoying simple pleasures and mediocrity, claiming to have invented happiness. And blinking.

Religion (with multiple variations) tells us to shun and be happy. Epicurus tells us not to shun and be happy. Socialism tells us to share and be happy. Capitalism tells us not to share and be happy. Maslow tells us to self-actualise and be happy. And the Buddha tells us that it is the suffering of change. Well, so what is this animal in the drawing room?

The fact is that all of the above have contributed to happiness in their own ways. (And in many ways, our different ways to define happiness may be responsible for the semblance of order in the world we live in.) But what all of these have contributed to certain identifiable and generalised facets of happiness. But unbroken happiness? That accommodates your temperament, phase and context? If you ask me, technology is the answer.(Go ahead, stone me.)

Technology can near-complete the elephant!

Happiness technology is built for a culture that is premised on an algorithmic model of the self. For this we have to understand individuals as a bundle of inputs (data collection), algorithmic processes (data analysis), and outputs (data use). Objectively, the algorithmic self is no more (or less) than what it consumes and the content it creates; subjectively, it converts input resources into outputs of emotion.

Mathematicians and statisticians (and now every marketer) can predict and influence our behavior and preferences based on the content we create online and on social media’s likes, dislikes and opinions. Which can easily be converted into customised recommendations for individual delight.

A confluence of technologies for personalised happiness.

Today we do have the technologies to gauge, understand and deliver personalised happiness. I already have all the information I need about the individual, I have the technology to bucket preferences and contexts. Now if I marry pop psychology and a little philosophy to your need context, I have a formula for personalised happiness. For you.

The delivery mechanism, we already have. All of us already know that social networks can impact moods. Studies have shown that emotions themselves might spread online to generate large-scale simultaneous clusters of happy and unhappy individuals.

And every time I deliver your happiness pill via social media, you will reveal how you could be made happier. And I keep getting better at keeping you continually happy.

Is this then the holy grail for the happiness industry? Well, the jury is out on that. And so is the ethical committee. I will just say that this is not the whole elephant but it is the closest we have come to defining this mammoth in our drawing room.

Is the happiness pill street legal?

Now this is where the ethical committee comes in. And with reason. For I, as an analytics merchant, can manipulate this knowledge to influence behaviour (create happiness) and of course, purchase.

“I am kind of paranoid in reverse. I suspect people of plotting to make me happy.” – J D Salinger.

Is it right to engineer happiness? It was not said in this context of course, but I really love Salinger’s quote, very apposite to this context. The moment we talk of Big Data, what comes to mind is Big Brother. War of the Worlds scared the few who were listening to radio at that point; but with Big Brother, Orwell has scared us for all eternity. He must be chuckling, now.

Will we be engineering happiness alone?

Or will it lead to what the occupiers of Wall Street fear – further accumulation in the hands of a few, now with the ability to keep the populace happy in the bargain? These are questions I have no answers to. I take recourse in Ockham’s les parsimoniae – the fewer the assumptions, the better.

Data can create continual happiness – with curated, personalised recommendations to connect with an individual’s contexts of mood and time. And this to me seems ‘the’ idea, as the fluctuating nature of the epistemology of happiness makes any one formula redundant.

Happiness has always been an industry, whatever -ism you call it by. With multiple and often fiercely-opposing prescriptions. But then again, theirs are formulae that you need to prescribe to, for happiness.

What we are on the cusp of, is exactly the opposite! My happiness formulae varies itself to suit the pill-popper’s subjective, contextual need. And everyone can have their own varying formulae!

This is the ultimate happiness pill (blue or pink or whatever), personalised to individual, mood and time. What man has been seeking all along – now brought to you by data sciences.

No mountains to climb.

Which brings us to the old man who sits patiently atop the mountain, waiting for seekers, including Hagar the Horrible, to clamber up to him to discover the sources and meaning of true happiness. Well, that old man now has a smartphone, with Big Data on cloud. And he can tell exactly what your recipe for happiness is. He knows that no one formula applies. He knows that Atul Jalan has been checking out the 1975 Porsche 911G but his wallet size can only accommodate a miniature.

But the old man also knows what else can make Atul Jalan happy. And what, can be delivered now. And he will. Without my having to climb any mountain.

If I have offended any old men atop mountains in the course of this note, apologies. And of course, to all who like my father, say ‘shun’. I do believe ‘things’ count. These are just the thoughts of someone whose pill is technology.

The other day I was watching my son break his head against math when it struck me that I have no idea what math will do for his future. My father’s script was much simpler – he knew that if Atul Jalan didn’t learn his math, hell waited ahead.

My worry is not the pace of change. Not at all. My worry is that the skills my son learns, might be completely unnecessary when he sets out to do whatever it is in life that he will do. (Which I hope is wine-tasting, an exceptional profession for my son to be in, especially in my golden years.)

I had to learn to use machines to automate. He will have to learn how to collaborate with machines. That, I think is the essential difference. But what does it mean for my son?

The Filters of Civilisation. I like to think of this entire progression as a series of filters that we as a race pass through. The first filter was when we learnt that there was more to tools than fingers. The (36x fast-forward) second filter, was the Industrial Revolution where we created machines to perform tasks. We moved through an automation filter next. And immediately after, the business intelligence filter, where machines were actually helping us take decisions based on data that we had to input. So, going by that logic, what would the next filter be?

Intelligent machines are the filter we are passing through currently. And to misquote the poet, bliss is it in this dawn to be alive. We are moving into a day when machines build on the understanding provided, themselves, in the direction you want them to. An age of smart machines that have the ability to slice data in any which dimension, understand it, build on it and offer us answers.

The age of the smart machine is not in the future.That phone in your pocket, is one.

And we already are influenced by smart machines. Sometimes without even realising how we are using it or how reliant we are on it. Like “Damn you, auto-correct”, for example. The smart keyboard uses language modelling for context (it knows how certain alphabets combine together in a language) and then adds machine learning to it (after a while it suggests words you use oftener) to tailor itself to your need. And if you keep abusing auto-correct enough, it is less likely to offend you as time goes by. My son depends on it!

Damn auto-correct often enough and it will correct itself. Now how do we import this technology into humans?

Your smartphone is the best indication of the future. Your machine already understands your voice. (Do not forget that this means your machine understands your accent and your individual modulation.) Gesture recognition is also available today, so now your machine understands your actions as well. Now add to this Emotion Recognition (this demands that the machine understand social and cultural contexts and you as an individual, again) and you really have a machine that understands you completely, maybe even better than you understand yourself.

This ability of machines to understand us, combined with the power of cloud computing, advanced analytical sciences and Big Data can bring us behavioural insights into every individual on this planet. That is what makes this dawn heaven! I know, it does sound like Carl Sagan, but it is true. We already see this around us.

Know how every human behaves. Know what every human feels.

Very soon (Manthan is working hard towards it), a retail organisation will be able to identify your emotions like trust, expectation, attention, interest or lust they moment your fingertips glide over your keyboard or from your gestures or body movement. Apply analytics and behavioural insights to this knowledge and you are able to trigger the right information flow to the customer.

Now comes the best part. No user needs to understand or even try bothering to understand underlying technologies – but all, can use it. This is what we call Siri-ization of analytics at Manthan.

Yesterday, the power of advanced analytics was limited to analysts and data scientists. In retail, the roles on the floor depended on information and recommendations that trickled down. With Siri-ization, everyone on the floor can use it, real-time, to figure what their next course of action should be. No skill requirement, no hierarchy, nothing.

Siri-ization consumerizes knowledge.

And that happens because the machine already knows your customer inside out. It knows your business inside out. It knows all possible business contexts. It knows competitor pricing. It It understands the categories. It understands trends as of this morning. It knows where the customer is at this precise point in time. It understands preferences. It knows Victoria’s secret. All you need to do, is ask the machine. And thou shalt receive a recommendation. Immediately.

I know this sounds very close to omnipotence and omniscience. But this is where we are today, this is the filter we must pass.

Synthetic Intelligence. Not Artificial.

This is what we used to call Artificial Intelligence not long ago. I personally, prefer to call it Synthetic Intelligence, though. We are not faking it; with all of these technologies that are simultaneously crossing our path, we are duplicating the neural pathways of the brain – to create machines that are intelligent. Intelligent enough, most importantly, to understand a million contexts, dice, analyze and bring you a defined set of right answers.

Which brings us to the next, natural question. With all of these capabilities already with the machine, what will my son do? He has guided recommendations on decisions to be taken, based on a scientific analysis of a large volume of data. With course corrections available every second. So then, what does he do now? What does he do with his innate capacity as a human?

If machine can do all, what will my son do?

I think my son is really fortunate to be born into this day. For I really believe that his tomorrow lies in discovering skills and practices where the quality of human involvement can be deepened. He will be able to bring humanisation and context to Big Data. He will be able to see the exciting insights that lie beneath. He will be able to bring to the fore his (yet) unduplicated skill as a human – empathy. And together they will collaborate to create a consumer environment and experience that will be unparalleled.

“Bliss was it in that dawn to be alive, But to be young was very heaven!” – Wordsworth

This is true augmentation. With all the new technologies we mentioned above, what we are enabling, is the true potential of the human mind. David Sarnoff said this (what appears like) aeons ago. But it is even truer today. With every advance in technology, we move a step ahead in our ability to frame the questions that machines should answer. What can the machine answer, in a way, becomes our new benchmark.

“The human brain must continue to frame the problems for the machine to solve.” – David Sarnoff

And in the Manthan context, with Siri-isation, every retailer will just need to ask questions. And, discover the next question. That, will truly exploit the creativity in each of us. And this will be true across businesses. Let me give you an example. Very soon, I will be able tell a driver-less car to take me to the supermarket of my choice. But prod the envelope a little more, and my car will be able to suggest the closest supermarket where my needs can be best met.

What we do at Manthan today, is bring together multiple technologies that enables everyone with the power of analytics; thus pushing him to explore his domain deeper. Helping help him and his business rise to a higher level in customer experience and delivery – a business environment where human ability can be better focussed, better optimised to create breakthroughs in the domains we operate in.

As part of its Siri-isation effort, Manthan is looking at Machine Learning, Natural Language Processing, Speech and Gesture Recognition, Facial and Emotion Recognition, Cognitive Reasoning, Guided Analytics, Adaptive Learning and Behavioural Analysis. With these and the power of cloud computing, the volume and complexity of Big Data can be made available, in a disciplined, reasoned fashion, to every business user.

The technologies already exist. Manthan Siri-izes it to bring you the best answers.

Most of what I am talking about, you already use. What Manthan does is bring these technologies together, to enable an analytics-unschooled business user to ask questions and find answers that have mapped the query against every byte of data available to deliver the best business decision.

With Siri-isation, I really believe Manthan (like many others) is moving from the analytics and technology business into the answers business.

Coming back to my son. He needs to develop empathy to ask the right questions tomorrow. For that, does he need math? I don’t know. Should I be teaching him empathy? I wouldn’t know how to do that. But what I do know for certain, is that he will be able to crunch Big Data with a gesture. Decision options will be placed in front of him on a platter. And he will find a way to move on to a higher level of human capability.

True, there are a lot of questions that machines cannot answer today. But wait, that’s today. I would like to believe that there are more filters for us to pass through. Till then, let’s ask and receive.
Ask yourself what you will do tomorrow when the machines get smarter. And you truly will discover what you are capable of.

At one time not too long ago, the retail industry and consumers were highly predictable.

Massive demographic and economic shifts, as well as historic levels of technology and media disruption, have turned this once predictable industry into a turbulent sea – leaving consumer behavior and how retailers respond, permanently altered.

Mobiles, social media, big data, cloud computing…. are driving us headlong into interconnected era or the Internet of Everything.

Consumerization and what it means

By “consumerization” of technology, the power of the consumer has been augmented and accelerated. Consumers now demand more – more convenience, more consistency, more collaboration, more customization.

Creating this 4C Shopper Experience is what the next evolutionary step in retail is.

This shift is so seismic, that even companies like Walmart and Procter & Gamble are scrambling to incorporate 4Cs into each and every customer touchpoint, across operations and processes.

Forward-thinking retailers are also working towards a level of preparedness to allow customers to dictate their business strategies and models.

And this will soon happen as customers get more and more informed about the shopping ecosystem and continuously find ways to tweak the system to better their experience.

We have reached a stage where if retail does not redefine itself, the consumer will redefine it for them. And if that is the nature of the beast, then the solution is to build a retail ecosystem that is proactive and can transform its strategies and models on-demand.

After all we live in an age of ‘on-demand economy’, what is being called as ‘uberification’ of economy.

The Right Retail Response

So how does one respond to the changes brought by consumerization of technology? The solution is for retail to employ the same tools that caused the disruption.

By harnessing the digital forces of consumerism – mobile and pervasive computing, big data and analytics, cloud computing, social media, artificial intelligence and robotics, retailers have the opportunity to ‘digitalize’ their businesses.

We believe that the final goal in retail, is an enhanced customer experience at every stage along the shopper journey. For this digitalization has to go beyond mimicking analog-based retail business models and redesign all processes from customer-facing processes to the supply chain.

We began this series of blog posts by saying that what the shopper expects today, is convenience, consistency, collaboration and customization. Some suggest that our consumerized, empowered, new customer, is the retailer’s biggest competition.
Therefore, retailers have to quickly adopt ways to charm, disarm and co-opt them. I think we can all agree that customer experience will be the sole basis of differentiation for retailers.

And if that is the nature of the beast, then the solution is to build a retail ecosystem that is proactive and can transform its strategies and models on-demand.

The future of software deployment has to be analogous to switching on the application, as opposed to the current methodology and practice, which is not in sync with the super response time one needs, to respond to situations.

And if IT is to achieve that task for retail, IT has to arm retailers with the ability to ‘Switch On’ all the big guns they have at their disposal on-demand.

Applications have to be truly intuitive, such that they don’t require huge efforts to train and should create user delight and depth of engagement.

Floating High on a Cloud

In its Magic Quadrant for Business Intelligence and Analytics Platforms, Gartner predicted that by 2017, most business users and analysts in organizations will have access to self-service tools to prepare data for analysis.

Gartner also reported that interest in cloud based business intelligence (BI) was at 42% in 2014. Survey respondents reported they either are (28%) or are planning to deploy (14%) BI in some form of private, public or hybrid cloud, and Gartner believes that this category will continue to grow as line of business (LOB) adoption increases over time.

And there is a good reason for this dramatic shift to cloud. CIOs have discovered that the flexibility, scalability, quick adoption timelines and the ability to handle huge volume of data in the cloud has made it a remarkably better option than on-premises offerings.

Cloud services architecture ensures that cloud analytics can be delivered to a wider audience within the organization and that usage is personalized by role, function, collaboration and competence levels.

Services can be scaled up or down depending on user need for processing power and the data repository volume required. This enables the retailer to explore data sources that previously were too big or too complex to handle.

At Manthan we have leveraged the powers of big data and cloud computing to kill deployment and develop the new paradigm – Switch On.Switch On, helps retailers to go from intent to analytics in couple of hours.

By harnessing the digital forces of consumerism – mobile and pervasive computing, big data and analytics, cloud computing, social media, artificial intelligence and robotics, retailers have the opportunity to ‘digitalize’ their businesses.

In our last post we discussed how cloud computing is offering CIOs the flexibility, scalability, and quick adoption timelines needed to handle huge volume of customer data, enabling them to ensure the shopper experience is consistent across their purchase journey.

Let’s look at some of the other ways digitalization can impact retail performance to deliver on the 4Cs of the shopper experience:

1. Customer centricity has to begin with building an N=1 understanding of the customer – Customer analytics.

Retailers need a 360 degree view of their customers, a comprehensive topography of each customer and their category, brand and channel preferences.

The retailer should know where the customer buys, what she buys, when she buys this, what else she buys, what is the context of her purchase – emotion-wise, location-wise and channel-wise.

The retailer has to stay close to the customer’s changing and emerging needs based on her life-stage and lifestyle; he has to know her preferred path to purchase and her preferred path to fulfillment.

2. Context based, math-driven Uber personalization of offers, deals, and information.

The mass production that came in the wake of industrial revolution, achieved democratization of access. From there we travelled to mass customization. And what we are now heading towards is uber-personalization.

Digitally empowered consumers are trending toward indulging their desire for unique, customized products and services.

This means that the “one size fits all” model for products or services is definitely out of date.

Imagine the possibilities. Customers could walk-in to stores enabled by consumer grade devices based on technologies such as 3D printing and have an experience that is entirely personalized!

Retailers need to able to serve up uber-personalized experience by offering – curated content, personalized offer and promotions, item-to-item collaborative filtering to offer shopping suggestions in real time, pushing personalized offers onto a customer’s mobile phone.

3. Clienteling.

Today, the store associate is right at the end of the value chain, and has very little information or tools at his or her disposal to create an engaging customer experience.

This situation is very similar to the airline business where the crew, which spends the maximum time interacting with customers, has the least information about them.

Airlines today are addressing this by enabling their crew with customer engagement solutions. We believe moving forward, store associates will coalesce into two specializations – customer-facing associates providing increasingly sophisticated customer experiences and fulfillment-centric associates, enabling complex delivery options.

Store associates will necessarily need to empowered with information on their mobiles about the customer, such as profile attributes, transactions and interactions, likes and dislikes; along with analytics-driven recommendations on up-sell and cross-sell, in-depth product information, previous customer feedback, and other relevant information.

Companies like Manthan are helping retailers with such functionalities by equipping store associates with a mobile/tablet device that combines, for example, inventory management, in-store operational analytics, walkie-talkie functionality, mobile POS capability, the ability to recommend up-sell and cross-sell through clienteling applications and connectivity to a receipt printer.

4. Collaboration with Customers.

The Consumerization of Technology means that customers, not retailers, are shaping and will continue to shape, the future of retailing.

This implies that retailers will have no choice but to collaborate with and include customers in the innovation process. In this regard, collaboration is not merely soliciting and managing customer feedback on customer service issues. Nor is it just about tapping into collaborative influencing techniques, such as crowdsourcing to gain customer feedback through the ‘wisdom of the crowds’.

To win the retail future, the retailer’s innovation process will have to dramatically involve way to leverage the human propensity for creating and co-creating, which consumers, through technology, are tapping into more and more.

Retailers can achieve in by including customers in initiatives such as product design, marketing campaigns, product launches, price and demand discovery, and viral marketing.

Manthan has taken a large part of its product portfolio onto cloud and has created a SaaS engagement model for its core customer – the retailer. This shift, which Manthan calls Switch On, is a further example of its interests in the consumerization of advanced technology.

Where Consumerization is taking us

The recent Gartner 2015 Magic Quadrant for Business Intelligence and Analytics Platforms elaborates extensive on how the market is undergoing a fundamental shift.

In the past decade, BI and analytics investments were always IT-driven. Analysts and information consumers created reports which were pushed to business users. Now, however, we see that business users are the ones driving analytical purchases, and demanding interactive styles of analysis and insights.

IDC forecasts the traditional analytics business to be worth $59.2 billion by 2018. And A T Kearney pegs big data, software and services at $114 billion by 2018. Remove the word ‘traditional’ from the equation and add ‘user-friendly’ and you know why Manthan is focusing on Consumerization of Analytics.