“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.
The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.
Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.
What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?
Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.
As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.
When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.
In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.
Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.
Abstract rules, not random environmental signals.
If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.
What if it cannot be encoded in any way?
The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.
Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.
If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?
However, let us go back to Artificial Intelligence machines and how they work.
In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.
At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.
From the beginning, expert systems were used in financial trading.
There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.
Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.
A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.
In 2011, deep learning (DL) added to the other “expert” systems.
It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.
As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.
DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.
In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.
Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.
The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.
BlackRock has just organized an AILab.
In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.
China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.
China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.
Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.
China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.
The Chinese intelligence industry zone near Tianjin is already active.
In the end, however, how does Artificial Intelligence change the financial sector?
AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.
Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.
Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.
The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.
In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.
There are other AI mathematical mechanisms that come into play here.
There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.
There is also market sentiment.
The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.
There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.
Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.
Here, in fact, 1,800 days of physical trading are reduced to seven minutes.
However, the algorithms developed from evidence work much better than human operators in predicting the future.
Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.
Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.
In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.
As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.
What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.
Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.
So far we have focused on Artificial Intelligence algorithms.
But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.
It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.
Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.
In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.
An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.
Iran has an integral role to play in Russian-South Asian connectivity
Iran is geostrategically positioned to play an integral role in Russian-South Asian connectivity. President Putin told the Valdai Club during its annual meeting in October 2019 that “there is one more prospective route, the Arctic – Siberia – Asia.
The idea is to connect ports along the Northern Sea Route with ports of the Pacific and Indian oceans via roads in East Siberia and central Eurasia.” This vision, which forms a crucial part of his country’s “Greater Eurasian Partnership”, can be achieved through the official North-South Transport Corridor (NSTC) and tentative W-CPEC+ projects that transit through the Islamic Republic of Iran.
The first one refers to the creation of a new trade route from Russia to India through Azerbaijan and Iran, while the second concerns the likely expansion of the China-Pakistan Economic Corridor (CPEC, the flagship project of China’s Belt & Road Initiative [BRI]) westward through Iran and largely parallel to the NSTC. W-CPEC+ can also continue towards Turkey and onward to the EU, but that branch is beyond the scope of the present analysis. The NSTC’s terminal port is the Indian-backed Chabahar, but delays in fully developing its infrastructure might lead to Bandar Abbas being used as a backup in the interim.
CPEC’s Chinese-backed terminal port of Gwadar is in close proximity to Chabahar, thus presenting the opportunity of eventually pairing the two as sister cities, especially in the event that rumored negotiations between China and Iran result in upwards of several hundred billion dollars worth of investments like some have previously reported. The combination of Russian, Indian, and Chinese infrastructure investments in Iran would greatly improve the country’s regional economic competitiveness and enable it to fulfill its geostrategic destiny of facilitating connectivity between Russia and South Asia.
What’s most intriguing about this ambitious vision is that Iran is proving to the rest of the world that it isn’t “isolated” like the U.S. and its closest allies thought that it would be as a result of their policy of so-called “maximum pressure” against it in recent years. While it’s true that India has somewhat stepped away from its previously strategic cooperation with Iran out of fear that it’ll be punished by “secondary sanctions” if it continued its pragmatic partnership with the Islamic Republic, it’s worthwhile mentioning that Chabahar curiously secured a U.S. sanctions waiver.
While the American intent behind that decision is unclear, it might have been predicated on the belief that the Iranian-facilitated expansion of Indian influence into Central Asia via Chabahar might help to “balance” Chinese influence in the region. It could also have simply been a small but symbolic “concession” to India in order not to scare it away from supporting the U.S. anti-Chinese containment strategy. It’s difficult to tell what the real motive was since American-Indian relations are currently complicated by Washington’s latest sanctions threats against New Delhi in response to its decision to purchase Russia’s S-400 air defense systems.
Nevertheless, even in the worst-case scenario that Indian investment and infrastructural support for Iran can’t be taken for granted in the coming future, that still doesn’t offset the country’s geostrategic plans. Russia could still use the NSTC to connect with W-CPEC and ultimately the over 200+ million Pakistani marketplaces. In theory, Russian companies in Pakistan could also re-export their home country’s NSTC-imported goods to neighboring India, thereby representing a pragmatic workaround to New Delhi’s potential self-interested distancing from that project which could also provide additional much-needed tax revenue for Islamabad.
Iran must therefore do its utmost to ensure Russia’s continued interest in the NSTC regardless of India’s approach to the project. Reconceptualizing the NSTC from its original Russian-Indian connectivity purpose to the much broader one of Russian-South Asian connectivity could help guarantee Moscow’s support. In parallel with that, Tehran would do well to court Beijing’s investments along W-CPEC+’s two branch corridors to Azerbaijan/Russia and Turkey/EU. Any success on any of these fronts, let alone three of them, would advance Iran’s regional interests by solidifying its integral geo-economic role in 21st-century Eurasia.
From our partner Tehran Times
The phenomenon of land grabbing by multinationals
Since 2012 the United Nations has adopted voluntary guidelines for land and forest management to combat land grabbing. But only a few people know about the guidelines, which aim to protect small farmers particularly in Third World countries.
When multinational investors buy up fields for their huge plantations, the residents lose their livelihood and means of support and will soon only be sleeping in their villages. If they are lucky, they might find work with relatives in another village. Many also try their luck in the city, but poverty and unemployment are high. What remains are depopulated villages and the huge palm oil plantations that have devoured farmland. People can no longer go there to hunt and grow plants or get firewood. The land no longer belongs to them!
Land grabbingis the process whereby mostly foreign investors deprive local farmers or fishermen of their fields, lakes and rivers. Although it has been widely used throughout history, land grabbing – as used in the 21st century – mainly refers to large-scale land acquisitions following the global food price crisis of 2007-2008.
From 2000 until 2019 one hundred million hectares of land have been sold or leased to foreign investors and the list of the most affected countries can be found here below:
Such investment may also make sense for the development of a country, but it must not deprive people of their rights: local people are starving while food is being produced and turned into biofuels for export right before their eyes.
In 2012, after three years of discussion, the UN created an instrument to prevent such land grabbing: the VGGTs (Voluntary Guidelines on the Responsible Governance of Tenure of Land, Fisheries and Forests in the Context of National Food Security:
Detailed minimum standards for investment are established, e.g. the participation of affected people or how to safeguard the rights of indigenous peoples and prevent corruption. Formally, the document provides a significant contribution to all people fighting for their rights.
The document, however, is quite cryptic. The guidelines should be simplified and explained. Only in this way can activists, but also farmers and fishermen, become aware of their rights.
Others doubt that much can be achieved through these guidelines because they are voluntary. After all, the UN has little or no say in the matter and can do no more than that. If governments implemented them, they would apply them as they will.
In Bolivia, for example, there are already laws that are supposed to prevent land grabbing. In the Amazon, however, Brazilian and Argentinian companies are buying up forests to grow soya and sugar cane, often with the approval and agreement of corrupt government officials. Further guidelines would probably be of little use.
At most, activists already use the guidelines to lobby their governments. Together with other environmental and human rights activists, they set up networks: through local radio stations and village meetings, they inform people of the fact that they right to their land.
Nevertheless, in many countries in Africa and elsewhere, there is a lack of documentation proving land ownership. Originally, tribal leaders vocally distributed rights of use. But today’s leaders are manipulated to pressure villagers to sell their land.
The biggest investors are Indians and Europeans: they are buying up the land to grow sugar cane and palm oil plantations. This phenomenon has been going on since 2008: at that time – as noted above – the world food crisis drove up food prices and foreign investors, but also governments, started to invest in food and biofuels.
Investment inland, which has been regarded as safe since the well-known financial crisis, must also be taken into account. Recently Chinese companies have also been buying up thousands of hectares of land.
In some parts of Africa, only about 6% of land is cultivated for food purposes, while on the remaining areas there are palm oil plantations. Once the plantations grow two or three metres high, they have a devastating effect on monocultures that rely on biodiversity, because of the huge areas they occupy. There is also environmental pollution due to fertilisers: in a village, near a plantation run by a Luxembourg company, many people have suffered from diarrhoea and some elderly villagers even died.
Consequently, the implementation of the VGGTs must be made binding as soon as possible. But with an organisation like the United Nations, how could this happen?
It is not only the indigenous peoples or the local groups of small farmers that are being deprived of everything. The common land used is also being lost, as well as many ecosystems that are still intact: wetlands are being drained, forests cleared and savannas turned into agricultural deserts. New landowners fence off their areas and deny access to the original owners. In practice, this is the 21st century equivalent of the containment of monastery land in Europe that began in the Middle Ages.
The vast majority of contracts are concentrated in poorer countries with weak institutions and land rights, where many people are starving. There, investors compete with local farmers. The argument to which the advocates of land grabbing hold -i.e. that it is mainly uncultivated land that needs to be reclaimed – is refuted. On the contrary, investors prefer well-developed and cultivated areas that promise high returns. However, they do not improve the supply of local population.
Foreign agricultural enterprises prefer to develop the so-called flexible crops, i.e. plants such as the aforementioned oil palm, soya and sugar cane, which, depending on the market situation, can be sold as biofuel or food.
But there is more! If company X of State Y buys food/fuel producing areas, it is the company that sells to its State Y and not the host State Z that, instead, assigns its future profits derived from international State-to-State trade to the aforementioned multinational or state-owned company of State Y.
Furthermore, there is almost no evidence of land investment creating jobs, as most projects were export-oriented. The British aid organisation Oxfam confirms that many land acquisitions took place in areas where food was being grown for the local population. Since local smallholders are generally weak and poorly educated, they can hardly defend themselves against the grabbing of the land they use. Government officials sell or lease it, often without even paying compensation.
Land grabbing is also present in ‘passive’ Europe. Russia, Ukraine, Romania, Lithuania and Bulgaria are affected, but also the territories of Eastern Germany. Funds and agricultural enterprises from “active” and democratic Europe, i.e. the West, and the Arab Gulf States are the main investors.
We might think that the governments of the affected countries would have the duty to protect their own people from such expropriations. Quite the reverse. They often support land grabbing. Obviously, corruption is often involved. In many countries, however, the agricultural sector has been criminally neglected in the past and multinationals are taking advantage of this under the pretext of remedying this situation.
No let-up in Indian farmers’ protest due to subconscious fear of “crony capitalism”
The writer has analysed why the farmers `now or never’ protest has persisted despite heavy odds. He is of the view that the farmers have the subconscious fear that the “crony capitalism” would eliminate traditional markets, abolish market support price and grab their landholdings. Already the farmers have been committing suicides owing to debt burden, poor monthly income (Rs. 1666 a month) and so on.”Crony capitalism” implies nexus between government and businesses that thrives on sweetheart deals, licences and permits eked through tweaking rules and regulations.
Stalemate between the government and the farmers’ unions is unchanged despite 11 rounds of talks. The farmers view the new farm laws as a ploy to dispossess them of their land holdings and give a free hand to tycoons to grab farmers’ holdings, though small.
Protesters allege the new laws were framed in secret understanding with tycoons. The farmers have a reason to abhor the rich businesses. According to an a January 2020 Oxfam India’s richest one per cent hold over four times the wealth of 953 million people who make up the poorest 70 per cent of the country’s population. India’s top nine billionaires’ Inc one is equivalent to wealth of the bottom 50 per cent of the population. The opposition has accused the government of “crony capitalism’.
Government has tried every tactic in its tool- kit to becloud the movement (sponsored y separatist Sikhs, desecrated Republic Day by hoisting religious flags at the Red ford, and so on). The government even shrugged off the protest by calling it miniscule and unrepresentative of 16.6 million farmers and 131,000 traders registered until May 2020. The government claims that it has planned to build 22,000 additional mandis (markets) 2021-22 in addition to already-available over 1,000 mandis.
Unruffled by government’s arguments, the opposition continues to accuse the government of being “suit-boot ki sarkar” and an ardent supporter of “crony capitalism” (Ambani and Adani). Modi did many favours to the duo. For instance they were facilitated to join hands with foreign companies to set up defence-equipment projects in India. BJP-ruled state governments facilitated the operation of mines in collaboration with the Ambani group just years after the Supreme Court had cancelled the allotment of 214 coal blocks for captive mining (MS Nileema, `Coalgate 2.0’, The Caravan March 1, 2018). Modi used Adani’s aircraft in March, April and May 2014 for election campaigning across the country.
“Crony capitalism” is well defined in the English oxford Living Dictionaries, Cambridge and Merriam –Webster. Merriam-Webster defines “crony capitalism” as “an economic system in which individuals and businesses with political connections and influence are favored (as through tax breaks, grants, and other forms of government assistance) in ways seen as suppressing open competition in a free market
If there’s one”.
Cambridge dictionary defines the term as “ an economic system in which family members and friends of government officials and business leaders are given unfair advantages in the form of jobs, loans, etc.:government-owned firms engaged in crony capitalism”.
A common point in all the definitions is undue favours (sweetheart contracts, licences, etc) to select businesses. It is worse than nepotism as the nepotism has a limited scope and life cycle. But, “crony capitalism” becomes institutionalized.
Modi earned the title “suit-boot ki sarkar” when a non-resident Indian, Rameshkumar Bhikabhai virani gifted him a Rs. 10 lac suit. To save his face, Modi later auctioned the suit on February 20, 2015. The suit fetched price of Rs, 4, 31, 31311 or nearly four hundred times the original price. Modi donated the proceeds of auction to a fund meant for cleaning the River Ganges. `It was subsequently alleged that the Surat-based trader Laljibhai Patel who bought the suit had been favoured by being allotted government land for building a private sports club (BJP returns ‘favour’, Modi suit buyer to get back land, Tribune June21, 2015).
Miffed by opposition’s vitriolic opposition, Ambani’s $174 billion conglomerate Reliance Industries Ltd. Categorically denied collusion with Modi’s government earlier this month. Reliance clarified that it had never done any contract farming or acquired farm land, and harboured no plans to do so in future. It also vowed to ensure its suppliers will pay government-mandated minimum prices to farmers. The Adani Group also had clarified last month that it did not buy food grains from farmers or influence their prices.
Like Modi, both Adani and Ambani hail from the western Indian state of Gujarat, just, who served as the state’s chief for over a decade. Both the tycoons are reputed to be Modi’s henchmen. Their industry quickly aligns its business strategies to Modi’s nation-building initiatives. For instance, Adani created a rival regional industry lobby and helped kick off a biannual global investment summit in Gujarat in 2003 that boosted Modi’s pro-business credentials. During 2020, Ambani raised record US$27 billion in equity investments for his technology and retail businesses from investors including Google and Face book Inc. He wants to convert these units into a powerful local e-commerce rival to Amazon.com Inc. and Wal-Mart Inc. The Adani group, which humbly started off as a commodities trader in 1988, has grown rapidly to become India’s top private-sector port operator and power generator.
Parallel with the USA
Ambani and Adani are like America’s Rockefellers and Vanderbilt’s in the USA’s Gilded Age in the second half of the 19th century (James Crabtree, The Billionaire Raj: a Journey through India’s New Gilded Age).
Modi government’s tutelage of Ambanis and Adanis is an open secret. Kerala challenged Adani’s bid for an airport lease is. A state minister said last year that Adani winning the bid was “an act of brazen cronyism.”
Threat of elimination of traditional markets
Farmers who could earlier sell grains and other products only at neighbouring government-regulated wholesale markets can now sell them across the country, including the big food processing companies and retailers such as WalMart.
The farmers fear the government will eventually abolish the wholesale markets, where growers were assured of a minimum support price for staples like wheat and rice, leaving small farmers at the mercy of corporate agri-businesses.
Is farmers’ fear genuine?
The farmers have a logical point. Agriculture yield less profit than industry. As such, even the USA heavily subsidies its agriculture. US farmers got more than $22 billion in government payments in 2019, the highest level of farm subsidies in the last 14 years, and the corporate sector paid for it. The Indian government is reluctant to give a permanent legal guarantee for the MSP. In contrast, the US and Western Europe buy directly from the farmers and build their butter and cheese mountains. Even the prices of farm products at the retail and wholesale levels are controlled by the capitalist government. In short, not the principles of capitalization but well-worked-out welfare measures are adopted to sustain the farm sector in the advanced West.
Threat of monopsonic exploitation
The farmers would suffer double exploitation under a monopsony (more sellers less buyers) at the hands of corporate sharks. They would pay less than the minimum support price to the producers. Likewise, consumers will have to pay more because the public distribution system is likely to be undermined as mandi (regulated wholesale market) procurement is would eventually cease to exist.
Plight of the Indian farmer
The heavily indebted Indian farmer has average income of only about Rs. 20000 a year (about Rs. 1666 a month). Thousands of farmers commit suicide by eating pesticides to get rid of their financial difficulties.
A study by India’s National Bank for Agriculture and Rural Development found that more than half of farmers in India are in debt. More than 20,000 people involved in the farming sector died by suicide from 2018-2019, with several studies suggesting that being in debt was a key factor.
More than 86 per cent of India’s cultivated farmland is owned by small farmers who own less than two hectares of land each (about two sports fields). These farmers lack acumen to bargain with bigger companies. Farmers fear the Market Support Price will disappear as corporations start buying their produce.
Modi sarkar is unwilling to yield to the farmers’ demand for fear of losing his strongman image and Domino Effect’. If he yields on say, the matter of the farm laws, he may have to give in on the Citizenship Amendment Act also. Fund collection in some foreign countries has started to sustain the movement. As such, the movement may not end anytime soon. Unless Modi yields early, he would suffer voter backlash in coming elections. The farm sector contributes only about 15 per cent of India’s $2.9 trillion economy. But, it employs around half its 1.3 billion people.
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