“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.
Emerging Global Market: The Arctic on Sale
The Arctic Region has been on a journey of geographical transformation induced by Climate Change. There has been an unprecedented percentage of what can be called as ‘Arctic metamorphosis’, witnessed as deterioration of climate twice as rapidly as in any other parts of the globe. There has been a decline in permafrost, sea ice, icesheets on ocean and glaciers in Canada, Alaska and Greenland. There has been a notable decrease in the snow cover that earlier occupied the land. These alarming changes in the physiography were first recorded in the 1980s, and have been on a surge ever since. Around 1 million sq. miles of sea ice has shrunk over the past 50 years, halving the size of Arctic icecap. The transition has been so dramatic that it actually cut the turf to Asia, revealing the fabled North West Passage that European voyagers sought for shipping, for over centuries. As of now, it is not a matter of ‘if’ but ‘when’ will the Arctic Passageway open for regular marine transportation and when would the exploration of lucrative natural energy-resources deposits be possible.
The regressing ecosystem has been the least of the concerns of our capitalist, market-oriented, energy-hungry world economy. The melting ice caps and glaciers are paving way to access the 13% of globe’s undiscovered oil and 30% of globe’s undiscovered natural gas lying at the Arctic Ocean seabed, a home for world’s largest unexplored hydrocarbon resources. These percentages translate to 1,669 trillion cubic ft. of natural gas and 90 billion barrels of oil. The economic potential for these energy resources exceeds $2.7 trillion for Russian and American Arctic claims alone. Moreover, there are massive reserve potential for rare mineral resources also referred to as “strategic minerals” including palladium, nickel and iron-ore which might prove to be a greater economic driver than the energy resources. Apart from these, Arctic has tremendous new opportunities for high sea fisheries. The Ocean has vast stocks of marine resources including shrimp, pollock, crab, pacific salmon, squid, scallop and halibut. It would prove to be a new arena of industrial-scale commercial fisheries.
Whether the sought resources are hydrocarbon or mineral, they must procure their route via pipelines or shipping routes to the receptive markets. Along with the transitory passageways, there would be need for improved icebreakers, satellite and communication and navigation, deep water ports, double-hulled shipping vessels, operational search and aviation infrastructure development.
An even better incentive would be the inception of new sea-lanes initiated by the great Arctic melt. The shipping shortcuts of Northwest Passage and Northern Sea Route would reduce the nautical transit times by days, saving the shipping corporations thousands of miles. The sailing distance between Yokohama and Rotterdam on the Northern Route would be reduced from over 11,200 nautical miles to 6,500 nautical miles, in comparison with the current Suez Canal Route which would amount to the savings of up to 40 percent of shipping expenses. Likewise, the voyage from Rotterdam to Seattle would be trimmed by the North West Passage by over 2000 nautical miles, reducing the distance up to 25 percent in comparison with the current Panama route.
Taking into consideration the fuel costs, canal fees and various other miscellaneous charges that amount to lofty freight rates, these alternative passages will cutback the charges of a single voyage down to at least 20%, saving around $17.5 million, saving billions of dollars per annum for the shipping industry. These savings would be far greater for the megaships that have to sail all the way down to Cape Horn and Cape of Good Hope.
The world’s shipyard’s have already started building ice-capable ships, beginning with the groundwork for the navigation through these sea-lanes and for the transport of Arctic’s natural gas and oil. Billions of dollars are being invested by the private sector for the fleet of Arctic tankers. As of now, around 496 ice-class ships have been built worldwide. The gas and oil markets are investing in development of the avant-garde technology and assemblage of advanced ships, possessing double-acting tankers, that have the dual technology of steam bowing through open waters and proceed stern to smash through deep ice. These ships are capable of sailing unobstructed to Arctic’s burgeoning gas and oil fields independent of ice-breakers. These breakthroughs will turn previously unviable commercial projects into booming businesses.
Of all the Arctic States, the largest stakeholder with greatest intrinsic interests in the region is Russia. A significant 20% of Russia’s GDP comes of Russian North, and accounts for 22% of all exports. The resources of Arctic are of strategic importance for Russia; therefore, it has been so far the largest investor in the region. It has invested in the fleet of nuclear-icebreakers, the only of their kind in the world. Further, Russia is planning on increasing this fleet of 4 to 13 with a cost of over $1.5 billion. Moreover, Russia has endeavored to aim for 92.6 million ton of cargo by 2030. These hefty investments indicate the importance of Arctic as a market. Russia aims at charging for providing the sea-routes since it has the largest geographical proximity to the ocean as well as providing shipping and infrastructure in the region. The claims of oil and gas reserves are only an addition to the gains Russia has planned to make.
Considering the economic and strategic importance of Arctic and its potential to add to the world’s oil, gas, minerals, fisheries and shipping reserves makes it an alluring marketplace. The region itself has been divided among the ‘Arctic States’ that include Russia, Denmark, Iceland, Finland, Sweden, Norway, Iceland, and United States. Instead of making efforts to preserve the deteriorating environmental conditions and the physiographic challenges, these states are only in a race of dividing the resources among themselves and reaping as much assets as they can. All domains of Arctic are on sale; including the sea, land, sea-life, mineral resources, and fossil fuels. The world has turned a blind eye towards the environmental consequences for the region of the planet which will surely cost more than the gains. Putting nature’s commodities on sale have never worked in anyone’s favor.
Covid-19 and food crisis
COVID-19 has hit at a time when food crisis and malnutrition are on the rise. According to the most recent UN projections, the pandemic-induced economic slump would cause as many as 132 million people to be hungry. This would be in addition to the 690 million people going hungry now. At the same time, 135 million people suffer from acute food insecurity and in need of urgent humanitarian assistance. Although the pandemic’s transmission has slowed in certain countries and cases have decreased, COVID-19 has resurfaced or is spreading rapidly in others. This is still a global issue that needs a worldwide solution.
This epidemic threatens both lives and livelihoods. COVID-19 has had a wide-ranging and disruptive influence on the agriculture system. We fear a worldwide food crisis unless we act quickly, which may have long-term consequences for hundreds of millions of children and adults. This is mostly due to a lack of food availability — as wages decline, remittances decline, and in certain cases, food prices rise. Food insecurity is increasingly becoming a food production concern in nations that already have high levels of acute food insecurity.
Agriculture continues to serve a reliable and major part in world economy and stability, and it remains the primary source of food, income, and work for rural communities, even in the face of a pandemic. The impact of the COVID-19 pandemic on the agricultural system and sector has been wide-ranging, causing unprecedented uncertainty in global food supply chains, including potential bottlenecks in labor markets, input industries, agriculture production, food processing, transportation and logistics, as well as shifts in demand for food and food services.
The COVID-19 epidemic not only created a new sort of agricultural catastrophe, but it also occurred at a difficult moment for farmers. In most years during the last few years, global commodity output has exceeded demand, resulting in lower prices. In 2013, the Food and Agricultural Organization (FAO) predicted decreased global agricultural output growth due to limited agricultural land development, rising production costs, expanding resource restrictions, and increasing environmental concerns.
An expanding global population remains the main driver of demand growth, although the consumption patterns and projected trends vary across countries in line with their level of income and development. Average per capita food availability is projected to reach about 3,000 kcal and 85 g of protein per day by 2029. Due to the ongoing transition in global diets towards higher consumption of animal products, fats and other foods, the share of staples in the food basket is projected to decline by 2029 for all income groups. In particular, consumers in middle-income countries are expected to use their additional income to shift their diets away from staples towards higher value products. Meanwhile, environmental and health concerns in high-income countries are expected to support a transition from animal-based protein towards alternative sources of protein.
When people suffer from hunger or chronic undernourishment, it means that they are unable to meet their food requirements – consume enough calories to lead a normal, active life – over a prolonged period. This has long-term implications for their future, and continues to present a setback to global efforts to reach Zero Hunger. When people experience crisis-level, acute food insecurity, it means they have limited access to food in the short-term due to sporadic, sudden crises that may put their lives and livelihoods at risk.
However, if people facing crisis-level acute food insecurity get the assistance they need, they will not join the ranks of the hungry, and their situation will not become chronic
It is clear: although globally there is enough food for everyone, too many people are still suffering from hunger. Our food systems are failing, and the pandemic is making things worse.
How Bangladesh became Standout Star in South Asia Amidst Covid-19
Bangladesh, the shining model of development in South Asia, becomes everyone’s economic darling amidst Covid-19. The per capita income of Bangladesh in the fiscal year 2020-21 is higher than that of many neighbouring countries including India and Pakistan. Recently, Bangladesh has agreed to lend $200 million to debt-ridden Sri Lanka to bail out through currency swap. Bangladesh, once one of the most vulnerable economies, has now substantiated itself as the most successful economy of South Asia. How Bangladesh successfully managed Covid-19 and became top performing economy of South Asia?
In March 1971, Sheikh Mujibur Rahman declared their independence from richer and more powerful Pakistan. The country was born through war and famine. Shortly after the independence of Bangladesh, Henry Kissinger, then the U.S. national security advisor, derisively referred to the country as a “Basket Case of Misery.” But after fifty years, recently, Bangladesh’s Cabinet Secretary reported that per capita income has risen to $2,227. Pakistan’s per capita income, meanwhile, is $1,543. In 1971, Pakistan was 70% richer than Bangladesh; today, Bangladesh is 45% richer than Pakistan. Pakistani economist Abid Hasan, former World Bank Adviser, stated that “If Pakistan continues its dismal performance, it is in the realm of possibility that we could be seeking aid from Bangladesh in 2030,”. On the other hand, India, the economic superpower of South Asia, is also lagging behind Bangladesh in terms of per capita income worth of $1,947. This also elucidates that the economic decisions of Bangladesh are better than that of any other South Asian countries.
Bangladesh’s economic growth leans-on three pillars: exports competitiveness, social progress and fiscal prudence. Between 2011 and 2019, Bangladesh’s exports grew at 8.6% every year, compared to the world average of 0.4%. This godsend is substantially due to the country’s hard-hearted focus on products, such as apparel, in which it possesses a comparative advantage.
The variegated investment plans pursued by the Bangladesh government contributes to the escalation of the country’s per capita income. The government has attracted investments in education, health, connectivity and infrastructure both from home and abroad. As a long-term implication, investing in these sectors helped Bangladesh to facilitate space for businesses and created skilled manpower to run them swiftly. Meanwhile, the share of Bangladeshi women in the labor force has consistently grown, unlike in India and Pakistan, where it has decreased. And Bangladesh has maintained a public debt-to-GDP ratio between 30% and 40%. India and Pakistan will both emerge from the pandemic with public debt close to 90% of GDP.
Bangladesh’s economy and industry management strategy during Covid-19 is also worth mentioning here since the country till now has successfully protected its economy from impact of pandemic. At the outset of pandemic, lockdowns and restrictions hampered the country’s overall productivity for a while. To tackle the pandemic effect, Bangladesh introduced improvised monetary policy and fiscal stimuli to bring them under the safety net which lifted the situation from worsening. Government introduced stimulus package which is equivalent to 4.3 percent of total GDP and covers all necessary sectors such as industry, SMEs and agriculture. These packages are not only a one-time deal, new packages are also being announced in course of time. For instance, in January 2021, government announced two new packages for small and medium entrepreneurs and grass roots populations. Apart from economic interventions, the government also chose the path of targeted interventions. The government, after first wave, abandoned widespread lockdown and adopted the policy of targeted intervention which is found to be effective as it allows socio-economic activities to carry on under certain protocols and helps the industries to fight back against the pandemic effect.
Another pivotal key to success was the management of migrant labor force and keeping the domestic production active amidst the pandemic. According to KNOMAD report, amidst the Covid-19, Bangladesh’s remittance grew by 18.4 percent crossing 21 billion per annum inflow where many remittance dependent countries experienced negative growth rate. Because of the massive inflow of remittance, the Forex reserve of Bangladesh reached at 45.1 billion US dollar.
Bangladesh’s success in managing COVID19 and its economy has been reflected in a recent report “Bangladesh Development Update- Moving Forward: Connectivity and Logistics to strengthen Competitiveness,” published by World Bank. Bangladesh’s economy is showing nascent signs of recovery backed by a rebound in exports, strong remittance inflows, and the ongoing vaccination program. Through financial assistance to Sri Lanka and Covid relief aid to India, Bangladesh is showcasing its rise as an emerging superpower in South Asia. That is why Mihir Sharma, Director of Centre for Economy and Growth Programme at the Observer Research Foundation, wrote in an article at Bloomberg that, “Today, the country’s 160 million-plus people, packed into a fertile delta that’s more densely populated than the Vatican City, seem destined to be South Asia’s standout success”. Back in 2017, PwC (PricewaterhouseCoopers) report also predicted the same that Bangladesh will become the largest economy by 2030 and an economic powerhouse in South Asia. And this is how Bangladesh, a development paragon, offers lessons for the other struggling countries of world after 50 years of its independence.
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