“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.
Tech layoffs and Indian IT Professionals
According to a report in the Washington Post, nearly 30%-40% of the 200000 workers laid off by IT companies like Google, Microsoft etc, since November 2022 are Indians. Several of these workers are on non-immigrant visas — H1B and L1 Visas. H1B is a non-immigrant work visa which enables US companies to employ individuals with specialised skills. If those on H1B visas do not find a new job within 60 days, they will have to leave the US. This would result in numerous logistical problems – for instance children of many of these individuals are enrolled in schools, and these individuals would also have to sell their properties. One suggestion which had been made is that these companies can extend the termination date for IT professionals on H1B visas by a few months.
It would be pertinent to point out, that several organisations are trying to help Information Technology (IT) professionals in their job search and also in influencing US policy makers of US Citizenship and Immigration Services (USCIS). While some of these individuals may be fortunate enough to find opportunities within the US, and a few would also be willing to relocate to India, if they are economically sound, others need to look at possible alternatives. While one possible alternative is Canada, which in recent years has been going all out to attract skilled tech professionals. After the inward immigration policies of the Trump administration, several professionals shifted to Canada (in 2019 the number of Indians who received permanent residency of Canada was over 80,000 while in 2016 less than 40,000 Indians received Canadian residency). Under the Global Skills Strategy program – the Canadian equivalent of the H1B – the processing time of immigration process for skilled workers to two weeks this has also resulted in Canada being a preferred destination for Indian IT professionals in recent years.
These professionals can also explore the possibility of options like UAE. UAE has been making a special effort to attract skilled professionals through its Golden Visa Program – a 10 year residency visa. Earlier, one of the reasons why the west was a preferred destination for Indian professionals vis-à-vis the UAE was that the latter did not provide long term visas. The introduction of the golden visa could make the UAE as a favoured destination for IT professionals given its proximity to India as well as the high living standards. Apart from this, the Golden Visa does not impose restrictions regarding dependents and family members can be sponsored regardless of their ages.
It is not just the UAE, even Japan is trying to attract professionals and has recently announced that individuals who have graduated from top universities can stay back for a period of two years (currently they can only stay for 90 days). Singapore which in recent years has emerged as preferred destination for Indian professionals has also emerged as an attractive destination for IT professionals. Between 2005 and 2020, the proportion of Indian professionals in Singapore has doubled and this has been driven to a large degree by the demand for tech professionals.
Many of the individuals who have been laid off by US companies can also take advantage of the increasing opportunities in India in the start-up sector and the recent thrust on digitalisation in India. Both the central and state governments should try to woo some of these individuals. A number of state governments, such as Kerala, have devised policies aimed at assisting expats who have returned from overseas to start business ventures.
Seeing the changes which are taking place in the IT Sector globally as well as some of the increasingly insular immigration policies of western countries, it is important that Indian students as well as professionals think innovatively and look at alternative avenues. Western companies as well as companies also need to bear in mind, that if other countries like UAE, Singapore, Japan and possibly countries like Vietnam and Taiwan, with strong research eco-systems and infrastructure, begin to open their doors to skilled IT professionals, then the west is unlikely to remain the primary choice for IT professionals in the longer run.
Free-Market Capitalism and Climate Crisis
Free market capitalism is an economic system that has brought about tremendous economic growth and prosperity in many countries around the world. However, it has also spawned a number of problems, one of which is the climate crisis. The climate crisis is a global problem caused by the emission of greenhouse gases, primarily carbon dioxide, into the atmosphere. These externalities are chiefly a consequence of day to day human activities, such as the burning of fossil fuels, deforestation, and conventional agriculture. The climate crisis is leading to rise in temperatures, sea levels, and more erratic weather patterns-The floods in Pakistan and depleting cedars of Lebanon are vivid instances for these phenomena, which are having a devastating impact on the planet.
One of the main reasons that free market capitalism has contributed to the climate crisis is that it prioritizes short-term economic growth over long-term environmental sustainability. Under capitalism, companies are primarily motivated by profit and are not required to internalize the costs of their pollution. This means that they are able to pollute without having to pay for the damage that they are causing. Additionally, the capitalist system is based on the idea of unlimited growth, which is not sustainable in the long-term. As long as there is an infinite demand for goods and services, companies will continue to produce them, leading to ever-increasing levels of pollution and resource depletion.
Another pressing issue that free market capitalism is recently going through is that it does not take into account the externalities of economic activities. Externalities are the unintended consequences of economic activities, such as pollution and climate change. Under capitalism, companies are not required to pay for the externalities of their activities, which means that they are able to continue polluting without having to pay for the damage that they are causing. In her book “This Changes Everything: Capitalism vs Climate” Naomi Klein argues that the current system of capitalism is inherently incompatible with the urgent action needed to address the Climate crisis.
To address the climate crisis, it is necessary to put checks and balances over the free market capitalism and/or make a way towards a more sustainable economic system. This can be done through a number of different effective policies, such as:
Carbon pricing: This can be done through a carbon tax or a cap-and-trade system, which would make companies pay for the carbon emissions that they are producing. In the article “The Conservative Case for Carbon Dividends” authors suggest that revenue-neutral carbon tax is the most efficient and effective way to reduce the carbon emissions.
Increasing renewable energy investments: an increment in the investments in clean energy technologies, such as solar and wind power, can result in the reduction in the use of fossil fuels.
Regulating pollution: Governments can regulate pollution to limit the amount of greenhouse gases that are emitted into the atmosphere.
Encouraging sustainable practices: Governments can encourage sustainable practices, such as recycling and conservation, to reduce the use of resources.
It is remarkable that evolving Capitalism can be harnessed to address the climate change. The private sector has the resources and innovation to develop and implement new technologies and sustainable practices, but they need the right incentives and regulations to do so. Finding the balance between economic growth and environmental protection must be a priority for capitalists.
The free market capitalism has been the driving force behind global economic growth, but at the same time, it has contributed to the ongoing climate crisis. The solution to this problem is not to reject capitalism, but rather to reform it to the societies’ suitable demands. Government should consider providing a level playing field so as to make the probable transition from fossil-based energy systems to Green energy technologies possible. The capitalists should not consider short-termism over long term environmental sustainability. Government intervention to put a price on carbon emissions, invest in renewable energy, regulate pollution, and encourage sustainable practices is necessary to avoid the worst impacts of the climate crisis and build a sustainable future for all. However, here is the catch: Is achieving net-zero-carbon emissions by mid-century a probable target? The answer is quite uncertain, however it is critical point to strive for in the face of escalating Climate Crisis.
Egypt’s “Too Big to Fail” Theory Once Again at Test
Authors: Reem Mansour & Mohamed A. Fouad
In the wake of 2022 FED’s hawkish monetary policy, the Arab world’s most populous nation, Egypt, saw an exodus of about USD20bn of foreign capital. A feat that exerted pressure on the value of its pound against the dollar slashing it by almost half. This led to USD12bn trade backlog accumulating in Egypt’s ports by December 2022.
Meanwhile, amidst foreign debt nearing USD170bn, inflation soaring to double digits, and a chronic balance of payment deficit, Egypt became structurally unfit to sustain global shocks; the country saw its foreign debt mounting to 35% of GDP, causing the financing gap to hover at USD20billion.
While it may seem all gloom and doom, friends from the GCC rushed to inject funds in the “too big to fail” country, sparing it, an arguably, ill-fate that was well reflected in its Eurobond yields spreads and credit default swaps, a measure that assesses a sovereign default risk.
For the same reason in early 2023, the IMF sealed a deal worth of USD3bn, with the government, which unlocked an extra USD14bn sources of financing from multilateral institutions, and GCC sovereign funds, to fill in a hefty portion of the annual foreign exchange gap, albeit a considerable amount averaging USD6bn per annum is yet to be sourced from portfolio investments.
With the IMF stepping in, the Egyptian government agreed on a structural reform program that requires a flexible exchange rate regime, where the Egyptian pound is set to trade within daily boundaries against the US dollar, rationalize government spending, especially in projects that require foreign currency; and most importantly the program entails stake-sales in publicly owned assets, paving the way for the private sector to play a bigger role in the economy.
In due course, through its sovereign fund, Egypt planned initial offerings for shares in companies worth about USD5-USD6bn, and expanded the sale of its shares in local banks and government holdings to Gulf investment funds.
Through the limited period of execution of these reforms, the EGP hit a high of 32 against the greenback, and an inflow of portfolio investments amounting to USD1bn took place, according to the Central Bank of Egypt.
Simultaneously, Citibank International, cited a possible near end of the devaluation of the Egyptian pound against the US dollar. Also, in a report to investors, Standard Chartered recommended to buy Egyptian treasury bills, and pointed to the return of portfolio flows to the local debt market in the early days of January, 2023. Likewise, Fitch indicated the ability of the Egyptian banking sector to face the repercussions of the depreciation of the pound, and that the compulsory reserve ratios within Egyptian banks are able to withstand any declines in the value of the pound because they are supported by healthy internal flows of capital.
While things seem to be poised for a recovery, the long term prospects may lack sustainability. The Egyptian government needs to accelerate its plans to shift gears towards a real operational economy capable of withstanding shocks and dealing with any global challenges. Egypt, however has implicitly held the narrative that the country is ‘too big to fail”. This is largely true to the country’s geopolitical relevance, but even this has its limitations when the price to bail far outweighs the price to fail.
Former President George W. Bush’s administration popularized the “too big to fail” (TBTF) doctrine notably during the 2008 financial crisis. The Bush administration often used the term to describe why it stepped in to bail out some financial companies to avert worldwide economic collapse.
In his book “The Myth of Too Big To Fail” Imad Moosa presented arguments against using public fund to bail out failing financial institutions. He ultimately argued that a failing financial institution should be allowed to fail without fearing an apocalyptic outcome. For countries, the TBTF theory comes under considerable challenge.
In August 1982, Mexico was not able to service its external debt obligations, marking the start of the debt crisis. After years of accumulating external debt, rising world interest rates, the worldwide recession and sudden devaluations of the peso caused the external debt bill to rise sharply, which ultimately caused a default.
After six years of economic reform in Russia, privatization and macroeconomic stabilization had experienced some limited success. Yet in August 1998, after recording its first year of positive economic growth since the fall of the Soviet Union, Russia was forced to default on its sovereign debt, devalue the ruble, and declare a suspension of payments by commercial banks to foreign creditors.
In Egypt, although the country remains to face a number of challenges, signs remain relatively less worrying than 2022, as global sentiment suggests that leverage will be provided in the short-term at least. Egypt’s diversified economy, size and relative regional clout may very well spare the country the fate of Lebanon. However, if reforms do not happen fast enough, the TBTF shield may become completely depleted.
Hence, in order to avoid an economic fallout scenario a full fledged support to the private sector’s local manufacturing activity and tourism is a must. Effective policies geared towards competitiveness are mandatory, and tax & export oriented concessions are required to unleash the private sector’s maximum potential and shift Egypt into gear.
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