Recent years have seen breakthroughs in neural network technology: computers can now beat any living person at the most complex game invented by humankind, as well as imitate human voices and faces (both real and non-existent) in a deceptively realistic manner. Is this a victory for artificial intelligence over human intelligence? And if not, what else do researchers and developers need to achieve to make the winners in the AI race the “kings of the world?”
Over the last 60 years, artificial intelligence (AI) has been the subject of much discussion among researchers representing different approaches and schools of thought. One of the crucial reasons for this is that there is no unified definition of what constitutes AI, with differences persisting even now. This means that any objective assessment of the current state and prospects of AI, and its crucial areas of research, in particular, will be intricately linked with the subjective philosophical views of researchers and the practical experience of developers.
In recent years, the term “general intelligence,” meaning the ability to solve cognitive problems in general terms, adapting to the environment through learning, minimizing risks and optimizing the losses in achieving goals, has gained currency among researchers and developers. This led to the concept of artificial general intelligence (AGI), potentially vested not in a human, but a cybernetic system of sufficient computational power. Many refer to this kind of intelligence as “strong AI,” as opposed to “weak AI,” which has become a mundane topic in recent years.
As applied AI technology has developed over the last 60 years, we can see how many practical applications – knowledge bases, expert systems, image recognition systems, prediction systems, tracking and control systems for various technological processes – are no longer viewed as examples of AI and have become part of “ordinary technology.” The bar for what constitutes AI rises accordingly, and today it is the hypothetical “general intelligence,” human-level intelligence or “strong AI,” that is assumed to be the “real thing” in most discussions. Technologies that are already being used are broken down into knowledge engineering, data science or specific areas of “narrow AI” that combine elements of different AI approaches with specialized humanities or mathematical disciplines, such as stock market or weather forecasting, speech and text recognition and language processing.
Different schools of research, each working within their own paradigms, also have differing interpretations of the spheres of application, goals, definitions and prospects of AI, and are often dismissive of alternative approaches. However, there has been a kind of synergistic convergence of various approaches in recent years, and researchers and developers are increasingly turning to hybrid models and methodologies, coming up with different combinations.
Since the dawn of AI, two approaches to AI have been the most popular. The first, “symbolic” approach, assumes that the roots of AI lie in philosophy, logic and mathematics and operate according to logical rules, sign and symbolic systems, interpreted in terms of the conscious human cognitive process. The second approach (biological in nature), referred to as connectionist, neural-network, neuromorphic, associative or subsymbolic, is based on reproducing the physical structures and processes of the human brain identified through neurophysiological research. The two approaches have evolved over 60 years, steadily becoming closer to each other. For instance, logical inference systems based on Boolean algebra have transformed into fuzzy logic or probabilistic programming, reproducing network architectures akin to neural networks that evolved within the neuromorphic approach. On the other hand, methods based on “artificial neural networks” are very far from reproducing the functions of actual biological neural networks and rely more on mathematical methods from linear algebra and tensor calculus.
Are There “Holes” in Neural Networks?
In the last decade, it was the connectionist, or subsymbolic, approach that brought about explosive progress in applying machine learning methods to a wide range of tasks. Examples include both traditional statistical methodologies, like logistical regression, and more recent achievements in artificial neural network modelling, like deep learning and reinforcement learning. The most significant breakthrough of the last decade was brought about not so much by new ideas as by the accumulation of a critical mass of tagged datasets, the low cost of storing massive volumes of training samples and, most importantly, the sharp decline of computational costs, including the possibility of using specialized, relatively cheap hardware for neural network modelling. The breakthrough was brought about by a combination of these factors that made it possible to train and configure neural network algorithms to make a quantitative leap, as well as to provide a cost-effective solution to a broad range of applied problems relating to recognition, classification and prediction. The biggest successes here have been brought about by systems based on “deep learning” networks that build on the idea of the “perceptron” suggested 60 years ago by Frank Rosenblatt. However, achievements in the use of neural networks also uncovered a range of problems that cannot be solved using existing neural network methods.
First, any classic neural network model, whatever amount of data it is trained on and however precise it is in its predictions, is still a black box that does not provide any explanation of why a given decision was made, let alone disclose the structure and content of the knowledge it has acquired in the course of its training. This rules out the use of neural networks in contexts where explainability is required for legal or security reasons. For example, a decision to refuse a loan or to carry out a dangerous surgical procedure needs to be justified for legal purposes, and in the event that a neural network launches a missile at a civilian plane, the causes of this decision need to be identifiable if we want to correct it and prevent future occurrences.
Second, attempts to understand the nature of modern neural networks have demonstrated their weak ability to generalize. Neural networks remember isolated, often random, details of the samples they were exposed to during training and make decisions based on those details and not on a real general grasp of the object represented in the sample set. For instance, a neural network that was trained to recognize elephants and whales using sets of standard photos will see a stranded whale as an elephant and an elephant splashing around in the surf as a whale. Neural networks are good at remembering situations in similar contexts, but they lack the capacity to understand situations and cannot extrapolate the accumulated knowledge to situations in unusual settings.
Third, neural network models are random, fragmentary and opaque, which allows hackers to find ways of compromising applications based on these models by means of adversarial attacks. For example, a security system trained to identify people in a video stream can be confused when it sees a person in unusually colourful clothing. If this person is shoplifting, the system may not be able to distinguish them from shelves containing equally colourful items. While the brain structures underlying human vision are prone to so-called optical illusions, this problem acquires a more dramatic scale with modern neural networks: there are known cases where replacing an image with noise leads to the recognition of an object that is not there, or replacing one pixel in an image makes the network mistake the object for something else.
Fourth, the inadequacy of the information capacity and parameters of the neural network to the image of the world it is shown during training and operation can lead to the practical problem of catastrophic forgetting. This is seen when a system that had first been trained to identify situations in a set of contexts and then fine-tuned to recognize them in a new set of contexts may lose the ability to recognize them in the old set. For instance, a neural machine vision system initially trained to recognize pedestrians in an urban environment may be unable to identify dogs and cows in a rural setting, but additional training to recognize cows and dogs can make the model forget how to identify pedestrians, or start confusing them with small roadside trees.
The expert community sees a number of fundamental problems that need to be solved before a “general,” or “strong,” AI is possible. In particular, as demonstrated by the biggest annual AI conference held in Macao, “explainable AI” and “transfer learning” are simply necessary in some cases, such as defence, security, healthcare and finance. Many leading researchers also think that mastering these two areas will be the key to creating a “general,” or “strong,” AI.
Explainable AI allows for human beings (the user of the AI system) to understand the reasons why a system makes decisions and approve them if they are correct, or rework or fine-tune the system if they are not. This can be achieved by presenting data in an appropriate (explainable) manner or by using methods that allow this knowledge to be extracted with regard to specific precedents or the subject area as a whole. In a broader sense, explainable AI also refers to the capacity of a system to store, or at least present its knowledge in a human-understandable and human-verifiable form. The latter can be crucial when the cost of an error is too high for it only to be explainable post factum. And here we come to the possibility of extracting knowledge from the system, either to verify it or to feed it into another system.
Transfer learning is the possibility of transferring knowledge between different AI systems, as well as between man and machine so that the knowledge possessed by a human expert or accumulated by an individual system can be fed into a different system for use and fine-tuning. Theoretically speaking, this is necessary because the transfer of knowledge is only fundamentally possible when universal laws and rules can be abstracted from the system’s individual experience. Practically speaking, it is the prerequisite for making AI applications that will not learn by trial and error or through the use of a “training set,” but can be initialized with a base of expert-derived knowledge and rules – when the cost of an error is too high or when the training sample is too small.
How to Get the Best of Both Worlds?
There is currently no consensus on how to make an artificial general intelligence that is capable of solving the abovementioned problems or is based on technologies that could solve them.
One of the most promising approaches is probabilistic programming, which is a modern development of symbolic AI. In probabilistic programming, knowledge takes the form of algorithms and source, and target data is not represented by values of variables but by a probabilistic distribution of all possible values. Alexei Potapov, a leading Russian expert on artificial general intelligence, thinks that this area is now in a state that deep learning technology was in about ten years ago, so we can expect breakthroughs in the coming years.
Another promising “symbolic” area is Evgenii Vityaev’s semantic probabilistic modelling, which makes it possible to build explainable predictive models based on information represented as semantic networks with probabilistic inference based on Pyotr Anokhin’s theory of functional systems.
One of the most widely discussed ways to achieve this is through so-called neuro-symbolic integration – an attempt to get the best of both worlds by combining the learning capabilities of subsymbolic deep neural networks (which have already proven their worth) with the explainability of symbolic probabilistic modelling and programming (which hold significant promise). In addition to the technological considerations mentioned above, this area merits close attention from a cognitive psychology standpoint. As viewed by Daniel Kahneman, human thought can be construed as the interaction of two distinct but complementary systems: System 1 thinking is fast, unconscious, intuitive, unexplainable thinking, whereas System 2 thinking is slow, conscious, logical and explainable. System 1 provides for the effective performance of run-of-the-mill tasks and the recognition of familiar situations. In contrast, System 2 processes new information and makes sure we can adapt to new conditions by controlling and adapting the learning process of the first system. Systems of the first kind, as represented by neural networks, are already reaching Gartner’s so-called plateau of productivity in a variety of applications. But working applications based on systems of the second kind – not to mention hybrid neuro-symbolic systems which the most prominent industry players have only started to explore – have yet to be created.
This year, Russian researchers, entrepreneurs and government officials who are interested in developing artificial general intelligence have a unique opportunity to attend the first AGI-2020 international conference in St. Petersburg in late June 2020, where they can learn about all the latest developments in the field from the world’s leading experts.
From our partner RIAC
Global Vaccine Divide: Covid-19 Pandemic and Opportunity for India
As the novel coronavirus (SARS CoV-2) pandemic is raging across the world, a race has already ensued for the discovery of a vaccine. Normally, vaccine development takes a decade. But given the urgent need for a vaccine to fight the novel coronavirus, different national drug regulatory agencies are observed to have fast tracked their approval process. Additionally, various regulatory bottlenecks were also removed in order to facilitate the development of a vaccine in an earliest possible time. According to the World Health Organization (WHO), as of 17th May 2020, there are eight vaccine candidates already in different phases of clinical trials around the world.
As the Covid-19 vaccine race picked pace, questions are once again raised over the affordability of the vaccines to the population in the Low and Middle Income Countries (LMIC). The question appears timely, given the past behavior of developed countries to place bulk orders and hoard vaccines, at the expense of other countries. Additionally, exorbitant prices will lead to denial of vaccines to the population in the global south. At the heart of this affordability debate is the “patent monopoly” usually enjoyed by the firm that discovers the vaccine first. In this context, the Doha Declaration on TRIPS and Public Health has allowed for “compulsory licensing” of pharmaceuticals during national health emergencies. Opponents of this emergency provision have made a counter-argument that invoking TRIPS exemption to break patent monopoly would disincentivize innovation. Their argument seems valid given the fact that vaccine discovery involves billions of dollars in sunk costs. Therefore, by following their line of argument, it can be stated that any measure taken to forcefully license a future Covid-19 vaccine would be counterproductive.
In the quest to make vaccines affordable, it is important to keep in mind the interest of both the patent holder and the populations in the LMIC. In recognition of this factor, the 73rd session of the World Health Assembly (WHA) passed a resolution to establish a ‘voluntary’ global patent pool. Another solution is to introduce a government-funded prizing system that would keep the incentives for vaccine development, and prevent the emergence of patent monopolies at the same time. Few others have called the future patent holders for Covid-19 vaccine to ‘unlock’ their patent for a short period. Such small steps would enhance vaccine availability during times of pandemic, by allowing vaccine manufacturers in LMIC to mass produce vaccines and distribute them at affordable prices. However, these global efforts and other suggestions made towards preventing the emergence of patent monopoly, has invoked opposition from the US, the UK and Switzerland, as these countries are the home to many of the world’s major pharmaceutical companies. In contrast, in his speech to the WHA made on 18th May 2020, President Xi of China declared that Chinese Covid-19 vaccine when available will be made a ‘global public good’, which will ensure “vaccine accessibility and affordability in developing countries”.
At the beginning of the current pandemic, states were seen to be behaving in their own self-interest by hoarding medical supplies. For instance, both Italy and Spain accused Germany for denying ventilators to them during their times of shortage. Similarly, the US was accused by a handful of countries for diverting critical medical supplies meant to be delivered to them. Eventually, as the pandemic control measures failed, states’ interests are seen to have shifted towards vaccine development and ensuring its future availability to their populations. In this regard, the Trump administration in the US was supposed to have paid the German firm Curevac to shift its vaccine research to the US, drawing Germany’s ire. The French were infuriated when its pharmaceutical major Sanofi announced that the US will be the first country to get access to its future vaccine. Also, the US did not participate in the EU-organized global fund raising event to support Covid-19 vaccine development, signaling its intention to go alone. Instead, the Trump administration offered $1 billion to UK firm AstraZeneca to support its vaccine development efforts. Thus, in ensuring vaccine accessibility, few developed countries are behaving in a similar way as they have done in the past.
The present situation has given an opportunity for Indian vaccine manufacturers to step in to ensure ‘equitable’ access to Covid-19 vaccines. India is known for mass production of vaccines and selling them at affordable costs to multilateral organizations like GAVI. Indian manufacturers also account for 60% of vaccines supplied to UNICEF’s global immunization programmes. India has also ramped up its efforts to indigenously develop a vaccine for Covid-19. If a successful vaccine emerges elsewhere, India could still leverage its strong biotech industrial base to partner with foreign firms and institutions for mass production of vaccines. In this respect, the University of Oxford has already partnered with Serum Institute of India in anticipation of a huge global demand for Covid-19 vaccines. Similarly, Bharat Biotech has joined hands with the University of Wisconsin and US firm FluGen to make 300 million doses of vaccines for global distribution. Given the uncertainty associated with vaccine development efforts, Indian pharmaceutical companies should choose the right partners based on the efficacy of their technology, and their suitability for mass production.
Majority of these partnerships are being formed while the vaccine is in the development phase and may fail to fructify in the future. This is because a successful vaccine candidate requires to pass rigorous clinical trials before it could be approved for wider usage. However, the need to mass produce vaccines to end the pandemic will lead to a number of post-development partnerships between biotech firms. In this respect, Indian vaccine manufacturers are better positioned to clinch more deals as they have the necessary ‘skilled’ workforce and R&D infrastructure for mass production. Still, the questions over the ‘equitable’ distribution of vaccines will remain unresolved, unless a percentage of vaccines produced by the manufacturers in India and other LMIC are reserved for local distribution at affordable prices.
Coronavirus in a Time of Chimeras
As the global COVID-19 scourge appears to recede, questions remain over the source and morphology of a virus that had locked down two-thirds of humanity over the first half of 2020. It may take years to satisfactorily decipher this extraordinary episode in human history.
Nonetheless, the novel coronavirus was not germinated in a vacuum. The type of research conducted at the Wuhan Institute of Virology had ominous analogues worldwide. These included the quest for super intelligence and the development of interspecies hybrids or chimeras.
What began as a scientific mission to remedy congenital defects has rapidly morphed into a global race to create designer babies, super soldiers and transhumans through the aid of biotechnology and Artificial Intelligence. 21st century eugenics is tacitly justified by the need to boost “national competitiveness”.
China leads the way here. In one revealing instance alone, genome sequencing giant BGI Shenzhen had procured and sequenced the DNA of more than 2,000 people – mostly Americans – with IQ scores of at least 160. According to Stephen Hsu, a theoretical physicist from Michigan State University and scientific adviser to BGI:
“An exceptional person gets you an order of magnitude more statistical power than if you took random people from the population…”
BGI Shenzhen intends to become a “bio-Google” that will collate the “world’s biological information and make it universally accessible and useful”. From 2012 onwards, it began to collaborate with the Bill & Melinda Gates Foundation.
Scientific endeavours like these are based on the assumption that an assemblage of smart samples will help in the identification and transplantation of optimal bits of DNA into future generations. It is not dissuaded by the nurture over nature debate, even after exhaustive studies have failed to establish genetic variants associated with intelligence. For example, a 2010 study led by Robert Plomin, a behavioural geneticist at King’s College London, had probed over 350,000 variations in single DNA letters across the genomes of 7,900 children but found no prized variant. Curiously, most of the smart samples procured by BGI Shenzhen were sourced from Plomin’s research activities.
Periodic setbacks will not deter the proponents of “procreative beneficence” who argue that it is a human duty to augment the genetic codes of future generations1. Failure to do so is couched in terms of “genetic neglect” and even child abuse2.If this sounds eerily familiar, look no further than the worldview that once animated Nazi Germany.
The eugenic zeitgeist has gripped China in a big way. Under its Maternal and Infant Health Care Law (1994), foetuses with potential hereditary diseases or deformities are recommended for abortion. At the rate Beijing is building its eugenic utopia, the definition of serious deformity may ultimately include a genetically-diagnosed lower IQ.
Instead of raising an eyebrow, the law precipitated a headlong rush to select “intelligent” babies through methods like preimplantation genetic diagnosis (PGD). The idea behind PGD isto screen and identify the most promising embryos for implantation and birth. Combined with CRISPR gene-editing tools, next generation Chinese citizens are expected to exhibit remarkably higher IQs – at least according to bioethicists who fret over a future marked by the “genetic haves” and “genetic have-nots”. China already has three CRISPR-edited babies whose current fate remains unclear.
In the aftermath of the COVID-19 contagion, the availability of “smart samples” would have increased exponentially and may dovetail nicely with the vaccination agenda oft he Rockefeller Foundation and Bill Gates. Incidentally, Gates grew up in a household that was heavily invested in population control and eugenics.
Our smart societies may inevitably face the existential question of “live-lets” and “live-nots” down the line. The orchestrated rebellion towards selective extinction, if it occurs, has a tragicomical public face: A 17-year-old Swede who unceasingly exhorts the world to “listen to the science” and “listen to the experts” but who has little time to listen to her own school teachers.
What can future designer babies contribute to society? For one thing, we will be missing individuals like Beethoven (deaf); Albert Einstein (learning disability/late development); John Nash (schizophrenia); Andrea Boccelli (congenital glaucoma) and Vincent van Gogh (chronic depression/anxiety). A future Stephen Hawkings (motor neurone disease) and Greta Thunberg (Asperger’s Syndrome – allegedly) may be genetically disqualified before birth.
It is now an inconvenience to consider intelligence as a result of peer interactions, human environment and personal adversity. Mapping out the complex and sometimes unpredictable interplay between 100 trillion synaptic connections in a human brain may take centuries. Genetic manipulation is implicitly regarded as the eugenic wormhole that will accelerate the emergence of a global smart society.
The late billionaire paedophile, Jeffrey Epstein, was a prominent proponent of this eugenics philosophy. Epstein intended to breed a“super race of humans with his DNA by impregnating women at his New Mexico ranch, genetic engineering and artificial intelligence.” Welcome to Lebensborn 2.0 and it is all about saving the environment and humanity. For now!
Prominent scientists linked to Epstein’s transhumanist fantasies included “molecular engineer George Church; Murray Gell-Mann, the discoverer of the quark; the evolutionary biologist Stephen Jay Gould; the neurologist and author Oliver Sacks; and the theoretical physicist Frank Wilczek.”The late Stephen Hawking – who will ironically flunk the genetic pre-screenings of tomorrow – was another Epstein associate. Are misanthropes in charge of humanity now?
Eugenics-driven national competitiveness is a tacitly growing obsession among major powers.Its hyper-materialistic focus is encapsulated by an analogy used by Russian scientist Denis Rebrikov:
“It currently costs about a million rubles ($15,500 at the time) to genetically change an embryo—more than a lot of cars—but prices will fall with greater use…I can see the billboard now: ‘You Choose: a Hyundai Solaris or a Super-Child?’”
Will that be an energy-efficient, coronavirus-resistant super child who will instinctively lead a low carbon-emitting lifestyle? The road to hell is indeed paved with fanciful intentions.
But why stop at children? From genetically engineered horses in Argentina that are supposedly faster, stronger and better jumpers to super-dogs in China that are comprehensively superior to the average mutt, the DNA of the entire natural world may be slated for a revolutionary redesign in the future.
Crouching Chimeras, Hideous Hybrids
We however cannot create a future generation of superhumans without being adept at recombining genetic sequences across species. That is the logic guiding eugenicists. As a result, a slew of chimeras or interspecies hybrids have been spawned with the aid of CRISPR. These include human-monkey hybrids, monkey-pig hybrids, human-rabbit hybrids and a host of other lab-manufactured monstrosities.
Chimeras are created when human embryonic stem cells are injected into embryos from another species. The goal, for the time being, is to induce the growth of targeted human organs. Those facing terminal illnesses will no longer have to worry about long organ waiting lists.A less controversial approach to human organ replacement is 3D bioprinting or its 4D bioprinting iteration. These techniques involve the “printing” of a replacement organ from the stem cells of a transplant recipient, thereby eliminating the odds of organ rejection.
But why stop at replacement organs when we can have replacement humans altogether? Future generations must think like Einsteins, be as nimble as leopards and possess owl-like night visions. And, of course, be virus-resistant as well!
The manipulation of the human genome is the new “grand response” to the venerable set of “grand challenges”. Thanks to globalization, China is the go-to place for such genetic tinkering as some of these undertakings are technically illegal in the West. Since 2014, the Wuhan Institute of Virology was the recipient of a two-stage grant worth $7.2 million from the United States government for gain-of-function research into bat coronaviruses. According to a Newsweek report:
Many scientists have criticized gain of function research, which involves manipulating viruses in the lab to explore their potential for infecting humans, because it creates a risk of starting a pandemic from accidental release.
Such caution has not deterred a flurry of research into microbial gene manipulation. It may have instead spawned COVID-19. Recombining genetic codes at the substrate levels is fraught with risks, as any systems theory scholar can attest3. COVID-19 was therefore not a Black Swan event but likely an “emergent”4outcome arising from complex genomic interactions and human folly.
To solely blame China for the coronavirus pandemic therefore may bea tad unfair. Just as China is the factory of the world for foreign corporations, it is also the genetic incubator for a variety of viruses and chimeras for foreign governments and foundations. Even so, the human-pig chimera was the creation of the Salk Institute in California. Research into the world’s first human-mouse hybrid was largely a Japanese affair. The Portuguese in the meantime had created a virus chimera.
The British, on their end, had spawned a human-cow hybrid embryo in 2008 – perhaps reflective of the bovine disposition of those who consume its mainstream media. Clinically-speaking, such analogies are not wholly unwarranted. It was in Britain where the game-changing Dolly the Sheep was cloned in 1996. The transition from sheep to sheeple may turn out to be a short 21st century Jurassic Park ride.
Coincidences and Consequences
Before the advent of gene-editing tools and supercomputing, it would have taken hundreds of years to create a viable chimera. The Genetics-Industrial Complex and contact tracing-type Panopticons constitute a new growth area for nearly-bankrupt Tech Titans5. Is it any wonder that the mainstream media and their Big Tech owners are furiously censoring contrarian expert views on COVID-19?
The dangers of genome editing were in fact included in the Worldwide Threat Assessment reports submitted to United States Congress in 2016 and 2017. They were either omitted or glossed over in the 2018 and 2019 reports– just as such risks were on the rise.
Is it a coincidence that the nations most affected by COVID-19 are the very ones that had either promoted or encouraged a variety of genetic experimentations that are contrary to nature? By the time this crisis is over, independent researchers should superimpose the maps of “genetic superpowers” with those of nations with either the highest COVID-19 fatality rates or the worst socioeconomic fallouts. There may likely be a good degree of overlap as the figure below indicates.
A Pandora’s Box has been opened and more hideous chimeras may emerge during this decade. It is quite an irony that a new generation of artificially-manufactured and cerebrally-deficient “thought leaders”, academics and activists are being groomed to promote “global governance” – a concept due for a portentous mission creep in tandem with the Second Great Depression. What will be their future worth in a eugenic global society that is centrally-controlled bya digital panopticon6?
“Designer babies” and “super humans” may also render many humans redundant. Will the genetic have-nots be reclassified as “live-nots” in the not-so-distant future?
India’s Digitalization: Big Data is the New Oil
Over the last few years, India has travelled the path of rapid digitalization. Not only has the current crisis failed to stop this process, on the contrary, it has served to accelerate it in many areas and make some trends more evident.
Government efforts, active work of India’s business and joint steps undertaken by India’s public bodies and private entrepreneurs who are equally cognizant of the digital transformation’s significance, difficulties and prospects for India’s economy and society as a whole have advanced the process of shaping India’s new digital realities.
In 2015, India’s Prime Minister Narendra Modi announced the launch of the Digital India campaign spanning a series of key government initiatives such as increasing the people’s digital literacy, developing infrastructure and creating an e-government. The most significant achievements include completing and putting into operation the Aadhar digital identification system; a single taxation system covering all Indian states that previously had individual taxation rules; and the Reserve Bank of India, jointly with the association of Indian banks, developing and introducing an instant payment system similar to that created by Russia’s Central bank.
Nandan Nilekani, a well-known Indian entrepreneur and public figure, leads the committee on deepening digital payments at the Reserve Bank of India. An engineer by training, together with Narayana Murthy and several other entrepreneurs, Nilekani co-founded Infosys, one of India’s most famous and successful companies working in software development and IT consulting. In 2009, Nilekani left Infosys and wrote several books about India’s development and the way he sees its future: Imagining India: The Idea of a Renewed Nation (2009); Rebooting India: Realizing a Billion Aspirations (2015). He also headed the Unique Identification Authority of India, the government body that developed Aadhar, a digital biometric identification system, and introduced it throughout the country; Aadhar has already been mentioned; its importance for India is hard to overestimate. Digitalization has already resulted in tectonic shifts within a very short time-span, no more than 5-7 years, in such areas as India’s e-payments and financial technologies, e-commerce, telemedicine and entertainment. The spread of digital technologies has great significance and potential in such areas as agriculture, education, increasing energy efficiency, regulating employment and the labour market, transportation, logistics and further development of e-government.
Yet, none of that would have been possible had government initiatives not been backed up by the ambitions and strategic approach of another Indian entrepreneur, Mukesh Ambani, who swiftly provided Indians with cheap Internet and accessible smartphones. As he advanced his digital business initiatives, Ambani called upon Narendra Modi’s government to achieve maximum localisation of Indian data in India and spoke about the need to fight a new type of colonialism, the country’s informational enslavement by global corporations, so-called data colonisation. He devoted all his resources to developing a new sovereign digital platform; back in 2016-2017, Ambani already said that data are the new oil and smart data are the new fuel of India’s economy.
Following the sectoral liberalisation at the turn of the 20th-21st century, India created a telecommunication services market characterised by high competition among players (both Indian and international companies) that came to the promising area via partnerships with national bodies holding the requisite licences. By around 2010, most companies working in India saw that their revenues coming from traditional services might potentially drop, so they planned to transition to selling data. None of the many telecommunication companies on India’s market have, however, succeeded in the attempt. The failure stems from several factors, including the policies of the regulator (which decided to change the rules of the game and check the terms and conditions of previously issued licences at a crucial time for the sector) and appearance of a new player with the requisite resources, who was willing to spend them on achieving his large-scale goals. That player was Mukesh Ambani and his company called Jio. The history of Ambani’s family business is an integral and characteristic part of India’s economy, and the development track of his companies, including Jio, is regularly discussed in business media and is the subject of several business cases in the world’s leading schools.
Dhirubhai Ambani, the father of Mukesh Ambani and Anil Ambani, launched his business empire in 1957 with a small Bombay-based company importing synthetic fibers and exporting spices. In 1977, following its successful IPO, Dhirubhai Ambani’s Reliance Group became synonymous with business success and guaranteed financial investment for many Indians. The company did not confine itself to the textile business and became a diversified holding that also worked in exploring and developing hydrocarbons, in oil processing, petrochemicals, as well as energy, finances, trade and other areas. In fewer than 30 years, Reliance Group became a fixture of Fortune Global 500 and India’s biggest private company, rivalling such famous family holdings as Tata, Birla, Godrej, Mahindra. Dhirubhai Ambani died in 2002, leaving his sons a multibillion fortune. The brothers Anil and Mukesh engaged in a series of high-profile and unrestrained quarrels that resulted in Reliance Group’s assets being split in 2006. The telecommunication company Mukesh Ambani formed in 2002 had to be transferred, among others, to Anil, but Mukesh had the powerful oil processing business left under his control. His company was now called Reliance Industries. Its assets included the famous high-tech complex in Jamnagar (Gujarat State) processing up to 1.4 million barrels of oil a day. 2010 marked an important stage in this story, when the brothers agreed on revising the terms and timeframe for the non-compete agreements, and subsequently, Mukesh had a chance to announce openly his intentions to embark on a qualitatively new approach to the telecommunication business.
It took Mukesh Ambani about six years to create a new company named Jio (Hindi for “live”). It was officially launched in September 2016. Back then, its telecommunication rivals realised that their already difficult situation would become far worse following the emergence of a powerful new player, but hardly anyone could imagine the cardinal and radical changes in store for the sector. India’s normally very active anti-monopoly agency, as well as other supervisory bodies, were prepared to close their eyes to many controversial points, since Ambani’s goals of swiftly spreading accessible Internet coincided with the course for digitalization steered by the government, while his statements that Indians’ data must be kept in India were very appealing for India’s political leadership. As of today, there are only two big players left in India’s telecommunication sector besides Jio, and these two are in a deep financial crisis. India’s government had to bail out both these companies by allowing large-scale foreign investment and by permitting all players to raise the prices for their services slightly, which had, over the last few years, fallen to an unprecedented low (between 2013 and 2017, the cost of 1 GB of data in India fell by 95%).
Today, Reliance Jio is part of the Jio Platforms holding company formed in 2019 as part of Reliance Industries. Mukesh Ambani’s two elder children hold top managerial positions in the family business. His son Akash, a graduate of Brown University, is in charge of strategy in Reliance Jio, while his daughter Isha, who graduated from Yale University, is on the board of directors in Reliance Jio and Reliance Retail.
The infrastructure and entire digital ecosystem of Reliance Jio was created and put into operation in under 2–3 years. The estimated costs of creating Reliance Jio vary between USD 20 and 45 bn., which is approximately the amount of Reliance Industries’ debt increase over the period of creating Jio. At the time of the company’s IPO in 2016, two-thirds of India’s population of over 1.3 bn. had no Internet access. The company set the goals of deploying an efficient 4G network throughout India, including its remotest areas, while securing a large tech margin for future improvements, and of providing its clients with cheap smartphones and access to various contents and services through its own apps. In the first few months of its operations, while the equipment and all systems were being checked, cheap mobile devices under Jio’s own brand were literally handed out to customers free of charge. Later, minimal tariffs were introduced that immediately made India the leader in mobile operator accessibility for both voice services (phone calls were essentially free) and high-speed data transfer. Once sales took off, the company endeavoured to achieve 100 million new clients in the first 100 days, and did not slack off later: in the first two years, Jio had 250 million subscribers, and today it has 388 million. The company plans to reach 500 million users by 2021.
Jio has a large number of apps and services that have quickly become fixtures in the lives of Indians. They include JioTV, JioCinema, JioSaavn (a music service), JioMoney, JioCloud, JioFiber (broadband Internet access service). Jio rather efficiently provided digital functions to the conglomerate’s commercial line: Reliance Retail, which is also the leader in its segment in India. JioMeet, a video call service, is the latest addition to this extensive range of services. Reliance Jio’s contribution to increasing India’s per capita GDP is estimated at 5.65% in 2018.
Internet access is, indeed, changing India’s image and lifestyle before our very eyes. Largely owing to the decisive actions of the Indian businessman Mukesh Ambani, India has, in just a few years, made a qualitative leap in many digitalization-related areas while avoiding many intermediary stages that other countries spent years on. Only Indonesia outstrips India in its digitalization pace. In 2018, only China exceeded India’s number of digital consumers (560 million users). A survey McKinsey conducted in 2019 showed that the pace of data consumption per user in India grew twice as fast as in the US and China, increasing by 152% annually. Various estimates put an Indian user’s average data consumption at up to 9.8 GB of mobile Internet a month (this indicator is 5.5 GB in China, 8–8.5 GB in South Korea, and the 2019 figure in Russia is about the same). The number of Internet users in India was expected to grow by about 40% by 2023, to 750–800 million people, and the number of smartphones is expected to double, reaching 650–700 million (as of 2018, India had 1.2 bn. mobile subscribers). We can be sufficiently confident that new conditions arising from the pandemic will speed up these trends significantly.
The development prospects of India’s digital economy and primarily its consumer segment stimulated an explosive growth of entrepreneurship that also relies on the traditionally strong stratum of Indian IT specialists. In 2017, Indian developers participated in creating over 100 000 apps for the App Store alone, while the total number of such apps is far higher, given that Indian specialists mostly create apps for Android. In the entrepreneurs’ major league, 30 Indian digital high tech companies are unicorns (their capitalisation is over USD 1 bn., and they are still owned by their founders). In 2017, there were ten such companies. The crucial thing is that would-be unicorns in India are also quite numerous: in 2019, there were over 50 potential future champions.
There have always been many difficulties in working on the Indian market. Suffice it to say that, today, the majority of new Internet users in India do not speak English and need interfaces and content in regional languages. The country has 22 such principal languages. WhatsApp, for instance, supports 11 of them. Still, international investors bank on Indian tech companies, which is greatly helped by government bodies constantly working to stimulate the sector’s investment appeal. Companies working in e-commerce, digital payment services, and tourism have long been the leaders in attracting investment among India’s tech startups. A telling recent example of the international capital race for digital India was the USA’s Walmart acquiring Flipkart, one of India’s many digital e-commerce platforms, in May 2018. Walmart had long tried to gain access to India’s offline market, all to no avail, and it finally came to India by buying 77% of Flipkart for USD 16 bn.
Several investment funds of Russian origin are among those making big investments in India. They continue actively selecting new projects for investment and for strategy adjustment, as do other investors.
Companies that appear not to have any tangible assets, not to make any money, and to accrue debt abound not only in developed countries but now in India as well and still continue to increase their investment potential, thus greatly befuddling traditionally-minded financiers. Yet, analysts increasingly have to admit that high-tech digital companies have unique sets of their clients’ big data, which allows these companies to increase their market share and make correct managerial decisions while constantly improving the functions or services they provide.
Big data is becoming more and more important for governments as well. The quality of analytical materials, development of AI technologies and efficiency of modelling processes depend directly on data volume used as learning material; it can be used, among other things, to manage processes and resources in smart homes and cities efficiently. This is the purpose of Smart Cities, one of India’s government programmes. By late 2020, Jio planned to present commercial solutions for the Internet of Things. The company’s technical capabilities make this possible. While the Indian government is only preparing to make the decision on deploying 5G, Mukesh Ambani says that he has already built a new infrastructure capable of working with 6G and he is now striving to make India one of the principal beneficiaries of the 4th industrial revolution. Jio has no rivals in India in its capacity for collecting up-to-date data of Indian consumers and it plans to improve its technologies for their most prompt and precise processing and further use, while simultaneously developing cloud computing, smart devices, blockchain, augmented reality and more.
The current crisis arising from the pandemic is both shaping new consumer habits and bolstering demand for qualitative changes in approaches to the future economic development of many countries. This is also important for Russia, where, despite all the efforts to diversify its economy, there still remains the threat linked to dependency on commodity exports and the high energy intensity of other Russian exports. And it is also important for India, where 80% of its economy depends on imports of coal, oil and gas.
It was previously announced that 20% in Reliance Industries’ petrochemical business would be sold to Saudi Aramco, Saudi Arabia’s oil giant, for USD 15 bn. With oil prices falling to record lows, however, in March the deal fell through.
Instead of the Saudi Aramco deal, Jio Platforms finalised three different sales: 9.99% was sold to Facebook for USD 5.7 bn., 2.32% of Jio Platforms is now owned by the Vista Equity Partners investment fund (the stock is worth USD 1.5 bn.), and an additional 1.15% of the company’s stock was purchased by investors at Silver Lake Partners for USD 747 m. Mukesh Ambani still holds 86.54% of the company. Other deals with other investors are likely to follow, which will allow the Indian businessman finally to pay off Reliance Industries’ debt (about USD 8 bn.) by March 2021, without losing control of Jio Platforms, just as he planned.
In their official statements concerning the deals, all the participants, including Mukesh Ambani and Mark Zuckerberg, emphasize their confidence in the promising Indian market and in Jio Platforms’ potential. In full accord with the expectations of the Indian government and regular Indian citizens, they say that the new collaboration does not entail data exchange between partner companies. Jio, Facebook, Vista and Silver Lake also say they intend to use their technologies for the benefit of India’s small and medium-sized businesses by connecting such entrepreneurs more actively to e-commerce platforms. They are talking street trade and the so-called kiranas, typical Indian “neighbourhood” grocery stores; they will be able to find a more efficient digital way to meet their customers’ demand. Facebook-owned WhatsApp, which is very popular in India, is expected to play an important role in this process. If talks with the regulator concerning granting WhatsApp payment-making functions are successful, then, by pooling efforts with JioMart, the company will be able to expand both sellers and buyers’ capabilities significantly and compete with India’s most widespread fintech service PayTM, whose investors include Alibaba Group (the Chinese company owns 40% in PayTM).
India, with its 300 million users, is Facebook’s biggest market. WhatsApp has over 400 million users in India. As for the two other investors in JioPlatforms, Vista Equity Partners is noted for its major presence in India’s tech sector: its Indian companies have over 13,000 employees, while its co-founder Brian Sheth is a native of Gujarat, like Mukesh Ambani and Narendra Modi. Like Vista, Silver Lake is based in Silicon Valley and has already invested over USD 40 bn. in tech companies such as Airbnb, Alibaba, Ant Financial owned by Alphabet Verily and Waymo, and also Dell Technologies and Twitter.
Observers with a lively imagination have long since noticed that the company’s name, Jio, is a mirror image of the word “oil.” It is not known for certain whether this is by its founder’s design, but the events of the last few months and transactions around Jio Platforms confirm that, instead of demand for oil, the world is demonstrating a growing demand for innovations. Consequently, compared to other countries, India has every chance of becoming part of the process and a big-time winner. Russia’s business cooperation with India needs, like never before, to have its current realities supplemented in new formats, be it financial technologies, information security, artificial intelligence, sustainable energy infrastructure, advanced materials or other innovative areas.
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