In August 2018, Czech Technical University in Prague simultaneously hosted several conferences on AI-related topics: human-level AI, artificial general intelligence, biologically inspired cognitive architectures, and neural-symbolic integration technology. Reports were presented by prominent experts representing global leaders in artificial intelligence: Microsoft, Facebook, DARPA, MIT and Good AI. The reports described the current status of AI developments, identified the problems facing society that have yet to be resolved, and highlighted the threats arising from the further development of this technology. In this review, we will attempt to briefly identify the main problems and threats, as well as the possible ways to counter these threats.
To begin with, let us provide definitions for some of the terms that are commonly used in conjunction with AI in various contexts: weak, or specialized, AI; autonomous AI; adaptive AI; artificial general intelligence (AGI); strong AI; human-level AI; and super-human AI.
Weak, or specialized, AI is represented by all existing solutions without exception and implies the automated solution of one specific task, be it a game of Go or face recognition with CCTV footage. Such systems are incapable of independent learning for the purpose of solving other problems: they can only be reprogrammed by humans to do so.
Autonomous AI implies a system’s ability to function for protracted periods of time without the intervention of a human operator. This could be a solar-powered UAV performing a multi-day flight from Champs-Elysees in Paris to Moscow’s Red Square or back, independently selecting its route and recharging stops while avoiding all sorts of obstacles.
Adaptive AI implies the system’s ability to adapt to new situations and obtain knowledge that it did not possess at the time of its creation. For example, a system originally tasked with conducting conversations in Russian could independently learn new languages and apply this knowledge in conversation if it found itself in a new language environment or if it deliberately studied educational materials on these new languages.
Artificial general intelligence implies adaptability of such a high level that the corresponding system could, given the appropriate training, be used in a wide variety of activities. New knowledge could either be self-taught or learned with the help of an instructor. It is in this same sense that the notion of strong AI is often used in opposition to weak or specialized AI.
Human-level AI implies a level of adaptability comparable to that of a human being, meaning that the system is capable of mastering the same skills as a human and within comparable periods of time.
Super-human AI implies even greater adaptability and learning speeds, allowing the system to masker the knowledge and skills that humans would never be able to.
Fundamental Problems Associated with Creating a Strong AI
Despite the multitude of advances in neuroscience, we still do not know exactly how natural intelligence works. For this same reason, we do not know for sure how to create artificial intelligence (AI). There are a number of known problems that need to be resolved, as well as differing opinions as to how these problems should be prioritized. For example, Ben Goertzel, who heads the OpenCog and SingularityNET, open-source international projects to create artificial intelligence, believes that all the requisite technology for creating an artificial general intelligence has already been developed, and that the only thing necessary is to combine them in a way that would ensure the necessary synergy. Other experts are more sceptical, pointing out that many of the problems that we will discuss below need to be resolved first. Also, expert estimates for when a strong AI may be created vary greatly, from ten or so years to several decades from now.
On the other hand, the emergence of a strong AI is logical in the framework of the general process of evolution as the emergence of molecules from atoms and cells from molecules, the creation of the central nervous system from specialized cells, the emergence of social structure, the development of speech and writing systems and, ultimately, the nascence of information technology. Valentin Turchin demonstrates the logic behind the increasing complexity of information structures and organizational mechanisms in the process of evolution. Unless humanity perishes first, this evolution will be inevitable and will, in the long run, rescue humankind, as only non-biological lifeforms will be able to survive the inevitable end of the Solar System and preserve our civilization’s information code in the Universe.
It is important to realize that the creation of a strong AI does not necessarily require the understanding of how the natural intelligence works, just as the development of a rocket does not necessarily require understanding how a bird flies. Such an AI will certainly be created, sooner or later, in one way or another, and perhaps even in several different ways.
Most experts identify the following fundamental problems that need to be solved before a general or strong AI can be created:
Few-shot learning: systems need to be developed that can learn with the use of a small amount of materials, in contrast to the current deep-learning systems, which require massive amounts of specifically prepared learning materials.
Strong generalization: creating problem recognition technologies allowing for recognizing objects in situations that differ from those in which they were encountered in the learning materials.
Generative learning models: developing learning technologies in which the objects to be memorized are not the features of the object to be recognised, but rather the principles of its formation. This would help in addressing the more profound characteristics of objects, providing for faster learning and stronger generalization.
Structured prediction and learning: developing learning technologies based on the representation of learning objects as multi-layered hierarchical structures, with lower-level elements defining higher level ones. This could prove an alternative solution to the problems of fast learning and strong generalization.
Solving the problem of catastrophic forgetting, which is pertinent to the majority of existing systems: a system originally trained with the use of one class of object and then additionally trained to recognize a new class of objects loses the ability to recognize objects of the original class.
Achieving an incremental learning ability, which implies a system’s ability to gradually accumulate knowledge and perfect its skills without losing the previously obtained knowledge, but rather obtaining new knowledge, with regard to systems intended for interaction in natural languages. Ideally, such a system should pass the so-called Baby Turing Test by demonstrating its ability to gradually master a language from the baby level to the adult level.
Solving the consciousness problem, i.e. coming up with a proven working model for conscious behaviour that ensures effective prediction and deliberate behaviour through the formation of an “internal worldview,” which could be used for seeking optimum behavioural strategies to achieve goals without actually interacting with the real world. This would significantly improve security and the testing of hypotheses while increasing the speed and energy efficiency of such checks, thus enabling a live or artificial system to learn independently within the “virtual reality” of its own consciousness. There are two applied sides to solving the consciousness problem. On the one hand, creating conscious AI systems would increase their efficiency dramatically. On the other hand, such systems would come with both additional risks and ethical problems, seeing as they could, at some point, be equated to the level of self-awareness of human beings, with the ensuing legal consequences.
Potential AI-Related Threats
Even the emergence of autonomous or adaptive AI systems, let alone general or strong AI, is associated with several threats of varying degrees of severity that are relevant today.
The first threat to humans may not necessarily be presented by a strong, general, human-level or super-human AI, as it would be enough to have an autonomous system capable of processing massive amounts of data at high speeds. Such a system could be used as the basis for so-called lethal autonomous weapons systems (LAWS), the simplest example being drone assassins (3D-printed in large batches or in small numbers).
Second, a threat could be posed by a state (a potential adversary) gaining access to weapons system based on more adaptive, autonomous and general AI with improved reaction times and better predictive ability.
Third, a threat for the entire world would be a situation based on the previous threat, in which several states would enter a new round of the arms race, perfecting the intelligence levels of autonomous weapon systems, as Stanislaw Lem predicted several decades ago.
Fourth, a threat to any party would be presented by any intellectual system (not necessarily a combat system, but one that could have industrial or domestic applications too) with enough autonomy and adaptivity to be capable not only of deliberate activity, but also of autonomous conscious target-setting, which could run counter to the individual and collective goals of humans. Such a system would have far more opportunities to achieve these goals due to its higher operating speeds, greater information processing performance and better predictive ability. Unfortunately, humanity has not yet fully researched or even grasped the scale of this particular threat.
Fifth, society is facing a threat in the form of the transition to a new level in the development of production relations in the capitalist (or totalitarian) society, in which a minority comes to control material production and excludes an overwhelming majority of the population from this sector thanks to ever-growing automation. This may result in greater social stratification, the reduced effectiveness of “social elevators” and an increase in the numbers of people made redundant, with adverse social consequences.
Finally, another potential threat to humanity in general is the increasing autonomy of global data processing, information distribution and decision-making systems growing, since information distribution speeds within such systems, and the scale of their interactions, could result in social phenomena that cannot be predicted based on prior experience and the existing models. For example, the social credit system currently being introduced in China is a unique experiment of truly civilizational scale that could have unpredictable consequences.
The problems of controlling artificial intelligence systems are currently associated, among other things, with the closed nature of the existing applications, which are based on “deep neural networks.” Such applications do not make it possible to validate the correctness of decisions prior to implementation, nor do they allow for an analysis of the solution provided by the machine after the fact. This phenomenon is being addressed by the new science, which explores explainable artificial intelligence (XAI). The process is aided by a renewed interest in integrating the associative (neural) and symbolic (logic-based) approaches to the problem.
Ways to Counter the Threats
It appears absolutely necessary to take the following measures in order to prevent catastrophic scenarios associated with the further development and application of AI technologies.
An international ban on LAWS, as well as the development and introduction of international measures to enforce such a ban.
Governmental backing for research into the aforementioned problems (into “explainable AI ” in particular), the integration of different approaches, and studying the principles of creating target-setting mechanisms for the purpose of developing effective programming and control tools for intellectual systems. Such programming should be based on values rather than rules, and it is targets that need to be controlled, not actions.
Democratizing access to AI technologies and methods, including through re-investing profits from the introduction of intellectual systems into the mass teaching of computing and cognitive technologies, as well as creating open-source AI solutions and devising measures to stimulate existing “closed” AI systems to open their source codes. For example, the Aigents project is aimed at creating AI personal agents for mass users that would operate autonomously and be immune to centralized manipulations.
Intergovernmental regulation of the openness of AI algorithms, operating protocols for data processing and decision-making systems, including the possibility of independent audits by international structures, national agencies and individuals. One initiative in this sense is to create the SingularityNET open-source platform and ecosystem for AI applications.
First published in our partner RIAC
The Development of Artificial Intelligence in China: Advantages and terms of development
Artificial intelligence in China is facing unprecedented development opportunities and has many advantages in terms of development. Let us make a few considerations in this regard.
1) International trends
Significant progress has been made in the IT environment and the technological level of human society between big data, cloud computing and the Internet, which are closely related to AI and have developed quickly. AI has started to have a significant impact on the structure of human society and the dual human-machine environment is gradually developing into the third human-machine-intelligent machine environment. The cooperation and coexistence of humans, machines and intelligent machines will become the new normal of the social structure. Such harmonious coexistence is hopefully not only a need for social development, but also provides a distinct place for AI.
Throughout the international community, the development trend of science and technology, as well as AI, is an important sign for human society to keep moving forward after entering the IT field at every level, and is the general trend of international scientific and technological development. Recovery and development inject positive energy into China, and this is also a period of unique development opportunities for China’s AI.
2) AI must be guided by the national strategy
Looking back to the process of AI development in China, we can see that the public’s understanding of AI, the development of its industry, and the government’s emphasis on it have all undergone major changes.
As mentioned above, the State’s top leadership encourages the development of artificial intelligence. President Xi Jinping, Prime Minister Li Keqiang and others have provided great support and clear instructions to the development of AI and robotics in China and have defined target requirements. The State Council, the government and the relevant departments have formulated and released relevant strategic and development plans, such as the three-year Internet+ Implementation Plan, Intelligent Manufacturing 2025 and, in the past, the Robotics Industry Development Plan 2016-2020, etc. The national strategy and government promotion are the source of the healthy development of China’s AI technology and industry. Without the country’s overall political coordination, AI will be impossible to achieve since it is only with the Chinese strategic support that it will be able to make great strides.
3) Internal development needs
AI development is a need for the transformation and upgrading of national industries. The development of intelligent industry and economy requires the continuous innovation of AI. The AI industrialisation is the general trend of national development.
China’s economic and social growth is facing new opportunities and challenges. The lack of dividends in labour, the advent of a society with a rising average age, the needs for elite talents and the development of key technologies must be solved one by one through development. The development of AI and intelligent machines can lead to the “replacement of humans by machines” and to industrial transformation and upgrading. It will provide new momentum and become a new trend for innovation. It cannot be said, however, that the development of AI can solve all economic and social problems, but it is safe to say that the AI industry can create good opportunities to solve the existing economic and social problems. The Chinese social progress and economic development urgently need the effective presence and participation of AI. The industrial transformation, upgrading and reconstruction of China’s growth also provide a “useful place” for the development of AI technology and industry.
4) The advantage of intellectual resources
Although China’s AI has started late and has gone through a long and winding development path, it has unique advantages in terms of intellectual resources.
Firstly, AI focuses on software and the Chinese have a good tradition and special wisdom in this regard. Wu Wenjun, known as the father of Chinese Artificial Intelligence, has emphasised that China is not only suitable ground for the mechanisation of mathematics as a typical mental work, but also fertile ground for the mechanisation of all kinds of AI. Ancient China was the birthplace of the transformation of mental work into factual achievement – although it was seen as an intellectual achievement and not as a practical application: suffice it to say that gunpowder was not used in wars, but was mostly used in recreational events.
Secondly, China currently has sound foundations, effective means and a wealth of experience necessary to develop the true mechanisation of mental work. The shushu method (by “art of predictions” we mean a series of methods for predicting the future developed in pre-imperial China, which played a significant role in the history and culture of the country), used to study even mathematics in Chinese history, is similar to the algorithm currently used to study AI.
China has a huge Internet user base, the largest number of netizens and talents, who form an important advantage in terms of resources of the AI group. Netizens are people who share a common interest and active engagement in improving the Internet, thus making it an intellectual and social resource. The term was widely adopted in the mid-1990s as a way to describe those who inhabit the new geography of the Internet. Internet pioneer and author Michael F. Hauben is credited with coining and popularising the term.
Thirdly, a large number of repatriated experts sent by China to study AI abroad have become the cornerstone and the academics of research and development on the subject, and are also extremely important for the industrial application and training of a new generation of teachers and professors.
Fourthly, China’s pro-reform and opening-up development environment will continue to attract more overseas students and foreign experts engaged in this field to join the common path of improving AI on a global scale.
China’s AI technology and industry is in the best period of development opportunities, provided that the talent strategy is well formulated and implemented, so that there is no longer the need to go abroad to learn, and a national school can be created.
5) The preliminary foundation of the industry
Compared with the robot industry, China’s AI industry started very late, but in recent years it has made great progress in its research achievements and industrial transformation, which is not in the same situation as it was years ago. In the current context of deep development and wide application of big data, cloud computing and the mobile Internet, national and foreign IT companies have seized the opportunity to implement the AI industry. Taking the smart voice sector as an example, its potential market is worth 10 billion US dollars: China’s Baidu, and the US Amazon and Google are conquering the top positions, and competition from smart voice cards of technology giants has begun to take shape.
The increase in the size of China’s voice industry is mainly due to the following three reasons: (i) the government’s political and financial support for the research, development and industrialisation of intelligent voice technology has created a favourable environment for the development of the voice industry; (ii) voice technology suppliers continue to optimise product performance, further deepening the application of intelligent voice in vehicle information service systems, smart homes and other fields; and (iii) the popularisation of 5G networks (5th Generation), big data development and cloud computing provide a strong guarantee for intelligent voice applications. These three reasons are also the fundamental basis for the development of this industry in China.
At present, information technology giants take intelligent voice as an entry point and proactively implement development in the field of AI. Internationally, Internet companies such as Google, Apple, Microsoft, Amazon, IBM, Facebook, etc., which have proactively promoted the research, development and application of intelligent voice technology, have taken this as an entry point to initiate the scheme of the entire AI field. At the same time, Chinese national companies such as Baidu, Tencent, Alibaba, iFLYTEK, Xiaoi Robot, Spichi, Yunzhisheng, BGI and Jietong Huasheng are proactively implementing AI based on intelligent interaction (voice and text).
Besides the intelligent voice industry, China also has some innovative products and industries in other aspects of natural language processing. Furthermore, image processing, machine learning, smart driving, smart home, smart sensors and other fields have also been planning the arrangement of elements that will make China’s related software independent. China’s AI industry is gradually taking shape and its standardisation also needs to be strengthened.
6) Financial Assistance
With the fast development of the economy and the unprecedented improvement of national strength, China’s monetary and financial supply has a respectable international status and has invested massively at home and abroad. In recent years, the national capital market has paved the way for the development of the AI sector. With such progress, it will be able to create cutting-edge industries. These new giants of national entrepreneurship could evolve exponentially in a short time. At the same time, a talent or a business idea could sprout from even a small company or a single, as yet unknown manufacturer.
The capital market’s enthusiasm for intelligent robots has caused the stock horizon to show a rare pattern. Investment in the robotics industry has increased and the amount of robotics industry’s financing has more than tripled. At the same time, the number of mergers and acquisitions in the robotics industry is also increasing year by year. Many listed companies have been involved in the mergers and acquisitions of robotics companies. Some national companies have started to turn to foreign markets, thus giving way to a larger scale of development.
With the further implementation of the Made in China 2025 plan, the potential energy of China’s robotics industry will be further released. There are signs that once the country has fully introduced an AI strategy, national and foreign financial capital will be invested in the AI industrial chain with the same enthusiasm as for intelligent robots. (9. continued)
New archaeology dives into the mysterious demise of the Neanderthals
BY SARAH WILD
For more than 350 000 years, Neanderthals inhabited Europe and Asia until, in a sudden change by evolutionary standards, they disappeared around 40 000 years ago. This was at around the same time the anatomically modern human Homo sapiens emerged from Africa.
With their distinctive sloped forehead, large pelvis and wide noses, Neanderthals leave in their wake one of the great mysteries of human evolution.
They lived during the middle to late Pleistocene Epoch, about 400 000 to 40 000 years ago. Neanderthals lived in Eurasia with traces discovered as far north as present-day Belgium and south to the Mediterranean and southwest Asia.
They were not the only hominid (human-like) species in existence on the planet at the time. Other archaic human groups such as Homo floresiensis and Denisovans, also walked the earth.
‘At the time of the Neanderthals, there were several human species and suddenly 40 000 years ago, all disappeared but one,’ said Prof Stefano Benazzi of the University of Bologna, Italy.
He is a physical anthropologist leading the Horizon-funded SUCCESS project to research the earliest migration of Homo sapiens in Italy. ‘It’s important to understand what happened,’ he said.
We already know more about Neanderthals than any other extinct humans, thanks to thousands of excavated artefacts and fossils, as well as several nearly-complete skeletons.
There are a number of competing theories as to why the Neanderthals disappeared, such as climate change, the aggression of Homo sapiens, possible competition for resources, or even that Neanderthals disappeared because they interbred with Homo sapiens. Some human populations alive in Europe and Asia today have as much as 3% Neanderthal DNA.
Benazzi investigated what happened to Neanderthals in Italy around the time that Homo sapiens arrived out of Africa.
‘In Italy, we have a lot of (dated) archaeological sites, and we have a good overview of the different (technological) cultures falling in the time period of interest,’ he said.
A number of scholars argue that climate change may have pushed Neanderthals towards extinction. While that may have been true in other places, it was not the case in Italy, Benazzi explained.
The SUCCESS project analysed the pollen from paleolake (ancient lake) cores using minerals collected from ancient stalactites. These calcium icicles which hang inside caves are effectively climate time machines, and researchers can decode what the climate was like when they formed.
Through this approach, the SUCCESS project reconstructed the paleoclimate (prehistoric climate) between 40-60 000 years ago. In contrast to ice-core analysis from Greenland, there were no data indicating catastrophic climate change in Italy, making it unlikely to have killed off the Neanderthals.
They closely examined a period of around 3 000 years when populations of Neanderthals and humans may have co-existed by excavating seven sites they once inhabited. They investigated the cultural and tool-making differences between the last Neanderthals and the first Homo sapiens in Italy.
Homo sapiens in Italy used specific types of technology including artefacts such as shell ornaments and projectiles like arrowheads. In fact, SUCCESS unearthed the earliest evidence for mechanically delivered projectile weapons in Europe.
Neanderthals would have found themselves at a severe disadvantage to their Homo sapiens relatives in terms of weapons technology. However, that meeting in Italy may never have happened.
Recently discovered remains in southern Europe show that at least one Neanderthal had been alive 44 000 years ago while the oldest Homo sapiens remains have been dated to 43 000 years ago. It is possible that they overlapped, but none of the current evidence shows that, Benazzi said.
Each region is different. ‘The result we get here (in Italy) doesn’t mean that we’re going to get the same results elsewhere,’ he said.
In the PALEOCHAR project, Carolina Mallol, a geoarchaeologist at the University of La Laguna in Spain and currently a visiting professor at UC Davis in the United States, is raking through the ashes of time, seeking traces of Neanderthals’ lives and hints of their demise.
The goal is to study microscopic and molecular charred matter from ancient fire sediments to see what organic material they left behind.
‘The handicap of the archaeologist is that the human world is organic, and we can’t get at it,’ said Mallol, who studies Neanderthal sites such as El Salt and Abric del Pastor in Spain.
When organic matter, such as meat or plants, is thrown in a fire, the heat dehydrates it, ultimately destroying its DNA and proteins. But fatty molecules called lipids can survive if the fire does not get hotter than about 350°C, as Mallol and colleagues show in their investigations.
‘PALEOCHAR was designed to explore how far we can take the analytical techniques to squeeze molecular information from the organic black layers (in the fire),’ she said.
Paleolipidomics (the study of ancient fats) has been used to study lipids in Roman amphorae, Egyptian mummies and even prehistoric leaves.
When it comes to ancient human sediments, ‘we are the first ones to apply (these techniques) systematically,’ she said. They also expanding the known lipid biomarkers, which are like molecular “barcodes” specific to species, families or even metabolic pathways.
‘With biomarkers, you can distinguish herbivores from carnivores, conifers from angiosperms,’ she said.
Mallol and colleagues set up the world’s first AMBILAB, which stands for the Archaeological Micromorphology and Biomarkers Research Lab, based in Tenerife, Spain, which trains researchers in the techniques of soil micromorphology and lipid biomarker analysis.
The questions about Neanderthals, such as why they went extinct, are very ambitious, said Mallol. ‘Those questions require that you first determine who they were and how they lived with a lot of information –– and we don’t have that information yet,’ she said.
With each new piece of information, archaeologists and scientists burrow deeper into the mystery of why our closest relatives suddenly disappeared while Homo sapiens managed to survive.
Research in this article was funded via the EU’s European Research Council and this article was originally published in Horizon, the EU Research and Innovation Magazine.
Distributed Ledger Technologies (DLTs)- as a counter to the growing threat of Centralisation
The Cyber era which found its genesis with the advent of the global internet- through the US Department of Defense funding ARPANET experimentations in the late 1960s – embodies the liberal spirit of laissez-faire and the freedom of expression and has grown rapidly from about 3 billion internet users in 2018, to over 5,385,798,406 internet users, – or 67.9% of our total population – as of June 30, 2022. Attempts, however, have been made to curtail users ability to access what is on the internet through the development of national intranets- examples being China’s great firewall, Russia’s sovereign internet, Iran’s National Information Network, and North Korea’s Kwangmyong network. What these national intranets have in common is centralised management in terms of websites and social media that are accessible, and information that is available to the citizens. Each of these states has the ability to distort truth, to rewrite the narrative, and to determine what is fact and force-feed it to their citizens by filtering opposing content and through restricting/blocking access to alternatives. ‘Centralisation’ here refers to concentrated power in the hands of one group, that allows for unilateral decisions to be made on behalf of the entire population or network.
The threat of centralisation also persists within the private sphere/realm, with tech companies having found themselves at the centre of a new debate in the press media. While tech companies focusing on social media may have first formulated/established themselves as outlets for expression of their user base they have now been taking strides in the opposite direction by censoring user posts and content. Recent instance include Pinterest which in 2019 began blocking any and all search results concerning vaccinations. Similarly, Facebook in 2020 began deleting ‘events’ that aimed at organising protests against home quarantine during the start of the COVID pandemic. Twitter in 2021 extricated over 70,000 accounts that were linked with the ‘QAnon’ conspiracy that threatened public order. This was part of Twitter’s policy of removing posts and deleting accounts that broke their platform’s rules. While in each of these instances the concerned tech company may have arguably acted with good intention, their ability to simply flick a switch and unanimously censor content is deeply troubling. The control that social media companies exerted even over influential people culminated with the permanent suspension of U.S ex-President Donald Trump’s Twitter handle. This incident is exemplary when it comes to determining the power of web censorship that is imbued within the hands of social media companies.
How can DLTs help counteract centrality?
‘Distributed Ledger Technologies’ (DLTs) can be used to counter the growing threat of centralisation from both state actors and monolithic tech-companies. There are various kinds of DLTs- such as Directed Acyclic Graphs (DAG), Holochain, Block Lattice, and the most renowned of all being- Blockchains. Quintessentially, DLTs are distributed peer-to-peer networks that utilise a majority consensus for transactions to be verified and then stored as data on a public ledger. For simplicity sake this article will utilise blockchains to illustrate how DLT networks function.
‘Nodes’ are individual peers within a Blockchain network that maintain a record of the ledger thereby being involved in the process of storing, verifying, and distributing the full set of data with other participant nodes on the blockchain. This “ledger of records” is immutable- allowing for data, events, and transactions to be time-stamped on chain, thus creating a verifiable log of all network user’s micro-history. The key features of a DLT are that they are ‘immutable’, ‘trustless’, and ‘verifiable’ with transactions being easily accessible/viewable by anyone in the entire network and it is these qualities that become instrumental when countering centralisation.
Take for instance the ostracisation of some Iranian national banks in 2012 and Russian national banks in 2022 from the ‘Society for Worldwide Interbank Financial Telecommunications’ (SWIFT)- an international banking system which executes international financial transactions. These instances demonstrate the current weaknesses/drawbacks of our existing financial system- as failure to comply with international norms has resulted in the enforcement of ‘one-sided’ economic sanctions. This cuts off these Banks’ and thereby the nation’s access to the global market as most exchanges occur using SWIFT via the U.S. Dollar and has the secondary effect of debilitating the economic strength of the local currency. Cryptocurrencies, however, are not restricted by these same limitations and were thereby used as a hedge by citizens both in Ukraine and in Russia to save the value of their savings by transferring them from fiat currencies into digital cryptocurrencies. DLTs moreover are resistant to external influence as the transactions (here referring to both financial transactions and any information that is ingrained on-chain) occurring on them are ‘immutable’, which means that once the network/chain is set up- the data time-stamped onto the ledger can no longer be tampered with by third-parties and will continue to exist on-chain permanently. Similarly, attempts made to restrict transactions to -and- between users from a particular region will prove ineffectual as no single entity has control over the entire network.
A second advantage/strength of DLTs is that the ‘consensus mechanism’- the process through which nodes coordinate to add transactions to the network- is designed in such a way so as to allow for the entire process to be ‘trustless’. The immutability of the DLT grants participants on the network the ability to engage in transactions with one-another without having to trust one-another or rely on a third intermediary such as banks or centralised tech platforms to execute the transaction. Moreover, these financial transfer are instant- a real life example being witnessed during the 2022 Russian invasion of Ukraine where individual netizens across the world were able to make direct donations totalling $42 million in 6 days to the Ukrainian Government after they posted their verified wallet address/public key- circumventing the restrictions imposed through bureaucracy. Furthermore it can be argued that the public nature of the digital ledger of transactions grants greater transparency to the public on how exactly the donated money was and can be spent. This is due to the fact all transactions made on-chain (the sending of cryptocurrency to the donated address and the spending of this donated amount on other things) become visible through inputting the public key address on tools such as BSCscan or Etherscan which display all existing transactions. The astounding feature of this whole process is that this can be executed while granting anonymity to the donators- as the only way to identify which address belongs to whom is if the donator somehow revealed that the corresponding wallet belonged to them.
To summarise DLTs have a low barrier to entry as anyone with an internet connection and who is willing to invest time and energy into understanding how the crypto space/system works is able to utilise it. DLTs are designed to be resistant to censorship as every node is independent and the network therefore decentralised. Therefore, a good way to test the strength of a DLT is to measure/assess how easy it would be for a government, corporation, or any external third party (venture capitalist firms, hackers, hacktivists) to shut down or interfere with the network. To shut down or effectively change a decentralized network would require ownership or control of over half the nodes or systems. Even individual countries are incapable of exacting their influence on these independent networks. Algeria, Nepal, Northern Macedonia, and China have all passed laws that decreed the trading and purchase of cryptocurrencies and the utilisation of their underlying blockchains as illegal further blocking user access to websites where cryptocurrencies could be purchased or exchanged making user access difficult but not impossible. Technologies such as Virtual Private Networks (VPNs) grant users the ability to circumvent censorship and allow citizens of even the most authoritarian regimes accessibility. The immutable, anonymous, and decentralized (cross-border/international) nature of DLTs therefore make it very hard for countries to police and regulate crypto transactions. In fact, this was a point argued by Indian Finance Minister Nirmala Sitharaman who called for mutual cooperation and a common solution between countries to tackle the global dilemma posed by cryptocurrencies during a high-level panel discussion organised by the International Monetary Fund (IMF) in April, 2022.
This makes it evident that for the first time in the history of humanity a series of systems are implemented that are capable of resisting State influence which has historically enjoyed its power unperturbed. What has made all of this possible is the invention of ‘smart contracts’, which is immutable computer code, existing on-chain, that allows for the terms and conditions of an agreement between two parties to be carried out without the intervention of a third-trusted party. The contractual clause- such as the release of funds in the form of cryptocurrencies (Bitcoin, Ethereum, Monero etc.) is executed automatically when the necessary preconditions (the fulfilment of services) have been successfully met. Smart contracts allow for the development of decentralised applications and offer increased versatility.
How to discern a centralised DLT from a truly decentralised DLT
DLT networks attempt to adhere to the tenets of decentralisation, at least ideologically, however, the harsh reality is that many of them simply masquerade themselves as being decentralised while falling short of the benchmark. ‘Solana’ is a fine example of an open-source blockchain that despite utilising smart contracts, and supporting decentralised applications (‘dapps’) is still quite centralised. For consensus and for adding transactions to its blockchain Solana utilises a hybrid proof-of-stake model combined with what it has termed as ‘proof-of-history’- where ‘leader’ nodes are chosen randomly for validation for fixed periods of time- thereby lowering latency and increasing throughput. While Solana currently has 1975 validator nodes running giving the illusion of decentralisation- just 32 nodes hold a third of the total staked supply of SOL (a.k.a cumulative stake) and thereby validate a third of all transactions! This is dangerous as this implies that 32 of the largest nodes could potentially collude to halt the network. Secondly, once a DLT is up and running outages should virtually be impossible provided the DLT is decentralised enough as no one can collude to temporarily shut off the network. Solana witnessed six outages during the month of January 2022 for periods lasting longer than 8 hours, during which time they halted the entire chain to identify and fix the issues before restarting the chain- something indicative of the centralised nature of this network. Finally, according to a 2021 report by Messari over 48% of Solana’s token allocation at its genesis were allotted to venture capital firms with only a very small fraction going to the public through lock drops or pre-launch sales. Any DLT having almost half of its initial token allocation allotted to VC firms cannot be said to adhere to the ideologies of decentralisation as only a sliver of the entire allocation was even purchasable/attainable by the public. It is for these reasons that Solana can be categorised as a fairly centralised blockchain.
Bitcoin- the original progenitor of all blockchains- currently having over 15000 reachable nodes active all throughout the world- serves as a prime example of a DLT that truly mirrors the ethos of decentralisation. Bitcoin fulfils the core tenets of decentralization with its blockchain being immutable, trestles through by utilising ‘proof-of-work’ (PoW) for consensus, and transparent with all the transactions on its blockchain being verifiable through services such as BScscan. Moreover, the initial coins were distributed through the mining of blocks- which could be carried out by anyone with a Graphic Processing Unit (GPU) available within Personal Computers- further implying/meaning that bitcoins were openly accessible/earn-able by the public. Furthermore, and in direct contrast to Solana, Bitcoin is leaderless and since its inception in 2008 has never experienced any outages. To enact any change or upgrade to the Bitcoin network requires over 51% of the nodes on the network to acquiesce. Some criticisms have arisen that make reference to the top 6 (centrally managed) mining pools that when combined amount to over 75% of the total computing power in Bitcoin- a fact which would allow them to validate or cancel transactions, conduct double-spending and create coins from thin air. However, the cold-undisputed truth remains that Bitcoin has never, since its inception, witnessed any such collusion that has resulted in a 51% attack- therefore, for all intents and purposes, Bitcoin stands as the apotheosis of decentralisation.
The conveniences afforded through the proliferation of the internet have simultaneously given rise to increasing avenues of centralised control to both national governments and monolithic state companies. In fact, Twitter Founder Jack Dorsey himself has grown despondent at this centralised nature of the internet and recently announced plans to create a new decentralised platform to combat it- terming this as the new Web5. However, this article has also made clear how DLTs and their underlying crypto assets provide a unique solution to countering the growing threat of centralisation. Truly decentralised networks cannot be stopped by the government through some obscure law because the only law in crypto is ‘immutable computer code.’ Neither can cryptocurrencies on these networks be confiscated as they are private assets truly owned by the individual key holder. Governments are aware that DLTs and cryptocurrencies are a frontier they do not exercise sovereignty over and are actively adopting stances to oppose them. Therefore, it can be said that the true test of a DLTs decentralised nature will be to observe how each of them respond to increasing censorship from state and tech influence. It is the author’s opinion and belief that DLTs will remain relevant and continue to grow undeterred because digital assets and their underlying technology are firmly located at the heart of the next technological revolution that is reshaping the world across societies and economies.
 Andrews, Evan. 2019. “Who Invented The Internet?”. HISTORY. https://www.history.com/news/who-invented-the-internet.
 Morgan, Steve. 2019. “Humans On The Internet Will Triple From 2015 To 2022 And Hit 6 Billion”. Cybercrime Magazine. https://cybersecurityventures.com/how-many-internet-users-will-the-world-have-in-2022-and-in-2030/.
 D’sa, Douglas Daniel. “How has China been using Artificial Intelligence (AI) to build a digital system of Social Control in East Turkistan (Xinjiang)?.” (2021).
 Epifanova, Alena. “Deciphering Russia’s “Sovereign internet law”: Tightening control and accelerating the Splinternet.” (2020): 10.
 Eyvazi, Mohammad Rahim, Safiye Rezaee, and Mohsen Mohammadi Khanghahi. “The role of the national information network in strengthening the independence and national security in the second step of the Islamic Revolution.” Islamic Revolution Research 10, no. 4 (2022): 29-55. (double check this one)
 Williams, Martyn. 2022. “A Peek Inside North Korea’s Intranet”. North Korea Tech – 노스코리아테크. https://www.northkoreatech.org/2015/07/06/a-peek-inside-north-koreas-intranet/.
 Telford, Taylor. 2019. “Pinterest Is Blocking Search Results About Vaccines To Protect Users From Misinformation”. The Washington Post. https://www.washingtonpost.com/business/2019/02/21/pinterest-is-blocking-all-vaccine-related-searches-all-or-nothing-approach-policing-health-misinformation/.
 Ghaffary, Shirin. 2020. “Facebook Is Taking Down Some, But Not All, Quarantine Protest Event Pages”. Vox. https://www.vox.com/recode/2020/4/20/21228224/facebook-coronavirus-covid-19-protests-taking-down-content-moderation-freedom-speech-debate.
 Conger, Kate. 2021. “Twitter, In Widening Crackdown, Removes Over 70,000 Qanon Accounts (Published 2021)”. Nytimes.Com. https://www.nytimes.com/2021/01/11/technology/twitter-removes-70000-qanon-accounts.html.
 Twitter. 2021. “Permanent Suspension Of @Realdonaldtrump”. Blog.Twitter.Com. https://blog.twitter.com/en_us/topics/company/2020/suspension.
 The Zenon Team. 2020. “Network Of Momentum- Leaderless BFT Dual Ledger Architecture”. https://github.com/zenon-network/zenon.network/releases/download/whitepaper/whitepaper.pdf.
 Florian, Martin, Sebastian Henningsen, Sophie Beaucamp, and Björn Scheuermann. “Erasing data from blockchain nodes.” In 2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), pp. 367-376. IEEE, 2019.
 Tulun, Teoman Ertuğrul. “SWIFT System Turns Into Economic Sanctions Instrument.” (2022).
 Lau, Yvonne. 2022. “They Fled Russia With Little Cash. Here’S How Cryptocurrency Saved Them”. Fortune. https://fortune.com/2022/03/30/russia-ukraine-war-cryptocurrency-sanctions-capital-controls-refugees-cash-oligarchs/.
 Taku, Nitasha, and Jeremy Merrill. 2022. “Ukraine Asked For Donations In Crypto. Then Things Got Weird.”. Washington Post. https://www.washingtonpost.com/technology/2022/03/03/ukraine-cryptocurrency-donations/.
 Cernera, Federico, Massimo La Morgia, Alessandro Mei, and Francesco Sassi. “Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and the Binance Smart Chain (BNB).” arXiv preprint arXiv:2206.08202 (2022).
 Orji, Chloe. 2022. “Bitcoin Ban: These Are The Countries Where Crypto Is Restricted Or Illegal”. Euronews. https://www.euronews.com/next/2022/04/27/bitcoin-ban-these-are-the-countries-where-crypto-is-restricted-or-illegal2.
 Varvello, Matteo, Inigo Querejeta Azurmendi, Antonio Nappa, Panagiotis Papadopoulos, Goncalo Pestana, and Benjamin Livshits. “VPN-Zero: A Privacy-Preserving Decentralized Virtual Private Network.” In 2021 IFIP Networking Conference (IFIP Networking), pp. 1-6. IEEE, 2021.
 PTI. 2022. “Cryptocurrency Could Be Used For Money Laundering And Terror Funding, Says Indian Finance Minister”. Business Insider. https://www.businessinsider.in/cryptocurrency/indian-finance-minister-says-that-cryptocurrency-could-be-used-for-money-laundering-and-terror-funding/articleshow/90933116.cms.
 Zheng, Zibin, Shaoan Xie, Hong-Ning Dai, Weili Chen, Xiangping Chen, Jian Weng, and Muhammad Imran. “An overview on smart contracts: Challenges, advances and platforms.” Future Generation Computer Systems 105 (2020): 475-491.
 Yakovenko, Anatoly. 2017. “Solana: A New Architecture For A High Performance Blockchain V0.8.13”. Solana.Com. https://solana.com/solana-whitepaper.pdf.
 Beach, Solana. 2022. “Dashboard | Solana Beach”. Solanabeach.Io. https://solanabeach.io/validators. Accessed on 6th September, 2022.
 Nicolle, Emily, and Bloomberg. 2022. “Solana’S Sixth Outage This Month—And Founder’S ‘Lol’ Tweet—Frustrates Traders”. Fortune. https://fortune.com/2022/01/25/solana-founder-anatoly-yakovenko-crypto-crash-blockchain-instability/.
 Watkins, Ryan. 2022. “Power And Wealth In Cryptoeconomies”. Messari.Io. https://messari.io/report/power-and-wealth-in-cryptoeconomies.
 Bitnodes. 2022. “Bitnodes”. Bitnodes.Io. https://bitnodes.io/. Accessed on 12th September, 2022
 Cernera, Federico, Massimo La Morgia, Alessandro Mei, and Francesco Sassi. “Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and the Binance Smart Chain (BNB).” arXiv preprint arXiv:2206.08202 (2022).
 Gervais, Arthur, Ghassan O. Karame, Vedran Capkun, and Srdjan Capkun. “Is bitcoin a decentralized currency?.” IEEE security & privacy 12, no. 3 (2014): 54-60.
 Ramage, Jack. 2022. “Move Over Web3. Former Twitter CEO Jack Dorsey Wants To Launch Web5 Based On Bitcoin”. Euronews. https://www.euronews.com/next/2022/06/15/move-over-web3-former-twitter-ceo-jack-dorsey-wants-to-launch-web5-based-on-bitcoin.
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