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A Brave New World without Work

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What’s the first thing that comes to mind when you think about the soon-to-come widespread introduction of robots and artificial intelligence (AI)? Endless queues of people waiting to get unemployment benefits? Skynet drones ploughing the sky over burnt-out slums? Or the opposite: idleness and equality provided by the labour of mechanical slaves? In all likelihood the reality will be less flashy, though that doesn’t mean we should ignore the social consequences of the technological changes taking place before our very eyes.

Revolution on the March

The Fourth Industrial Revolution with its robotics, bio and nanotechnologies, 3D printing, Internet of things, genetics, and artificial intelligence is rapidly spreading across the world [1]. The coming technological changes will have direct consequences for a number of existing professions and promise in the very least to transform the labour market in developed countries.

The high speed of change (suffice it to say that 10 of the most popular professions of 2010 did not exist in 2004) makes it difficult to predict the impact on society. In this regard, the assessments of experts and international organizations range from optimistic to alarmist. However, even if we were to eliminate the most extreme case scenarios, we could still say with certainty that a fundamental restructuring of the global economy, comparable to the one that took place in the 18th–19th centuries during the First Industrial Revolution, awaits us in the foreseeable future.

According to the World Economic Forum (WEF) Future of Jobs report, 65% of today’s primary school students will have hitherto unheard-of professions. McKinsey came to the same conclusion, highlighting in their report that at the current level of technological development, 30% of the functions of 60% of professions can be automated. M. Osborne and C. Frey of Oxford University give an even more pessimistic forecast. According to their research, 47% of jobs in the US risk being automated within 20 years.

Who will robots replace?

What professions are at risk? First at risk is, of course, unskilled labour. The Osborne and Frey study found clerks, data entry workers, librarians, machine operators, plumbers, sales specialists, and equipment adjusters among others to be those most vulnerable.

According to WEF, from 2015 to 2020, job reductions will have the greatest effect on office professions (4.91%) and the manufacturing sector (1.63%). Employment in areas such as design, entertainment, construction, and sales should also decline by 1%. In turn, the most significant growth in jobs is predictably expected in the field of computer technology (3.21%), architectural and engineering specialties (2.71%), and management (just under 1%).

Predictably, professions related to transport risk automation in the medium term. The development of self-driving vehicles could radically change both the passenger and freight traffic markets. In the US alone, 8.7 million people are employed in the long-distance freight traffic industry. If you take into account all of the business operations connected to trucking (motels, roadside cafes, etc.), the number increases to 15 million or about 10% of the country’s labour force. Reductions in passenger transport and the public transport sector are likely to be even more significant. It is also probable that self-guiding technologies will be introduced into sea freight traffic in the near future. The development of artificial intelligence should also bring down hard times on lawyers, teachers, miners, middle management, and journalists among others.

It can be said that on the whole, employment will gradually move from services to other sectors of the economy, many of which have yet to be created. The possibility is a confirmation of the revolutionary nature of the changes that are taking place rather than something unique. Before the First Industrial Revolution, over 70% of the population was occupied with agriculture, whereas nowadays the number hovers around a few percent in developed countries. The percentage of those employed in manufacturing continued to grow until the mid-twentieth century, though it has now fallen to 24% in the EU and 19% in the US (27% in Russia) as a result of the Digital Revolution. Meanwhile, although there are fewer workers, production volume continues to rise steadily. It would now appear to be time to automate services.

The Golden Age of Engineers and Psychiatrists?

Professions associated with intellectual work or direct personal contact with clients are least likely to suffer in the short term. According to the study from Oxford University, professions least susceptible to automation include various jobs in medicine and psychology, as well as coaches, social workers, programmers, engineers, representatives of higher management and creative professionals.

In other words, those whose work requires a creative approach and is not limited to the performance of predictable combinations will be best prepared to deal with the new reality. If we were to speak of engineers in this regard, it would have to be clarified that design engineers are generally safe, while operating engineers, on the contrary, are at risk.

Three key factors are keeping automation away from the creative professions. To successfully perform their tasks, artificial intelligence must possess intuition and an ability to manipulate material objects (touch) and make use of creative and social intelligence. Technology at its current level of development does not actually allow for the resolution of these problems. However, as strong AI continues to develop, the range of jobs available to it will invariably increase as well. It will expand the limits of automation that have already been achieved with existing technologies and will make it possible for computers to make managerial decisions and even, perhaps, engage in creative activity. Therefore, it cannot be ruled out that in the medium or long term, machines might successfully replace writers and artists along with engineers and managers. Furthermore, precedents do exist for AI’s successfully composing literary texts.

Thus, it is quite conceivable that the majority of the labour force will find itself back in school in the foreseeable future. The problem, however, is that no one really knows what to study. It has been estimated, that as many as 85% of the professions that will be in demand in 2030 do not yet exist. Even in developed countries, the education systems have yet to adapt to the new reality.

What will become of our country and of us?

Today, most researchers have little doubt that developed countries will successfully adapt to the changes coming one way or another (which does not rule out the possibility of social tension and growth in income inequality). New technologies could help create additional jobs to replace those that have been lost, as it was not long ago following the rapid development of the Internet. It is assumed that the new professions will be more creative and better paid.

A new balance will gradually be established in the labour market. The nature of manufacturing will also change. The development of automation and 3D printing will make it possible to create efficient local production facilities focused on the specific needs of consumers. This will facilitate the return of a part of production from developing countries to developed (so-called reshoring).

In turn, the consequences of automation could be much more negative for countries of the third world. The percentage of non-skilled jobs in developing countries decreased by 8% between 1995 and 2012. Reshoring could significantly accelerate this process in the short term. Since the proportion of people engaged in low-skilled work in low and middle-income countries is much higher, the growth of unemployment would threaten to become a major global problem. The situation would be further aggravated by the underdevelopment of labour protection institutions in these countries.

It must be noted that risks of this sort are endemic to Russia as well. Despite the significantly higher level of education of its citizens in comparison to that in developing countries, the Russian economy could hardly be called high-tech. A significant part of the working population is engaged in routine low-skilled labour, and productivity remains low as well. At the present time, Russia lags significantly behind other developed countries in regards to this indicator (and behind the US by more than 100%), and according to some estimates falls below the world average. What’s more, factory jobs are not the only ones at stake – an army of many millions of bureaucrats and clerks is also under threat of redundancy as a result of digitalization.

Another disaster waiting to happen to the Russian economy is related to outdated industry and the decline of domestic engineering. At present, institutions of higher education produce mainly operational engineers trained to maintain tools and machines. What’s more, even the limited innovative potential of Russian engineers is not needed by Russian industry.

Furthermore, it cannot be ruled out that in the near future Russia will launch a massive programme to introduce robotic automation and artificial intelligence. All the more since it fits in perfectly with the desire to modernize and digitalize the national economy repeatedly spoken of by the Russian leadership. Because of the lack of a strong trade union movement and the prevalence of hybrid and grey forms of employment, labour automation could lead to much more severe social consequences in Russia than in Western countries. Finally, it is entirely possible that the catch-me-if-you-can nature of such modernization will result in Russia introducing more primitive technologies than in more developed countries. Editor-in-Chief of Russia in Global Affairs magazine and RIAC Member Fyodor Lukyanov cleverly described a similar scenario in his article.

Saving the Rank and File

Ways to reduce the social consequences of labour automation have long been at the heart discussions surrounding the Fourth Industrial Revolution and the development of AI. The Robot Tax is one measure being considered. Microsoft Founder Bill Gates supports the idea and has proposed collecting income tax and social payments on robot labour to slow down the pace of automation. “Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, social security tax, all those things. If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level,” he declared in an interview for the Internet publication Quartz. It is his opinion that the funds received from payments of this sort should be used by governments to create social security systems for those who have lost their jobs as a result of automation.

The first country to resort to this measure is South Korea, which introduced an indirect tax on robots in August 2017. The European Union also discussed the introduction of a similar tax, though the clause proposed by Progressive Alliance of Socialists and Democrats Representative Mady Delvaux was rejected by the European Parliament was rejected by the European Parliament because it could slow the development of innovations. At the same time, the parliament approved the resolution itself, which calls for granting robots the status of legal entities.

A universal basic income could also soften the effect of rising unemployment and inequality. Elon Musk supports the initiative together with numerous other businessmen and experts. At the same time, a lack of work to afford one the opportunity to fulfil one’s potential poses a significant social risk. Significant unemployment, even in the absence of poverty, can contribute to the marginalization of the population and the growth of crime – the first jobs to go are those of low-skilled employees, who are unlikely to spend all of their permanent free time engaged in yoga and self-improvement activities.

Possible ways of mitigating the consequences of the upcoming restructuring of the world economy include a change in the nature of employment. Technological changes and expanding access to the Internet allow more and more people to work remotely. Thus, some of those who lose their jobs will be able to find themselves a place in the new economy without having to change their place of residence.

Some believe that automation will increase and not reduce the total number of jobs by accelerating the pace of economic development over the long term. Amazon is one example of how automation has not resulted in staff reduction. While increasing the number of robots employed in its warehouses from 1,400 to 45,000, it has managed to retain the same number of jobs. It has also been noted that automation is becoming increasingly necessary due to a decrease in the working-age population (primarily in developed countries).

It should be noted that these measures are all limited in nature and hardly correspond to the scale of changes that stand to be swept in by the Fourth Industrial Revolution. To avoid mass unemployment and social instability, governments must develop comprehensive short-term strategies for adapting the population to the new reality. It is very likely that new programs will be needed to retrain citizens en masse for new professions.

Russia is no exception here; on the contrary, it is of vital importance that our country reform its education system in the near future, especially as regards technical education. It is equally important to develop targeted support programs for those parts of the population that are most vulnerable to automation and digitalization. Moreover, it would seem advisable to make use of existing experience to mitigate the social consequences of factory closures in Russian single-industry towns. If we continue to move as sluggishly as we are moving at present, we risk turning into a kind of reserve for yesterday’s technologies with a population becoming ever more rapidly marginalized.

First published in our partner RIAC

[1] Marsh, P. The New Industrial Revolution. Consumers, Globalization, and the End of Mass Production. M.: Gaidar Institute Press, 2015.

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The Promise of Blockchain in Mega Sport Events

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Authors: Dr. Aiman Erbad and Dr. Mohamed Abdallah

Amid the excitement and anticipation of the FIFA World Cup Qatar 2022TM, sport remains a business. Like other global industries, the adoption of technology innovations is driving greater efficiency and transparency to generate benefits for sports organizations, leagues, clubs, and fans.

Researchers at the College of Science and Engineering (CSE), Hamad Bin Khalifa University (HBKU), make the case for adopting blockchain-powered solutions in delivering seamless sport mega events by outlining some of the top use cases.

Understanding blockchain

“Blockchain can solve many real-world problems,” explains Dr. Mohamed Abdallah, Associate Professor in the Division of Information and Computing Technology (ICT) at CSE.

“For mega sport events, the benefits can be exceptional. Because of its transparent data structure, blockchain can facilitate secure and reliable data exchange at the individual, institutional, or national systems level as needed, without the need for intermediaries to ensure mutual trust and the authenticity of the data exchanged.”

The chaos of the UEFA Champions League final between English team Liverpool and Spanish club Real Madrid in May 2022, which resulted from the illegal distribution of non-validated tickets, is likely to have accelerated the recognition of blockchain’s benefits for the sport industry. The ensuing government inquiry unequivocally called for using blockchain for ticketing to prevent a similar fiasco at future events. A closer look at the nature of this cutting-edge technology reveals why.

How blockchain works

By its functional nature, a blockchain is a distributed (or shared) digital ledger that stores encrypted blocks of transaction data securely chained together in chronological order. Unlike other ledgers or databases, blockchain combines unique security features based on cryptographic techniques and its chronological chain structure.

In its standard form, blockchain provides immutability (data entered is permanently recorded), transparency (data is visible to everyone involved), and decentralization (all computers in the network have a copy of the blockchain to collectively maintain control). These features facilitate a tamper-proof, reliable way of storing, exchanging, and tracking information.

A key use case for mega events: preventing ticketing scandals

Dr. Abdallah and Dr. Aiman Erbad, Associate Professor and Head of ICT at CSE, add their expert voices to arguments that the UEFA Champions League final chaos could have been prevented using a blockchain platform with a self-enforcing contract capability to facilitate a secure ticket purchase process.

In practical terms, tickets can be stored on the blockchain denoted with unique cryptographic tokens. Each ticket can be linked to the authentic owner, providing traceability and accountability that prevents forgery. In this way, it can effectively reduce the impact of bots and/or scammers buying large numbers of tickets for illegal resale.

Using blockchain-based “smart contract” technology, ticketing entities can set the required resale rules to ensure a fair and secure market. These digital contracts can facilitate transactions between buyers and sellers while maintaining data accountability and traceability.

A related use case is storing the chain of ticket ownership. These records cannot be forged since changes are verified and tracked, ensuring data integrity. It can help customers validate the authenticity of tickets to avoid being trapped by ticketing scams.

Other use cases in sport

Blockchain-powered fan engagement is a growing use case for the sports industry. Several professional leagues and clubs are using blockchain to establish trustworthy fan databases that facilitate the distribution of “fan tokens”. With the status of a digital asset (created on a blockchain), the tokens can be redeemed by fans for rewards such as VIP experiences or ticket promotions. The increased fan engagement can potentially create new revenue streams for clubs; for example, incentivizing them to attend more events in person. Fan tokens have been rolled out by professional sports teams all over the world, including Paris Saint-Germain and FC Barcelona.

In another use case, the market for sports collectibles and memorabilia can leverage blockchain to establish trust and traceability. Experts have warned that fraud is rampant in the sports collectibles and memorabilia market. Blockchain can ensure the authenticity of special items through the use of digital identities.

CSE’s own blockchain-based applications

CSE faculty members are developing innovative use cases for blockchain in a range of applied settings.

“Our research focuses on the applicability of blockchain in solving real-world problems, such as securing data access in healthcare and decentralized trading,” says Dr. Erbad.

“We also study the technical aspects of blockchain to enhance its security, privacy, and efficiency. We have investigated the possibility of reducing energy consumption in public blockchains and developed an energy-efficient consensus algorithm. In other areas, we have also investigated using artificial intelligence in combination with blockchain smart contracts, called Rational Contracts, to provide smart resource trading with optimal prices in smart city applications.”

Among CSE’s blockchain-based applications are a trading platform for electric vehicle charging in smart cities, a decentralized ride-sharing service, a privacy-preserving decentralized stock exchange platform, a scalable energy trading sealed-bid auction mechanism, real-time secure health data exchange system, and a cooperative spectrum management system for 5G networks.

A national blueprint for Qatar

CSE had a leading role in developing the Qatar National Blockchain Blueprint in collaboration with the Communications Regulatory Authority and Qatar University. The blueprint highlights how blockchain can advance Qatar’s innovative and growing IT sector.

Essential blockchain requirements and recommendations for building a solid regulatory framework drive its pivotal goal of facilitating blockchain’s adoption at the national level, in support of Qatar National Vision 2030 and Qatar National Development Strategy. To achieve this, the blueprint outlines the conditions and incentives each sector must provide for the level of technology adoption needed to allow start-ups, pilot projects, and new companies to emerge. The strategy is an important step for Qatar, its sports, and other leading industries, to reap the societal benefits of this innovative technology.

For more information on the work of the College of Science and Engineering, please visit cse.hbku.edu.qa. To know more about Qatar National Blockchain Blueprint, please visit: https://www.cra.gov.qa/document/national-blockchain-blueprint

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Interesting archaeological discovery in Israel

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An ancient scarab from three thousand years ago was surprisingly discovered during a school trip to Azor, near Tel Aviv, Israel. The scene depicted on the scarab probably represents the conferral of legitimate power and authority on a local ruler.

“We were wandering around, when I saw something that looked like a small toy on the ground,” told Gilad Stern of the Education Centre of the Israeli Antiquities Authorityntre, who was leading the school trip. “An inner voice told me: ‘Pick it up and turn it over.’ I was amazed: it was a scarab with a clearly engraved scene, the dream of every amateur archaeologist. The pupils were really enthusiastic!”.

The visit of the Rabin Middle School eight graders took place as part of a tour guide course organised by the Education Centre of the Israel Antiquities Authority for the third consecutive year. The course enables students to teach the residents of Azor about the local archaeological heritage.

The scarab was designed in the shape of the common dung beetle. The ancient Egyptians saw in the gesture of the tiny scarab, which rolls a ball of dung twice its size where it stores its future offspring, the embodiment of creation and regeneration, similar to the gesture of the Creator God.

According to Dr. Amir Golani, an expert of the Israeli Antiquities Authority specialized in the Bronze Age period, “the scarab was used as a seal and was a symbol of power and status. It could be inserted into a necklace or a ring. It is made of silicate earthenware covered with a bluish-green glaze. It could have fallen from the hands of an important and influential personage passing through the area, or it could have been deliberately buried in the ground with other objects and after thousands of years returned to the surface. It is difficult to determine the precise original context.”

The lower, flat part of the scarab seal depicts a figure seated on a chair in front of a standing figure, whose arm is raised above that of the seated person. The standing figure has an elongated head, which seems to represent the crown of an Egyptian pharaoh. It is possible that we are seeing here a snapshot of a scene in which the Egyptian pharaoh confers power and authority on a local Canaanite.

“This scene fundamentally reflects the geopolitical reality that prevailed in the Land of Canaan during the Late Bronze Age (approx. 1500-1000 BC), when local Canaanite rulers lived under Egypt’s political and cultural hegemony (and sometimes rebelled against it)” – said Dr. Golani. “It is therefore very likely that the seal dates back to the Late Bronze Age, when the local Canaanites were ruled by the Egyptian Empire”.

Scarab seals are indeed distinctly Egyptian, but their widespread use extended beyond the borders of ancient Egypt. Hundreds of scarabs were discovered in the Land of ancient Israel, mostly in tombs, but also in settlement layers. Some of them were imported from Egypt, many others were imitated in ancient Israel by local craftsmen under Egyptian influence. The level of workmanship of the particular scarab found is not typical of Egypt and may be a product of local craftsmen.

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Towards Efficient Matrix Multiplication

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Algorithms have, over the years, helped mathematicians/scientists solve numerous fundamental operations. From the early use of simple algorithms by Egyptian, Greek, and Persian mathematicians to the shift towards more robust AI-enabled algorithms, their evolution has manifested incredible progress in the technological realm. While Artificial Intelligence (AI) and Machine Learning (ML) are extending their reach and contributions in various military and civilian domains, it is interesting to witness the application of the technology on itself, i.e., using ML to improve the effectiveness of its underlying algorithms.

Despite the increased familiarisation with algorithms over time, it remains fairly strenuous to find new algorithms that can prove reliable and accurate. Interestingly, ‘Discovering faster matrix multiplication algorithms with reinforcement learning,’ a recent study by DeepMind, a British AI subsidiary in London, published in Nature, has demonstrated some interesting findings in this regard. It revealed new shortcuts simulated by AI for faster mathematical calculations vis-à-vis matrix multiplication.

DeepMind developed an AI system called ‘AlphaTensor’, to expedite matrix multiplication. Matrix multiplication – which uses two grids of numbers multiplied together – is a simple algebraic expression often taught in high school. However, its ubiquitous use in the digital world and computing has considerable influence.

‘AlphaTensor’ was tasked with creating novel, correct, and efficient algorithms to carry out matrix multiplication with the least number of steps possible. The algorithm discovery process was treated as a single-player game. It used AlphaZero – the same AI agent which gained global traction when it displayed extraordinary intelligence in board games like Chess and Go.

AlphaTensor conceptualised the board into a 3-D array of numbers which, through a limited number of moves, tried to find the correct multiplication algorithms. It uses reinforcement learning, where the neural networks interact with the environment toward a specific goal. If the results are favourable, the internal parameters are updated. It also uses Tree Search, in which the ML explores the results of branching possibilities to choose the next action. It seeks to identify the most promising action at each step. The outcomes are used to sharpen neural networks, further helping the tree search, and providing more successes to learn from.

As per the paper’s findings, AlphaTensor discovered thousands of algorithms for various sizes for multiplication matrices, some of which were able to break decades-long computational efficiency records of the previously existing algorithms. They overshadowed the towering complexity of the best-known Strassen’s two-level algorithm for multiplying matrix. For example, AlphaTensor found an algorithm for solving a 4 x 4 matrice in 47 steps overperforming the Strassen algorithm, which used 49 steps for the same operation. Similarly, if a set of matrices was solved using 80 multiplication steps, AlphaTensor reduced it to only 76 steps. This development has caused quite a stir in the tech world as it is being claimed that a fifty-year old record has been broken in Computer Science.

However, the episode underlines some important implications. Given that matrix multiplication is a core component of the digital world, companies around the world have invested considerable time and resources in computer hardware for matrix multiplication. Since it is used across a wide range of domains, including computing, processing images, generating graphics, running simulations, digital communication, and neural networks etc. – to name a few, even minor improvements in matrix multiplication’s efficiency could have a notable and widespread impact in the concerned fields.

The findings manifest the potential of ML to solve even more complicated mathematical problems. The automatic discovery of algorithms via ML offers new capacities to surpass the existing best human-designed algorithms. It introduces new ML techniques, which have the potential to increase computing speed by 20 percent leading to much more feasible timelines. It is pertinent to mention that a lesser number of operations lead to not only lesser time but also less amount of energy spent.

The finding has presented a model to gamify ML to solve mathematical operations. It exhibited that AlphaZero is a potent algorithm that could be used beyond winning traditional games and be applied to solving complex mathematical operations/tasks.

This DeepMind discovery can pave the way for future research on understanding matrix multiplication algorithms and be an inspiration to use AI for algorithm discovery for other computing tasks and set the stage for a possible breakthrough in the field. 

The increased efficiency of matrix multiplication has once again brought into light the ever-expanding potential of AI. To be fair, such developments do not infer that human programmers would be out of the job soon; rather, at least for now, it should be seen as an addition of an optimisation tool in the coder’s arsenal, which could lead to more innovative discoveries in the future with remarkable implications for the world.

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