Wagner and Furst exhaustively explore the inner workings and implications of AI in their new book, “AI Supremacy: Winning in the Era of Machine Learning”. Each chapter focuses on the current and future state of AI within a specific industry, country or society in general. Special emphasis is placed on how AI will shape the domestic, diplomatic and military landscapes of the US, EU and China.
Here is an interview with Daniel Wagner
Can you briefly explain the differences between artificial intelligence, machine learning, and deep learning?
Artificial intelligence (AI) is the overarching science and engineering associated with intelligent algorithms, whether or not they learn from data. However, the definition of intelligence is subject to philosophical debate-even the terms algorithms can be interpreted in a wide context. This is one of the reasons why there is some confusion about what AI is and what is not, because people use the word loosely and have their own definition of what they believe AI is. People should understand AI to be a catch-all term for technology which tends to imply the latest advances in intelligent algorithms, but the context in how the phrase is used determines its meaning, which can vary quite widely.
Machine learning (ML) is a subfield of AI that focuses on intelligent algorithms that can learn automatically (without being explicitly programmed) from data. There are three general categories of ML: supervised machine learning, unsupervised machine learning, and reinforcement learning.
Deep learning (DL) is a subfield of ML that imitates the workings of the human brain (or neural networks) in the processing of data and creating patterns for use in decision-making. It is true that the way the human brain processes information was one of the main inspirations behind DL, but it only mimics the functioning of neurons. This doesn’t mean that consciousness is being replicated, because we really do not understand all the underlying mechanics driving consciousness. Since DL is a rapidly evolving field there are other more general definitions of it, such as a neural network with more than two layers. The idea of layers is that information is processed by the DL algorithm at one level and then passes information on to the next level so that higher levels of abstraction and conclusions can be drawn about data.
Is China’s Social Credit Score system about to usher in an irreversible Orwellian nightmare there? How likely is it to spread to other dictatorships?
The social credit system that the Chinese government is in the process of unleashing is creating an Orwellian nightmare for some of China’s citizens. We say “some” because many Chinese citizens do not necessarily realize that it is being rolled out. This is because the government has been gradually implementing versions of what has become the social credit system over a period of years without calling it that. Secondly, most Chinese citizens have become numb to the intrusive nature of the Chinese state. They have been poked and prodded in various forms for so long that they have become accustomed to, and somewhat accepting, of it. That said, the social credit system has real consequences for those who fall afoul of it; they will soon learn about the consequences of having done so, if they have not learned already.
As we note in the book, the Chinese government has shared elements of its social credit system technology with a range of states across the world. There is every reason to believe that authoritarian governments will wish to adopt the technology and use it for their own purposes. Some have already done so.
How can we stop consumer drones from being used to aid in blackmail, burglary, assassination, and terrorist attacks?
As Daniel notes in his book Virtual Terror, governments are having a difficult time keeping track of the tens of millions of drones that are in operation in societies around the world. Registering them is largely voluntary and there are too few regulations in place governing their use. Given this, there is little that can be done, at this juncture, to prevent them from being used for nefarious purposes. Moreover, drones’ use on the battlefield is transforming the way individual battles will be fought, and wars will be waged. We have a chapter in the book devoted to this subject.
Google, YouTube, Twitter and Facebook have been caught throttling/ending traffic to many progressive (TeleSur, TJ Kirk) and conservative (InfoWars, PragerU) websites and channels. Should search engines and social media platforms be regulated as public utilities, to lend 1st Amendment protections to the users of these American companies?
The current battle being waged–in the courts, legislatures, and the battlefield of social media itself- are already indicative of how so many unanswered questions associated with the rise of social media are being addressed out of necessity. It seems that no one–least of all the social media firms–wants to assume responsibility when things go wrong or uncomfortable questions must be answered. Courts and legislatures will ultimately have to find a middle ground response to issues such as first amendment protections, but this will likely remain a moving target for some time to come, as there is no single black or white answer, and, as each new law comes into effect, its ramifications will become known, which means the laws will undoubtedly need to become subsequently modified.
Do you think blockchain will eventually lead to a golden era of fiscal transparency?
This is hard to say. On one hand, the rise of cryptocurrencies brought with them the promise of money outside the control of governments and large corporations. However, cryptocurrencies have been subject to a number of high-profile heists and there are still some fundamental issues with them, such as the throughput of Bitcoin which is only able to process around a few transactions per second. This makes some cryptocurrencies less viable for real world transactions and everyday commerce.
The financial services industry has jumped on the blockchain bandwagon, but they have taken the open concept of some cryptocurrencies and reinvented it as distributed ledger technology (DLT). To be part of DLTs created by financial institutions, a joining member must be a financial institution. For this reason, the notion of transparency is not relevant, since the DLT will be controlled by a limited number of members and only they will determine what information is public and what is not.
The other issue with the crypto space right now is that is filled with fraud. At the end of the day, crypto is an asset class like gold or any other precious metal. It does not actually produce anything; The only real value it has is the willingness of another person to pay more for it in the future. It is possible that a few cryptocurrencies will survive long-term and become somewhat viable, but the evolution of blockchain will likely continue to move towards DLT that more people will trust. Also, governments are likely to issue their own cryptocurrencies in the future, which will bring it into the mainstream.
Taiwan has recently started using online debate forums to help draft legislation, in a form of direct democracy. Kenya just announced that they will post presidential election results on a blockchain. How can AI and blockchain enhance democracy?
Online debate forums are obviously a good thing, because having the average person engage in political debate and being able to record and aggregate voting results will create an opportunity for more transparency. The challenge becomes how to verify the identities of the people submitting their feedback. Could an AI program be designed to submit feedback millions of times to give a false representation of the public’s concerns?
Estonia has long been revered as the world’s most advanced digital society, but researchers have pointed out serious security flaws in its electronic voting system, which could be manipulated to influence election outcomes. AI can help by putting in place controls to verify that the person providing feedback for legislation is a citizen. Online forums could force users to take a pic of their face next to their passport to verify their identity with facial recognition algorithms.
Should an international statute be passed banning scientists from installing emotions-specially pain and fear-into AI?
Perhaps, for now at least, the question should be: should scientists ban the installation of robots or other forms of AI to imitate human emotions? The short answer to this is that it depends. On one hand, AI imitating human emotions could be a good thing, such as when caring for the elderly or teaching a complex concept to a student. However, a risk is that when AI can imitate human emotions very well, people may believe they have gained a true friend who understands them. It is somewhat paradoxical that the rise of social media has connected more of us, but some people still admit that they lack meaningful relationships with others.
You don’t talk much about India in your book. How far behind are they in the AI race, compared to China, the US & EU?
Surprisingly, many of the world’s countries have only adopted a formal AI strategy in the last year. India is one of them; It only formally adopted an AI strategy in 2018 and lags well behind China, the EU, the US, and variety of other countries. India has tremendous potential to meaningfully enter the race for AI supremacy and become a viable contender, but it still lacks a military AI strategy. India already contributes to advanced AI-oriented technology through its thriving software, engineering, and consulting sectors. Once it ramps up a national strategy, it should quickly become a leader in the AI arena–to the extent that it devotes sufficient resources to that strategy and swiftly and effectively implements it. That is not a guaranteed outcome, based on the country’s prior history with some prior national initiatives. We must wait and see if India lives up to its potential in this arena.
On page 58 you write, “Higher-paying jobs requiring creativity and problem-solving skills, often assisted by computers, have proliferated… Demand has increased for lower skilled restaurant workers, janitors, home health aides, and others providing services that cannot be automated.” How will we be able to stop this kind of income inequality?
In all likelihood, the rise of AI will, at least temporarily, increased the schism between highly paid white-collar jobs and lower paid blue-collar jobs, however, at the same time, AI will, over decades, dramatically alter the jobs landscape. Entire industries will be transformed to become more efficient and cost effective. In some cases this will result in a loss of jobs while in others it will result in job creation. What history has shown is that, even in the face of transformational change, the job market has a way of self-correcting; Overall levels of employment tend to stay more or less the same. We have no doubt that this will prove to be the case in this AI-driven era. While income inequality will remain a persistent threat, our expectation is that, two decades from now, it will be no worse than it is right now.
AI systems like COMPAS and PredPol have been exposed for being racially biased. During YouTube’s “Adpocalypse”, many news and opinion videos got demonetized by algorithms indiscriminately targeting keywords like ‘war’ and ‘racism”. How can scientists and executives prevent their biases from influencing their AI?
This will be an ongoing debate. Facebook removed a PragerU video where a woman was describing the need for strong men in society and the problem with feminizing them. Ultimately, Facebook said it was a mistake and put the video back up. So the question becomes who decides what constitutes “racist” or “hate speech” content? The legal issues seem to emerge, if it can be argued that the content being communicated are calling on people to act in a violent way.
Could the political preferences of a social media company’s executives overrule the sensibilities of the common person to make up their own mind? On the other hand, India has a string of mob killings from disinformation campaigns on WhatsApp, mostly from people who were first time smartphone users. Companies could argue that some people are not able to distinguish between real and fake videos so content must be censored in that case.
Ultimately, executives and scientists will need to have an open and ongoing debate about content censorship. Companies must devise a set of principles and adhere to them to the best of their ability. As AI becomes more prevalent in monitoring and censoring online content there will have to be more transparency about the process and the algorithms will need to be adjusted following a review by the company. In other words, companies cannot prevent algorithmic biases, but they can monitor them and be transparent with the public about steps to make them better over time.
Amper is an AI music composer. Heliograf has written about 1000 news blurbs for WaPo. E-sports and e-bands are starting to sell out stadiums. Are there any human careers that you see as being automation-proof?
In theory, nearly any cognitive or physical task can be automated. We do not believe that people should be too worried, at least for the time being, about the implications of doing so because the costs to automate even basic tasks to the level of human performance is extremely high, and we are a good ways away from being technically capable of automating most tasks. However, AI should spark conversations about how we want to structure our society in the future and what it means to be human because AI will improve over time and become more dominant in the economy.
In Chapter 1 you briefly mention digital amnesia (outsourcing the responsibility of memorizing stuff to one’s devices). How else do you anticipate consumer devices will change us psychologically in the next few decades?
We could see a spike in schizophrenia because the immersive nature of virtual, augmented, and mixed reality that will increasingly blur the lines between reality and fantasy. In the 1960s there was a surge of interest in mind-expanding drugs such as psychedelics. However, someone ingesting LSD knew there was a time limit associated with the effects of the drug. These technologies do not end. Slowly, the real world could become less appealing and less real for heavy users of extended reality technology. This could affect relationships between other humans and increase the nature and commonality of mental illness. Also, as discussed in the book, we are already seeing people who cannot deal with risk in the real world. There have been several cases of animal mauling, cliff falls, and car crashes among individuals in search of the perfect “selfie”. This tendency to want to perfect our digital personas should be a topic of debate in schools and at the dinner table.
Ready Player One is the most recent sci-fi film positing the gradual elimination of corporeal existence through Virtual Reality. What do you think of the transcension hypothesis on Fermi’s paradox?
The idea that our consciousness can exist independently from our bodies has occurred throughout humanity’s history. It appears that our consciousness is a product of our own living bodies. No one knows if a person’s consciousness can exist after the body dies, but some have suggested that a person’s brain still functions for a few minutes after the body dies. It seems we need to worry about the impact of virtual reality on our physical bodies before it will be possible for us to transcend our bodies and exist on a digital plane. This is a great thought experiment, but there is not enough evidence to suggest that this is even remotely possible in the future.
What role will AI play in climate change?
AI will become an indispensable tool for helping to predict the impacts of climate change in the future. The field of “Climate Informatics” is already blossoming, harnessing AI to fundamentally transform weather forecasting (including the prediction of extreme events) and to improve our understanding of the effects of climate change. Much more thought and research needs to be devoted to exploring the linkages between the technology revolution and other important global trends, including demographic changes such as ageing and migration, climate change, and sustainable development, but AI should make a real difference in enhancing our general understanding of the impacts of these, and other, phenomena going forward.
Central Banks Becoming Leaders in Blockchain Experimentation
Although central banks are among the most cautious institutions in the world, they are, perhaps surprisingly, among the first to implement and experiment with blockchain technology. Central banks have been quietly researching its possibilities since 2014. Over the past two years, the beginning of a new wave has emerged as more central banks launch large-scale pilots and research efforts, including rapid and complete cross-border interbank securities.
The Blockchain and Distributed Ledger Technology team at the World Economic Forum interviewed dozens of central bank researchers and analysed more than 60 reports on past and current research efforts. The findings were released today in a white paper, Central Banks and Distributed Ledger Technology: How are Central Banks Exploring Blockchain Today?
“As the blockchain hype cools, we are starting to see the real use cases for blockchain technology take the spotlight,” said Ashley Lannquist, Blockchain Project Lead at the World Economic Forum. “Central bank activities with blockchain and distributed ledger technology are not always well known or communicated. As a result, there is much speculation and misunderstanding about objectives and the state of research. Dozens of central banks around the world are actively investigating whether blockchain can help solve long-standing challenges such as banking and payments system efficiency, payments security and resilience, as well as financial inclusion.”
It is not widely known, for instance, that the Bank of France has fully replaced its centralized process for the provisioning and sharing of SEPA Credit Identifiers (SCIs) with a decentralized, blockchain-based solution. SEPA, or Single Euro Payments Area, is a payment scheme created by the European Union and managed on a country-by-country basis for facilitating efficient and secure cross-border retail debit and card payments across European countries. The solution is a private deployment of the Ethereum blockchain network and has been in use since December 2017. It has enabled greater time efficiency, process auditability and disaster recovery.
The fact that dozens of central banks are exploring, and in some cases implementing, blockchain technology is significant, according to the white paper. It is an early indicator of the potential use of this emerging technology across financial and monetary systems. “Central banks play one of the most critical roles in the global economy, and their decisions about implementing distributed ledger and digital currency technologies in the future can have far-reaching implications for economies,” Lannquist said.
Top 10 central bank use cases
Following interviews and analysis, how central banks are experimenting with blockchain can be highlighted by 10 top use cases.
Retail central bank digital currency (CBDC) – A substitute or complement for cash and an alternative to traditional bank deposits. A central-bank-issued digital currency can be operated and settled in a peer-to-peer and decentralized manner, widely available for consumer use. Central banks from several countries are experimenting, including those from the the Eastern Caribbean, Sweden, Uruguay, the Bahamas and Cambodia.
Wholesale central bank digital currency (CBDC) – This kind of digital currency would only be available for commercial banks and clearing houses to use the wholesale interbank market.Central bank-issued digital currency would be operated and settled in a peer-to-peer and decentralized manner. Central banks from several countries are experimenting, including those from South Africa, Canada, Japan, Thailand, Saudi Arabia, Singapore and Cambodia.
Interbank securities settlement – A focused application of blockchain technology, sometimes involving CBDC, enabling the rapid interbank clearing and settlement of securities for cash. This can achieve “delivery versus payment” interbank systems where two parties trading an asset, such as a security for cash, can conduct the payment for and delivery of the asset simultaneously. Central banks exploring this include the Bank of Japan, Monetary Authority of Singapore, Bank of England and Bank of Canada.
Payment system resiliency and contingency – The use of distributed ledger technology in a primary or back-up domestic interbank payment and settlement system to provide safety and continuity in case of threats, including technical or network failure, natural disaster, cybercrime and others. Often, this use case is coupled with others as part of the set of benefits that a distributed ledger technology implementation could potentially offer. Central banks exploring this include the Central Bank of Brazil and Eastern Caribbean Central Bank.
Bond issuance and lifecycle management – The use of distributed ledger technology in the bond auction, issuance or other life-cycle processes to reduce costs and increase efficiency. This may be applied to bonds issued and managed by sovereign states, international organizations or government agencies. Central banks or government regulators could be “observer nodes” to monitor activity where relevant. Early implementation is being conducted by the World Bank with their 2018 “bond-i” project.
Know-your-customer (KYC) and anti-money-laundering (AML) – Digital KYC/AML processes that leverage distributed ledger technology to track and share relevant customer payment and identity information to streamline processes. This may connect to a digital national identity platform or plug into pre-existing e-KYC or AML systems. Central banks exploring this include the Hong Kong Monetary Authority.
Information exchange and data sharing – The use of distributed or decentralized databases to create alternative systems for information and data sharing between or within related government or private sector institutions. Central banks exploring include the Central Bank of Brazil.
Trade finance – The employment of a decentralized database and functionality to enable faster, more efficient and more inclusive trade financing. Improves on today’s trade finance processes, which are often paper-based, labour-intensive and time-intensive. Customer information and transaction histories are shared between participants in the decentralized database while maintaining privacy and confidentiality where needed. Central banks exploring this include the Hong Kong Monetary Authority.
Cash money supply chain – The use of distributed ledger technology for issuing, tracking and managing the delivery and movement of cash from production facilities to the central bank and commercial bank branches; could include the ordering, depositing or movement of funds, and could simplify regulatory reporting. Central banks exploring this include the Eastern Caribbean Central Bank.
Customer SEPA Creditor Identifier (SCI) provisioning – Blockchain-based decentralized sharing repository for SEPA credit identifiers managed by the central bank and commercial banks in the SEPA debiting scheme. This is a faster, streamlined and decentralized system for identity provisioning and sharing. It can replace pre-existing manual and centralized processes that are time- and resource-intensive, as seen in the Bank of France’s Project MADRE implementation.
Emerging economies may benefit most: Cambodia, Thailand and South Africa and others experimenting
The National Bank of Cambodia will be one of the first countries to deploy blockchain technology in its national payments system for use by consumers and commercial banks. It is implementing blockchain technology in the second half of 2019 as an experiment to support financial inclusion and greater banking system efficiency.
The Bank of Thailand and the South African Reserve Bank, among others, are experimenting with CBDC in large-scale pilots for interbank payment and settlement efficiency. The Eastern Caribbean Central Bank is exploring the suitability of distributed ledger technology (DLT) to advance multiple goals, from financial inclusion and payments efficiency to payment system resilience against storms and hurricanes.
“Over the next four years, we should expect to see many central banks decide whether they will use blockchain and distributed ledger technologies to improve their processes and economic welfare,” Lannquist said. “Given the systemic importance of central bank processes, and the relative freshness of blockchain technology, banks must carefully consider all known and unknown risks to implementation.”
How Nuclear Techniques Help Feed China
With 19% of the world’s population but only 7% of its arable land, China is in a bind: how to feed its growing and increasingly affluent population while protecting its natural resources. The country’s agricultural scientists have made growing use of nuclear and isotopic techniques in crop production over the last decades. In cooperation with the IAEA and the Food and Agriculture Organization of the United Nations (FAO), they are now helping experts from Asia and beyond in the development of new crop varieties, using irradiation.
While in many countries, nuclear research in agriculture is carried out by nuclear agencies that work independently from the country’s agriculture research establishment, in China the use of nuclear techniques in agriculture is integrated into the work of the Chinese Academy of Agricultural Sciences (CAAS) and provincial academies of agricultural sciences. This ensures that the findings are put to use immediately.
And indeed, the second most widely used wheat mutant variety in China, Luyuan 502, was developed by CAAS’s Institute of Crop Sciences and the Institute of Shandong Academy of Agricultural Sciences, using space-induced mutation breeding (see Space-induced mutation breeding). It has a yield that is 11% higher than the traditional variety and is also more tolerant to drought and main diseases, said Luxiang Liu, Deputy Director General of the Institute. It has been planted on over 3.6 million hectares – almost as large as Switzerland. It is one of 11 wheat varieties developed for improved salt and drought tolerance, grain quality and yield, Mr Liu said.
Through close cooperation with the IAEA and FAO, China has released over 1,000 mutant crop varieties in the past 60 years, and varieties developed in China account for a fourth of mutants listed currently in the IAEA/FAO’s database of mutant varieties produced worldwide, said Sobhana Sivasankar, Head of the Plant Breeding and Genetics Section at the Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture. The new mutation induction and high-throughput mutant selection approaches established at the Institute serve as a model to researchers from around the world, she added.
The Institute uses heavy ion beam accelerators, cosmic rays and gamma rays along with chemicals to induce mutations in a wide variety of crops, including wheat, rice, maize, soybean and vegetables. “Nuclear techniques are at the heart of our work, fully integrated into the development of plant varieties for the improvement of food security,” Liu said.
The Institute has also become a key contributor to the IAEA technical cooperation programme over the years: more than 150 plant breeders from over 30 countries have participated in training courses and benefited from fellowships at CAAS.
Indonesia’s nuclear agency, BATAN, and CAAS are looking for ways to collaborate on plant mutation breeding and Indonesian researchers are looking for ways to learn from China’s experience, said Totti Tjiptosumirat, Head of BATAN’s Center for Isotopes and Radiation Application. “Active dissemination and promotion of China’s activities in plant mutation breeding would benefit agricultural research across Asia,” he said.
From food safety to authenticity
Several of CAAS’ other institutes use nuclear-related and isotopic techniques in their research and development work and participate in several IAEA technical cooperation and coordinated research projects. The Institute of Quality Standards and Testing Technology for Agro-Products has developed a protocol to detect fake honey, using isotopic analysis. A large amount of what is sold in China as honey is estimated to be produced synthetically in labs rather than by bees in hives, so this has been an important tool in cracking down on fraudsters, said Professor Chen Gang, who leads the research work using isotopic techniques at the Institute. A programme is also in place to trace the geographical origin of beef using stable isotopes, he added.
The Institute uses isotopic techniques to test the safety and to verify the authenticity of milk and dairy products – work that was the outcome of IAEA technical coordinated research and cooperation projects that lasted from 2013 to 2018. “After a few years of support, we are now fully self-sufficient,” Mr Gang said.
Improving nutrition efficiency
Various CAAS institutes use stable isotopes to study the absorption, transfer and metabolism of nutrients in animals. The results are used to optimize feed composition and feeding schedules. Isotope tracing offers higher sensitivity than conventional analytical methods, and this is particularly advantageous when studying the absorption of micronutrients, vitamins, hormones and drugs, said Dengpan Bu, Professor at the Institute of Animal Science.
While China has perfected the use of many nuclear techniques, in several areas it is looking to the IAEA and the FAO for support: the country’s dairy industry is dogged by the low protein absorption rate of dairy cows. Less than half of the protein in animal feed is used by the ruminants, the rest ends up in their manure and urine. “This is wasteful for the farmer and the high nitrogen content in the manure hurts the environment,” Mr Bu said. The use of isotopes to trace nitrogen as it travels from feed through the animal’s body would help improve nitrogen efficiency by making the necessary adjustments to the composition of the feed. This will be particularly important as dairy consumption, currently at a third of global average per person, continues to rise. “We are looking for international expertise, through the IAEA and the FAO, to help us tackle this problem.”
When neuroscience meets AI: What does the future of learning look like?
Meet Dr. Nandini Chatterjee Singh, a cognitive neuroscientist at UNESCO MGIEP (Mahatma Gandhi Institute of Education for Peace and Sustainable Development) where she has been leading the development of a new framework for socio-emotional learning. MGIEP focuses on mainstreaming socio-emotional learning in education systems and innovating digital pedagogies.
Dr. Singh answered five questions on the convergence of neuroscience and Artificial Intelligence in learning, ahead of the International Congress on Cognitive Science in Schools where she will be speaking this week.
What are the links between neuroscience and Artificial Intelligence when it comes to learning?
The focus of both neuroscience and AI is to understand how the brain works and thus predict behaviour. And the better we understand the brain, the better designs we can create for AI algorithms. When it comes to learning, the neuroscience – AI partnership can be synergistic. A good understanding of a particular learning process by neuroscience can be used to inform the design of that process for AI. Similarly, if AI can find patterns from large data sets and get a learning model, neuroscience can conduct experiments to confirm it.
Secondly, when neuroscience provides learning behaviours to AI, these behaviours can be translated into digital interactions, which in turn are used by AI to look at learning patterns across large numbers of children worldwide. The power of AI is that it can scale this to large numbers. AI can track and search through massive amounts of data to see how that learning happens, and when required, identify when learning is different or goes off track.
A third feature is that of individualized learning. We increasingly also know that learning has a strong individual component. Yet our classrooms are structured to provide common learning to all children. Sometimes these individual differences become crucial to bring out the best in children, which is when we might tailor learning. Neuroscience research on individual differences has shown that detailed information on that individual can reveal a wealth of information about their learning patterns. However, this is extremely cost and labour intensive. Yet, this detailed learning from neuroscience can be provided to AI in order to scale. AI can collect extensive detailed data at the personal level, to design a path to learning for that child. Thus, what neuroscience can study in small groups, AI can implement in large populations. If we are to ensure a world where every child achieves full potential, such personalized learning offers a great promise.
How do we create a structure around AI to ensure learning standards globally?
One thing AI capitalizes on and constantly relies on is large volumes of data. AI algorithms perform better if they are being fed by continuous distributed data. We need to keep in mind that humans are the ones designing these algorithms. This means that the algorithms will only do as well as the data that they have been trained on. Ensuring that we have access to large amounts of data that comes from various situations of learning is crucial. What sometimes becomes an issue for AI algorithms is that most of the training data has been selected from one particular kind of population. This means that the diversity in the forms of learning is missing from the system.
To return to reading and literacy as an example, in neuroscience, a large part of our research and understanding of how the brain learns to read has come from individuals learning to read English and alphabetic languages. However, globally, billions of people speak or read non-alphabetic languages and scripts that are visually complex, which are not really reflected in this research. Our understanding is built on one particular system that does not have enough diversity.
Therefore, it is important that AI algorithms be tested in varied environments around the world where there are differences in culture. This will create more robust learning models that are able to meet diverse learning requirements and cater to every kind of learner from across the world. If we are able to do that, then we can predict what the learning trajectory will look like for children anywhere.
Human beings have similarities in the way they learn, but pedagogies vary across different situations. In addition, those differences must be reflected in the data provided. The results would be much more pertinent if we are able to capture and reflect those differences in the data. This will help us improve the learning of AI, and ultimately understand how the brain works. We would then be better suited to leverage the universal principles of learning that are being used across the world and effects that are cultural in nature. That is also something that we want to hold on to and capitalize on in trying to help children. People designing AI algorithms so far have not given a lot of attention to this, but they are now beginning to consider it in many places across the world.
How do you see AI’s role in inclusive education today, especially in the context of migration?
Societies have become multicultural in nature. If you go to a typical classroom in many countries, you will find children from diverse cultures sitting in the same learning space. Learning has to be able to meet a variety of needs and must become more inclusive and reflect cultural diversity. Innovative pedagogy such as games, interactive sessions and real-life situations are key because they test learning capabilities focused on skills that children should acquire. AI relies on digital interactions to understand learning and that comes from assessing skills and behaviours. We now recognize that what we need to empower our children with are skills and behaviours – not necessarily tons of information.
Digital pedagogies like interactive games are among the ones emerging rapidly to assess children’s skills. They are powerful because they can be used in multicultural environments and can assess different competencies. They are not necessarily tied to a specific language or curricula but are rather performance-based. How do you assess children for collaboration in a classroom? In the context of migration and 21st century skills, these are necessary abilities and digital games provide a medium to assess these in education. When such interactive games are played by children across the world, they provide digital interactions to AI. AI might discover new patterns and ways to collaborate since children have ways of doing things that are often out of the box. A skills-based approach can be applied anywhere, whether it is in a classroom in India, France or Kenya. In contrast, curriculum-based methods are context-specific and show extensive cultural variation.
What are the risks and the challenges?
Data protection and security is of course still a huge issue and is the biggest challenge in this sphere. We have to ensure that children are never at risk of exposure and that the data is not misused in any way. This is something that needs more global attention and backing.
Another crucial point is that learning assessments should not be restricted to just one domain. There are multiple ways, and time and space to learn. Learning is continuous in nature and should be able to be adapted to the child’s needs at that particular point. The assessment should also be continuous in order to get a full picture of the improvement that the child is demonstrating. If there is no improvement, then we can provide interventions to help and find out why learning is not happening. From what we know from neuroscience, the earlier you can provide intervention, the better is the chance of the child to be able to change and adapt. The ability of the brain to learn and change is much easier and faster in childhood compared to adulthood.
Yet, we want to be cautious about the conclusions we draw about how to intervene with children. Poor academic performance might have a social or emotional reason.
Thus, learning today needs to be multi-dimensional. Along with academic competencies, social and emotional skills also need to be assessed. If this information is used wisely, it can provide a lot of insight about the child’s academic and emotional well-being. Based on the combination of the two, the right intervention can be provided. Unless multiple assessments all converge on the same result, the child’s learning abilities should not be labeled. AI gives a great opportunity to conduct multi-skills assessments, rather than just one. And that is something that we should leverage, rather than abandon. The standards for the baselines for the algorithms must be properly taken into consideration for any type of assessment. They must come from a large quantity of distributed data in order to provide more accurate results. That is something that we should not compromise under any condition.
How is the teaching community responding to this new way of learning and assessing?
There are teachers who worry about the future of learning but that is also because they do not necessarily have the full picture. People working and promoting the use of AI in learning must play a crucial role in telling teachers that they will not be obsolete. Teachers will be more empowered and be able to meet the needs of every kind of learner in their classrooms. The ideal world would be to have one teacher per child but that is of course impossible. AI is a tool to guide teachers when it comes to finding the right intervention for a student that might be struggling to learn. That intervention comes from data that has been checked for bias and diversity and does not use ‘a one size fits all ‘approach and therefore teachers can be more certain that it will fit the needs of the child. AI gives the opportunity for the teacher to tailor learning for the child. In addition, we do not really know all the different kinds of learning. Sometimes we have to be prepared to learn from children themselves. Children can give us insights into the different ways that learning actually happens, and teachers should be able apply them back into the classroom. Teachers are extremely powerful individuals who are able to shape the brains of so many children. If they are doing a good job, they are making individuals for life.
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The Luxury Collection®, part of Marriott International Inc., today announced the opening of Parklane, a Luxury Collection Resort & Spa,...
Economic reform in the Gulf: Who benefits, really?
For Gulf leaders, long-overdue economic reforms were never going to be easy. Leaders like the crown princes of Saudi Arabia...
China needs further reforms to make growth sustainable, greener and more inclusive
The Chinese economy continues to slow as it rebalances, with headwinds including trade frictions and the weakening global economy undermining...
ADB Releases Annual Report, Financial Results for 2018
The Asian Development Bank (ADB) released its Annual Report for 2018 today. The report presents ADB’s important operational and organizational...
The Rabidly Hypocritical EU
Unlike America under Donald Trump, who is proudly psychopathic and went so far as to blurt out that his followers...
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