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.
From nanotechnology to solar power: Solutions to drought
While the drought has intensified in Iran and the country is facing water stress, various solutions from the use of solar power plants to the expansion of watershed management and nanotechnology are offered by experts and officials.
Iran is located in an arid and semi-arid region, and Iranians have long sought to make the most of water.
In recent years, the drought has intensified making water resources fragile and it can be said that we have reached water bankruptcy in Iran.
However, water stress will continue this fall (September 23-December 21), and the season is expected to be relatively hot and short of rain, according to Ahad Vazifeh, head of the national center for drought and crisis management.
In such a situation, officials and experts propose various solutions for optimal water management.
Alireza Qazizadeh, a water and environment expert, referring to 80 percent of the arid regions in the country, said that “Iran has one percent of the earth’s area and receives only 36 percent of renewable resources.
The country receives 250 mm of rainfall annually, which is about 400 billion cubic meters, considering 70 percent evaporation, there is only 130 billion cubic meters of renewable water and 13 billion cubic meters of input from border waters.”
Referring to 800 ml of average rainfall and 700 mm of global evaporation, he noted that 70 percent of rainfall in Iran occurs in only 25 percent of the country and only 25 percent rains in irrigation seasons.
Pointing to the need for 113 billion cubic meters of water in the current year (began on March 21), he stated that “of this amount, 102 billion is projected for agricultural use, 7 percent for drinking and 2 percent for industry, and at this point water stress occurs.
In 2001, 5.5 billion cubic meters of underground resources were withdrawn annually, and if we consider this amount as 20 years from that year until now, it means that we have withdrawn an equivalent of one year of water consumption from non-renewable resources, which is alarming.”
The use of unconventional water sources can be effective in controlling drought, such as rainwater or river runoff, desalinated water, municipal wastewater that can be reused by treatment, he concluded.
Rasoul Sarraf, the Faculty of Materials at Shahid Modarres University, suggests a different solution and states that “To solve ease water stress, we have no choice but to use nanotechnology and solar power plants.
Pointing to the sun as the main condition for solar power plant, and while pointing to 300 sunny days in the country, he said that at the Paris Convention, Iran was required to reduce emissions by 4 percent definitively and 8 percent conditionally, which will only be achieved by using solar power plants.
Hamidreza Zakizadeh, deputy director of watershed management at Tehran’s Department of Natural Resources and Watershed Management, believes that watershed management can at least reduce the effects of drought by managing floods and extracting water for farmers.
Amir Abbas Ahmadi, head of habitats and regional affairs of Tehran Department of Environment, also referring to the severe drought in Tehran, pointed to the need to develop a comprehensive plan for water management and said that it is necessary to cooperate with several responsible bodies and develop a comprehensive plan to control the situation.
He also emphasizes the need to control migration to the capital, construction, and the implementation of the Comprehensive Plan of Tehran city.
While various solutions are proposed by officials and experts to manage water and deal with drought, it is necessary for the related organizations to work together to manage the current situation.
Mohammad Reza Espahbod, an expert in groundwater resources, also suggested that while the country is dealing with severe drought due to improper withdrawal of groundwater and low rainfall, karst water resources can supply the whole water needed by the country, only if managed.
Iran is the fifth country in the world in terms of karst water resources, he stated.
Qanats can also come efficient to contain water scarcity due to relatively low cost, low evaporation rates, and not requiring technical knowledge, moreover, they proved sustainable being used in perpetuity without posing any damages to the environment.
According to the Ministry of Energy, about 36,300 qanats have been identified in Iran, which has been saturated with water for over 2,000 years.
In recent years, 3,800 qanats have been rehabilitated through watershed and aquifer management, and people who had migrated due to water scarcity have returned to their homes.
Water resources shrinking
Renewable water resources have decreased by 30 percent over the last four decades, while Iran’s population has increased by about 2.5 times, Qasem Taqizadeh, deputy minister of energy, said in June.
The current water year (started on September 23, 2020) has received the lowest rain in the past 52 years, so climate change and Iran’s arid region should become a common belief at all levels, he lamented.
A recent report by Nature Scientific Journal on Iran’s water crisis indicates that from 2002 to 2015, over 74 billion cubic meters have been extracted from aquifers, which is unprecedented and its revival takes thousands of years along with urgent action.
Three Iranian scientists studied 30 basins in the country and realized that the rate of aquifer depletion over a 14-year period has been about 74 billion cubic meters, which is recently published in Nature Scientific Journal.
Also, over-harvesting in 77 percent of Iran has led to more land subsidence and soil salinity. Research and statistics show that the average overdraft from the country’s aquifers was about 5.2 billion cubic meters per year.
Mohammad Darvish, head of the environment group in the UNESCO Chair on Social Health, has said that the situation of groundwater resources is worrisome.
From our partner Tehran Times
Technology and crime: A never-ending cat-and-mouse game
Is technology a good or bad thing? It depends on who you ask, as it is more about the way technology is used. Afterall, technology can be used by criminals but can also be used to catch criminals, creating a fascinating cat-and-mouse game.
Countless ways technology can be used for evil
The first spear was used to improve hunting and to defend from attacking beasts. However, it was also soon used against other humans; nuclear power is used to produce energy, but it was also used to annihilate whole cities. Looking at today’s news, we’ve learned that cryptocurrencies could be (and are) used as the preferred form of payments of ransomware since they provide an anonymous, reliable, and fast payment method for cybercriminals.
Similarly, secure phones are providing criminal rings with a fast and easy way to coordinate their rogue activities. The list could go on. Ultimately, all technological advancements can be used for good or evil. Indeed, technology is not inherently bad or good, it is its usage that makes the difference. After all, spears served well in preventing the extinction of humankind, nuclear power is used to generate energy, cryptocurrency is a promise to democratize finance, and mobile phones are the device of choice of billions of people daily (you too are probably reading this piece on a mobile).
However, what is new with respect to the past (recent and distant) is that technology is nowadays much more widespread, pervasive, and easier to manipulate than it was some time ago. Indeed, not all of us are experts in nuclear material, or willing and capable of effectively throwing a spear at someone else. But each of us is surrounded by, and uses, technology, with a sizeable part of users also capable of modifying that technology to better serve their purposes (think of computer scientists, programmers, coding kids – technology democratization).
This huge reservoir of people that are capable of using technology in a way that is different from what it was devised for, is not made of just ethical hackers: there can be black hats as well (that is, technology experts supporting evil usages of such technology). In technical terms, the attack vector and the security perimeter have dramatically expanded, leading to a scenario where technology can be easily exploited for rogue purposes by large cohorts of people that can attack some of the many assets that are nowadays vulnerable – the cybersecurity domain provides the best example for the depicted scenario.
Fast-paced innovation and unprecedented threats
What is more, is that technology developments will not stop. On the contrary, we are experiencing an exponentially fast pace in technology innovation, that resolves in less time between technology innovations cycles that, while improving our way of living, also pave the way for novel, unprecedented threats to materialize. For instance, the advent of quantum computers will make the majority of current encryption and digital signature methods useless and what was encrypted and signed in the past, exposed.
The tension between legitimate and illegitimate usages of technology is also heating up. For instance, there are discussions in the US and the EU about the need for the provider of ICT services to grant the decryption keys of future novel secure applications to law enforcement agencies should the need arise –a debatable measure.
However, technology is the very weapon we need to fight crime. Think of the use of Terahertz technology to discover the smuggling of drugs and explosives – the very same technology Qatar has successfully employed. Or the infiltration of mobile phone crime rings by law enforcement operators via high tech, ethical hacking (as it was the case for the EncroChat operation). And even if crime has shown the capability to infiltrate any sector of society, such as sports, where money can be laundered over digital networks and matches can be rigged and coordinated via chats, technology can help spot the anomalies of money transfer, and data science can spot anomalies in matches, and can therefore thwart such a crime – a recent United Nations-sponsored event, participated by the International Centre for Sport Security (ICSS) Qatar and the College of Science and Engineering (CSE) at Hamad Bin Khalifa University (HBKU) discussed the cited topic. In the end, the very same technology that is used by criminals is also used to fight crime itself.
Don’t get left behind
In the above-depicted cybersecurity cat-and-mouse game, the loser is the party that does not update its tools, does not plan, and does not evolve.
In particular, cybersecurity can help a country such as Qatar over two strategic dimensions: to better prevent/detect/react to the criminal usage of technology, as well as to advance robustly toward a knowledge-based economy and reinforce the country’s presence in the segment of high value-added services and products to fight crime.
In this context, a safe bet is to invest in education, for both governments and private citizens. On the one hand, only an educated workforce would be able to conceptualize/design/implement advanced cybersecurity tools and frameworks, as well as strategically frame the fight against crime. On the other hand, the same well-educated workforce will be able to spur innovation, create start-ups, produce novel high-skill products, and diversify the economy.
In this context, Qatar enjoys a head start, thanks to its huge investment in education over the last 20 years. In particular, at HBKU – part of Qatar Foundation – where we have been educating future generations.
CSE engages and leads in research disciplines of national and global importance. The college’s speciality divisions are firmly committed to excellence in graduate teaching and training of highly qualified students with entrepreneurial capacity.
For instance, the MS in Cybersecurity offered by CSE touches on the foundations of cryptocurrencies, while the PhD in Computer Science and Engineering, offering several majors (including cybersecurity), prepares future high-level decision-makers, researchers, and entrepreneurs in the ICT domain – the leaders who will be driving the digitalization of the economy and leading the techno-fight against crime.
Enhancing poverty measurement through big data
Authors: Jasmina Ernst and Ruhimat Soerakoesoemah*
Ending poverty in all its forms is the first of the 17 Sustainable Development Goals (SDGs). While significant progress to reduce poverty had been made at the global and regional levels by 2019, the Covid-19 pandemic has partly reversed this trend. A significant share of the population in South-East Asia still lacks access to basic needs such as health services, proper nutrition and housing, causing many children to suffer from malnutrition and treatable illnesses.
Delivering on the commitments of the 2030 Agenda for Sustainable Development and leaving no one behind requires monitoring of the SDG implementation trends. At the country level, national statistics offices (NSOs) are generally responsible for SDG data collection and reporting, using traditional data sources such as surveys, census and administrative data. However, as the availability of data for almost half of the SDG indicators (105 of 231) in South-East Asia is insufficient, NSOs are exploring alternative sources and methods, such as big data and machine learning, to address the data gaps. Currently, earth observation and mobile phone data receive most attention in the domain of poverty reporting. Both data sources can significantly reduce the cost of reporting, as the data collection is less time and resource intensive than for conventional data.
The NSOs of Thailand and the Philippines, with support from the Asian Development Bank, conducted a feasibility study on the use of earth observation data to predict poverty levels. In the study, an algorithm, convolutional neural nets, was pretrained on an ImageNet database to detect simple low-level features in images such as lines or curves. Following a transfer learning technique, the algorithm was then trained to predict the intensity of night lights from features in corresponding daytime satellite images. Afterwards income-based poverty levels were estimated using the same features that were found to predict night light intensity combined with nationwide survey data, register-based data, and geospatial information. The resulting machine learning models yielded an accuracy of up to 94 per cent in predicting the poverty categories of satellite images. Despite promising study results, scaling up the models and integrating big data and machine learning for poverty statistics and SDG reporting still face many challenges. Thus, NSOs need support to train their staff, gain continuous access to new datasets and expand their digital infrastructure.
Some support is available to NSOs for big data integration. The UN Committee of Experts on Big Data and Data Science for Official Statistics (UN-CEBD) oversees several task teams, including the UN Global Platform which has launched a cloud-service ecosystem to facilitate international collaboration with respect to big data. Two additional task teams focus on Big Data for the SDGs and Earth Observation data, providing technical guidance and trainings to NSOs. At the regional level, the weekly ESCAP Stats Café series provides a knowledge sharing platform for experiences related to the impact of COVID-19 on national statistical systems. The Stats Café includes multiple sessions dedicated to the use of alternative data sources for official statistics and the SDGs. Additionally, ESCAP has published policy briefs on the region’s practices in using non-traditional data sources for official statistics.
Mobile phone data can also be used to understand socioeconomic conditions in the absence of traditional statistics and to provide greater granularity and frequency for existing estimates. Call detail records coupled with airtime credit purchases, for instance, could be used to infer economic density, wealth or poverty levels, and to measure food consumption. An example can be found in poverty estimates for Vanuatu based on education, household characteristics and expenditure. These were generated by Pulse Lab Jakarta – a joint innovation facility associated with UN Global Pulse and the government of Indonesia.
Access to mobile phone data, however, remains a challenge. It requires long negotiations with mobile network operators, finding the most suitable data access model, ensuring data privacy and security, training the NSO staff and securing dedicated resources. The UN-CEBD – through the Task Team on Mobile Phone Data and ESCAP – supports NSOs in accessing and using mobile phone data through workshops, guides and the sharing of country experiences. BPS Statistics Indonesia, the Indonesian NSO, is exploring this data source for reporting on four SDG indicators and has been leading the regional efforts in South-East Asia. While several other NSOs in Asia and the Pacific can access mobile phone data or are negotiating access with mobile network operators, none of them have integrated it into poverty reporting.
As the interest and experience in the use of mobile phone data, satellite imagery and other alternative data sources for SDGs is growing among many South-East Asian NSOs, so is the need for training and capacity-building. Continuous knowledge exchange and collaboration is the best long-term strategy for NSOs and government agencies to track and alleviate poverty, and to measure the other 16 SDGs.
*Ruhimat Soerakoesoemah, Head, Sub-Regional Office for South-East Asia
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