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When neuroscience meets AI: What does the future of learning look like?

MD Staff

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Photo: MGIEP

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.    

UNESCO

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3 ways leaders can help employees embrace Artificial Intelligence

MD Staff

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Most people may be unaware how much Artificial Intelligence (AI) has become part of their lives. From GPS and predictive text on smartphones to search engines and customer service chatbots, AI is changing the way people live and work. AI involves the process of using algorithms to make sense of large amounts of data beyond human capacity to manage, with the potential to tackle more tasks than ever before imagined.

Researchers at Oxford University asked AI/machine-learning experts for their future predictions, and they said AI will replace truck drivers by 2027 and do a surgeon’s work by 2053. They said there is a relatively high chance AI will beat humans at all tasks within 45 years, and that AI could automate all human jobs by 2063.

AI is increasingly used in the workplace, including for productivity tracking and numerous HR functions. Virtual assistants use proprietary AI algorithms to identify and screen candidates.

Employees first using AI may have difficulty making this transition, according to director of research and thought leadership for Dale Carnegie Mark Marone, PhD. In his publication, Preparing People for Success in the Era of AI, Marone discusses issues employees encounter adapting to AI, and steps leadership can take to help employees prepare for working alongside machines.

Marone outlines three ways leaders can ensure employee success in embracing AI: instilling trust in organizational leadership; providing transparency for employees to understand what AI does; and increasing employee confidence in their own skills to adapt to AI.

Trust.

Employees may worry about the true purpose of using AI in their company. If a solid foundation of trust in leadership is already established, it’s more likely changes will be accepted positively. Trust is gained by leaders exhibiting honesty and consistency in what they say and do.

Without underlying trust, implementing AI could be perceived as a threat. Marone recommends assessing the level of trust employees have in leadership using tools like engagement assessments, pulse surveys and exit interviews. If the trust level is not optimal, leaders must improve their consistency of communication and demonstrate the organization’s stated values in deed as well as word. Building trust is vital to helping employees accept challenging transitions.

Transparency.

Related to trust is transparency. Employees must understand what AI is and how it will be used in their workplace. People often fear what they don’t understand. While employees may not comprehend every technical detail, leadership should explain the use of AI as clearly as possible.

Marone gives an example of employees’ willingness to accept an appraisal given by AI rather than a human supervisor. In their research, 62% of respondents were more willing to accept an AI appraisal if criteria for the appraisal were completely transparent. Without transparency, only 32% of respondents would accept that appraisal. Marone explains, “People want to be sure that AI is delivering decisions that are fair and in a way that can be explained.”

Transparency contributes to employees’ perceptions of fairness. In the survey, 63% of respondents expressed concern about human biases built into AI systems, such as the inappropriateness of using predictive power for HR applications. Marone cites the example of an AI algorithm determining potential hires based on current company leadership, which “may suggest the desirability of hiring more white males.”

Employees who trust the role of human leaders to provide AI oversight, and who perceive transparency in how AI is used, will more likely accept these new technologies.

Confidence.

Employees feeling threatened by technological advances may be insecure about their own skills to adapt. Marone found that what makes an organization more agile in the face of change is the ability of employees and leadership to adapt, learn and assess new information, ask questions and analyze situations. Agility in the age of AI requires soft skills that machines cannot replace: creativity, social skills and judgment.

With sufficient training and development of those soft skills, employees demonstrate increased confidence in accepting and using AI effectively.

It’s vital that organizations lay the foundation for employees to cope with technological change. With preparation, gains achieved by adapting new technology won’t be offset by losses in employee engagement. Preparing the workforce with the right attitudes, understanding and skills will make future changes more successful.

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Girls Don’t Code? In The Caribbean, They Lead Tech Startups

MD Staff

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Research shows that science, technology, engineering, and mathematics (STEM) are still male-dominated fields. According to the U.S. Department of Commerce, in 2011 women occupied less than 25% of STEM jobs. Automation and advancements in technology seem to penalize women: the World Economic Forum estimates that per every 20 jobs lost to the fourth industrial revolution, women will only gain one new STEM job. For men, there will be a new STEM job for every four lost.

Luckily, a growing number of women is pursuing STEM careers, as developers, coders, or even tech entrepreneurs. The success of these women not only creates jobs and promotes economic growth; it also inspires more and more women to look beyond conventional career roles and take full advantage of the new opportunities offered by the digital revolution.

Last month, in the Caribbean, women entrepreneurs swept all five top places in the second PitchIt Caribbean Challenge, a mobile-tech startup competition organized by the Entrepreneurship Program in the Caribbean (EPIC) and sponsored by the World Bank’s infoDev program and the government of Canada.

Here, these talented women talk about their journey to the finals.

Quickplate, Jamaica

For a long time, Monique Powell worked late hours. By the time she got home, she would have no choice than to order food for delivery. “I realized you were more or less limited to pizza,” she recalls. “There was no reliable way to order from different restaurants and have the food delivered.”

She found that many Jamaicans shared her frustration.

It didn’t take Monique long to reach for a web- or app-based solution. “My professional background spanned web development, e-commerce, and marketing. With this knowledge, plus my determination to make the business work, I’d be able to lead the team and get the company off the ground.”

After partnering with many of Kingston’s popular eateries, in 2016 she launched QuickPlate, a mobile app that promises to help people “get good food fast” from anywhere. Customers can easily pay for meals online and track their delivery status from their phone or computer.

She sees the irony of the scarcity of female tech entrepreneurs in an increasingly industrialized world. “I’m always excited when women stand out and shine in male-dominated fields,” she says. “There are more and more programs designed to introduce girls to coding and web development, and I can’t wait to see what the next generation of female tech entrepreneurs will come up with.”

The Interview JM, Jamaica 

“My parents were always trying to help someone find a job,” Angela Tait says. “As I grew older, they would ask me to review resumes or look for openings. Later, I started an informal job network to help match youth with entry-level jobs at small businesses. The struggles I saw on both sides, the job seekers’ and the small business owners’, eventually led to The Interview JM.” 

Angela’s company facilitates the recruitment process for both employers and job seekers by using “innovative and modern assessment/training tools to help clients leverage their strengths.” She plans to grow the business into something that can change the talent management landscape in the Caribbean.

Angela commands her company’s technology. She handles operations and strategy, as well as negotiations with software partners. This, she says, requires “an intimate knowledge of all technical processes and core software we use.”

“It’s important to have diversity when we are talking about solving problems, which is what tech innovation does most of the time. We’ve seen that women can do anything and everything, so I decided that 2016 would be my ‘year of yes.’ I registered for the PitchIt pre-accelerator and then the competition.”

Indetours App, Montserrat

Nerissa Golden always considered herself ‘a solutionist.’ In Montserrat, she has been looking for “ways to leverage our uniqueness in a way that preserves our identity but allows residents to make money from it.” Her team has built an app that will help taxi drivers and tour operators find travelers quickly and inexpensively.

With more than 17 years of Internet experience, Nerissa is used to the idea of women as mobile tech entrepreneurs. “I taught myself about web development. I code. In 2014, I did a Caribbean Girls Can Code campaign to feature a few of the women I know who do code and are using it to change lives,” she says. “Now I leave the coding to others but I try to keep up with what is changing.”

SKED, Trinidad & Tobago

For Kelly-Ann Bethel it all began in August 2016 at the TV contest ‘Planting Seeds’. Her $30,000 prize gave her the funding she needed to develop SKED, a business appointment-management app that allows consumers to book meetings with a wide range of businesses without having to make a phone call.

“I always loved technology businesses. Although I am not a developer myself, the scalability of tech was always attractive,” she says, highlighting that the gist of her business idea is “simpler appointment booking for the Caribbean.”

Kelly-Ann agrees that women are underrepresented in technology, “but that doesn’t negate the women’s ability to be awesome tech entrepreneurs. Although we were outnumbered at the beginning of this PitchIT competition, we still managed to win big! Girls don’t code? Really?”

She wears an impressive number of hats: “I am the quintessential go-getter: I do everything except for the actual tech. I know the vision for SKED, I am the product manager, business development lead, marketer, pitch maker, finance manager…”

In the first quarter of 2017, SKED will focus on finishing the beta stage before launching, as she says, “to the many businesses that are excited and have expressed interest in using the product.”

Kelly-Ann hopes to participate in an international accelerator and get enough funding to realize the vision.

D Carnival Scene, Trinidad & Tobago

In 2011, Ayanna St. Louis started to work on her idea of ‘a mobile carnival concierge’ to serve revelers at the carnival in Trinidad & Tobago. “I have always loved carnivals and took an interest in carnivals around the world,” she explains. “Being from Trinidad & Tobago – where the carnival is the best in the world – I have always found that most other carnivals in the Caribbean are lacking in elements of ‘completeness’ and ‘structure.’ ”

For PitchIt Caribbean, she competed her registration at the last minute, and she wasn’t quite ready with a ‘defined’ pitch. When she had to pitch, she says, “everything truly came from within. For the Q&A round, I answered truthfully — as if I were using the product — how it would impact my life positively.”

“I am no ‘techie’,” Ayanna confesses. Unlike several of the other winners, Ayanna admits that she focused primarily on a problem-solving idea, rather than a new technology. As for her next move, Ayanna is currently working on an application for another pitch event. 

This feature is an outcome of infoDev, a multi-donor program administered by the World Bank Group, with a focus on entrepreneurs in developing economies.

World Bank

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Technological Superiority at the Heart of China-US Confrontation

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The US defense and technology sectors have become genuinely worried about Chinese significant strides in the technological sphere. Various reports have over the past couple of weeks stated that the Chinese military and technology sectors are close to achieving parity with the US.

One such report, published several days ago by the Center for New America Security, stated that China now “appears increasingly close to achieving technological parity with US operational systems and has a plan to achieve technological superiority.”

In a way, the current confrontation between the US and China fits into the biggest struggle in history: a battle between sea and land peoples. China is more of a continental power than a sea one, while the US is clearly an oceanic country. The US, like its historical predecessors, be they ancient Greeks, medieval Venetian merchants, or British and French seafarers in the 19th century, has so far successfully managed to limit Eurasian powers from rising to a prime position in the major world continent.

But with China it is different. One simple example suffices to state a surprising development. Since about 1885, the United States has not had to face a competitor or even a group of competitors with a combined Gross Domestic Product (GDP) larger than its own. China surpassed the United States in purchasing power parity in 2014 and is on track to have the world’s largest GDP in absolute terms by 2030. In comparison, America’s Cold War adversary, the Soviet Union, was bogged down by a truly unsustainable economic system that ultimately crumbled under pressure in the 1980s. At the height of the Soviet power, its GDP was roughly 40% the size of the United States’.

As said, a guarantee to win the Cold War was the US’ technological and economic preeminence. This is still at the heart of today’s global competition. Both Washington and Beijing understand that bilateral trade issues are in fact disguised by a deeper rivalry which opens up in the technology and innovation sector.

It has always been the case that sea powers possessed much fewer human resources, but attenuated this problem with much larger technological advances in comparison with continental powers. What is worrying for the US, and this constitutes a fundamental shift in global history, is China’s ability as a land power not only to confront the Americans with a larger population pool, but also with a highly competitive technological sector.

Several moves made by the Chinese government in the past week show China’s massive technological prowess. According to state media, Beijing is allegedly creating a system to protect its technology. Exactly what this system is, is not clear, but it was suggested that the system will build a strong firewall to strengthen the nation’s ability to innovate and to accelerate the development of key technologies.

The Chinese also announced that they, like the Americans are considering restricting export of Chinese technologies abroad, primarily to the US. This follows similar US moves to restrict sales to Huawei Technologies and other Chinese tech firms on national security grounds.

Thus, there are major concerns as to how the US would be able to offset the Chinese geopolitical challenge. There has always been a simple understanding in the US ruling circles, and among strategists, that it is America’s technological edge which gives it fundamental superiority. If this is no longer the case, then the very foundation of the US grand strategy is at stake. US general Paul J. Selva, Vice Chairman of the Joint Chiefs of Staff, has warned that the Chinese military could reach technological parity with the United States in the early 2020s and outpace the Pentagon in the 2030s, if the US military doesn’t respond to the challenge.

The course is set for future global instability, where the Americans will be more worried and the Chinese more assertive in pursuing their goals. This does not necessarily mean that a military confrontation would ensue, but it is highly likely that both states might end up investing billions, if not trillions, to develop future technologies.

Author’s note: first published in Georgia Today

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