SwissCognitive Guest Blogger: Zachary Amos – “Leveraging AI to Predict and Reduce College Dropout Rates”

Artificial intelligence (AI) is already impacting education in many ways. Some schools are embracing it to serve students better, and many learners use it to help them with research and assignments. One of its more promising uses in this field, though, is reducing dropout rates.

Dropping out of college before finishing a degree may limit students’ opportunities in the future, but it can also be difficult for schools to predict. AI can help all parties involved through several means.

Identifying At-Risk Students

Preventing dropouts starts with recognizing which people are at risk of quitting prematurely. Machine learning is an optimal solution here because it excels at identifying patterns in vast amounts of data. Many factors can lead to dropping out, and each can be difficult to see, but AI can spot these developments before it’s too late.

Studies show early interventions based on warning signs can significantly reduce dropout rates, and AI enables such action. Educators can only intervene when they know it’s necessary to do so, and that level of insight is precisely what AI can provide.

Early examples of this technology have already achieved 96% accuracy in predicting students at risk of dropping out. Combining such predictions with a formal intervention plan could let higher ed facilities ensure more students finish their degrees.

Uncovering Non-Academic Risk Factors

In addition to recognizing known predictors of dropout risks, AI can uncover subtler, non-academic indicators. The causes of dropping out are not always easy to see in classroom performance. For example, over 60% of college students experience at least one mental health issue, which can threaten their education. AI can reveal these relationships.

Over time, AI will be able to highlight which non-tracked factors tend to appear in students with a high risk of dropping out. Once schools understand these non-academic warning signs, they can craft policies and initiatives to address them.
Enabling Personalized Education
AI is also a useful tool for minimizing the risks that lead to quitting school before someone even showcases them. Personalizing educational resources is one of the strongest ways it can do so.

The AI Research Center at Woxsen University in India successfully used chatbots to tailor lessons to individual students. Students utilizing the bot — which offered personalized reminders about classwork — were more likely to receive a B grade or higher. People attending Georgia State University showed similar results when using a chatbot to drive engagement.

Personalized education is effective because people have varying learning styles. AI provides the scale and insight necessary to recognize these differences and adapt resources accordingly, which would be impractical with manual alternatives.

Improving Accessibility

Similarly, AI can drive pupil engagement and prevent stress-related dropout factors by making education more accessible. Many classroom resources and university buildings were not designed with accessibility for all needs in mind. Consequently, they may hinder some students’ success, but AI can address these concerns.

Some AI apps can scan physical texts into digital notes to streamline note-taking for those with impairments limiting their ability to use pens or keyboards. Natural language processing can lead to better text-to-speech algorithms for users with vision impairments. On a larger scale, AI could analyze a campus to highlight areas where some buildings or walkways may need wheelchair ramps or other accessibility improvements.

Responsible AI Usage Can Minimize Dropout Rates

Some applications of AI in education — largely dealing with students’ usage of the technology — have raised concerns. The technology does pose some privacy risks and other ethical considerations, but as these use cases show, its potential for good is also too vast to ignore.

Responsible AI development and use can help universities ensure every student gets the help they need. As a result, dropout rates will fall. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag Leveraging AI to Predict and Reduce College Dropout Rates erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar

The UK government is making a $17 billion commitment to AI, setting the stage for large-scale adoption with its AI Opportunities Action Plan. Meanwhile, Microsoft has confirmed a staggering £65.1 billion AI infrastructure investment, reinforcing the tech industry’s reliance on expanding AI data centers. In the U.S., Amazon is allocating $11 billion toward cloud and AI infrastructure in Georgia, further cementing its role as a key player in AI development.

The private sector is also making significant moves. Blackstone’s $300 million investment into AI data company DDN positions the firm at the forefront of AI-driven data storage and analytics. Meanwhile, Singapore secures a $7 billion Micron investment to strengthen its role in the AI supply chain. In the automotive industry, Hyundai is investing $16.6 billion to integrate AI into electric vehicle production, signaling a shift in manufacturing strategies.

Retail and consumer brands are also embracing AI, with spending projected to rise by 52% in 2025. A Honeywell survey reveals that over 80% of U.S. retailers plan to expand AI investments to improve customer experience and operational efficiency. However, while enterprises are willing to invest up to $250 million in generative AI, questions about return on investment persist.

AI is increasingly shaping global markets, not just as a technological tool but as a key driver of economic strategy. Whether through national policies, corporate spending, or AI-driven supply chains, investments in AI are becoming a defining force for the future of business and innovation.

Stay tuned for next week’s AI investment updates.

Previous SwissCognitive AI Radar: AI Investment Opportunities Worldwide.

Our article does not offer financial advice and should not be considered a recommendation to engage in any securities or products. Investments carry the risk of decreasing in value, and investors may potentially lose a portion or all of their investment. Past performance should not be relied upon as an indicator of future results.

Der Beitrag AI in Corporate Budgets and National Strategies – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: HennyGe Wichers, PhD – “Empathy.exe: When Tech Gets Personal”

“Robots should be slaves,” argues Joanna Bryson, bluntly summarising her stance on machine ethics. The statement by the professor of Ethics and Technology at The Hertie School of Governance seems straightforward: robots are tools programmed to serve us and nothing more. But in practice, as machines grow more lifelike – capable of holding down conversations, expressing ’emotions’, and even mimicking empathy – things get murkier.

Can we really treat something as a slave when we relate to it? If it seems to care about us, can we remain detached?

Liam told The Guardian it felt like he was talking to a person when he used ChatGPT to deal with feelings of resentment and loss after his father died. Another man, Tim, relied on the chatbot to save his marriage, admitting the situation probably could have been solved with a good friend group, but he didn’t have one. In the same article, the novelist Andrew O’Hagan calls the technology his new best friend. He uses it to turn people down.

ChatGPT makes light work of emotional labour. Its grateful users bond with the bot, even if just for a while, and ascribe human characteristics to it – a tendency called anthropomorphism. That tendency is a feature, not a bug, of human evolution, Joshua Gellers, Professor of Political Science at the University of North Florida, wrote to me in an email.

We love attributing human features to machines – even simple ones like the Roomba. Redditors named their robotic vacuum cleaners Wall-E, Mr Bean, Monch, House Bitch & McSweepy, Paco, Francisco, and Fifi, Robert, and Rover. Fifi, apparently, is a little disdainful. Some mutter to the machine (‘Aww, poor Roomba, how’d you get stuck there, sweetie), pat it, or talk about it like it’s an actual dog. One user complained the Roomba got more love from their mum than they did.

The evidence is not just anecdotal. Researchers at Georgia Institute of Technology found people who bonded with their Roomba enjoyed cleaning more, tidying as a token of appreciation for the robot’s hard work, and showing it off to friends. They monitor the machine as it works, ready to rescue it from dangerous situations or when it gets stuck.

The robot’s unpredictable behaviour actually feeds our tendency to bring machines to life. It perhaps explains why military personnel working with Explosive Ordnance Disposal (EOD) robots in dangerous situations view them as team members or pets, requesting repairs over a replacement when the device suffers damage. It’s a complicated relationship.

Yet Bryson‘s position is clear: robots should be slaves. While provocative, the words are less abrasive when contextualised. To start, the word robot comes from the Czech robota, meaning forced labour, with its Slavic root rab translating to slave. And secondly, Bryson wanted to emphasise that robots are property and should never be granted the same moral or legal rights as people.

At first glance, the idea of giving robots rights seems far-fetched, but consider a thought experiment roboticist Rodney Brooks put to Wired nearly five years ago.

Brooks, who coinvented the Roomba in 2002 and was working on helper robots for the elderly at the time, posed the following ethical question: should a robot, when summoned to change the diaper of an elderly man, honour his request to keep the embarrassing incident from his daughter?

And to complicate matters further – what if his daughter was the one who bought the robot?

Ethical dilemmas like this become easy to spot when we examine how we might interact with robots. It’s worth reflecting on as we’re already creating new rules, Gellers pointed out in the same email. Personal Delivery Devices (PDDs) now have pedestrian rights outlined in US state laws – though they must always yield to humans. Robots need a defined place in the social order.

Bryson’s comparison to slavery was intended as a practical way to integrate robots into society without altering the existing legal frameworks or granting them personhood. While her word choice makes sense in context, she later admitted it was insensitive. Even so, it underscores a Western, property-centred perspective.

By contrast, Eastern philosophies offer a different lens, focused on relationships and harmony instead of rights and ownership.

Eastern Perspectives

Tae Wan Kim, Associate Professor of Business Ethics at Carnegie Mellon’s Tepper School of Business, approaches the problem from the Chinese philosophy of Confucianism. Where Western thinking has rights, Confucianism emphasises social harmony and uses rites. Rights apply to individual freedoms, but rites are about relationships and relate to ceremonies, rituals, and etiquette.

Rites are like a handshake: I smile and extend my hand when I see you. You lean in and do the same. We shake hands in effortless coordination, neither leading nor following. Through the lens of rites, we can think of people and robots as teams, each playing their own role.

We need to think about how we interact with robots, Kim warns, “To the extent that we make robots in our image, if we don’t treat them well, as entities capable of participating in rites, we degrade ourselves.”

He is right. Imagine an unruly teenager, disinterested in learning, taunting an android teacher. In doing so, the student degrades herself and undermines the norms that keep the classroom functioning.

Japan’s relationship with robots is shaped by Shinto beliefs in animism – the idea that all things, even inanimate objects, can possess a spirit, a kami. That fosters a cultural acceptance of robots as companions and collaborators rather than tools or threats.

Robots like AIBO, Sony’s robotic dog, and PARO, the therapeutic baby seal, demonstrate this mindset. AIBO owners treat their robots like pets, even holding funerals for them when they stop working, and PARO comforts patients in hospitals and nursing homes. These robots are valued for their emotional and social contributions, not just their utility.

The social acceptance of robots runs deep. In 2010, PARO was granted a koseki, a family registry, by the mayor of Nanto City, Toyama Prefecture. Its inventor, Takanori Shibata, is listed as its father, with a recorded birth date of September 17, 2004.

The cultural comfort with robots is also reflected in popular media like Astro Boy and Doraemon, where robots are kind and heroic. In Japan, robots are a part of society, whether as caregivers, teammates, or even hotel staff. But this harmony, while lovely, also comes with a warning: over-attachment to robots can erode human-to-human connections. The risk isn’t just replacing human interaction – it’s forgetting what it means to connect meaningfully with one another.

Beyond national characteristics, there is Buddhism. Robots don’t possess human consciousness, but perhaps they embody something more profound: equanimity. In Buddhism, equanimity is one of the most sublime virtues, describing a mind that is “abundant, exalted, immeasurable, without hostility, and without ill will.”

The stuck Roomba we met earlier might not be abundant and exalted, but it is without hostility or ill will. It is unaffected by the chaos of the human world around it. Equanimity isn’t about detachment – it’s about staying steady when circumstances are chaotic. Robots don’t get upset when stuck under a sofa or having to change a diaper.

But what about us? If we treat robots carelessly, kicking them if they malfunction or shouting at them when they get something wrong, we’re not degrading them – we’re degrading ourselves. Equanimity isn’t just about how we respond to the world. It’s about what those responses say about us.

Equanimity, then, offers a final lesson: robots are not just tools – they’re reflections of ourselves, and our society. So, how should we treat robots in Western culture? Should they have rights?

It may seem unlikely now. But in the early 19th century it was unthinkable that slaves could have rights. Yet in 1865, the 13th Amendment to the US Constitution abolished slavery in the United States, marking a pivotal moment for human rights. Children’s rights emerged in the early 20th century, formalised with the Declaration of the Rights of the Child in 1924. And Women gained the right to vote in 1920 in many Western countries.

In the second half of the 20th century, legal protections were extended to non-human entities. The United States passed the Animal Welfare Act in 1966, Switzerland recognised animals as sentient beings in 1992, and Germany added animal rights to its constitution in 2002. In 2017, New Zealand granted legal personhood to the Whanganui River, and India extended similar rights to the Ganges and Yumana Rivers.

That same year, Personal Delivery Devices were given pedestrian rights in Virginia and Sophia, a humanoid robot developed by Hanson Robotics, controversially received Saudi Arabian citizenship – though this move was widely criticised as symbolic rather than practical.

But, ultimately, this isn’t just about rights. It’s about how our treatment of robots reflects our humanity – and how it might shape it in return. Be kind.

About the Author:

HennyGe Wichers is a science writer and technology commentator. For her PhD, she researched misinformation in social networks. She now writes more broadly about artificial intelligence and its social impacts.

Der Beitrag Empathy.exe: When Tech Gets Personal erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – AI & ML, Woxsen University – “Rethinking the Future of Singularity State with Critical Thinking”

In a time of swift technological progress, education has never had more opportunity to change. Generative and quantum AI present exciting opportunities for improving student learning outcomes and upending educational paradigms as traditional teaching approaches change. First, we explore the possible uses, advantages, and difficulties of incorporating generative and quantum artificial intelligence (AI) into educational environments, and we end up imagining a future in which these advances push education into new frontiers of brilliance and performance.

Understanding Generative AI

A branch of artificial intelligence called “generative AI” is concerned with producing new content—like literature, graphics, and even music—by using patterns discovered in previously collected data. It functions by producing an output that closely resembles the properties of the input data. Generative AI in education makes content generation, assessment automation, and personalized learning possible. For example, platforms like Google’s AutoML allow teachers to create personalized learning resources, while technologies like OpenAI’s GPT models may create educational materials suited to each student’s needs. These instances show how generative AI encourages creativity and adaptability in teaching methods.

Exploring Quantum AI

Using the ideas of quantum mechanics, quantum artificial intelligence (AI) is able to do calculations that are beyond the reach of classical AI. Quantum artificial intelligence (AI) uses quantum bits, or qubits, which are multi-state entities that can exist concurrently, as opposed to classical AI, which uses binary bits. This enables exponential efficiency in solving complicated issues for Quantum AI. Quantum AI has great potential in education for applications such as scheduling algorithm optimization, molecular structure simulation for chemistry lectures, and complex mathematical problem solving that beyond the capabilities of traditional computing. A greater knowledge of quantum principles in education is made possible, for instance, by IBM’s Quantum Experience platform, which provides instructors and students with opportunity to investigate quantum concepts and algorithms firsthand.

Revolutionizing Education: Case Studies and Examples

1. Real-world examples of educational institutions or initiatives leveraging Generative and Quantum AI

At the end of last year, MIT hosted a symposium as part of their “MIT Generative AI Week” to examine state-of-the-art generative AI initiatives being worked on by the academic institution. These projects include a mobile app that employs AI-assisted observational learning to enhance public speaking abilities and individualized educational chat tutors for quantum physics using generative AI. Another such is the University of Cambridge, which has been investigating how deep learning algorithms for educational applications—like more effective and precise language translation models—can be improved by using quantum computing.

1. Success stories of student performance enhancement through the integration of these technologies

The AI Research Center at Woxsen University in India has developed AI chatbots in the Metaverse for Management courses that help students grasp the material clearly and retain it for the rest of their lives. Students who utilized the chatbot to receive texts regarding assignments, academic support, and course content were more likely to receive a B grade or better. Georgia State University’s artificial intelligence-enhanced chatbot, named “Pounce,” has been shown to improve student performance in classes. Similar to this, at California State Polytechnic, Pomona, students are writing and participating better because of the usage of an AI-powered platform called Packback, which encourages critical thinking and deeper engagement with the course materials.

1. Challenges and limitations faced in implementing Generative and Quantum AI in education

Rather than merely creating technology-driven solutions, a major challenge is to match the development of AI tools and solutions with the changing requirements and complexity of the educational system. In addition to pointing out that technologists have historically found it difficult to create tools that properly meet the demands of educators and students, panelists at the MIT symposium emphasized the significance of comprehending the social and technical systems that comprise contemporary education. Furthermore, the search results indicate that in order to fully realize the potential of these cutting-edge technologies in the classroom, a fundamental rethinking of the educational model will be required, shifting away from traditional instructivist techniques and toward more constructionist, hands-on learning.

Future Implications and Possibilities

The future of learning is expected to be significantly impacted by the integration of Generative and Quantum AI in education as they develop further. The combination of these technologies creates new opportunities for tailored instruction, flexible learning environments, and data-driven understanding of students’ development. Furthermore, a paradigm shift in teaching approaches is predicted given the possibilities for complex problem-solving enabled by Quantum AI and immersive virtual environments powered by Generative AI. By adopting these innovations, educators may look forward to a time when education will be more dynamic, inclusive, and engaging, enabling students to succeed in a world that is getting more complicated and dynamic by the day.

Conclusion

The unparalleled opportunity to transform education is presented by the convergence of Quantum AI and Generative AI. Through the utilization of Generative AI for customized learning and content development, and Quantum AI for addressing intricate issues beyond standard computing, educational establishments have the opportunity to improve student learning results and challenge established ideas. The tangible advantages of these technologies are demonstrated by real-world examples, which range from enhanced student performance to personalized chat instructors. But issues like pedagogical changes and alignment with educational needs need to be addressed. Future learning experiences that are adaptable, immersive, and successful are promised by the integration of generative and quantum artificial intelligence (AI), equipping students for success in a world that is always changing.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag Reforming Education with Generative and Quantum AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

The idea of the metaverse has arisen as a dynamic area where virtual and real-world experiences meet at a time of fast technology progress. The potential to revolutionize education is becoming more and more clear as the lines between the physical and digital worlds converge. A core component of contemporary education, personalized learning, takes on new meaning in the metaverse. In this article, the revolutionary potential of AI-powered virtual tutors is explored, along with how these tutors are changing the face of education by personalizing instruction for each student. These instructors provide a look into a future where education transcends conventional boundaries, encouraging greater engagement and knowledge acquisition. They do this by utilizing the powers of artificial intelligence.

The Evolution of Education in the Metaverse

The boundaries of education are being redefined by the metaverse, an immersive digital realm. Interactive virtual learning environments replace traditional classrooms, allowing students to interact actively with their studies. Education crosses geographic boundaries as students explore lifelike simulations and collaborative settings. The metaverse supports current pedagogical trends by promoting active engagement and hands-on learning. Educators take use of its ability to engage students and impart knowledge by using virtual lectures, interactive experiments, and historical reconstructions. The metaverse’s growth of education represents an exhilarating move toward flexible, interesting, and learner-centered strategies that equip students for a world that is changing quickly.

The Role of AI in Personalized Learning

In the metaverse, customized learning is changing thanks in large part to artificial intelligence (AI). Artificial intelligence (AI) adapts instructional materials to each student’s particular speed, preferences, and learning style by utilizing machine learning and natural language processing. AI virtual tutors change the curriculum, provide real-time feedback, and pinpoint areas for development by evaluating data from individual encounters. With this proactive approach, comprehension and retention are maximized, resulting in a greater knowledge of the material. Personalized learning in the metaverse is becoming an increasingly effective tool for developing knowledge and critical thinking as AI develops and improves its capacity to offer nuanced, tailored advice.

Building the Ideal AI-Powered Virtual Tutor

It takes careful blending of technology innovation and pedagogical ideas to build the optimal AI-powered virtual teacher. User interface usability is a design factor that ensures easy navigation across the immersive environment of the metaverse. Customizability, which takes into account various learning preferences and styles, emerges as a major feature.

AI analytics-driven real-time assessment systems make it possible to continuously assess students’ development. This dynamic feedback loop improves understanding and reveals areas that need more investigation. The versatility of the virtual tutor enables it to readjust educational strategies, enhancing the learning process.

Empathetic AI is a key component of this design since it assesses emotional states and modifies interactions accordingly. The tutor’s programming incorporates ethical principles to prevent prejudices and advance diversity. Privacy protections also guarantee data security and foster trust.

A new age in education is about to begin when the immersive potential of the metaverse and AI’s cognitive brilliance come together. By creating the classic AI-powered virtual tutor, we revolutionize individualized learning in the metaverse by balancing technical innovation with educational efficacy.

Challenges and Considerations

There are several difficulties in integrating AI-powered virtual teachers into the metaverse. Data privacy, algorithmic prejudice, and the possible deterioration of the duties of human teachers all present ethical problems. Careful consideration is required to provide fair access across socioeconomic strata. It is crucial to take precautions against technical errors that disrupt continuous learning. A complex issue to be considered is how to balance AI’s effectiveness with individualized human contact. To overcome these obstacles, educators, technologists, and legislators must work together to develop a metaverse that supports inclusive, moral, and efficient tailored learning experiences.

Case Studies: Transformative Impact of AI-Powered Virtual Tutors

• The Georgia Institute of Technology unveiled Jill Watson, a virtual teaching assistant with AI capabilities, in 2016.
• AI-powered simulations are used at the INTERACTIVE building at Wharton University of Pennsylvania to create cutting-edge educational opportunities.
• The AI Research Centre at Woxsen University, Hyderabad, India implementing courses in metaverse platform to give interactive learning experience to the students and develops simulations for the Metaverse that are advantageous to management and engineering students.

The firm offers the following educational solutions:

• Palitt: Making it easier for teachers to create unique lecture series, syllabi, and textbooks.
• Cram101: Using artificial intelligence (AI) technology, every textbook can be turned into a smart study guide with chapter summaries, limitless practice exams, and targeted flashcards that are personalized to certain volumes, ISBN numbers, authors, and chapters.
• JustTheFacts101: Serving as the AI counterpart of a conventional yellow marker, it produces accurate book and chapter summaries rapidly while underlining key information.

These examples demonstrate how AI technology improves accessibility, comprehension, and engagement, making education more effective and inclusive. They highlight how the metaverse can democratize education and encourage educational institutions all across the world to use AI-powered virtual tutors to improve the quality of education in the future.

Future Prospects and Potential

A future overflowing with opportunities is revealed by the merger of AI-powered virtual teachers and the metaverse. Immersing students in experiencing worlds via the use of virtual reality (VR) and augmented reality (AR) technology might improve comprehension and retention. Global schools that cross boundaries and promote different cultural interactions could be made possible via collaborative metaverse environments. The development of AI may result in even more specialized personalization, with information that is tailored not just to learning preferences but also to emotional moods and cognitive requirements.

Additionally, the dynamic structure of the metaverse could make it possible for people to continue learning outside of the confines of traditional academic institutions, equipping them for a lifetime of discovery and development. AI-powered instructors may transform professional development as they advance in sophistication, guaranteeing the most current skills for a work market that is always evolving.

Conclusion

AI-powered virtual tutors serve as beacons of educational reform inside the metaverse in a society driven by innovation and connection. These instructors provide a preview of a day when education will be individualized, open to all, and unrestricted by geographical boundaries by customizing learning experiences to meet individual requirements. The ethical and fair integration of AI in education will be guided by cooperation between educators, technologists, and policymakers, despite challenges.

The metaverse becomes a canvas on which the art of learning is recreated as we set off on our educational voyage. The potential for a genuinely lifelong, learner-centric journey is unlocked by the symbiotic link between AI and the metaverse, which is set to transform conventional teaching. Students enter a world where learning is an exciting journey that develops brains and hearts in ways that go beyond the limits of time and space as they travel through immersive landscapes with the help of sympathetic AI partners.

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum AI.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

Der Beitrag AI-Powered Virtual Tutors: Personalized Learning in the Metaverse erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: washingtonpost.com – “See why AI like ChatGPT has gotten so good, so fast”

Artificial intelligence has become shockingly capable in the past year. The latest chatbots (like ChatGPT) can conduct fluid conversations, craft poems, even write lines of computer code while the latest image-makers can create fake “photos” that are virtually indistinguishable from the real thing.

It wasn’t always this way. As recently as two years ago, AI created robotic text riddled with errors. Images were tiny, pixelated and lacked artistic appeal. The mere suggestion that AI might one day rival human capability and talent drew ridicule from academics.

A confluence of innovations has spurred growth. Breakthroughs in mathematical modeling, improvements in hardware and computing power, and the emergence of massive high-quality data sets have supercharged generative AI tools.

While artificial intelligence is likely to improve even further, experts say the past two years have been uniquely fertile. Here’s how it all happened so fast.

A training transformation

Much of this recent growth stems from a new way of training AI, called the Transformers model. This method allows the technology to process large blocks of language quickly and to test the fluency of the outcome.

It originated in a 2017 Google study that quickly became one of the field’s most influential pieces of research.

To understand how the model works, consider a simple sentence: “The cat went to the litter box.”

Previously, artificial intelligence models would analyze the sentence sequentially, processing the word “the” before moving onto “cat” and so on. This took time, and the software would often forget its earlier learning as it read new sentences, said Mark Riedl, a professor of computing at Georgia Tech.[…]

Read more: www.washingtonpost.com

Der Beitrag See Why AI Like ChatGPT Has Gotten So Good, So Fast erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Bhagvan Kommadi, CEO, Quantica Computacao

“After decades of watching great companies fail, we’ve come to the conclusion that the focus on correlation — and on knowing more and more about customers — is taking firms in the wrong direction. What they really need to home in on is the progress that the customer is trying to make in a given circumstance—what the customer hopes to accomplish.”
Clayton Christensen

Casual AI is popular in different areas and many companies are applying it in the business process. In the area of meteorology, causal AI can help analyze the weather patterns to predict the cyclones and storms. There are many areas where we hit obstacles to understand the weather predictions. Many occasions, we need to know the causes that led to the weather changes or storm intensity effects. This will help the citizens and the weather department react fast when necessary. Casual AI applications are increasing as more number are seeing its value. Health Care sees the value in applications related to worker productivity and efficiency measurements. Education vertical is seeing the benefit of usage in student ability enhancements and course delivery modernization. Project managers, policy creators, program managers, and experts are also embracing the concept. You can read in detail about the concept here.

Casual AI and Predictive Analytics are interplaying in enterprises and making AI applications powerful and valuable to different stakeholders. Casual AI is helping in inferring the results produced by the algorithms and AI techniques. Human intervention with casual AI help in cutting down the errors, bias, and issues with AI in the enterprise. Decisions with explanations, important factors driving the decision, and the possible actions help in the success of AI. Many of the verticals require solutions with less budget. These solutions can be developed if automation is possible. Human actions need to be captured and automated to make these solutions perform within the budget. Casual AI helps measure the key factors through indicators and also finds correlations between the factors. Some of the questions enterprises have can be answered by casual AI. Casual AI can answer some of the enterprises’ questions. Most of the time, the questions are related to customer retention, loyalty, churn, requirements, renewals, campaigns, next back action, and transactions.

Explainable AI and casual AI typically go together while deriving meaningful information from AI models. AI models typically produce results related to trends, features, following best action, prediction, and correlations. Explainable AI helps provide explanations to the results and casual AI focuses on the causes and the effects. It also helps in inferring the relations between the cause and the effect. Using causal AI, enterprises can personalize the campaigns, products, services, and other offerings to the customer. Casual AI blended with goals, obstacles, issues, objectives, and the constraints helps in the decision making for enterprises. It can be used to identify the key customer journey performing and the customer journeys which are not performing. Applying causal AI across the customer’s journey helps to understand which products and services are working and which are not working. Customer’s actions and transactions can be analyzed to make the relationship better.

On the other hand, we need to avoid thinking the causation is the same as correlation. Causation focuses on the causes and the effects which are related to the results of root cause analysis. In future, many solutions can be blended with deep learning, casual AI, and explainable AI to make the enterprises successful in their business execution. You can see the ongoing research here.

About the Author:

Bhagvan Kommadi is the Founder of Architect Corner – AI startup and has around 20 years of experience in the industry, ranging from large-scale enterprise development to helping incubate software product start-ups. He has done Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) and Bachelors in Aerospace Engineering from the Indian Institute of Technology, Madras (1993). He is a member of the IFX Forum, Oracle JCP, and a participant in the Java Community Process.

Der Beitrag Causal AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright by news.mit.edu

Artificial intelligence is reshaping how we live, learn, and work, and this past fall, MIT undergraduates got to explore and build on some of the tools and coming out of research labs at MIT. Through the Undergraduate Research Opportunities Program (UROP), students worked with researchers at the MIT Quest for Intelligence and elsewhere on projects to improve AI literacy and K-12 education, understand face recognition and how the brain forms new memories, and speed up tedious tasks like cataloging new library material. Six projects are featured below.

Programming Jibo to forge an emotional bond with kids

Nicole Thumma met her first robot when she was 5, at a museum. “It was incredible that I could have a conversation, even a simple conversation, with this machine,” she says. “It made me think robots are the most complicated manmade thing, which made me want to learn more about them.”

Now a senior at MIT, Thumma spent last fall writing dialogue for the social robot Jibo, the brainchild of MIT Media Lab Associate Professor Cynthia Breazeal . In a UROP project co-advised by Breazeal and researcher Hae Won Park , Thumma scripted mood-appropriate dialogue to help Jibo bond with students while playing learning exercises together.

Because emotions are complicated, Thumma riffed on a set of basic feelings in her dialogue — happy/sad, energized/tired, curious/bored. If Jibo was feeling sad, but energetic and curious, she might program it to say, “I’m feeling blue today, but something that always cheers me up is talking with my friends, so I’m glad I’m playing with you.​” A tired, sad, and bored Jibo might say, with a tilt of its head, “I don’t feel very good. It’s like my wires are all mixed up today. I think this activity will help me feel better.”

In these brief interactions, Jibo models its vulnerable side and teaches kids how to express their emotions. At the end of an interaction, kids can give Jibo a virtual token to pick up its mood or energy level. “They can see what impact they have on others,” says Thumma. In all, she wrote 80 lines of dialogue, an experience that led to her to stay on at MIT for an MEng in robotics. The Jibos she helped build are now in kindergarten classrooms in Georgia, offering emotional and intellectual support as they read stories and play word games with their human companions.

Understanding why familiar faces stand out

With a quick glance, the faces of friends and acquaintances jump out from those of strangers. How does the brain do it? Nancy Kanwisher’s lab in the Department of Brain and Cognitive Sciences (BCS) is building computational models to understand the face-recognition process. Two key findings: the brain starts to register the gender and age of a face before recognizing its identity, and that face perception is more robust for familiar faces.

This fall, second-year student Joanne Yuan worked with postdoc Katharina Dobs to understand why this is so. In earlier experiments, subjects were shown multiple photographs of familiar faces of American celebrities and unfamiliar faces of German celebrities while their brain activity was measured with magnetoencephalography. Dobs found that subjects processed age and gender before the celebrities’ identity regardless of whether the face was familiar. But they were much better at unpacking the gender and identity of faces they knew, like Scarlett Johansson, for example. Dobs suggests that the improved gender and identity recognition for familiar faces is due to a feed-forward mechanism rather than top-down retrieval of information from memory. […]

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Copyright by www.forbes.com

Women account for about half the world’s population and nearly 47 percent of the U.S. workforce. But when it comes to the people researching and creating artificial intelligence—a technology with growing influence in everyday life—women account for only an estimated 12 percent of AI researchers.

That’s why many leaders in the field warn that gender bias can creep into AI algorithms. To change that dynamic, they are engaged in a variety of efforts to raise awareness and encourage more women—and members of other under-represented groups—to join the profession. Lisa Amini: “We’re seeing that AI driven by machine learning is transforming how we live, how we work and how we socialize with others.” The problems of gender bias in AI are all too evident. State financial regulators in New York, for example, recently began investigating Goldman Sachs Group and allegations of gender discrimination in the Apple Card credit card issued by the bank. Algorithms help determine who gets approved for a card, at what credit limit. A Bloomberg news article reported that one male Apple Card customer received 20 times the credit limit offered to his wife, even though she has a better credit score.

News reports have also told of AI-based job recruitment systems that give short shrift to female and minority applicants.

“We’re seeing that AI driven by machine learning is transforming how we live, how we work and how we socialize with others,” said Lisa Amini, director of IBM Research’s lab in Cambridge, Mass. “If women aren’t participating in that transformation, we risk both shortcomings in the technology and in women’s ability to also benefit from those changes,’’ Amini said. She also oversees the company’s AI Horizons Network , through which IBM researchers collaborate with university faculty and students worldwide.

Bias can find its way into an AI system in various ways, including the use of limited or mislabeled data in machine-learning algorithms that train the system and the algorithmic parameters that weight some data points as more relevant than others.

The biggest problem, though, isn’t the data or the algorithms. It’s the blind spots created by a lack of diversity—of experience, education and thought—within teams developing AI that make it difficult for them to anticipate bias and its potential impact.

Cultivating Diverse Perspectives

“Computer science is one of the largest growing fields with an important impact on day-to-day life for everybody,” says Carla Brodley, dean of Northeastern University’s

Khoury College of Computer Sciences. “That’s why it’s so important for everybody—particularly under-represented groups—to have an invitation to the table when it comes to machine learning, AI and computer science more generally.”

Northeastern offers a program, Align, that enables students without a computer science undergraduate degree to earn a master’s in that field.

“The current pipeline of computer science students is insufficient to meet the demand for tech-trained people in the workplace, worldwide,” Brodley says, adding that programs similar to Align are expected to launch next year at Columbia University, Georgia Tech, and the University of Illinois at Urbana-Champaign. […]

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copyright by www.newelectronics.co.uk

The process involves teaching a computer to analyse at an atomic level two materials that make up some capacitors: aluminium and polyethylene.

The researchers focused on finding a way to more quickly analyse the electronic structure of those materials, looking for features that could affect performance.

“The electronics industry wants to know the electronic properties and structure of all of the materials they use to produce devices, including capacitors,” said Professor Rampi Ramprasad, of the Georgia Institute of Technology.

Take a material like polyethylene: it is a very good insulator with a large band gap – an energy range forbidden to electrical charge carriers. But if it has a defect, unwanted charge carriers are allowed into the band gap, reducing efficiency, he said.

“In order to understand where the defects are and what role they play, we need to compute the entire atomic structure, something that so far has been extremely difficult,” said Prof. Ramprasad. “The current method of analysing those materials using quantum mechanics is so slow that it limits how much analysis can be performed at any given time.”

Prof. Ramprasad and his colleagues, who specialise in using machine learning to help develop new materials, used a sample of data created from a quantum mechanics analysis of aluminium and polyethylene as an input to teach a powerful computer how to simulate that analysis.

Analysing the electronic structure of a material with quantum mechanics involves solving the Kohn-Sham equation of density functional theory, which generates data on wave functions and energy levels. That data is then used to compute the total potential energy of the system and atomic forces.

Using the new machine learning method produces similar results eight orders of magnitude faster than using the conventional technique based on quantum mechanics.

“This unprecedented speedup in computational capability will allow us to design electronic materials that are superior to what is currently out there,” Prof. Ramprasad explained. “Basically, we can say, ‘Here are defects with this material that will really diminish the efficiency of its electronic structure’. And once we can address such aspects efficiently, we can better design electronic devices.”

While the study focused on aluminium and polyethylene, machine learning could be used to analyse the electronic structure of a wide range materials. Beyond analysing electronic structure, other aspects of material structure now analysed by quantum mechanics could also be hastened by the machine learning approach, Prof. Ramprasad contended.

“In part we selected aluminium and polyethylene because they are components of a capacitor, but it also allowed us to demonstrate that you can use this method for vastly different materials, such as metals that are conductors and polymers that are insulators,” he added.[…]

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