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 Expansion and This Week’s Top Investments – SwissCognitive AI Investment Radar

The AI investment landscape is more active than ever, with tech giants, governments, and venture capitalists committing billions to AI-driven projects and infrastructure. This week’s headlines include Meta’s potential $200 billion data center project, Apple’s record-breaking $500 billion investment in the U.S., and Microsoft reaffirming its $80 billion AI push. Meanwhile, Alibaba is set to invest $52 billion in AI over the next three years, reinforcing China’s commitment to AI leadership.

Europe is also making bold moves, with the EU mobilizing €200 billion for AI development, alongside France’s €109 billion investment plan, as President Macron aims to position the country at the heart of AI innovation. Additionally, Brookfield has pledged €20 billion towards AI projects in France, further strengthening Europe’s AI ecosystem.

On the venture capital side, Perplexity AI is launching a $50 million fund to support early-stage AI startups, while Atria AI secures £720K for its legal AI solutions. At the same time, Marlin Equity Partners has taken a majority stake in Napier AI, signaling growing interest in AI-powered financial crime prevention.

Asia is not staying behind in the race. Saudi Arabia secured $14.9 billion in AI investments at LEAP 25, while India sees 76% of companies already reporting positive AI returns, driving further long-term investments. Meanwhile, Wistron is boosting AI investment by 77%, showing growing confidence in AI’s role in business transformation.

Beyond corporate investments, AI’s impact on sustainability is becoming more evident, with new AI-driven green tech solutions gaining traction amid increasing regulatory scrutiny. Clarity AI launched a new tool for sustainable investing, helping fund managers navigate the evolving ESG landscape.

With AI funding accelerating across industries, next week’s developments will likely bring even more major announcements and strategic shifts. Stay tuned for the next edition of AI Investment Radar.

Previous SwissCognitive AI Radar: Where the AI Money is Going.

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 Expansion and This Week’s Top Investments – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

EU and France Go Big on AI – SwissCognitive AI Investment Radar

The AI investment landscape is taking another major leap forward this week, as governments and private investors pour billions into AI infrastructure and innovation. The European Union announced a massive €200 billion AI investment initiative, including a dedicated fund for AI gigafactories, signaling Europe’s intent to strengthen its global AI position. Meanwhile, French President Emmanuel Macron revealed a €109 billion AI investment package, just ahead of the AI Summit in Paris, where industry leaders like OpenAI’s Sam Altman are expected to weigh in on the future of AI policy and development.

Beyond Europe, AI capital continues to flow across the world. Saudi Arabia secured $14.9 billion in AI investments at the LEAP 25 tech conference, further reinforcing its ambition to become a major AI hub. Additionally, the kingdom has committed $1.5 billion to AI chip firm Groq, boosting its semiconductor and AI infrastructure efforts. Meanwhile, in India, 76% of companies already report positive AI returns, driving a wave of long-term AI investments as businesses look to integrate AI across new applications.

Private sector players are equally active. The Iliad Group announced a $3 billion AI investment, strengthening its position as Europe’s leading AI cloud provider. At the same time, Brookfield is committing €20 billion to AI projects in France, reinforcing the country’s growing reputation as a European AI powerhouse. SoftBank, however, reported a $2.4 billion loss in its Vision Fund but remains focused on long-term AI investments despite short-term financial turbulence.

Amid this investment frenzy, AI-driven sustainability solutions are gaining traction, as companies face mounting regulatory pressures. Clarity AI’s latest platform helps fund managers streamline ESG compliance, while Standard Chartered and the London Stock Exchange Group (LSEG) are deploying AI-powered investment tools to enhance retail investor strategies.

With Europe easing AI regulations to encourage competitiveness and Baidu’s CEO defending aggressive AI investments, the conversation is shifting from investment size to long-term returns and strategic positioning.

Next week’s developments are sure to bring more insights into where capital is flowing and how investors are adapting. Stay tuned!

Previous SwissCognitive AI Radar: AI Market Adjustments and Billion-Dollar Bets.

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 EU and France Go Big on AI – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar

We can all agree that this week, the spotlight was firmly on DeepSeek, whose budget-friendly AI model sent shockwaves through the market, triggering the largest single-day market cap loss in history for Nvidia. Investors reacted sharply, fearing reduced demand for high-end semiconductor chips. While the immediate sell-off was staggering, some experts argue that DeepSeek’s innovation could expand AI adoption rather than collapse the market, potentially opening up new investment opportunities rather than diminishing them.

Beyond the DeepSeek turmoil, Microsoft continues its aggressive AI strategy, committing $80 billion to cloud expansion, leveraging OpenAI’s technology to solidify Azure’s competitive edge. Meanwhile, Meta’s $65 billion AI expansion aims to scale its infrastructure with massive data center investments, signaling confidence in AI’s long-term role in the tech industry.

Venture capital activity remains strong, with SoftBank eyeing a major investment in robotics startup Skild AI, valued at $4 billion. The startup aims to develop an AI-powered “brain” for more agile and dexterous robots, further integrating AI into automation and real-world applications. In the AI data space, Turing has tripled its revenue to $300 million, demonstrating the growing demand for AI training data as more companies scale up their AI models.

Looking beyond big tech, geopolitical AI strategies continue to unfold. India faces challenges in AI infrastructure, with investors warning that a lack of GPUs and data centers could hinder its global competitiveness. Meanwhile, the U.S. is contemplating a $500 billion AI infrastructure initiative, dubbed the Stargate Project, though experts question its feasibility given the sheer scale and energy demands.

As the AI market rapidly evolves, investors are looking for ways to maximize the value of their AI investments, from optimizing AI integration to structuring data and equipping teams with language models. Pharma investors are also weighing AI’s long-term potential, balancing high expectations with the reality of AI adoption hurdles in healthcare.

Despite the ups and downs of the market, AI investment remains a dominant force, shaping industries and redefining long-term strategies. Stay tuned for next week!

Previous SwissCognitive AI Radar: Who’s Investing and Why in AI.

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 The AI Market Shake-Up: Where the Investments Are Headed – 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: Vishal Kumar Sharma – “Leveraging AI and Blockchain for Privacy and Security in Cross-Border Data Transfers”

The globalized world of today depends on the flow of data across boundaries for the operations of international companies to function effectively. Organizations have great difficulties controlling the privacy and security of data across borders as they depend more and more on abroad operations. Different privacy rules, legal systems, and security measures between countries create complexity. So, cross-border data transfers become a major issue for companies trying to keep compliance while guaranteeing seamless corporate operations.

The Growing Concern of Cross-Border Data Transfers

Cross-border data transfers are fraught with legal and operational challenges. Data privacy regulations vary significantly from country to country, leading to uncertainty about compliance and accountability. Regulations such as the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and China’s Data Security Law have stringent guidelines for the protection of personal data and restrict the transfer of sensitive information outside their jurisdictions.

Data breaches are one of the main worries about cross-border data exchanges. Data moving across borders could pass via several governments, increasing the possibility of illegal access or mistreatment. Companies have to make sure enough security systems are in place to guard this information against cyberattacks, espionage, and data theft.

Compliance with local rules is another important problem since many times they put severe restrictions on how personal data may be exchanged or used internationally. Ignoring these rules could lead to big fines, bad reputation, and lost client confidence. Moreover, the variations in privacy models can lead to operational inefficiencies since companies must apply multiple data security solutions to satisfy different local needs.

AI for Enhanced Data Privacy in Cross-Border Transfers

By automating and optimizing privacy protections, artificial intelligence (AI) can transform management and security of cross-border data transfers. Some main ways AI might improve data privacy are below:

1. Automated Data Classification and Encryption: AI systems can automatically find sensitive data depending on pre-defined criteria and apply suitable encryption before exporting it internationally. Different sensitivity level data classification helps AI to guarantee that the most important data gets the best degree of protection. This lessens the possibility of exposure during storage or transportation.
2. Data Anonymization and Pseudonymization: AI-driven systems can anonymize personal data before it leaves a country’s borders, transforming sensitive information into pseudonymous or anonymized data sets that are more difficult to trace back to individuals. This minimizes privacy risks, especially when handling health, financial, or personally identifiable information (PII).
3. Real-time Threat Detection and Response: Real-time data transfer and monitoring by artificial intelligence allows it to identify any irregularities or threats in motion. By means of network traffic pattern analysis and risk identification, machine learning models help companies to react fast to new hazards and prevent data breaches before they materialize.
4. Compliance Monitoring: AI can enable companies to monitor and preserve compliance with many worldwide data protection regulations. AI guarantees that cross-border data transfers follow the necessary legal criteria by always searching for regulatory changes and automatically adjusting data handling systems. This greatly lessens the work for compliance teams and the danger of non-compliance.

Blockchain for Secure and Transparent Data Transfers

With its distributed and unchangeable character, blockchain technology offers a strong basis for improving security and privacy in international data exchanges. Blockchain’s contributions can be as follows:

1. Decentralized Data Ownership: Establishing unambiguous ownership of data as it passes across several countries can be difficult in cross-border data exchanges. Blockchain lets people and companies keep ownership and control over their data even while it is shared across borders, hence enabling distributed control. Every transaction or data move is noted on a distributed ledger guarantees complete traceability and openness.
2. Immutable Audit Trails: Blockchain generates an unchangeable audit record of all data transactions, therefore enabling any cross-border data movement to be followed back to its source. This tool is especially helpful in satisfying legal criteria for responsibility and documentation. By presenting an unchangeable record of data transfers, companies can demonstrate proof of compliance and help to prevent legal conflicts and regulatory fines.
3. Smart Contracts for Automated Compliance: Built on blockchain systems, smart contracts—which represent automated compliance with data privacy rules—can enforce compliance across borders. These agreements can contain clauses guaranteeing that data is managed in compliance with pre-defined policies and that it is transmitted just to countries with sufficient privacy regulations. Should a region fall short of the required privacy criteria, the smart contract can stop the flow, therefore guaranteeing respect to legal systems.
4. Enhanced Encryption and Data Access Control: Blockchain allows encrypted, peer-to–peer data exchanges, therefore improving security by means of data access control and encryption. Blockchain allows companies to regulate access, therefore guaranteeing that only authorised users may read or change private information while it travels across borders. Moreover, the encryption systems used by blockchain systems make it quite impossible for illegal players to access or control data.

The Synergy of AI and Blockchain in Data Privacy
Even further privacy and security advantages can come from using AI and blockchain together in cross-border data exchanges. While blockchain guarantees safe, open, and auditable data transfers, artificial intelligence may offer intelligent data classification, real-time threat detection, and automatic compliance monitoring.

While blockchain guarantees that every transaction is recorded immutably, thereby offering a reliable log for auditing and legal purposes, artificial intelligence may monitor cross-border transactions, warning potential dangers or compliance issues. Even in difficult international settings, these technologies taken together can create a strong framework for safe and compliant data moves.

Conclusion

International corporations depend on cross-border data exchanges, but they also carry major privacy and security concerns. By means of automated data security, safe transfer methods, and regulatory compliance, artificial intelligence (AI) and blockchain present strong instruments to reduce these threats. Adopting these technologies would help companies to negotiate the complexity of cross-border data transfers with more confidence, therefore ensuring that sensitive data stays encrypted and allowing seamless worldwide operations.

Organizations trying to keep ahead of the curve and safeguard their most important asset data will depend critically on the integration of artificial intelligence and blockchain in data privacy plans as the global regulatory scene changes.

References:

• T. Scherer, “Data Privacy and Cross-Border Data Flows: Impact of GDPR on International Businesses,” Journal of Data Protection & Privacy, vol. 3, no. 2, pp. 120-132, 2022.
• Kosciuszko, and P. Heikkilä, “Blockchain-Based Data Management for Secure Cross-Border Transactions,” in Proc. Int. Conf. on Blockchain Technology, 2021, pp. 45-54.
• Narayanan, V. Shmatikov, “Privacy Concerns in Cross-Border Data Transfer: A Review of Encryption Techniques,” IEEE Security & Privacy, vol. 17, no. 4, pp. 33-40, July-Aug. 2020.
• Y. Lu and R. Zhao, “Artificial Intelligence for Data Classification and Protection in Cross-Border Transfers,” IEEE Transactions on Big Data, vol. 7, no. 3, pp. 536-545, 2021.
• Zhang et al., “Smart Contracts for Enforcing Data Privacy Regulations in International Data Transfers,” IEEE Access, vol. 8, pp. 32543-32554, 2020.
• Behl and K. Pal, “Blockchain-Based Secure Framework for Cross-Border Data Flow and Privacy Preservation,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2179-2189, 2020.
• C. Lin and D. Xu, “AI and Blockchain in Cross-Border Data Transfer: A Synergistic Approach to Privacy Protection,” IEEE Communications Surveys & Tutorials, vol. 23, no. 1, pp. 326-342, 2021.
• S. W. Brenner, “Global Data Privacy and Cross-Border Data Transfers: Legal Challenges and Solutions,” Harvard Journal of Law & Technology, vol. 34, no. 1, pp. 125-140, 2021.
• C. O. Martins and M. T. O’Connor, “Blockchain for Cross-Border Data Transfers: Enhancing Security and Compliance,” Journal of Cybersecurity and Privacy, vol. 5, no. 3, pp. 1-18, 2022.
• K. Hughes, “Artificial Intelligence and Data Privacy: How AI Can Help Manage Cross-Border Data Transfers,” Journal of International Data Privacy Law, vol. 10, no. 2, pp. 85-95, 2020.
• T. F. Siegel, “Blockchain and Data Sovereignty: Implications for International Data Transfers,” Journal of Global Privacy Law and Security, vol. 3, no. 4, pp. 211-229, 2021.
• R. K. Gupta and L. Yang, “Leveraging AI for Real-Time Data Protection in Cross-Border Transfers,” Future Internet, vol. 12, no. 6, pp. 1-14, 2020.
• P. M. Schwartz, “Global Data Flows and the EU-U.S. Privacy Shield: Toward Improved Transatlantic Data Protection,” California Law Review, vol. 106, no. 4, pp. 115-150, 2018.
• M. Montoya and J. Wells, “Data Anonymization and Blockchain Solutions for Cross-Border Transfers,” International Journal of Information Management, vol. 55, pp. 102-110, 2020.

About the Author:

Vishal Kumar Sharma, Senior Project Engineer of AI Research Centre, Woxsen University, India, with over 8 years of experience in team management, PCB design, programming, robotics manufacturing, and project management. He has contributed to multiple patents and is passionate about merging smart work with hard work to drive innovation in AI and robotics.

Der Beitrag Leveraging AI and Blockchain for Privacy and Security in Cross-Border Data Transfers erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Analysing the Importance of Artificial Intelligence and Robotics in Agriculture”

Introduction

As the foundation of human civilization, agriculture is undergoing a revolution right now. The agriculture sector faces hitherto unheard-of challenges given the rising world population and growing effects of climate change. Though throughout has proven successful, conventional agricultural methods are failing to meet the demands of modern society. Two innovative technologies poised to transform our farming, monitoring, and collecting of crops are artificial intelligence (AI) and robotics. The article investigates the reasons behind the necessity of artificial intelligence (AI) and robotics into agriculture rather than just a passing trend.

The challenge is supplying food for a population growing

By year 2050, the world’s population is expected to reach 10 billion. Feeding this many people calls for a 70% increase in food output, claims the Food and Agriculture Organization (FAO). Still, the resources needed for agriculture land, water, labor are few and in many cases declining. Conventional methods usually damage the environment and demand a lot of human effort. Thus, it is quite necessary to improve farming’s efficiency, sustainability, and output.

The Significance of Artificial Intelligence (AI) in Agriculture

In agriculture, artificial intelligence is the use of data-based, more intelligent decisions making. Large amounts of data in real-time analysis made possible by AI-driven systems gives farmers insightful information that may be utilized to monitor soil condition and project crop harvests. Using satellite images and weather data, artificial intelligence systems can predict ideal planting times, spot disease outbreaks, and suggest effective pest control tactics. Such a great degree of accuracy can lead to notable increases in waste reduction, crop output, and the limitation of the usage of harmful pesticides.

Furthermore, artificial intelligence powered instruments have the capacity to improve resource use efficiency. Precision agriculture driven by artificial intelligence helps farmers to precisely apply pesticides, fertilizers, and water in ideal amounts and targeted areas. This method solves the entwined problems of sustainability and financial viability by lowering costs and mitigating the negative effects of agriculture.

The Significance of Robotics in Agriculture

By automating tasks requiring a lot of manual work, robotics improves artificial intelligence and hence increases farming’s productivity and scalability. Robots are used gradually for harvesting, weeding, and planting jobs. While robotic harvesters can pick fruits and vegetables with no damage, a task difficultly accomplished with human workers, autonomous tractors can plow fields with perfect accuracy. In fields without personnel or where agricultural chores demand great physical effort, this technique is very important.

Precision farming depends much on robotic tools. With sensors and cameras, unmanned aerial vehicles can monitor crop conditions from above and provide current data that lets farmers make wise decisions. Terrestrial robots can do complex tasks including weed removal, therefore reducing the need for herbicides. These technologies not only increase output but also reduce the boring character of manual farming, so appealing agriculture is to younger generations.

Sustainability and environmental impact

Using robotics and artificial intelligence in agriculture has a clear advantage since it helps farming methods to be more sustainable. Often requiring resources, traditional agricultural techniques can lead to soil degradation, water shortage, and a decline in biodiversity. Artificial intelligence (AI) driven analytics can give farmers direction on using sustainable practices such crop rotation, minimum soil disturbance, and irrigation optimization. By enabling precise farming techniques that cut waste and environmental effect, robotics can help to forward this goal.

Artificial intelligence might, for instance, look at soil moisture data and project irrigation needs, therefore ensuring the effective use of water. By selectively distributing fertilizers and pesticides, robots can help to lower the overall consumption and thereby minimize the flow into nearby ecosystems. By maintaining soil health and biodiversity, these technologies not only protect the surroundings but also raise agricultural output.

Advantages in the field of economics

In the context of agriculture, artificial intelligence (AI) and robotics provide clear financial benefits. For farmers, these technologies could help to lower costs, increase crop output, and raise the quality of agricultural goods. By means of predictive capabilities of artificial intelligence, farmers may efficiently reduce risks related to market volatility, pests, and weather conditions, so promoting more stable income. By automating chores requiring a lot of physical labor, robotics can significantly cut labor costs. In places where agricultural labor is either scarce or highly expensive, this is particularly helpful.

Moreover, the information generated by robotics and artificial intelligence can provide farmers with other revenue streams. For example, precise information on crop quality could be used to negotiate better prices or enter special markets. Furthermore, the application of these technologies can improve farming output, therefore raising its competitiveness and maintaining the livelihoods of farmers in both developed and underdeveloped countries.

Challenges and the road forward

Though robots and artificial intelligence (AI) have great potential in agriculture, several factors prevent their general application. Mostly because of high startup costs, lack of technology knowledge, and concerns about data privacy, smallholder farmers in underdeveloped areas have great difficulties. Governments, research labs, and businesses must cooperate to provide training, subsidies, and support systems that make this technology available to all farmers thereby overcoming these challenges.

Moreover, the development of robotics and artificial intelligence in agriculture has to be guided by ideas of durability and fairness. It is imperative to ensure that these technologies benefit smallholder farmers, the basis of world food supply, as well as big-scale commercial farms as they develop.

Conclusion

Rather than only a technical development, artificial intelligence and robots are essential tools for the direction of agriculture. These technologies offer a way to reach a more efficient, ecologically friendly, and flexible agricultural system within the worldwide fight to solve the problems of feeding an increasing population and preserving the environment. Including robotics and artificial intelligence (AI) into agricultural practices has moved from a luxury to a necessary need. These technologies will help us to ensure that agriculture meets the needs of the present generation without endangering the capacity of next generations to support themselves.

References:

1. Food and Agriculture Organization (FAO). (2017). The future of food and agriculture: Trends and challenges.
2. Aravind, K. R., Raja, P., & McKee, G. (2017). A review of agriculture robotics: Current trends and future directions. Computers and Electronics in Agriculture, 142, 379-394. doi:10.1016/j.compag.2017.09.030
3. Shamshiri, R. R., Kalantari, F., Ting, K. C., et al. (2018). Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. International Journal of Agricultural and Biological Engineering, 11(1), 1-22. doi:10.25165/j.ijabe.20181101.3790
4. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69-80. doi:10.1016/j.agsy.2017.01.023
5. Balafoutis, A., Fountas, S., Cavalaris, C., et al. (2017). Precision Agriculture Technologies Positively Contributing to GHG Emissions Mitigation, Farm Productivity and Economics. Sustainability, 9(8), 1339. doi:10.3390/su9081339
6. Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: A review. Precision Agriculture, 13(6), 693-712. doi:10.1007/s11119-012-9274-5

About the Author:

Vishal Kumar Sharma, Senior Project Engineer of AI Research Centre, Woxsen University, India, with over 8 years of experience in team management, PCB design, programming, robotics manufacturing, and project management. He has contributed to multiple patents and is passionate about merging smart work with hard work to drive innovation in AI and robotics.

Der Beitrag Analysing the Importance of Artificial Intelligence (AI) and Robotics in Agriculture erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI Investments in the Spotlight – SwissCognitive AI Investment Radar

OpenAI’s $6 billion funding round stands out, showcasing investor confidence as the company targets a $150 billion valuation. Meanwhile, BlackRock and Microsoft are setting the stage for large-scale AI infrastructure with a $30 billion fund to fuel data centers and energy projects, further cementing AI’s role in global development.

Investors are increasingly drawn to specialized AI applications built on open-source models, as seen at this year’s AI Summit. Financial advisors and venture capitalists alike are placing AI at the top of their investment priorities, with U.S. VCs leading the charge in comparison to their global counterparts. In India, Google and Nvidia are expanding their AI initiatives, underscoring the region’s importance in the broader AI narrative.

As AI continues to intertwine with sustainability goals, we also see a growing interest in how AI can address global challenges such as climate change and resource management, with investors keeping a keen eye on the potential of AI to advance the UN’s Sustainable Development Goals. Alongside this, partnerships between nations like the UAE and U.S. demonstrate the strategic push to leverage AI for mutual growth in technology and economic development.

Join us as we delve deeper into the key developments and insights shaping AI investments this week, from groundbreaking megadeals to emerging opportunities that are set to drive the next phase of AI-driven innovation.

Previous SwissCognitive AI Radar: AI Funds, Energy Needs, and Tech Giants.

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 Investments in the Spotlight – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Saul Robot, A Frontier in Hospital Disinfection”

The Saul Robot uses strong UV (UVC) rays to fight harmful bacteria like Ebola and C. difficile, therefore transforming hospital cleanliness. Saul provides a quick, environmentally friendly, automated disinfection system unlike conventional chemical techniques, therefore lowering infection rates and safeguarding of healthcare professionals. Robots like Saul will set new benchmarks in worldwide healthcare cleanliness as technology develops.

The Concern Before efficient cleaning tools were developed

Hospitals faced enormous challenges controlling the spread of contagious diseases. Usually reliant on chemical agents, conventional disinfection methods had several disadvantages. These methods took time, harmed the environment and human health, and often proved futile against viruses like Ebola and powerful bacteria like Clostridium difficile (C. diff). The focus on hand washing also increases the risk of exposing medical professionals to harmful microorganisms, therefore complicating initiatives at infection control.

The Methodical Approach of Saul Robot in Problem Solving

Designed in concert with the 633rd Medical Group at Joint Base Langley-Eustis, Virginia, Xenex Healthcare Services presents a significant advance in hospital disinfection using the Saul Robot. Originally first presented in 2014, Saul effectively sterilizes hospital rooms and healthcare environments with intense UV (UVC) photons.

The Saul Robot boasts the following clear benefits and features:
25,000 times brighter than fluorescent lights, Saul’s UVC ray pass through bacterial and virus cell walls, therefore neutralizing dangerous diseases. Changing their DNA and RNA helps the UVC rays prevent diseases by stopping germs from growing.

Saul says his virus killing capacity is 99.9%. It rapidly sterilizes a room, therefore reducing the cleaning time required as compared to more traditional methods. This efficiency will help one to keep high rates of patient turnover without compromising safety.
Mercury-based compounds, which are harmful to human health and the environment, can form basis for conventional disinfection methods. Saul offers a green replacement that enables safer and more sustainable healthcare operations by employing UV light.
By automating the disinfection process, Saul can minimize the demand for human cleaning and thereby lower the possibility of healthcare workers getting into touch with harmful germs. This relates to general safety criteria for medical practitioners.

Future Course and Demand

The introduction of the Saul Robot has changed the way hospitals handle cleaning, thereby improving patient outcomes and reducing infection rates. As technology advances, the use of robots such as Saul in healthcare is expected to expand. The following lists some necessities as well as possible future repercussions:
Global Adoption increases as after Covid, hospitals all around aim to improve infection control, the acceptance of advanced disinfection robots like Saul is most likely to increase. These robots will set new standards for medical facility hygiene standards.
Constant advances in robotics and UVC technology will increase the capacity of disinfection robots, therefore enhancing their efficiency and adaptability in the battle against a larger spectrum of illnesses.

Future developments could show integration with hospital systems whereby disinfection robots coupled with hospital management systems for automatic and optimum cleaning schedules could be observed, therefore improving patient safety and efficiency.
Dealing with Emerging Threats as new infectious diseases develop, robots such as Saul will be indispensable in providing quick and effective responses to curtail outbreaks and protect public health.

Conclusion

Apart from being a technical marvel, the Saul Robot shows how creatively able public health can be enhanced. Using UVC radiation, Saul presents a safe, rapid, eco-friendly answer to the crucial issue of hospital disinfection. As we continue facing the challenges presented by infectious diseases, the Saul Robot demonstrates the path to a cleaner, safer, and healthier future in healthcare.

References:

1. Weber, D.J., Rutala, W.A. (2013). Understanding Hospital Disinfection Techniques. American Journal of Infection Control, 41(5), S31-S35.
2. Wilson, A.P., et al. (2018). The Role of Automated UV-C Devices in Infection Control. Journal of Hospital Infection, 99(3), 249-257.
3. Anderson, D.J., et al. (2017). Effectiveness of UV Disinfection in Healthcare Settings. The Lancet, 389(10071), 805-814.

About the Author:

Vishal Kumar Sharma, Senior Project Engineer of AI Research Centre, Woxsen University, India, with over 8 years of experience in team management, PCB design, programming, robotics manufacturing, and project management. He has contributed to multiple patents and is passionate about merging smart work with hard work to drive innovation in AI and robotics.

Der Beitrag Saul Robot, A Frontier in Hospital Disinfection erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Revanth Kumar Guttena – “AI as a Companion: A Blessing or a Curse in Modern Times?”

Marriage has long been a cornerstone of human society, serving as a foundation for family life and social stability. However, in recent years, there has been a noticeable decline in marriage rates across various countries, driven by factors such as financial burdens, compatibility issues, and evolving societal norms. Concurrently, advancements in artificial intelligence (AI) have opened new avenues for emotional support and companionship, suggesting that AI could potentially play a role in fulfilling human emotional needs. This article explores the reasons behind the decline in marriage rates and considers AI’s potential as a supplement to, rather than a replacement for, human companionship.

Decline in Marriage Rates: Complex Factors at Play

The decline in marriage rates is a multifaceted issue influenced by various factors. The financial burden associated with weddings, housing, and child-rearing has made marriage less appealing to many, as seen in Japan, where young people increasingly view marriage as a financial strain. Compatibility issues also play a significant role, with emotional and psychological alignment becoming crucial in modern relationships. Many individuals now prioritize personal values and goals, making it more challenging to find compatible partners. Additionally, a growing focus on individual achievements and personal growth has led people to prioritize careers and personal aspirations over marital commitments. Cultural shifts and changing societal norms further contribute to the decline, with greater acceptance of alternative lifestyles reducing the societal pressure to marry. This trend is evident in countries like India, where a significant percentage of young people express little interest in marriage.

Human-AI Relationships: Navigating New Territories

While human-AI relationships were once the domain of science fiction, the rapid growth of AI technology has brought these concepts into reality. The AI market is projected to reach $407 billion by 2027, with AI increasingly integrated into daily life. As technology continues to evolve, AI is beginning to reshape social interactions and influence how individuals connect emotionally. However, while some may form deep attachments to AI, it is essential to recognize that these relationships should complement rather than replace human connections.

The Role of Anthropomorphism in Human-AI Interaction

Anthropomorphism, the tendency to attribute human-like traits to non-human entities, plays a significant role in how people interact with AI. AI systems that exhibit behaviors and conversational styles reminiscent of human personalities can evoke emotional responses from users. This can include qualities such as empathy, humor, and kindness, making AI feel more personable and engaging. However, it is important to remember that these interactions, while valuable, are still based on algorithms rather than genuine emotions.

The Role of AI Companionship: Supplementing Human Interaction

As traditional forms of companionship face challenges, AI is emerging as a potential supplement for emotional support. However, it is essential to view AI companionship as an addition to, rather than a replacement for, human relationships. AI can provide personalized emotional support by analyzing emotions and responding empathetically, offering tailored comfort. Yet, while AI can help combat loneliness, it lacks the genuine understanding and emotional depth inherent in human relationships. Its 24/7 availability is beneficial for those feeling isolated, but it should not replace efforts to build and maintain human connections. Additionally, AI companions can facilitate social engagement and encourage individuals to connect with others, but they cannot replicate the authenticity and richness of human emotional bonds.

The Triarchic Theory of Love and Its Limitations in AI

Some studies suggest that based on the triarchic theory of love—intimacy, passion, and commitment—it is possible for individuals to experience affection for AI. However, while AI may simulate aspects of love, it lacks the depth and mutuality that define human relationships. True intimacy, passion, and commitment are grounded in shared experiences, emotional reciprocity, and personal growth, elements that AI cannot fully replicate.

Navigating the Complexities: Balancing Innovation with Responsibility

While AI offers promising avenues for emotional support and companionship, it is important to consider its limitations. AI, despite advancements, cannot fully replicate the depth and complexity of human emotions. Unlike humans, AI cannot share personal experiences or provide genuine emotional reciprocity. Overreliance on AI companions could lead to a decline in human interactions and social skills, potentially contributing to feelings of isolation and loneliness. Additionally, AI systems collect and analyze vast amounts of personal data, raising privacy and security concerns. Furthermore, AI algorithms may inadvertently perpetuate biases or discrimination present in the data they are trained on. There is also a risk that AI companions could inadvertently replace human relationships, leading to a decline in social cohesion. AI systems can be designed to manipulate emotions by providing tailored responses based on user data, potentially leading to a strong emotional dependency on AI. While AI technology is rapidly evolving, there may be limitations in its ability to fully understand and respond to complex human emotions. AI systems can sometimes exhibit unexpected or unintended behaviors, which can be disconcerting for users. In conclusion, while AI companions offer potential benefits, it’s essential to approach them with caution and consider the potential drawbacks. A balanced approach that integrates AI companionship with human interactions is likely to be the most beneficial for individuals and society as a whole.

References

Christina Pazzanese (2024). Lifting a few with my chatbot. (Accessed: 06 September 2024).

Deepak Maggu (2022). Youth in India Report 2022: 23 percent of young people are not interested in marriage. (Accessed: 06 September 2024).

Jaap Arriens (2024). AI companions can relieve loneliness – but here are 4 red flags to watch for in your chatbot ‘friend’. (Accessed: 06 September 2024).

Manish Raj Malik (2024). Rarest of the Rare: Japan Government Asks Young People Reason Behind Not Marrying Amid Population Crisis. (Accessed: 06 September 2024).

Neuroscience News. (2024). AI companions and loneliness. (Accessed: 06 September 2024).

Sian Zaman (2024). AI champions – Exploring the ethical concerns, promises and perils. (Accessed: 06 September 2024).

Surbhi Bhatia and Sriharsha Devulapalli (2020). Are India’s youth giving up on marriage? (Accessed: 06 September 2024).

The conversations (2024). AI ‘companions’ promise to combat loneliness, but history shows the dangers of one-way relationships. (Accessed: 06 September 2024).

Uma Shashikant (2024). Why women refuse marriage. (Accessed: 06 September 2024).

About the Author:

Dr. Revanth Kumar Guttena, Assistant Professor in Marketing, Woxsen University, India has more than 15 years of experience in industry an academics. The author obtains a PhD degree in Business Administration, specialized in marketing from National Dong Hwa University, Taiwan. Master in Imagineering from Breda University of Applied Sciences, The Netherlands. The author practices appreciative inquiry in his daily life and feels the importance in student’s  behavior, motivated to write this article.

Der Beitrag AI as a Companion: A Blessing or a Curse in Modern Times? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.