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 Funding Highlights – SwissCognitive AI Investment Radar

This week’s AI investment landscape has been defined by diverging strategies, capital flows, and a widening discussion around equity, access, and economic consequence. On one side, the U.S. and EU are outlining ambitious visions for leadership. While the Stargate initiative pushes scale and speed, the EU’s dual strategy of financial commitment and regulatory positioning is placing ethical trust at the heart of its long game.

At the institutional level, signals of maturity are surfacing. Stanford’s AI Index highlighted pressure points shaping enterprise tech strategy, while BCG’s IT Spending Pulse underlined a shift: budgets are recalibrating as generative AI moves from novelty to core capability. Large investors are responding in kind—Bay Area-based SignalFire closed a $1 billion fund focused solely on applied AI companies, and Microsoft’s AI alliance with MSCI emphasizes the financial sector’s shift to AI-informed strategies.

From a regional angle, the Gates Foundation is betting $7.5 million on Rwanda as a launch point for AI scaling hubs in health, agriculture, and education. Canada attracted a CAD$150 million investment from Siemens for a global AI R&D center focused on battery production, while Italy’s Axyon AI secured €4.3 million for financial forecasting, and Ukraine’s QurieGen raised €2.2 million for AI-driven cancer drug R&D.

Meanwhile, a different class of firms is recalibrating customer interaction models. Arta Finance unveiled a suite of AI agents for portfolio insight, and startups skipping traditional funding stages—especially in Europe—signal a shift toward faster, more efficient capital strategies. But UNCTAD’s report reminds us that AI’s projected $4.8 trillion global impact comes with significant risks: unless addressed, the gap between early adopters and the rest could deepen.

This week’s updates confirm that the race is no longer about who adopts AI—it’s about how, and at what cost.

Previous SwissCognitive AI Radar: From Mega Rounds to Market Ripples .

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 Funding Highlights – SwissCognitive AI Investment Radar 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 in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation”

As cyber threats become more complex, traditional security controls have real challenges to stay in pace. AI-powered self-healing mechanisms are set to revolutionize cybersecurity with real-time threat detection, automated response, and self-healing by itself without human intervention. These machine-learning-based intelligent systems, behavioral analytics, and big data allow detection of vulnerabilities, disconnection from infected devices, and elimination of attacks while they are occurring. The shift to a proactive defense with AI-enabled cybersecurity solutions will reduce time to detect and respond to attacks and strengthen digital resilience. Forcing businesses and organizations to fight to keep pace with the fast-paced cyber threat landscape, self-healing AI systems have become a cornerstone of next-gen cyber defense mechanisms.

Introduction to Self-Healing Systems

Definition and Functionality of Self-Healing Cybersecurity Systems

In self-healing cybersecurity, an AI-based cyber security system determines, cuts off, and heals a cyber attack or security danger inflicted without the intervention or oversight of a human. Such systems utilize an automated recovery process to fix attacked networks with the least disturbance to restore normalcy. Unlike conventional security measures that require human operations, self-healing systems learn from experiences and detect and respond to dangers reactively and very efficiently.

Role of AI and Machine Learning in Detecting, Containing, and Remediating Cyber Threats

Artificial Intelligence and machine learning facilitate the cyber security-based technologies with self-healing abilities. An AI-enabled threat detection will analyze huge data wealth in real-time to spot anomalies, suspicious behaviors, and possible breaches in security. When a threat gets detected, ML algorithms analyze severity levels, triggering automated containment actions such as quarantining infected devices or blocking bad traffic. In AI-supported repair, self-healing measures are taken, where infected systems are automatically cleaned, healed, or rebuilt, hence shortening the time span of human intervention and damage caused by attacks.

How Big Data Analytics and Threat Intelligence Contribute to Self-Healing Capabilities

Processing of large data sets is a large concern for making autonomous cybersecurity systems more efficient by integrating real-time threat intelligence from multiple sources, including network logs, user behavior patterns, and global cyber threat databases. By processing and analyzing that data, self-healing systems may predict threats as they arise and provide proactive defense against cyberattacks. Continuous updates on emerging vectors of attack by threat intelligence feeds will enable AI models to learn and update security protocols on real time. The convergence of big data, artificial intelligence, and machine learning creates a robust and dynamic security platform, hence amplifying the efficiency of digital resilience.

Key Features of Self-Healing Systems

Self-healing cyber defense systems use artificial intelligence (AI) and automation to isolate and respond to threats as they surface and in real-time. They have the ability to react straight off, identifying and doing away with intruders in less than a millisecond. Autonomous intrusion detection employs machine learning and behavioral analysis to preemptively eradicate the chance of a successful cyber-attack. Self-healing capabilities enable a system to patch vulnerabilities, restore a breached network, and revive the security system without any human aid. These systems learn constantly in real-time and are therefore able to adapt to changing threats and enhance cyber resilience. Self-healing security solutions effectively protect organizations against sophisticated cybercrime and potential business disruption by lessening the load of human intervention and response times.

Advantages Over Traditional Cybersecurity Methods

AI-sustained self-healing systems enable instantaneous threat detection and responses to decrease the Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) to orders of magnitude below conventional cybersecurity practices.

Unlike reactive security, these systems pro-actively do live monitoring, predict, and neutralize threats before they can expand. They preclude reliance on human intervention, hence reducing errors and delays.

Self-healing systems learn and adapt to open-ended cyber threats, creating a long-standing extra-zero-day exploit, ransomware, and advanced persistent threat (APT) resilience. Automated threat mitigation and system recovery raise cybersecurity efficiency, scalability, and cost-effectiveness for the modern organization.

Challenges and Limitations

The self-healing cyber security solutions, despite understanding their benefits, pose serious challenges to integration, making it imperative to deploy and support AI-powered security systems with the specialist skills of professionals. The issue of false positives persists as automated responses can ascribe threats to actions that are though correct, putting business continuity in jeopardy. Compliance with international data protection legislation, such as the General Data Protection Regulation (GDPR) and the Family Educational Rights and Privacy Act (FERPA), is also a big hurdle for AI-assisted security in order to have strong privacy provisions. Compatibility with current legacy systems can be a roadblock to seamless adoption, forcing organizations to renew their superannuated infrastructure. Ethical issues on AI bias in threat detection should also receive due diligence so that fairness and accuracy in decision-making continue to receive encouragement in the field of cybersecurity.

Real-World Applications of Self-Healing Systems

Financial Institutions

AI-based self-healingcybersecurity enables banks and financial institutions to identify and block fraudulent transactions, breaches, and cyberattacks. With constant surveillance over financial transactions, AI detects anomalies to improve fraud detection and automate security controls, thereby decreasing financial losses and maintaining data integrity in the process.

Healthcare Industry

With the threats posed to patient data by cyber warfare on healthcare networks and hospitals, self-healing systems will be used in protecting patient data. These self-healing systems are built for searching for intrusions, isolating the affected parts of a system, and restored by an automated reset process to guarantee compliance with HIPAA and other healthcare regulations.

Government and Defense

National security agencies count on AI-based cybersecurity systems to protect sensitive data, deter cyber war and protect critical infrastructure. Autonomous self-healing AI systems respond to nation-state-sponsored cyberthreats and are able to react failure-point-to-failure-point around an attack’s continual adaptation while providing real-time protection against potential breaches or intrusions in the space around them.

Future Outlook

With someday ever-weaving variation of possible cyber attacks, therefore enhancing most probably probable requirement of AI self-healing cyber security systems. Futuristic advancements such as blockchain for enforcing secure data inter-exchange, quantum computing for championing encryption strength, and AI deception to falsify some attacker’s cognition. It will allow even the SOCs( Security Operation Centers) and add more autonomy, this much will further curtail human intervention and thus make the security proactive, scalable and able to thwart advanced persistent threats.

Conclusion

AI self-healing systems emerge as the next-generation of cyber defense models which will impersonate the real-time threat detection, execute the automated response, and conduct self-correction without human intervention. By utilizing machine learning, big data analytics, and self-adaptive AI, the accomplishment of these systems will be such that no one could dream of lessenedness of their efficacy in providing security and business continuity. As organizations become increasingly more susceptible to advanced cyber threats, self-healing cybersecurity will be key in future-proofing digital infrastructures and establishing cyber resilience.

References

1. https://www.xenonstack.com/blog/soc-systems-future-of-cybersecurity
2. https://fidelissecurity.com/threatgeek/threat-detection-response/future-of-cyber-defense/
3. https://smartdev.com/strategic-cyber-defense-leveraging-ai-to-anticipate-and-neutralize-modern-threats/

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 AI in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Transforms EV Charging Networks”

People who drive gas-powered vehicles can lug a fuel can around if they ever run out while driving. For electric vehicle (EV) owners, it isn’t as easy. Many fear being stranded on the side of the road, which is why charging infrastructure is so important. However, chargers are often unreliable or outright out of order. Is artificial intelligence the solution?

Why EV Charging Networks Need an Overhaul

The current state of EV charging networks is less than ideal. Harvard Business School research revealed that charging stations are largely unreliable — and drivers are aware and dissatisfied. They can only successfully recharge using nonresidential stations an estimated 78% of the time, meaning one in five chargers in the United States don’t work. This makes them less reliable than the average gas pump.

Omar Asensio — the Harvard Business School fellow who led the study — said the main reason for this substandard reliability is that no one’s maintaining the stations. While these complex machines require extensive maintenance to keep the circuitry in peak shape, they are often neglected.

When electrical systems break down, equipment damage is not the only outcome. Potentially dangerous situations will occur unless companies perform electrical system maintenance regularly. Loose connections and fried circuits can ignite materials or shock users, causing injuries or death.

While the seemingly obvious solution is for drivers to recharge at home, people use home chargers just 10% of the time, according to one software company. Although modern batteries can reach hundreds of miles on a single charge, many people fear theirs will run out of power before they reach their destination, leaving them stranded. Besides, installation can be expensive, depending on their location and the type of at-home station they choose.

Companies Could Change EV Charging With AI

AI could help companies resolve the sector’s current charging challenges. For starters, it could autonomously manage loads, distributing power efficiently and safely among multiple stations. Reducing grid load — especially during peak hours — helps prevent EV charging equipment from damaging transmission lines, circuit breakers or transformers.

A study from the University of Michigan’s Transportation Research Institute proves this point. It states that large-scale, unmanaged EV charging could cause sudden current draw fluctuations, damaging the electrical grid. This inconsistency can lead to inefficient energy consumption, resulting in transformer strain. An outage is the likely outcome of accelerated equipment wear and energy waste.

Much of the U.S. power grid is already on its last legs. For instance, around 70% of the transmission lines are nearly three decades old, nearing their expected life span of 50 to 80 years. Minimizing strain with AI-powered smart load management can prevent outages while ensuring every battery is fully recharged.

A more comprehensive solution leverages predictive maintenance. Machine learning models can anticipate possible outcomes. They can use embedded, internet-enabled sensors to identify faults like a fried circuit or frayed wire. Maintenance teams would get real-time alerts, minimizing unplanned downtime.

AI could even improve battery health monitoring, maximizing charging efficiency. A research team from the United Kingdom’s Cambridge and Newcastle Universities discovered a machine learning method is 10 times more accurate than the current industry standard technique. It measures electrical pulses instead of tracking current and voltage during charge and discharge cycles. Improving EV battery reliability could transform the charging network’s layout.

Where would companies place new stations? With AI, they could analyze metrics like EV demand, travel frequency and location to determine where to build them. They could also optimize charging network design by plugging their budget, desired density and grid capacity into the algorithm.

Improving EV Charging Infrastructure With AI

Access to a reliable charging network is tightly intertwined with people’s opinions of EVs themselves — meaning companies can only make this mode of transportation more popular if they improve the reliability of the underlying infrastructure. AI is one of the few technologies that could help them fast-track this achievement.

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 How AI Transforms EV Charging Networks erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Samir Anil Jumade – “The Relentless Tide of Technological Disruption: Are You Ready?”

The world is evolving at an unprecedented pace, driven by rapid technological advancements. Many industries that once seemed invincible have either vanished or are on the verge of collapse due to their failure to adapt. The rise of artificial intelligence (AI), automation, blockchain, and digital platforms is fundamentally reshaping how businesses operate.

In this article, we explore how past giants like Kodak and Nokia disappeared, how today’s industries are facing a similar existential crisis, and how individuals and businesses must prepare for this inevitable transformation.

The Rise and Fall of Industry Giants

Remember Kodak? In 1997, they employed 160,000 people and dominated the photography market, with their cameras capturing 85% of the world’s images. Fast forward a few years, and the rise of mobile phone cameras decimated Kodak, leading to bankruptcy and the loss of all those jobs. Kodak’s story isn’t unique. A host of once-dominant companies, like HMT, Bajaj, Dyanora, Murphy, Nokia, Rajdoot, and Ambassador, failed to adapt and were swept aside by the relentless tide of technological change. These weren’t inferior products; they simply couldn’t evolve with the times.

This isn’t just a nostalgic look back. It’s a stark warning. The world is changing faster than ever, and we’re on the cusp of another massive transformation – the Fourth Industrial Revolution. Think about how much has changed in the last decade. Now imagine the next ten years. Experts predict that 70-90% of today’s jobs will be obsolete within that time frame. Are we prepared?

Look at some of today’s giants. Uber, the world’s largest taxi company, owns no cars. Airbnb, the biggest hotel chain, owns no hotels. These companies, built on software and connectivity, are disrupting traditional industries and redefining how we live and work. This disruption is happening across all sectors.

Consider the legal profession. AI-powered legal software like IBM Watson can analyze cases and provide advice far more efficiently than human lawyers. Similarly, in healthcare, diagnostic tools can detect diseases like cancer with greater accuracy than human doctors. These advancements, while offering immense potential benefits, also threaten to displace a significant portion of the workforce.

The automotive industry is another prime example. Self-driving cars are no longer science fiction; they’re a rapidly approaching reality. Imagine a world where 90% of today’s cars are gone, replaced by autonomous electric or hybrid vehicles. Roads would be less congested, accidents drastically reduced, and the need for parking and traffic enforcement would dwindle. But what happens to the millions of people whose livelihoods depend on driving, car insurance, or related industries?

Even the way we handle money is transforming. Cash is becoming a relic of the past, replaced by “plastic money” and, increasingly, mobile wallets like Paytm. This shift towards digital transactions offers convenience and efficiency, but also raises questions about security, privacy, and the future of traditional banking.

From STD Booths to Smartphones: A Revolution in Communication

Think back to the time when STD booths lined our streets. These public call offices were once essential for long-distance communication. But the advent of mobile phones sparked a revolution that swept STD booths into obsolescence. Those who adapted transformed into mobile recharge shops, only to be disrupted again by the rise of online mobile recharging. Today, mobile phone sales are increasingly happening directly through e-commerce platforms like Amazon and Flipkart, further highlighting the rapid pace of change.

The Evolving Definition of Money

The concept of money itself is undergoing a radical transformation. We’ve moved from cash to credit cards, and now mobile wallets are gaining traction. This shift offers convenience and efficiency, but it also has broader implications. As we move towards a cashless society, we need to consider the potential impact on financial inclusion, security, and privacy.

The Message is Clear: Adapt or Be Left Behind

The message is clear: adaptation is no longer a choice; it’s a necessity. We must embrace lifelong learning and upskilling to navigate this rapidly changing landscape. We need to foster creativity, critical thinking, and problem-solving skills – qualities that are difficult for machines to replicate. The future belongs to those who can innovate, adapt, and thrive in a world increasingly shaped by technology. The question is: will you be ready?

Additional Points to Consider:

· The environmental impact of technological advancements, both positive and negative.

· The ethical considerations surrounding AI and automation.

· The role of government and education in preparing the workforce for the future.

· The potential for new industries and job roles to emerge. By staying informed and proactive, we can harness the power of technology to create a better future for all.

References:

1. D. Deming, P. Ong, and L. H. Summers, “Technological Disruption in the Labor Market,” National Bureau of Economic Research, Working Paper No. 33323, Jan. 2025.
2. K. Hötte, M. Somers, and A. Theodorakopoulos, “Technology and Jobs: A Systematic Literature Review,” arXiv preprint arXiv:2204.01296, Apr. 2022.
3. D. Acemoglu and P. Restrepo, “Assessing the Impact of Technological Change on Similar Occupations,” Proceedings of the National Academy of Sciences, vol. 119, no. 40, e2200539119, Oct. 2022.
4. D. Acemoglu and P. Restrepo, “Occupational Choice in the Face of Technological Disruption,” National Bureau of Economic Research, Working Paper No. 29407, Oct. 2021. 5.S. 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.

About the Author:

Samir Jumade is a passionate and experienced Blockchain Engineer with over three years of expertise in Ethereum and Bitcoin ecosystems. As a Senior Blockchain Engineer at Woxsen University, he has led innovative projects, including the Woxsen Stock Exchange and Chain Reviews, leveraging smart contracts, full nodes, and decentralized applications. With a strong background in Solidity, Web3.js, and backend technologies, Samir specializes in optimizing transaction processing, multisig wallets, and blockchain architecture.

Der Beitrag The Relentless Tide of Technological Disruption: Are You Ready? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

By Martina Fuchs

Credits: The Female Quotient / The Frequency School – “First Song Ever to Unite Women from All Countries Using Sound Healing Frequencies”

The Frequency School – in partnership with The Female Quotient – launched “195”, the first song in history featuring women from all of the world’s 195 countries using sound healing frequencies and the 528 Hz ‘Love Frequency’ in a bid to ignite a global movement and raise awareness about gender equality across various industries including technology.

The Frequency School co-founded by Grammy-nominated and multi-platinum music producer Maejor, Kingsley Maduka, Brandon Lee, Aaron Dawson and Martina Fuchs premiered this visionary, powerful and universal campaign at a side-event in Davos, Switzerland which took place during the first day of the 55th Annual Meeting of the World Economic Forum (WEF).

Using the sounds of birds and the heartbeat as instrumental elements, “195” aims to raise the world’s frequency, elevate humanity, and trigger a positive impact by uniting and empowering women worldwide.

One woman in every one of the world’s 195 countries recognized by the United Nations said one word: “EQUALITY” in her national language or native tongue.

Martina Fuchs, business journalist and executive producer of the 195 women song, said:

“It has always been my dream to produce the first song in history featuring every country on the planet. Our vision was to unite 195 women from all walks of life from around the globe in this pioneering and groundbreaking initiative to advocate for gender equality and the rights of women and girls, and to help people struggling with mental health issues. Gender equality is not only a fundamental human right, but a necessary foundation for a peaceful, prosperous and sustainable world.”

U.S. Grammy-nominated and multi-platinum music producer Maejor who produced the song using sound healing frequencies said:

“We chose to use 528 Hz for the track which is often referred to as the ‘love frequency’, or the frequency of transformation and miracles. It provides a powerful energetic foundation for creating an environment where equality can flourish. By resonating with a frequency linked to unconditional love, we wanted to promote more respect and fairness for women, as well as deep inner healing and a state of peace. The transformative vibration of 528 Hz can inspire positive action and empathy and people to act more kindly and inclusively.”

According to scientific studies and music theory, Solfeggio frequencies, ranging from 174 Hz to 963 Hz, offer unique sound patterns that promote relaxation, stress relief, and overall well-being. These frequencies have been shown to positively impact mental, emotional, and physical health by generating vibrations that help achieve a state of calm and balance of the mind, body and spirit.

Miriam Moriati, President of the Kiribati Rotaract Youth Club and a Women and Youth representative for OARS (Ocean Alliance for Resilience and Sustainability), said:

“I’m from Kiribati, a small island nation in the Pacific, where our highest point is just 3 meters above sea level. Our women in Kiribati are vulnerable due to gender equality not being part of our culture and traditions. They are often the first to be affected by crises and the last to recover. Being part of this initiative to support women on an international stage is an incredible honor. Opportunities to represent our small country are rare, and I am grateful for this platform to amplify the voices of Kiribati women.”

Dalith Steiger, Co-Founder of SwissCognitive and the voice of Israel in the song said:

“Equality is not just an ideal—it’s our collective responsibility. The lack of gender equality still remains a huge challenge in the technology space that we need to tackle. I’m convinced that AI can support us to unbias the bias! I am grateful to be part of this global women empowerment initiative to raise awareness across various sectors.”

A new white paper by IMD in collaboration with Microsoft Switzerland and EqualVoice released at the WEF on Thursdy provides in-depth analysis and guidance for organizations to mitigate against risks and use AI responsibly.

More than 80% of the world’s organizations are expected to be using GenAI tools in production environments by 2026, and the productivity gains will likely boost global GDP by $7tn in the next decade. Yet even as innovation races ahead, important questions need to be asked about the potential risks.

Read the full report “Mind the Gap Addressing the risk of bias in Generative AI”.

The “195” song is planned to be submitted for a Guinness World Records for the title: “Most nationalities to contribute vocals to a musical recording (single song)”.

@Brandon Lee Shelley Zalis The Female Quotient The Frequency School

Der Beitrag First Song Ever to Unite Women from All Countries Using Sound Healing Frequencies erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger:  HennyGe Wichers, PhD – “What Happens When AI Commodifies Emotions?”

Imagine your customer service chatbot isn’t just solving your problem – it’s listening, empathising, and sounding eerily human. It feels like it cares. But behind the friendly tone and comforting words, that ‘care’ is just a product, finetuned to steer your emotions and shape your decisions. Welcome to the unsettling reality of empathetic AI, where emotions and mimicked – and monetised.

In 2024, empathetic AI took a leap forward. Hume.AI gave large language models voices that sound convincingly expressive and a perceptive ear to match. Microsoft’s Copilot got a human voice and an emotionally supportive attitude, while platforms like Character.ai and Psychologist sprouted bots that mimic therapy sessions. These developments are paving the way for a new industry: Empathy-as-a-Service, where emotional connection isn’t just simulated, it’s a product: packaged, scaled, and sold.

This is not just about convenience – but about influence. Empathy-as-a-Service (EaaS), an entirely hypothetical but now plausible product, could blur the line between genuine connection and algorithmic mimicry, creating systems where simulated care subtly nudges consumer behaviour. The stakes? A future where businesses profit from your emotions under the guise of customer experience. And for consumers on the receiving end, that raises some deeply unsettling questions.

A Hypothetical But Troubling Scenario

Take an imaginary customer service bot. One that helps you find your perfect style and fit – and also tracks your moods and emotional triggers. Each conversation teaches it a little more about how to nudge your behaviour, guiding your decisions while sounding empathetic. What feels like exceptional service is, in reality, a calculated strategy to lock in your loyalty by exploiting your emotional patterns.

Traditional loyalty programs, like the supermarket club card or rewards card, pale in comparison. By analysing preferences, moods, and triggers, empathetic AI digs into the most personal corners of human behaviour. For businesses, it’s a goldmine; for consumers, it’s a minefield. And it raises a new set of ethical questions about manipulation, regulation, and consent.

The Legal Loopholes

Under the General Data Protection Regulation (GDPR), consumer preferences are classified as personal data, not sensitive data. That distinction matters. While GDPR requires businesses to handle personal data transparently and lawfully, it doesn’t extend the stricter protections reserved for health, religious beliefs, or other special categories of information. This leaves businesses free to mine consumer preferences in ways that feel strikingly personal – and surprisingly unregulated.

The EU AI Act, introduced in mid-2024, goes one step further, requiring companies to disclose when users are interacting with AI. But disclosure is just the beginning. The AI Act doesn’t touch using behavioural data or mimicking emotional connection. Joanna Bryson, Professor of Ethics & Technology at the Hertie School, noted in a recent exchange: “It’s actually the law in the EU under the AI Act that people understand when they are interacting with AI. I hope that might extend to mandating reduced anthropomorphism, but it would take some time and court cases.”

Anthropomorphism, the tendency to project human qualities onto non-humans, is ingrained in human nature. Simply stating that you’re interacting with an AI doesn’t stop it. The problem is that it can lull users into a false sense of trust, making them more vulnerable to manipulation.

Empathy-as-a-Service could transform customer experiences, making interactions smoother, more engaging, and hyper-personalised. But there’s a cost. Social media already showed us what happens when human interaction becomes a commodity – and empathetic AI could take that even further. This technology could go beyond monetising attention to monetising emotions in deeply personal and private ways.

A Question of Values

As empathetic AI becomes mainstream, we have to ask: are we ready for a world where emotions are just another digital service – scaled, rented, and monetised? Regulation like the EU AI Act is a step in the right direction, but it will need to evolve fast to keep pace with the sophistication of these systems and the societal boundaries they’re starting to push.

The future of empathetic AI isn’t just a question of technological progress – it’s a question of values. What kind of society do we want to build? As we stand on the edge of this new frontier, the decisions we make today will define how empathy is shaped, and sold, in the age of AI.

About the Author:

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

Der Beitrag What Happens When AI Commodifies Emotions? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

AI Investment Opportunities Worldwide – SwissCognitive AI Investment Radar

The AI Investment Radar is here with another week of major funding rounds, strategic expansions, and emerging trends in artificial intelligence. Microsoft’s $80 billion investment in AI-driven data centers marks a significant move toward expanding cloud infrastructure, reinforcing AI’s growing demand. Meanwhile, Vietnam’s new incentives for semiconductor and AI R&D aim to attract global tech investors by covering up to 50% of initial investment costs.

Nvidia has funneled $1 billion into AI startups in 2024, solidifying its role as a key player in the sector. Financial firms are also integrating Artificial Intelligence into decision-making, with Banca Investis partnering with Bain & Company to launch an AI-powered investment platform. These initiatives reflect how AI is being embedded across industries to drive efficiency and innovation.

AI adoption continues to gain traction in startups and financial services. Irish Artificial Intelligence startup Jentic secured €4 million to support enterprise automation, while Hong Kong-based Arbor is targeting 100,000 investment professionals with its AI-powered reasoning chatbot. Meanwhile, AI-driven trading tools are reshaping financial analysis, offering investors an edge in managing complex portfolios.

Education and workforce development are also adapting to AI’s rise. Colleges are expanding AI-focused programs to equip students with essential skills, while investment research is shifting toward AI-driven insights, challenging traditional analyst roles. The market’s enthusiasm remains high, with AI startups dominating venture capital funding and securing multi-million-dollar investments.

From infrastructure investments to AI-powered financial tools and startup innovation, the Artificial Intelligence boom continues to reshape global industries. As funding surges and adoption scales, AI’s influence on economies and businesses grows stronger.

Stay tuned for more Artificial Intelligence investment updates in the coming weeks.

Der Beitrag AI Investment Opportunities Worldwide – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “AI for Disabilities: Quick Overview, Challenges, and the Road Ahead”

AI has enormous power in improving accessibility and inclusivity for people with disabilities. This power lies in the potential of this technology to bridge gaps that traditional solutions could not address. As we have demonstrated in the series of articles devoted to AI for disabilities, AI-powered products can really change a lot for people with various impairments. Such solutions can allow users to live more independently and get access to things and activities that used to be unavailable to them before. Meanwhile, the integration of AI into public infrastructure, education, and employment holds the promise of creating a more equitable society. These are the reasons that can show us the importance of projects building solutions of this type.

Yes, these projects exist today. And some of them have already made significant progress in achieving their goals. Nevertheless, there are important issues that should be addressed in order to make such projects and their solutions more efficient and let them bring real value to their target audiences. One of them is related to the fact that such solutions are often built by tech experts who have practically no understanding of the actual needs of people with disabilities.

According to the survey conducted in 2023, only 7% of assistive technology users believe that their community is adequately represented in the development of AI products. At the same time, 87% of respondents who are end users of such solutions express their readiness to share their feedback with developers. These are quite important figures to bear in mind for everyone who is engaged in the creation of AI-powered products for disabilities.

In this article, we’d like to talk about the types of products that already exist today, as well as potential barriers and trends in the development of this industry.

Different types of AI solutions for disabilities

In the series of articles devoted to AI for disabilities, we have touched on types of products for people with different states, including visual, hearing, mobility impairments, and mental diseases. Now, let us group these solutions by their purpose.

Communication tools

AI can significantly enhance the communication process for people with speech and hearing impairments.

Speech-to-text and text-to-speech apps enable individuals to communicate by converting spoken words into text or vice versa.

Sign language interpreters powered by AI can translate gestures into spoken or written language. It means that real-time translation from sign to verbal languages can facilitate communication, bridging the gap between people with disabilities and the rest of society.

Moreover, it’s worth mentioning AI-powered hearing aids with noise cancellation. They can improve clarity by filtering out background sounds, enhancing the hearing experience in noisy environments.

Advanced hearing aids may also have sound amplification functionality. If somebody is speaking too quietly, such AI-powered devices can amplify the sound in real time.

Mobility and navigation

AI-driven prosthetics and exoskeletons can enable individuals with mobility impairments to regain movement. Sensors and AI algorithms can adapt to users’ physical needs in real time for more natural, efficient motion. For example, when a person is going to climb the stairs, AI will “know” it and adjust the movement of prosthetics to this activity.

Autonomous wheelchairs often use AI for navigation. They can detect obstacles and take preventive measures. This way users will be able to navigate more independently and safely.

The question of navigation is a pressing one not only with people with limited mobility but also for individuals with visual impairments. AI-powered wearable devices for these users rely on real-time environmental scanning to provide navigation assistance through audio or vibration signals.

Education and workplace accessibility

Some decades ago people with disabilities were fully isolated from society. They didn’t have the possibility to learn together with others, while the range of jobs that could be performed by them was too limited. Let’s be honest, in some regions, the situation is still the same. However, these days we can observe significant progress in this sphere in many countries, which is a very positive trend.

Among the main changes that have made education available to everyone, we should mention the introduction of distance learning and the development of adaptive platforms.

A lot of platforms for remote learning are equipped with real-time captioning and AI virtual assistants. It means that students with disabilities have equal access to online education.

Adaptive learning platforms rely on AI to customize educational experiences to the individual needs of every learner. For students with disabilities, such platforms can offer features like text-to-speech, visual aids, or additional explanations and tasks for memorizing.

In the workplace, AI tools also support inclusion by offering accessibility features. Speech recognition, task automation, and personalized work environments empower employees with disabilities to perform their job responsibilities together with all other co-workers.

Thanks to AI and advanced tools for remote work, the labor market is gradually becoming more accessible for everyone.

Home automation and daily assistance

Independent living is one of the main goals for people with disabilities. And AI can help them reach it.

Smart home technologies with voice or gesture control allow users with physical disabilities to interact with lights, appliances, or thermostats. Systems like Alexa, Google Assistant, and Siri can be integrated with smart devices to enable hands-free operation.

Another type of AI-driven solutions that can be helpful for daily tasks is personal care robots. They can assist with fetching items, preparing meals, or monitoring health metrics. As a rule, they are equipped with sensors and machine learning. This allows them to adapt to individual routines and needs and offer personalized support to their users.

Existing barriers

It would be wrong to say that the development of AI for disabilities is a fully flawless process. As well as any innovation, this technology faces some challenges and barriers that may prevent its implementation and wide adoption. These difficulties are significant but not insurmountable. And with the right multifaceted approach, they can be efficiently addressed.

Lack of universal design principles

One major challenge is the absence of universal design principles in the development of AI tools. Many solutions are built with a narrow scope. As a result, they fail to account for the diverse needs that people with disabilities may have.

For example, tools designed for users with visual impairments may not consider compatibility with existing assistive technologies like screen readers, or they may lack support for colorblind users.

One of the best ways to eliminate this barrier is to engage end users in the design process. Their opinion and real-life experiences are invaluable for such projects.

Limited training datasets for specific AI models

High-quality, comprehensive databases are the cornerstone for efficient AI models. It’s senseless to use fragmented and irrelevant data and hope that your AI system will demonstrate excellent results (“Garbage in, Garbage out” principle in action). AI models require robust datasets to function as they are supposed to.

However, datasets for specific needs, like regional sign language dialects, rare disabilities, or multi-disability use cases are either limited or nonexistent. This results in AI solutions that are less effective or even unusable for significant groups of the disability community.

Is it possible to address this challenge? Certainly! However, it will require time and resources to collect and prepare such data for model training.

High cost of AI projects and limited funding

The development and implementation of AI solutions are usually pretty costly initiatives. Without external support from governments, corporate and individual investors, many projects can’t survive.

This issue is particularly significant for those projects that target niche or less commercially viable applications. This financial barrier discourages innovation and limits the scalability of existing solutions.

Lack of awareness and resistance to adopt new tools

A great number of potential users are either unaware of the capabilities of AI or hesitant to adopt new tools. Due to the lack of relevant information, people have a lot of concerns about the complexity, privacy, or usability of assistant technologies. Some tools may stay just underrated or misunderstood.

Adequate outreach and training programs can help to solve such problems and motivate potential users to learn more about tools that can change their lives for the better.

Regulatory and ethical gaps

The AI industry is one of the youngest and least regulated in the world. The regulatory framework for ensuring accessibility in AI solutions remains underdeveloped. Some aspects of using and implementing AI stay unclear and it is too early to speak about any widely accepted standards that can guide these processes.

Due to any precise guidelines, developers may overlook critical accessibility features. Ethical concerns, such as data privacy and bias in AI models also complicate the adoption and trustworthiness of these technologies.

Such issues slow down the development processes now. But they seem to be just a matter of time.

Future prospects of AI for disabilities: In which direction is the industry heading?

Though the AI for disabilities industry has already made significant progress in its development, there is still a long way ahead. It’s impossible to make any accurate predictions about its future look. However, we can make assumptions based on its current state and needs.

Advances in AI

It is quite logical to expect that the development of AI technologies and tools will continue, which will allow us to leverage new capabilities and features of new solutions. The progress in natural language processing (NLP) and multimodal systems will improve the accessibility of various tools for people with disabilities.

Such systems will better understand human language and respond to diverse inputs like text, voice, and images.

Enhanced real-time adaptability will also enable AI to tailor its responses based on current user behavior and needs. This will ensure more fluid and responsive interactions, which will enhance user experience and autonomy in daily activities for people with disabilities.

Partnerships

Partnerships between tech companies, healthcare providers, authorities, and the disability community are essential for creating AI solutions that meet the real needs of individuals with disabilities. These collaborations will allow for the sharing of expertise and resources that help to create more effective technologies.

By working together, they will ensure that AI tools are not only innovative but also practical and accessible. We can expect that the focus will be on real-world impact and user-centric design.

New solutions

It’s highly likely that in the future the market will see a lot of new solutions that now may seem to be too unrealistic. Nevertheless, even the boldest ideas can come to life with the right technologies.

One of the most promising use cases for AI is its application in neurotechnology for seamless human-computer interaction.

A brain-computer interface (BCI) can enable direct communication between the human brain and external devices by interpreting neural signals related to unspoken speech. It can successfully decode brain activity and convert it into commands for controlling software or hardware.

Such BCIs have a huge potential to assist individuals with speech impairments and paralyzed people.

Wrapping up

As you can see, AI is not only about business efficiency or productivity. It can be also about helping people with different needs to live better lives and change their realities.

Of course, the development and implementation of AI solutions for disabilities are associated with a row of challenges that can be addressed only through close cooperation between tech companies, governments, medical institutions, and potential end users.

Nevertheless, all efforts are likely to pay off.

By overcoming existing barriers and embracing innovation, AI can pave the way for a more accessible and equitable future for all. And those entities and market players who can contribute to the common success in this sphere should definitely do this.

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Der Beitrag AI for Disabilities: Quick Overview, Challenges, and the Road Ahead erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: ucsf.edu – “4 Ways Artificial Intelligence (AI) is Poised to Transform Medicine”

AI can compare thousands of images to uncover dangerous patterns, create ultra-high resolution scans from low-res images and see what the human eye misses.

The radiologist was dead.

Or at least that’s what artificial intelligence (AI) experts prophesized in 2016 when they said AI would outperform radiologists within the decade.

Today, AI isn’t replacing imaging specialists, but its use is leading health care providers to reimagine the field. That’s why UC San Francisco was among the first U.S. universities to combine AI and machine learning with medical imaging in research and education by opening its Center for Intelligent Imaging.

Take a look at how UCSF researchers are pioneering human-centered AI solutions to some of medicine’s biggest challenges.

Spot illnesses earlier

Tens of thousands of Americans suffer pneumothoraces, a type of collapsed lung, annually. The condition is caused by trauma or lung disease – and serious cases can be deadly if diagnosed late or left untreated.

The problem:

This type of collapsed lung is difficult to identify: The illness can mimic others both in symptoms and in x-rays, in which only subtle clues may indicate its presence. Meanwhile, radiologists must interpret hundreds of images daily, and some hospitals do not have around-the-clock radiologists.

The solution:

UCSF researchers created the first AI bedside program to help flag potential cases to radiologists. In 2019, the tool was the first AI innovation of its kind to be licensed by the U.S. Food and Drug Administration. Today, it’s used in thousands of GE Healthcare machines around the world.

How did they do it?

Researchers from the Department of Radiology and Biomedical Imaging created a database of thousands of anonymous chest X-rays. Some of these images showed cases of collapsed lungs and others not.[…]

Read more: www.ucsf.edu

Der Beitrag 4 Ways Artificial Intelligence (AI) is Poised to Transform Medicine erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.