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.

Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar

As global AI funding levels remain elevated, this week’s investment activity reveals a tightening pattern: fewer hubs, bigger bets, and sharper focus. Silicon Valley, Beijing, and Paris now account for 80% of global AI funding, while other regions navigate capital scarcity and look for niche leverage. Meanwhile, Amazon’s CEO used his annual letter to justify billions already spent, calling AI investments a necessity for long-term competitiveness.

In San Francisco, startup Virtue AI secured $30 million to tackle deployment risk, a concern that’s becoming more pronounced as adoption scales. UK-based Synthesia reported $100 million in revenue and welcomed Adobe Ventures as a new backer, underscoring the value of enterprise AI tools that are already delivering results. And in China, a newly launched $8 billion AI fund backed by government and finance ministries will channel early-stage investments into foundational research and startup formation.

CEE continues to gain investor attention as a cost-efficient and increasingly capable AI development region, while Korea saw a domestic political pledge of $70 billion toward AI initiatives. On the infrastructure front, Nvidia’s $500 billion long-term strategy—including chips and supercomputing partnerships—continues to drive share price gains, while nEye Systems closed a $58 million round to push optical chip development further into the AI stack.

Big tech players aren’t staying out of the startup scene either. Alphabet and Nvidia reportedly invested in SSI, the new venture by OpenAI co-founder Ilya Sutskever, and ex-OpenAI CTO Mira Murati’s startup is reportedly eyeing a massive $2 billion seed round. CMA CGM’s €100 million partnership with Mistral AI brings logistics into the funding spotlight, and the trend toward agentic AI for financial research continues to spread across fintech.

Previous SwissCognitive AI Radar: AI Funding Highlights.

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 Who’s Betting, Where, and Why in AI – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Here’s a fresh batch of stories shaping the AI conversation:

Europe plans €20B AI gigafactories to boost innovation
US tariffs risk weakening AI alliances, say experts
Alzheimer’s research taps into AI through new training program
4,000 researchers share optimism on AI’s benefits
AI analysts emerge as key to enterprise transformation
…and more!

Let’s keep tracking how AI continues to unfold!

Kind regards,

The Team of SwissCognitive

Der Beitrag AI Alliences at Risk erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

This is your last chance to nominate! The Global AI Ambassador Program 2025 closes next week—don’t miss the opportunity to recognize AI leaders shaping the future.

In the meantime AI is advancing in research, defense, healthcare, and business—here are this week’s highlights:

AI deciphers genetic mysteries in biomedical research
US Space Force outlines AI-driven space strategies
AI-powered brain implant enables robotic arm control
Self-healing AI systems strengthen cyber defense
…and more!

Stay ahead in AI—catch you next week with more updates!

Kind regards,

The Team of SwissCognitive

Der Beitrag Last Chance for Recognition 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.

New funding, new discoveries, and new challenges. Look into the latest in AI:

The Global AI Ambassador Program by SwissCognitive is open for nominations
Google’s AI tool speeds up research
Microsoft unveils new quantum chip
AI detects diseases from blood samples
Musk’s Grok-3 gets ‘Big Brain’ upgrade
…and more!

More AI developments are just around the corner—see you next week.

Warm regards,

The Team of SwissCognitive

Der Beitrag AI Needs Global Collaboration erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Team – “Why AI Needs Global Collaboration – Call for Nomination”

Every morning, as we open our news feeds, we encounter the latest breakthroughs in Artificial Intelligence. New LLM models, emerging startups, record-breaking AI investments, and novel applications that push the boundaries of what’s possible. The competition among tech giants like OpenAI, Google, Meta, Microsoft, Nividia etc., alongside rising startups, has never been more intense, accelerating AI development at an unprecedented scale.

Here are just a few highlights from the past weeks:

• DeepSeek’s energy-efficient AI model triggered a significant shift in AI investments, causing stock declines, tech sell-offs, and a reevaluation of costly AI development strategies.
• xAI, Elon Musk’s AI venture, launched Grok-3, a model with over ten times the computing power of its predecessor.
• The New York Times is integrating AI tools into its newsroom for editing, summarizing, and writing tasks.
• The European Union announced a €50 billion investment to boost AI development and adoption across industries.
• Anthropic secured $6 billion in investments from Amazon and Google.
• Google unveiled an AI-powered “co-scientist” designed to accelerate biomedical research.

And this is just a small, randomly selected fraction of the developments in the field of AI that’s been happening globally since the beginning of the year.

The Critical Role of Collaboration in AI Development

With such high-speed advancements and large-scale AI adoption, our greatest responsibility is ensuring these developments serve humanity and society as a whole. Artificial Intelligence must be shaped through transparent communication, collaboration, and collective responsibility.

SwissCognitive has been committed to this mission since 2016, acting as a global AI facilitator—bridging knowledge gaps, fostering responsible AI adoption, and ensuring AI reaches its full potential as an economic booster.

One of our key initiatives to support this vision is the Global AI Ambassador Program, where AI leaders unite to spread knowledge and collaborate for the ethical, responsible, and transparent development of Artificial Intelligence.

Global AI Ambassador Program 2025 – A New Era of Collaboration

The Global AI Ambassador Program 2025 by SwissCognitive is designed to bring together  leading AI professionals across industries—fostering knowledge exchange, cross-sector innovation, and responsible AI governance.

This year, we are expanding the program on a larger scale than ever before. For the first time, we are introducing a peer-nominated selection process — ensuring that the most brilliant minds in AI are recognized and empowered to drive positive change.

Call for Nominations

Nominations are officially open until 28th of March 2025.
Unlike previous years, we have moved from self-nomination to a peer-nomination process, requiring two sponsors to nominate an AI expert.

We believe in the power of collaboration—because impactful AI leadership is stronger if we use our collective intelligence to shape the future together.

You can find all details, nomination criteria, and the application form at the link below.

“Ultimately, the global AI race will be won not by any one region alone, but through collaboration, knowledge-sharing, and a commitment to the responsible development and deployment of AI for the benefit of all.”

– Pascal Bornet Global AI Ambassador 2023, in the SwissCognitive AI Navigator 02/2024

Der Beitrag Why AI Needs Global Collaboration – Call for Nomination erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Eleanor Wright – “Navigating the Adoption of AI by the Public Sector”

Perfectly positioned to transform government efficiency and public services, governments globally are investing heavily in AI. From the UK’s plan to ramp up AI adoption to the Emirati investment in project Stargate, no government wants to be left behind.

AI however has more to offer governments than transforming public services, and government contracts will accelerate AI companies to industry dominance.

The public sector adoption of AI will require infrastructure, expertise, and a risk appetite. Data centers will be built, and vast amounts of energy will be used. Beyond the financial and material investment, engineers will be needed to code and develop these systems, and government expertise will be required to procure and integrate AI into antiquated legacy systems.

AI, however, has more to offer governments than transforming public services, and governments have the power to transform the business of AI. By gatekeeping access to data and procuring long-term contracts, public sector contracts can rapidly accelerate AI companies into big businesses and deliver the capital needed to beat out the competition, enabling a new wave of incumbents.

This model of public sector procurement from the private sector, however, may not be in the best interest of the citizens and taxpayers who will ultimately fund these large contracts. As AI efficiency and capabilities develop and public sector jobs are replaced, the greater the dependency will be on these companies to maintain critical public services. Thus, it is fair to assume that a critical point will be reached where these companies become too big to fail. If public services become reliant on the capabilities and services of a handful of providers, the balance of power will shift.

This dependency however should not discourage the adoption of AI by the public sector, but shape how contracts are procured and the business model underpinning them. Whether it be public-private partnerships, state-owned or implementing a cooperative structure, the business models underlying the roll-out of AI into the public sector could determine how AI is procured and implemented.

Whilst state-owned assets or companies can be inefficient, open to political interference, and lack a drive for innovation, they offer public-focused interest. Capital saved can be reinvested into the impact of public services and jobs that will have been outsourced to the private sector can be internally generated.

In the same way, state-owned companies operate in the interest of the public, public-private partnerships and cooperative companies may represent a strong middle ground between purely public or privately sourced contracts. Public-private partnerships will limit the amount of control private companies exert, and cooperative companies could enable the development and procurement of AI systems that meet a common economic and social goal.

It should be noted however that neither public-private partnerships nor cooperatives are fully resilient against political or private interference. Decisionmakers will always be susceptible to desiring increased control and securing financial gain.

Finally, another alternative may be to implement an open-source procurement model. By procuring solely from companies utilising open-sourced base models, public service contracts built on open-source models could help mitigate incumbency dominance and level the playing field. These base models could even use university knowledge and expertise to drive and maintain innovation.

No matter how public service agencies and providers choose to procure and maintain AI contracts, the business model underpinning the procurement both internally and externally will heavily shape the future of AI. A carefully thought-out business model could provide a strategic advantage and deliver greater value to stakeholders.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles

Der Beitrag Navigating the Adoption of AI by the Public Sector erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.