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.

SwissCognitive Guest Blogger: Zachary Amos – “Is Healthcare AI Prioritizing People or Profit?”

In many sectors, artificial intelligence (AI) is largely a tool for driving efficiency, but in healthcare, it can save lives. However, medical practices are still businesses at the end of the day, so AI’s cost-saving benefits are hard to overlook. While that’s not an issue in and of itself, the push to save money can lead to healthcare organizations prioritizing profit over people.

How Healthcare AI May Put Profit Before People

AI is a powerful financial management tool. It can analyze vast amounts of data to highlight opportunities to increase profits and emphasize areas that may not pay back investment. 

AI insight in healthcare could lead private practices to drive high-value drug or treatment sales instead of focusing on care accessibility. It may also lead to preferential treatment of more profitable patients. Some hospital systems claim they have lost as much as $640 million on Medicare recipients. AI-driven cost analysis may drive hospitals to reduce their investment in these populations because of the lower financial incentive.

AI’s profit-driving capabilities can influence healthcare ethics in subtler ways, too. Staff may over-rely on automation and machine learning because it saves them time. However, AI hallucinations are still possible. Similarly, the underrepresentation of diverse patients in training datasets can lead to biased AI results, which may negatively impact a medical system’s ability to care for historically underserved groups.

Prioritizing convenience and efficiency goals over avoiding these missteps may come at the cost of effective and equitable care. Even if medical profits increase, patient outcomes and healthcare disparities could worsen.

How to Ensure Responsible AI Usage in Healthcare

Despite these risks, AI has many beneficial implications for patients, so the industry cannot ignore it. Healthcare organizations can use these steps to ensure ethical, patient-centric AI usage.

1. Focus on Direct Patient-Impacting AI Applications

First, hospitals must prioritize AI use cases that directly impact patients over those that drive economic or efficiency gains for the organization. Medical imaging and diagnostic tools are among the most crucial. 

AI can identify Alzheimer’s with 99.95% accuracy and achieve similar results with many cancers and other conditions. Investing in these applications rather than in AI-based financial analysis will ensure AI’s benefits go directly to promoting better care standards.

Personalized treatment is another promising area for responsible AI usage. Machine learning models can analyze an individual patient’s medical history and physiology to determine which courses of action will help them most. This application is more ethical than using AI to compare the profitability of different treatment options.

2. Ensure Responsible AI Development

Healthcare organizations must address the bias issue in their AI models. Studies have found that removing specific biased factors from training datasets can maintain model accuracy while reducing the risk of prejudice. Common examples of these factors include names, ethnicities, age and gender-related labels.

Having a diverse team of AI developers who regularly inspect models for signs of bias or hallucinations can help. Relying on synthetic data is also a useful strategy, as this can make up for gaps in historical real-world information that may lead to unreliable or biased results.

3. Train Medical Staff on AI Best Practices

Finally, medical companies should train their staff so they’re familiar with how AI can affect care equality. When users understand how misusing AI or failing to catch errors can harm patients, they’ll be more likely to use it responsibly.

Cybersecurity deserves attention, too. A criminal can hinder reliable AI results by poisoning just 0.01% of its data, which can lead to harmful results if unnoticed. Training employees to follow strict access policies and resist phishing attempts will mitigate some of these concerns.

Healthcare teams should also write formal policies to ensure a human expert always makes the final decision on anything affecting patients. AI can provide insights to inform human choices, but it should never be the ultimate authority, given the risk of bias and the temptation to prioritize profit over equitable care.

Ethical Healthcare AI Is Possible

When organizations use it responsibly, healthcare AI can make the industry a safer, more equitable place. However, failing to account for possible shortcomings and errors will create the opposite effect. Learning about how AI can influence both ethics and profitability is the first step in creating a better future for patients and their care providers.

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 Is Healthcare AI Prioritizing People or Profit? 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: Eleanor Wright, COO at TelWAI – “A New Era of Intelligent Robots”

Imagine a world where humanoid robots cook for you, care for your loved ones, and streamline your workday – all powered by AI smarter than ever before. The global AI in robotics market, projected to surpass $124 Billion by 2030, is set to make this vision a reality. As the capabilities of AI evolve, these machines will become our companions, caregivers, and coworkers, they’ll make mobility more affordable, transform access to services, and redefine the value of human effort.

From Amazon’s fleet of 750,000 warehouse robots to Tesla’s ambitions to build 10,000 humanoid Optimus robots this year, the age of robots is upon us. Dependent on sensors and actuation systems to navigate and interact with the physical environment, this new age of robotics hinges on the developments of AI, designed to mimic and learn from its biological makers. Equipping these robots with intelligence, engineers working across various domains of expertise, utilise AI to enable vision, natural language processing, sound processing, pressure sensing, and more.

Beyond sensing, AI also enables robots to reason, adapt, and learn, using approaches including—but not limited to—reinforcement learning, neural networks, and Bayesian networks. These models and methods enable robots to assess risks and determine actions, and by learning from experience, robots can adapt to new tasks and environments. Thus, AI enables robots to perceive, act, learn, and adapt, allowing them to perform tasks with greater autonomy and precision.

However, integrating AI into robotics isn’t seamless, it comes with hurdles. Robots struggle with real-time processing delays, adapting to messy unpredictable environments, squeezing efficiency from limited hardware, and understanding human quirks like vague commands or gestures. These challenges constrain capabilities and the pace at which robots enter and dominate markets.

So, how can these challenges be addressed?

Some developments in addressing these challenges include:

1. Parallel computing

Parallel computing involves dividing larger tasks into smaller, independent tasks that can be processed simultaneously rather than sequentially. This enables increased computational efficiency, reduced latency, and improved cost efficiency. In robotics, parallel computing allows robots to process inputs from LIDAR, radar, and cameras simultaneously, enabling them to navigate environments more effectively and efficiently.

2. Transfer learning

Transfer learning leverages pre-trained models to solve new, but similar, problems. In this approach, a model trained on one task or dataset is reused and fine-tuned for a related task. For example, in machine vision for defect detection in manufacturing, fine-tuning a pre-trained model on a smaller dataset of images allows it to quickly adapt to detect specific defects, such as cracks or dents, without needing to train a model from scratch.

3. Self-calibrating AI

Self-calibrating refers to AI systems that autonomously adjust their parameters, models, or processes to maintain optimal performance without manual intervention. In robotics, self-calibrating AI enables robots to adapt to changes in their environment, hardware, or tasks, ensuring they operate with optimized accuracy and efficiency over time.

4. Federated learning

Federated learning is a technique that enables AI systems to learn from distributed data sources whilst ensuring privacy and security. It allows AI to collaboratively train a shared model without transferring sensitive data, preserving privacy and reducing reliance on centralised storage. For example, delivery robots use federated learning to optimise pathfinding without sending raw data, such as sensor inputs or location, to a central server. Instead, they locally update their models and share improvements, preserving both privacy and security.

These developments indicate a key focus on efficiency, adaptability, and learning – all of which are essential for the continued evolution of robotics in complex, real-world environments. Additionally, these advancements contribute to a future where robots collaborate with humans, leveraging their ability to learn from experience and improve over time.

So, what’s next for AI in Robotics?

Just as AI agents are taking over the digital realm, they are about to flood robotics too. AI agents embedded in robotics will supercharge the autonomy and flexibility of robots, enabling them to communicate with humans and even interpret intentions by analysing gestures and potentially emotional cues. Crucial to human-robot interactions, AI agents may prove highly effective in assisted care, hospitality, and other service industries.

Additionally, as technologies like federated learning and edge computing evolve, robots will share knowledge without compromising privacy or relying on centralised data. This will improve scalability and efficiency by reducing the need for costly centralised storage and processing, and enable additional robots to integrate rapidly into existing networks.

So, where does this leave us?

Although there are abundant market opportunities for AI in robotics, the pace at which different markets adopt robotics will vary; with AI being a key factor driving this adoption. Crucial for overcoming challenges related to autonomy, adaptability, and decision-making, AI will empower robots to perform tasks once considered too complex or risky for automation. As AI continues to evolve, it will not only raise important concerns about safety, ethics, and integration but help address them; ensuring robots can work seamlessly alongside humans and contribute to a more productive future.

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 A New Era of Intelligent Robots – AI and Robotics 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.

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.

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

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

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

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

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

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

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

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

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

Der Beitrag The AI Market Shake-Up: Where the Investments Are Headed – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Featured Guest Article: Patrick Hebron – “AI and the Revolution in Design, Engineering, and Problem-Solving Methodology”

A Note to the Reader

This illustrated essay invites you to imagine how we can create a more sustainable, creative, and livable world by applying the transformative power of AI to design, engineering, and everyday problem-solving. It examines how reimagining design and engineering processes can empower both novices and experts to bring ambitious ideas to life.

For the past 15 years, I’ve worked on creating tools that connect AI research to real-world applications with the goal of making design and engineering more accessible and impactful. This essay draws on those experiences to envision how AI can shape the future of our tools and the built systems around us. Starting with a broad vision and foundational premises, it then focuses on specific interaction mechanisms, optimization opportunities, industry implications, and areas where AI can have a significant impact through the orchestration of design and engineering pipelines.

Whether you’re a researcher, designer, engineer, or simply curious about the future of the built world, I invite you to join me in this exploration.

Introduction

If I had asked people what they wanted,
they would have said faster horses.
— Attributed to Henry Ford

Knowing what to want is a skill. It requires a systematic approach to defining goals, evaluating options, analyzing available data and assessing potential outcomes. Above all, it requires the audacity to imagine that things could be different, that an existing need could be met in a better way, or that something entirely new could emerge, transforming how we live, work, or understand the world.

It’s impossible to keep up with the latest developments across every field, so we rely on a kind of innovation republic, where domain experts and visionaries like Henry Ford and Steve Jobs represent our interests by recognizing the transformative potential of new technologies and shaping them into impactful products.

AI is enabling a shift towards something more like a direct democracy of innovation, where individuals can bypass traditional gatekeepers to create solutions for themselves.

Over the last few years, we have seen the beginnings of the revolution in AI-driven scientific discovery. DeepMind’s Nobel Prize-winning protein structure prediction system, AlphaFold, and tools like Sakana AI’s AI Scientist highlight how AI can enable foundational breakthroughs.

These discoveries may lay the groundwork, but they do not directly constitute the downstream solutions needed to address real-world problems. To bridge this gap, it is essential to augment the methodologies of both foundational sciences and applied fields like functional design and engineering, where AI-driven innovation can help to tackle humanity’s toughest challenges and improve everyday life.

Outcomes in design and engineering work can be enhanced by the advanced reasoning, holistic planning, and deep technical knowledge present in agentic AI systems. However, for AI to select real-world problems that matter to humans and solve them in ways that align with our sensibilities, it stands to reason that human participation of some kind is needed.

Human contributions to this work will inevitably evolve and take many forms, from direct collaboration with AI to indirect influence on its behavior, with participation ranging from hands-on tool use and intent expressions to passive guidance by individuals, groups, and even the broader public.

Tools of this kind will enable the development of more efficient, sustainable, and inspiring products and buildings. They can also supplement the work of organizations like the Peace Corps, the International Red Cross, and the U.S. Army Corps of Engineers, while directly empowering communities and individuals to tackle challenging problems.

The full realization of this future will require significant technical advancement, a re-envisioning of design and engineering software, and a reconsideration of fundamental assumptions, such as what constitutes a “user.”

Importantly, we do not need to wait for AGI to get started. By taking a scaffolding approach that pairs problem selection with the iterative extension of capabilities, we can tackle progressively harder problems and steadily increase the system’s real-world impact.[…]

Read more: www.patrickhebron.com

Der Beitrag AI and the Revolution in Design, Engineering, and Problem-Solving Methodology erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Shivi Gupta – “Who Owns the Sound? AI in Music and the Legal Landscape”

Attending a live gig, enjoying the music from your favourite artist or band? What if it can come to your couch, with the feeling that they are performing right there in front of you? But hey, who is producing the music? Is it AL or Al

The creation is revered, but more than the creation, the creators are worshipped. Recently, Sony, Universal, and Warner have sued Suno and Udio (GenAI music startups), claiming copyright infringement in training models, to protect the artists affiliated with these giants.

Major record labels are protecting their clients, the artists, the great ones who produce music that can rarely be replicated. But in the day and age of generative AI or (GPT Generative Pretrained transformer), music is also replicated by machine learning algorithms to make songs sound like the original creators.

As one of the popular web3 music websites Unchainedmusic.io wrote in their article “Deepfake vocal synthesizers, an innovation in AI technology, can make a singer’s voice sound like a famous artist. Under English and EU law, it is unlikely that a style of singing, whether generated through deep learning, AI or vocal imitation, is protectable by copyright. However, other forms of intellectual property, such as passing off, may be relevant in some jurisdictions.”

There is no common universal law against intellectual property, and most countries have their own rules, copyrights, patents. Any commercial use begets a request or a permission from the creator who owns the intellectual property of their voice.

Problem:

All music can be created eclectically with different styles, lyrics and genres. GenAI music might saturate the market with more and more music generated by machine learning algorithms.

Possibility:

Music lovers will rely on humans creating songs as it has the emotional factor, the timber, tone, pitch, stretch, diction, accent are some of the unique human characteristics which helps us being empathetic and understanding of the singer’s mindset.

Probability:

These AI created songs will be used by ad companies and video editors to feature a product or sell a service with an attractive UX.

Musicians will continue creating great records and go on tours, and fill stadiums.

Editors, marketers, sales representatives will use GenAI music in elevators, advertisements, branding, showcase of their products and services. The GenAI music will complement the product *-as-a-service.

Proposed Solution:

Follow rules created by the countries in which these AI tools are used. For classical music the law states that as mentioned by edwardslaw.ca “it covers original literary, dramatic, musical, and artistic works of authorship. This is during the lifetime of the author, the remainder of the calendar year in which the author dies, plus 70 additional years (the Canadian copyright lifespan recently increased from 50 to 70 years in June of 2022). Once this term expires, the work becomes public domain. “ So works from Beethoven, Mozart et al. can be performed in public without permission or paying a fee – royalty free. So any music which has been recorded prior to 1974 can be used since it has entered the public domain, but if you have the London symphony orchestra uploaded their recording of Beethoven’s symphony number 5, one can’t use that without the permission from the orchestra.

For example this particular youtube video can’t be reused without BBC’s permission:

More on copyright of voices: “According to Herndon, much of vocal mimicry comes down to personality rights. “You cannot copyright a voice, but an artist retains exclusive commercial rights to their name and you cannot pass off a song as coming from them without their consent,” she wrote in a recent Twitter thread, citing previous legal cases related to vocal impersonation.”

More about ethics in AI.

About the Author:

Shivi Gupta is a  passionate data scientist and full-stack developer, working in the industry for over a decade. An AI expert navigating through the world of real vs generated content. With a focus on ethics , he creates websites, mobile applications, chatbots all powered by AI.

Der Beitrag Who Owns the Sound? AI in Music and the Legal Landscape erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.