Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar

AI funding momentum hasn’t slowed. From global infrastructure projects to nuanced questions about investor confidence, this week brought high-dollar commitments alongside critical reflections on where the money is flowing—and why.

The United Arab Emirates made headlines with a bold $1.4 trillion, 10-year commitment to invest in the United States, a move that reflects the centrality of AI and tech collaboration in long-term statecraft. Meanwhile, BlackRock’s joint initiative with Microsoft, NVIDIA, and xAI signals continued investor appetite for large-scale AI infrastructure, with $100 billion earmarked for global data centers and energy solutions.

Several firms are also reinforcing their US presence: Hyundai announced a $21 billion investment, Siemens followed with $10 billion, and Schneider Electric added another $700 million—all aimed at fortifying AI-driven manufacturing and operations amid ongoing trade policy uncertainty.

Vietnam’s small businesses are setting the tone in Asia-Pacific, where 44% named AI their top tech investment for 2024. Fractal Analytics’ $13.7 million investment into India’s first reasoning model and Germany’s €2.1 million seed round for enterprise AI search show how national AI goals are increasingly shaped by local strategies and use cases.

Yet, not all attention is on infrastructure. Thought leaders at Man Group and other investment firms raised flags about the sustainability of AI stock valuations. An AI model under a top-performing fund has been flashing warnings on mega-cap tech stocks, including Nvidia. Still, audiences from pharma to finance are assessing AI’s value not just in terms of returns, but in ethics and relevance, particularly when it comes to pharma’s future and the realities of Artificial General Intelligence claims.

As global interest in AI capital remains high, this week’s updates highlight a shift from novelty to operational depth. More investment—yes—but also more scrutiny.

Previous SwissCognitive AI Radar: New AI Investment Funds and Strategic Expansions.

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 Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar 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.

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SwissCognitive Guest Blogger: Zachary Amos – “How Countries Are Using AI to Predict Crime”

Industry 4.0 and 5.0 is using robotics to bring supply chains into the future. The last decade has been fraught with challenges, including delays, worker shortages and market volatility. Mitigating costs and enhancing the workforce are the goals of swarm robotics, and artificial intelligence (AI) is making them even more competent. See how these workers make supply chains resilient and competitive.

What Are Swarm Robotics?

Swarm robotics is a field focusing on large quantities of simple yet practical robots. These robots work best in groups to achieve straightforward tasks, making them optimal for reducing labor burdens. They also shine in industries like supply chains, where repetitive tasks take up a major portion of the working day.

Supply chains need to use swarm robotics because they are easy to manage simultaneously. They are autonomous, respond to environmental stimuli and are easy to reprogram to new tasks. The collective efforts of these machines can make decisions on the fly, covering ground from last-mile delivery to utilizing resources in a smarter way.

How Do Supply Chains Use Swarm Robotics?

These robots enhance operations while allowing supply chains to overcome common pain points. Each application for swarm robots is also made better by AI. What does this look like?

Dynamic Operations

Because swarm robots take tedious tasks away from workers, they allow people to focus on more high-level processes. In the meantime, the bots can tally inventory, navigating complex warehouses in large numbers. They are immediately deployable to do automatic updates, sending instant notifications to procurement, fulfillment and distribution teams.

Swarm robots are also ideal in changing, unstructured environments. With AI and sensor technology, they can map areas no matter how complicated they are. As they learn to navigate, they become more proficient when interacting with similar environments because of machine learning algorithms. This informs routing and navigation and allows perpetual scaling potential.

Cost Reduction

Delegating tasks to robots saves supply chains tons of money. Human error costs corporations between $50-$300 for every mistake. The increased accuracy is only one aspect of the financial savings. The robots save businesses time and money in talent acquisition processes, which take efforts away from fulfilling client needs.

However, the most prominent financial gain may be from warehouse savings. Refined inventory management prevents objects from taking up square footage and energy as they collect dust. Instead, there is detailed metadata on each item, their expiration date, market values and more, which swarm robots can collect with AI.

Productivity Gains

ot only do AI-powered swarm robots save money, they make everything more efficient. Preventing errors, defects and more can shorten lead times from suppliers. In one study, several industries experienced shortened fulfillment lead times by an average of 6.7 days.

They can also allow parallel task execution. While some robots pick up objects, others can transport them and even more can pack them. This yields numerous time savings across lengthy processes with multiple intermediaries.

There are also other productivity gains because swarm robots make supply chain environments safer for workers. They can constantly monitor unsafe conditions in real time, saving employees the trouble of entering dangerous circumstances. This means fewer workers experience injuries and incidents, allowing them to work with higher morale in safer conditions.

Preparing the Swarm

Much like swarms of ants group together to achieve a common goal, these types of robots optimize supply chains. Combining them with AI makes them even more powerful. As they advance, swarm robotics consistently prove they are a must-have fixture for supply chain management in the future.

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 Enables Swarm Robotics in the Supply Chain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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

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

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

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

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

Stay tuned for next week’s AI investment updates.

Previous SwissCognitive AI Radar: AI Investment Opportunities Worldwide.

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

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

Breaking Barriers to AI Funding – SwissCognitive AI Investment Radar

Der Beitrag Breaking Barriers to AI Funding – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Leveraging AI and Blockchain for Privacy and Security in Cross-Border Data Transfers”

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

The Growing Concern of Cross-Border Data Transfers

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

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

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

AI for Enhanced Data Privacy in Cross-Border Transfers

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

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

Blockchain for Secure and Transparent Data Transfers

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

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

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

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

Conclusion

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

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

References:

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

About the Author:

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

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

Participants will benefit from tailored solutions within three critical verticals: Industry/Medtech, Fintech/Blockchain, and SMEs (Small and Medium Enterprises). Whether addressing innovations in Industry 4.0, exploring blockchain’s transformative potential in financial technologies, or identifying scalable IT solutions for SMEs, the Summit offers a rich platform for Swiss companies to connect with highly skilled CEE professionals.

The day will feature B2B-meetings, panel discussions and hands-on workshops, providing insights into the latest trends and fostering long-term business relationships. This mix of structured and spontaneous networking will help companies benchmark their IT strategies, find innovative solutions, and explore nearshoring opportunities.

Der Beitrag CEE Swiss IT -Solutions & Talent Summit erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Artificial Intelligence: Insights and AI Leadership Strategy from Industry Experts – The SwissCognitive AI Navigator – Issue 02 – 2024

Artificial Intelligence is restructuring industries at a pace we haven’t experienced before. Staying ahead requires more than just awareness—it demands insight, strategy, and visionary leadership.

The high-speed evolution of AI technologies, coupled with the complexities of ethical considerations and regulatory landscapes, presents both immense opportunities and significant challenges for businesses worldwide.

These circumstances were the driving forces, why we decided to interview 16 distinguished AI experts and leaders.  We are thrilled to announce the release of the 2024 AI Navigator – Issue 02, a comprehensive guide designed to equip leaders with the knowledge and AI leadership strategy needed to navigate the intricate world of AI. Building on the success of our previous edition, this year’s AI Navigator delves deeper into the critical aspects of AI adoption, offering actionable insights from the AI professionals at the forefront of AI innovation.

Why the AI Navigator Matters

As AI developments, regulations, and trends advance at lightning speed, the decisions we make today are more critical than ever. The AI Navigator serves as a trusted resource, cutting through the hype to provide clear, practical guidance on harnessing AI’s transformative potential responsibly and effectively.

Whether you’re in healthcare, finance, manufacturing, mobility, technology, or any other industry, this guide is designed to help you:

• Strategically Integrate AI: Understand real-world applications that drive efficiency and innovation within your organization.
• Navigate Ethical Frameworks and Compliance: Gain insights into the complex landscape of AI ethics and global regulations to ensure responsible deployment.
• Adapt Leadership and Workforce Strategies: Prepare your team for an AI-driven future with adaptive leadership styles and continuous upskilling.
• Embrace Collaboration and Plan for Scalability: Learn how co-innovation and strategic planning can foster sustainable growth and long-term success.

What’s Inside the 2024 AI Navigator

The guide is thoughtfully structured into five comprehensive chapters, each delving into essential facets of AI integration and leadership:

1. A Comprehensive Exploration of AI Adoption and Integration in the Modern Era
   • Key Developments and Innovations
   • Adoption and Integration of AI Across Industries
   • Challenges in Scaling and Practical Integration of AI
   • Future Directions and Predictions for AI
   • Key Takeaways
2. Leadership Strategy, Workforce Adaptation, and Global Perspectives
   • Strategic Implementation of AI in Business Processes
   • The Future of Work: AI and Human Collaboration
   • Leadership and Workforce Adaptation to AI
   • Investing in AI for Future Growth
   • Global Perspectives on AI Adoption and Innovation
   • Key Takeaways
3. Navigating the Landscape of Regulation and Compliance
   • Regulatory Challenges and Opportunities
   • Implementing Effective AI Governance and Frameworks
   • Global Coordination and Harmonization of AI Regulations
   • Regulatory Compliance in AI Development: Socioeconomic and Ethical Considerations
   • The Future of AI Regulation and Compliance
   • Key Takeaways
4. Strategic Integrity: Navigating the Complexities of AI Ethics and Public Trust
   • Public Perception and Trust in AI
   • Integrating AI Ethics into Business Strategy and Proactive Deployment
   • Ensuring Data Ethics and Building Trust Through Transparency
   • The Role of Leadership in Shaping AI Culture and Ethics
   • Key Takeaways
5. From Theory to Practice: A Comprehensive Guide to Implementing AI in Business
   • Cultivate an AI-Literate Culture
   • Prioritize Practical AI Integration
   • Build an Ethical AI Framework
   • Invest in AI-Driven Innovation
   • Enhance Data Infrastructure
   • Embrace Collaboration and Co-Innovation
   • Plan for Long-Term AI Scalability
   • Key Takeaways

Insights from Industry Leaders

We are grateful for the participation of our esteemed AI experts, whose insights have been instrumental in shaping this guide:

• Alessandro Curioni
• Dr. Anita Puppe
• Aruna Pattam
• David Wood
• Kalogeropoulos Dimitris
• Prof. Dr. Ingrid Vasiliu-Feltes
• Dr. Jacques Ludik
• Jair Pinto Ribeiro
• Jarrod Anderson
• Jeff Winter
• Jim Harris
• José Pedro Almeida
• Pascal Bornet
• Patrick Bangert
• Semih Kumluk
• Valeria Sadovykh

What is the “AI Navigator”?

An insightful exploration of AI’s evolving landscape, focusing on ethical considerations, human-centric values, and practical leadership guidance. It offers a balanced and in-depth perspective essential for any leader navigating today’s digital age.

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

Introduction

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

The challenge is supplying food for a population growing

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

The Significance of Artificial Intelligence (AI) in Agriculture

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

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

The Significance of Robotics in Agriculture

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

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

Sustainability and environmental impact

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

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

Advantages in the field of economics

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

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

Challenges and the road forward

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

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

Conclusion

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

References:

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

About the Author:

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

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