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: 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 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.

Transformative AI Investments and Market Leaders – SwissCognitive AI Investment Radar

This week, our coverage spans from venture capital driving the generative AI boom to major corporate investments and strategic partnerships.

We begin with top VC investors fueling soaring AI valuations, highlighting key players like Cohere and Perplexity. Moving to Europe, French startup Mistral AI’s substantial $645 million funding positions it as a strong competitor to OpenAI.

The cybersecurity sector sees a resurgence in venture capital, driven by generative AI innovations. Havas is gearing up for a potential IPO with a significant €400 million AI investment. Meanwhile, NATO’s €1 billion fund underscores AI’s growing role in global defense.

Major firms like Amazon, Baidu, and Cisco are making significant bets through specialized AI funds, while SAP enhances its AI initiatives with Joule and eyes a partnership with Microsoft. Gracia AI’s $1.2 million funding aims to advance photorealistic volumetric video technology.

From the UAE, Polynome Group launches a $100 million AI fund, and Brazil’s B3 introduces an AI assistant to aid new investors. Our podcast segment emphasizes the need for practical generative AI use cases, and UNICEF’s fund supports blockchain and AI for social impact. Finally, we explore AI’s growing role in enhancing investment decisions.

Join us as we delve into these developments and chart the future of AI investments and market leaders.

Previous SwissCognitive AI Investments Radar: AI Market Movements and Strategic Investments.

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 Transformative AI Investments and Market Leaders – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwssCognitive Guest Blogger: Felipe Castro Quile – “Rethinking Investments in the Age of AI Towards Economic Thinking”

In today’s technological advancement, the rapid progression of artificial intelligence stands out as a transformative force reshaping our societies from the ground up. In the heart of this storm of innovation, traditional concepts like money and investment find themselves under the microscope, their roles and meanings challenged against the backdrop of profound societal shifts.

Money, at its core, is a social construct—a medium of exchange infused with value by collective agreement. Its purpose extends beyond mere transactions, serving as a symbol of trust and a store of wealth. Yet, in the realm of investment, money takes on a different guise, becoming a tool for allocating resources with the expectation of future returns. This relationship between money and investment is deeply entrenched in economic and social systems, shaping the fabric of our societies.

However, the advent of AI introduces new complexities into this age-old equation. The relentless march of automation and machine learning algorithms disrupts traditional notions of value creation, employment, and wealth distribution. As AI permeates financial systems, it redefines the dynamics of decision-making in investments and challenges established valuation paradigms.

The fusion of AI and finance ushers in a new era—one where algorithms parse through vast troves of data, identifying patterns and making split-second decisions that elude human cognition. This shift not only streamlines processes but also introduces a level of efficiency and accuracy previously unimaginable. Despite this, accountability, transparency, and the human element in financial decision-making still remain at risk of being diminished.

The evolving landscape prompts a reevaluation of the relationship between money, investment, and societal value. As AI-driven technologies reshape industries and redefine labor markets, the traditional metrics of economic success—GDP growth, profit margins, and shareholder value—seem increasingly inadequate. In this transformative era, the pursuit of sustainability, social impact, and equitable wealth distribution takes center stage, challenging the status quo and demanding a recalibration of our economic priorities.

In fact, the intersection of AI and finance underscores the urgent need for adaptable frameworks capable of accommodating emerging technologies and evolving societal values. This calls for a paradigm shift in economic thinking—one that embraces complexity, prioritizes inclusivity, and fosters resilience in the face of uncertainty.

One promising avenue for exploration lies in decentralized finance (DeFi)—an emerging paradigm that leverages blockchain technology to democratize access to financial services and redefine the nature of trust in economic transactions. By eliminating intermediaries and empowering individuals to directly participate in financial markets, DeFi holds the potential to usher in a more equitable and transparent financial ecosystem.

Additionally, as AI continues to evolve, so too must our governance frameworks; governance in the sense of reevaluating power dynamics and AI regulation before it becomes too entrenched with vested interests that hinder innovation or worst. Ensuring that AI-driven financial systems are accountable and aligned with the societal values of a new era requires proactive measures to safeguard against potential risks and mitigate unintended consequences. How this is achieved is just a matter of strategy.

Ultimately, the convergence of AI and finance signals a new frontier of possibilities, where innovation and disruption go hand in hand with benefit and foresight. As we navigate this uncharted territory, let us pay attention to the lessons of the past while embracing the opportunities of the future. By embracing adaptable frameworks that prioritize sustainability (which is only reached through mindful consumption and allocating the same resources we discuss above), social impact, and decentralized finance, we can pave the way for a more resilient and inclusive economic paradigm—one that truly serves the needs of all. AI is the key to unlocking unprecedented efficiency and insight, but it’s our responsibility to ensure that its benefits are distributed equitably and its risks are mitigated effectively, empowering us to transform.

About the Author:

Felipe Castro Quile is an accomplished international entrepreneur, leads as CEO at Emerging Rule and GENIA Latinoamérica. With dual MBA credentials and expertise in deep learning, blockchain technology, and virtual learning, he excels in crafting and deploying AI solutions to address intricate business dilemmas and drive societal progress. Felipe’s fervor for leveraging technology to enrich education, coupled with his adeptness as a virtual teaching specialist, is matched by his reputation as a pioneering expert in blockchain technology, particularly in transforming supply chain management. Renowned for his innovative thinking and meticulous execution, he is a sought-after consultant and mentor within the tech industry, leaving a significant imprint on global technological advancements.

Der Beitrag Rethinking Investments in the Age of AI Towards Economic Thinking erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Look into the latest top AI insights that have landed on our radar:

A Web3 Revolution: AI running “on” blockchain as a smart contract
YouTube’s new deep fake disclosure rule
Microsoft’s AI leap with DeepMind’s co-founder
The reality of AI chatbots in enterprises
AI scores in football strategy
Apple & Google’s potential AI collaboration
Privacy-focused AI with a new framework
…and more!

Stay ahead with the key updates in the world of AI!

Warm regards,

The Team of SwissCognitive

Der Beitrag A World First: AI Running “On” Blockchain erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Ethan Millar – “Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail?”

Gaining deep insight with artificial intelligence tools is the trend for businesses to operate. Both small businesses and large enterprises are compelled to use AI technologies. AI chatbots communicate with more complex sessions. Companies that have already completed digital transformation should be moving towards a new generation of chatbots. SMEs can also take advantage of this new trend.

The new generation of AI chatbots comes with complex neural connections to have conversations. It is scalable as developers use deep learning tools. It eventually helps enterprises to bankroll AI-based intents with a high-tech approach. Unless the developer knows the pros, cons, and effects of deep learning tools on training chatbots, the very purpose of accurate deliverables gets lost in translation.

This 10-minute reading material is a virtual assistant for the developer to understand how deep learning tools maximize their potential. Moreover, it is also aimed at leadership with companies to understand why a bot-building project has met with failure. How can be brought back to work?

As both are inter-connected, this post focuses on IT developers in large enterprises and lean departments of small businesses.

Lessons to learn from the developer’s perspective

Have you just met with a failure in an AI Chatbot-based project, recently? It is not success that teaches. Failure adds a valued experience while dealing with different approaches to creating chatbots. Many companies fail initially in their efforts. It becomes the ideal base for understanding how a CRM developer can help an enterprise monetize through deep learning tools.

Three things count:

1. Deep learning does not involve or solve everything for business solutions. Some applications can do without it.
2. All enterprises cannot deal with specialized tools unless they have the requirement.
3. All developer tools are not meant for monetizing.

If an enterprise uses only deep learning tools, then only about 1/3^rd of its potential will be realized and the rest will remain untapped. The developer needs an overall understanding to tap it.

Two systems for learning

An IT team of a company) will need to research AI Chatbots and their specific requirements. It will avoid aberrations related to conversations with humans and machines. Earlier virtual assistants like Cortana, Siri, and Alexa set the bar for new bots. They still work with smartphones, appliances, and other home-based devices. They work on 2 systems – Supervised learning and unsupervised learning which require natural language processing capabilities. Since 2020, 85% of customers have been dealing with chatbots by making inquiries. The human connections have reduced.

Supervised Learning

The software is developed after getting data from real-world requests. Correlations are established between ‘tags’ and ‘user-intents’ which are marked for learning and engaging the customer. In such a case, deep learning tools achieve a high level of accuracy. Specialized tools are developed for this purpose. The only hitch here is if the data collected is insufficient or not suitable then the functionality and success are trapped.

Unsupervised learning

Again, in this case, too, a good database is required to understand the customer intent of the chatbot. When it is not supervised, it works independently. There is no need for human supervision while it functions nor does it require specific tags to prompt it to work.

The failure rate increases if the database does not provide a wide range of variables. The quality is not good enough for it to be released in the public domain. Even if it does come out, it will have limited success. The data volumes required are large for deep learning tools to be effective. And, it goes without saying that poor data does not give the required results and also affects business.

Chatbots will continue to grow

Despite the failure rate, AI chatbots will grow and many companies experiment with their capabilities. Consumers are already hooked on them and enjoy the services of such virtual assistants. They find an opportunity to add value to their routine tasks. Every public company wants to reduce customer care efforts, and this is a solution that has promise in the real world.

The only reason why it fails is due to the data required for tags and the user intent in each company is diverse. In some cases, it is limited to a certain extent. Hence, deep learning tools need careful deployment by the developer. They require a well-structured database and good examples for training the system. Getting advanced systems to work requires a good degree of inference latency, interpretability, and reproducibility to understand the data and train the program.

Developer’s skills are tested

A complex toolset may not be the answer for a training program to converse. It took years and several failed tests for Siri or Alexa to reach the stage where they are now. E-commerce giants using machine learning tools have survived as they have a constant flow of data to test and train. In the final analysis, a complete overview of components is required before they can be channeled and ready for public use or limited enterprise utility.

If developers choose hybrid systems, advanced NLP, and AI algorithms and do not rely on the 2 main systems there are bright chances of creating the right chatbot.

Now we turn our focus on the functionality and advantages of AI bots for real-time business needs.

AI chatbots are the new Jeeves

Your wish is my command!

Are you still confused about the diverse functions of AI deep learning? chatbots? Here is a simple description of the new automated ‘Jeeves’ in the corporate world. They are computer programs that communicate with the user as messengers. Some are advanced enough to handle instructions in the absence of the programmer.

It may sound like sci-fi but it is gaining traction as it is a time saver and do various tasks efficiently for different departments. For small businesses, it reduces overheads while multi-tasking.

How can it be deployed?

Most people are used to texting messages to each other as their main form of communication on social media or FB messenger even for work. This is the way even customer care is handled worldwide. Now chatbots are designed to take over.

Once you are familiar with deep learning and how it influences business processes the possibilities of its uses are unlimited. For example, they can be embedded in websites to answer 24×7 any customer queries. It is a live chat and once the user signs up on the website, the chatbot is functional.

Where is it most influential and popular? In businesses where customer services need to be handled with care. Today, pharma, real estate, and financial companies also use AI chatbots successfully.

Smart business advantage

Ai Chatbots are more common than you think. Google Assistant, Apple’s Siri and Amazon’s Alexa are all chatbots serving various functions. They are not only useful but are extremely popular. A smart chatbot increases your company’s visibility thereby boosting sales.

Earlier it was possible only for large companies to invest in AI deep learning. Now more avenues have opened up for small businesses to take advantage of this feature. Chatbots can be integrated into many areas of a company’s business. Chatbots use natural language processing in combination with machine learning to respond accurately to a customer’s requests.

They have been created to recognize an inquiry and provide an appropriate answer. With advances in the tool and features, they record previous questions and answers. They are geared to offer a personal experience to the user. As a service, it upgrades the company’s overall profile to settle disputes and provide customer satisfaction.

Ideal social media tools

AI chatbots have proven to be excellent social media marketing tools. Their efficiency is only set to increase in the coming years. AI provides personalized, real-time content targeting that produces 20 percent more sales opportunities. It can also be utilized for behavioral targeting methods for specific buyers. This is a sleek advantage for small companies that cannot hire expensive marketing managers.

Using this technology data and statistics prove to be useful to make decisions through predictive analysis. Machine learning can be applied in marketing to optimize for successful campaigns. Automaton reduces time gaps for performances and many sectors are turning towards bots to increase productivity and interactions.

Evolving innovation

With new developments, the way conversations are perceived is changing. This platform has already introduced voice bots and crypto tokens, messengers for blockchain.  Companies like Google, Apple, and Amazon are already developing new conversational platforms for better customer interaction. Perhaps this evolution will help solutions to be more forthcoming.

As 2024 is underway, the use of AI chatbots is no longer a luxury. It has become essential. With ChatGPT, Gemini, Bing, and Claude making an influential impact, it is hard to ignore them for business operations. Leaders require content generation and customization to streamline. AI bots can reason with limited inaccuracies with the user.

Closing thoughts

If you have failed once, now with experience take advantage of the new ‘Jeeves’ and its sophisticated commands. It’s time your developers take a fresh take on creating the right chatbot and reduce operational challenges.

About the Author:

Ethan Millar is a technical writer at Aegis Softtech especially for computer programming like artificial intelligence, emergency technology, Big Data, data analytics, and CRM for more than 8 years. Also, have basic knowledge of AI and technology are vast fields with numerous experts contributing to various aspects of research, development, and application.

Der Beitrag Born To Be A Bot: Then Why Does Building AI Chatbots For Enterprises Fail? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Andy Fitze, Co-Founder of SwissCognitive, World-Leading AI Network
Copyright: inside-it.ch – Andy’s AI Almanac: Technology Trilogy Engineer – die Köche der Zukunft

Let’s start from the beginning. We humans still believe that we understand non-linear processes. But in reality, we don’t. We can’t even get a bathtub halfway to a comfortable bathing temperature after the hundredth time. We are not made for things that we cannot grasp linearly. We are so overwhelmed by a simple weather forecast that we usually don’t even know what the weather is going to be like after reading the weather report. Intuition doesn’t want to enter our heads.

This leads me to 3 conclusions:

1. Things are complex, and we have simplified them so that we can understand and apply them. And that’s a good thing otherwise we’d all be overwhelmed. Anyone who goes skiing in winter, for example, knows this. There are the 3×3 rules of avalanche safety. That’s how it’s understood and it’s practical, but the reality is much more complex. So complex that we don’t understand it and can’t process it, especially not in the terrain at minus 20 degrees.
2. We deduce the future from experience, even and especially with highly volatile systems. We call this intuition, but what we mean is “competent behaviour in the face of complete cluelessness”. We tend to behave in this way when complexity is involved, perhaps in order to create a certain logic. For example, we believe that the next logical step after assisted driving is autonomous driving. In other words, we describe this change as “one step”. In reality, the technology is 1000 times more complex in this single step. When Steve Ballmer made fun of Apple in 2007, Microsoft sold millions of smartphones a year, and Apple none. Today, Apple sells that amount in one day.
3. We like to forget so badly we are practically world champions in it. Individual and social amnesia, so to speak. Before boarding the plane, we check our boarding pass to remember our seat number, but as soon as we get on the plane, we have forgotten everything.

And now we are surprised about AI, as if we had forgotten that calculators, Wikipedia, Google search, Excel, smartphones and even our kitchen stove have long surpassed us.

Wake up! With our experience, logic, and knowledge, we will hardly grasp technological developments, far less predict them.

We need to engage more intensively with technology. Much more!

What we see today with AI and its rapid development dazzles us. We are amazed, stunned and therefore blind to see what is coming.

My prognosis:

1. AI applied directly will boost productivity.
2. AI applied in technologies will define new markets.
3. AI merged with multiple technologies will change the foundations of our world.

We are already experiencing the first one today. The second one is also already advancing in the B2B sector. But the third one will be the most exciting. For example, the combination of AI, crypto, and blockchain will fundamentally change the trade of all assets in this world. And AI, biotech, and quantum will completely alter our understanding of ourselves, nature, and the healthcare industry.

For the first time in history, we will be able to answer complex questions with complex systems. I admit that I am very excited. This is beyond our current understanding.

Therefore, I suggest a new professional category: Technology Trilogy Engineer – chefs who understand how to rediscover technology recipes.

Original article: www.inside-it.ch

Der Beitrag Technology Trilogy Engineer – The Chefs of the Future erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Unpacking the Latest Trends and Transitions – SwissCognitive AI Investment Radar

Welcome to the latest dispatch from SwissCognitive’s AI Investment Radar, where we dissect and distribute the most recent and riveting developments within the AI investment sphere. This week, we traverse from the innovation hubs crafting the future of AI applications to the boardrooms where decisions are shaping the trajectory of industries and economies worldwide.

Fetch.ai is pushing the boundaries with a $100 million investment into AI development infrastructure. Across the Atlantic, Theia Insights in Cambridge is leveraging AI to refine investment strategies, embodying the fusion of cutting-edge technology and traditional finance.

ChainGPT’s initiation of a $1 million grant earmarks the fusion of AI and blockchain. Meanwhile, Peppermint Innovation is setting its sights on revolutionizing financial services with AI-driven enhancements.

In an ambitious national strategy, India announces a $1.25 billion AI push. This movement is mirrored in the financial districts of Wall Street, where giants like Goldman Sachs and Blackstone are integrating AI to redefine efficiency and innovation in finance.

On the cybersecurity front, Reach Security’s funding aims to transform security operations with AI, offering a glimpse into the future of fortified digital defenses. The venture capital market is predicted to soar, with AI, sustainability, and technological innovation as the primary catalysts, indicating a prosperous horizon for AI investments.

Thomson Reuters is setting a precedent with an $8 billion AI investment, aiming to transition into a content-driven tech firm, while Amazon’s Climate Pledge Fund invests in AI for a greener future through innovative recycling methods. Lastly, Jeff Bezos’ investment in Perplexity AI, a startup aiming to refine AI’s usability, illustrates the high stakes and high rewards in the evolving AI investment landscape.

Join us on this exploration of AI’s present and future and the upcoming trends, illuminated by SwissCognitive’s AI Investment Radar.

Previous SwissCognitive AI Investments Radar: Last Week’s New Investments and Innovations.

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 Unpacking the Latest Trends and Transitions – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.