SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – “AI in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation”

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

Introduction to Self-Healing Systems

Definition and Functionality of Self-Healing Cybersecurity Systems

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

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

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

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

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

Key Features of Self-Healing Systems

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

Advantages Over Traditional Cybersecurity Methods

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

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

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

Challenges and Limitations

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

Real-World Applications of Self-Healing Systems

Financial Institutions

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

Healthcare Industry

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

Government and Defense

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

Future Outlook

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

Conclusion

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

References

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

About the Authors:

Dr. Raul Villamarin Rodriguez is the Vice President of Woxsen University. He is an Adjunct Professor at Universidad del Externado, Colombia, a member of the International Advisory Board at IBS Ranepa, Russian Federation, and a member of the IAB, University of Pécs Faculty of Business and Economics. He is also a member of the Advisory Board at PUCPR, Brazil, Johannesburg Business School, SA, and Milpark Business School, South Africa, along with PetThinQ Inc, Upmore Global and SpaceBasic, Inc. His specific areas of expertise and interest are Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Robotic Process Automation, Multi-agent Systems, Knowledge Engineering, and Quantum Artificial Intelligence.

Dr. Hemachandran Kannan is the Director of AI Research Centre and Professor at Woxsen University. He has been a passionate teacher with 15 years of teaching experience and 5 years of research experience. A strong educational professional with a scientific bent of mind, highly skilled in AI & Business Analytics. He served as an effective resource person at various national and international scientific conferences and also gave lectures on topics related to Artificial Intelligence. He has rich working experience in Natural Language Processing, Computer Vision, Building Video recommendation systems, Building Chatbots for HR policies and Education Sector, Automatic Interview processes, and Autonomous Robots.

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

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

A world where police departments can predict when, where and how crimes will occur seems like something from a science fiction novel. Thanks to artificial intelligence, it has become a reality. Already, countries are using this technology to forecast misconduct.

How Do AI-Powered Crime Prediction Systems Work?

Unlike regular prediction systems — which typically use hot spots to determine where and when future misconduct will be committed — AI can analyze information in real time. It may even be able to complete supplementary tasks like summarizing a 911 call, assigning a severity level to a crime in progress or using surveillance systems to tell where wanted criminals will be.

A machine learning model evolves as it processes new information. Initially, it might train to find hidden patterns in arrest records, police reports, criminal complaints or 911 calls. It may analyze the perpetrator’s demographic data or factor in the weather. The goal is to identify any common variable that humans are overlooking.

Whether the algorithm monitors surveillance camera footage or pours through arrest records, it compares historical and current data to make forecasts. For example, it may consider a person suspicious if they cover their face and wear baggy clothes on a warm night in a dark neighborhood because previous arrests match that profile.

Countries Are Developing AI Tools to Predict Crime

While these countries don’t currently have official AI prediction tools, various research groups and private police forces are developing solutions.

• United States

Violent and property crimes are huge issues in the United States. For reference, a burglary occurs every 13 seconds — almost five times per minute — causing an average of $2,200 in losses. Various state and local governments are experimenting with AI to minimize events like these.

One such machine learning model developed by data scientists from the University of Chicago uses publicly available information to produce output. It can forecast crime with approximately 90% accuracy up to one week in advance.

While the data came from eight major U.S. cities, it centered around Chicago. Unlike similar tools, this AI model didn’t depict misdemeanors and felonies as hot spots on a flat map. Instead, it considered cities’ complex layouts and social environments, including bus lines, street lights and walkways. It found hidden patterns using these previously overlooked factors.

• South Africa

Human trafficking is a massive problem in South Africa. For a time, one anti-human trafficking non-governmental organization was operating at one of the country’s busiest airports. After the group uncovered widespread corruption, their security clearance was revoked.

At this point, the group needed to lower its costs from $300 per intercept to $50 to align with funding and continue their efforts. Its members believed adopting AI would allow them to do that. With the right data, they could save more victims while keeping costs down.

Some Are Already Using AI Tools to Predict Crime

Governments have much more power, funding and data than nongovernmental organizations or research groups, so their solutions are more comprehensive.

• Japan

Japan has an AI-powered app called Crime Nabi. The tool — created by the startup Singular Perturbations Inc. — is at least 50% more effective than conventional methods. Local governments will use it for preventive patrols.

Once a police officer enters their destination in the app, it provides an efficient route that takes them through high-crime areas nearby. The system can update if they get directed elsewhere by emergency dispatch. By increasing their presence in dangerous neighborhoods, police officers actively discourage wrongdoing. Each patrol’s data is saved to improve future predictions.

Despite using massive amounts of demographic, location, weather and arrest data — which would normally be expensive and incredibly time-consuming — Crime Nabi processes faster than conventional computers at a lower cost.

• Argentina

Argentina’s Ministry of Security recently announced the Artificial Intelligence Applied to Security Unit, which will use a machine learning model to make forecasts. It will analyze historical data, scan social media, deploy facial recognition technology and process surveillance footage.

This AI-powered unit aims to catch wanted persons and identify suspicious activity. It will help streamline prevention and detection to accelerate investigation and prosecution. The Ministry of Security seeks to enable a faster and more precise police response.

• South Korea

A Korean research team from the Electronics and Telecommunications Research Institute developed an AI they call Dejaview. It analyzes closed-circuit television (CCTV) footage in real time and assesses statistics to detect signs of potential offenses.

Dejaview was designed for surveillance — algorithms can process enormous amounts of data extremely quickly, so this is a common use case. Now, its main job is to measure risk factors to forecast illegal activity.

The researchers will work with Korean police forces and local governments to tailor Dejaview for specific use cases or affected areas. It will mainly be integrated into CCTV systems to detect suspicious activity.

Is Using AI to Stop Crime Before It Occurs a Good Idea?

So-called predictive policing has its challenges. Critics like the National Association for the Advancement of Colored People argue it could increase racial biases in law enforcement, disproportionately affecting Black communities.

That said, using AI to uncover hidden patterns in arrest and police response records could reveal bias. Policy-makers could use these insights to address the root cause of systemic prejudice, ensuring fairness in the future.

Either way, there are still significant, unaddressed concerns about privacy. Various activists and human rights organizations say having a government-funded AI scan social media and monitor security cameras infringes on freedom.

What happens if this technology falls into the wrong hands? Will a corrupt leader use it to go after their political rivals or journalists who write unfavorable articles about them? Could a hacker sell petabytes of confidential crime data on the dark web?

Will More Countries Adopt These Predictive Solutions?

More countries will likely soon develop AI-powered prediction tools. The cat is out of the bag, so to speak. Whether they create apps exclusively for police officers or integrate a machine learning model into surveillance systems, this technology is here to stay and will likely continue to evolve.

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 Countries Are Using AI to Predict Crime erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “5 Archaeological Discoveries Made by AI”

Artificial intelligence (AI) transforms many industries, and archaeology is no exception. It leverages machine learning and advanced data analysis to make it easier for researchers to discover and analyze ancient artifacts and sites.

Whether using satellite imagery to locate lost civilizations, deciphering ancient texts or predicting excavation sites, AI enhances the speed and accuracy of archaeological discoveries. As interest grows in AI’s ability to uncover hidden historical insights, it’s becoming a powerful tool for shedding new light on past mysteries.

1. Mapping Lost Civilizations

AI has proven invaluable in analyzing satellite imagery to uncover ancient cities and structures that have long been hidden from view. One remarkable example is the Nazca region in Peru. Deploying an AI system led to the discovery of 303 new figurative geoglyphs in just six months. This accomplishment would have taken years with traditional methods.

AI uses machine learning algorithms to sift through vast amounts of satellite data and quickly identify patterns and anomalies human eyes might miss. This ability to process large datasets rapidly and precisely makes AI far more efficient and accurate. This allows archaeologists to make discoveries faster and on a much larger scale.

2. Uncovering Hidden Texts

AI is a trailblazer for archeologists trying to read ancient texts that are too damaged for the human eye to decipher. One groundbreaking example is the Herculaneum scrolls, buried under volcanic ash and charred beyond recognition. Deep learning techniques allow researchers to read beneath the surface of these fragile artifacts.

Machine learning algorithms identified ink regions in the flattened papyrus, which would have otherwise remained invisible. Deep learning’s ability to sort and interpret massive numbers of images revolutionizes how these texts are classified and understood. This method reveals previously unreadable content and speeds up the analysis of ancient languages to accelerate discoveries in historical research.

3. Predicting Excavation Sites

AI is increasingly used to predict the most promising excavation sites by analyzing geographical data, historical records and patterns from past discoveries. Examining these large datasets can accurately identify likely locations for hidden artifacts and ancient structures.

Technologies like retrieval augmented generation (RAG) further enhance this process by providing access to the latest reliable information and enabling archaeologists to verify their claims in real time. This combination of AI’s data processing power and advanced technologies ensures efficiency and precision. It allows researchers to focus on areas with the highest potential and reduce time and resources spent on less promising sites.

4. Restoring and Reconstructing Artifacts

AI is crucial in reconstructing fragmented artifacts and structures by helping archaeologists visualize and restore damaged or lost pieces. It uses generative adversarial networks to rapidly manipulate portraits and landscapes and predict missing elements. One notable example is the RePAIR project, which aims to piece together ancient frescoes from thousands of fragments discovered in Pompeii.

AI systems analyze these fragments, predict how they fit together and help restore the art. This technology has also been applied to ancient pottery and sculptures, where AI predicts the shape of missing pieces, allowing archaeologists to recreate the original forms. Speeding up the reconstruction process and improving accuracy transforms restoration work, saving time and making it possible to recover more historical treasures.

5. Studying Human Evolution

AI enhances the study of ancient human migration patterns by analyzing genetic material and fossil evidence with unprecedented precision. Researchers can process complex datasets using deep learning models to trace how early humans moved and settled across different regions.

For example, deep learning models used to study the Mesopotamian floodplain environment achieved an impressive 80% detection accuracy in identifying archaeological sites. This level of precision allows scientists to understand human migration routes and settlement patterns. It also offers insights into the movements of ancient populations that would be difficult to uncover through traditional methods.

Why Staying Informed About AI Advancements Matters

Staying informed about the role of AI in archaeology opens the door to understanding new, groundbreaking discoveries that change how people view the past. AI’s potential to uncover even more hidden historical insights is immense as technology advances.

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 5 Archaeological Discoveries Made by AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “Beyond the Hype: Key Components of an Effective AI Policy”

In today’s rapidly evolving technological landscape, artificial intelligence (AI) plays a pivotal role in transforming businesses across various sectors. From enhancing operational efficiency to revolutionizing customer experiences, AI offers immense potential. However, with great power comes great responsibility. Creating a robust AI policy is imperative for companies to address the ethical, legal and operational challenges that come with AI implementation.

Understanding the need for an AI policy

As AI technologies become more sophisticated, concerns around privacy, bias, transparency and accountability have intensified. Companies must address these issues proactively through well-defined policies that guide AI development, deployment and usage. An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives.

For instance, companies in sectors like manufacturing or consumer goods often leverage AI to optimize their supply chain. While this leads to efficiency, it also raises questions about transparency and data usage. A clear policy helps ensure that AI not only improves operations but also aligns with legal and ethical standards.

Key components of an effective AI policy

Ethical principles and values

It’s important to define the ethical principles that guide AI development and deployment within your company. These principles should reflect your organization’s values and commitment to responsible AI use, such as fairness, transparency, accountability, safety and inclusivity. If your company uses AI for targeted marketing, for example, ensure that its use respects customer privacy and prevents discriminatory targeting practices.
Data governance

Strong data governance is the foundation of any successful AI strategy. Companies need to establish clear guidelines for how its data is collected, stored and used, and ensure compliance with data protection regulations like GDPR in the EU, CCPA in California, LGPD in Brazil, PIPL in China and AI regulations such as EU AI Act.[…]

Read more: www.cio.com

Der Beitrag Beyond the Hype: Key Components of an Effective AI Policy erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

A Record Year of AI Investments and Rising Expectation – SwissCognitive AI Investment Radar

The AI investment landscape continues to reshape industries at a global scale, with both emerging startups and tech giants driving growth in this transformative sector. This week’s AI Investment Radar as every week, we highlight for you the most significant developments in AI funding. Startung from Zhipu AI’s state-backed investment boost in China to Celestica’s upgrade amid strong Ethernet-related AI growth.

Despite investor concerns about profitability timelines, the drive toward generative AI remains strong, with companies like Walliance embedding AI into real estate platforms and Claro Colombia preparing its infrastructure for AI applications with a $200M fiber network investment.

Even as some segments of the tech sector experience slowdowns, all-in-all AI investments continue to surge. AI startups have raised $48.4 billion so far this year, surpassing 2023’s totals, hitting its record and experts predict that China’s AI industry could see $1.4 trillion in investments over the next six years. As AI continues to evolve, balancing opportunity with risk management is becoming essential for companies across the globe.

Join us, once again, and explore with us these key shifts in AI funding and innovation.

Previous SwissCognitive AI Radar: Investment Horizons – SwissCognitive AI Investment Radar.

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 A Record Year of AI Investments and Rising Expectation – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – AI & ML, Woxsen University – “AI As A Tool for Enhancing Wisdom: A Comparative Analysis”

Exploring Artificial Intelligence as a Tool for Enhancing Wisdom: A Comparative Analysis Using Webster’s SAWS Scale and Ardelt Scales

Well-informed decisions are guided by wisdom, which includes in-depth comprehension, emotional control, and critical thinking. AI has the capacity to improve human knowledge because of its capacity to analyze large amounts of data and provide insights. Three evaluation measures are used in this article to examine how AI might augment wisdom: the Ardelt Wisdom Scale, the Three-Dimensional Wisdom Scale (3D-WS) developed by Monika Ardelt, and the Self-Assessed Wisdom Scale (SAWS) developed by Webster. We hope to gain insight into how well AI aligns with the dimensions of wisdom by assessing its performance using these scales, identifying areas of strength and improvement, and providing guidance for future advancements in AI decision-making.

Webster’s Self-Assessed Wisdom Scale (SAWS)

Webster’s Self-Assessed Wisdom Scale (SAWS) measures wisdom across five dimensions: experience, emotional regulation, reminiscence and reflectiveness, openness, and humor [1]. Applying this scale to AI systems offers insights into how AI aligns with these facets. AI excels in the “experience” dimension by analyzing vast datasets to provide valuable insights. Its data-driven strategies support emotional regulation, while its ability to identify patterns in personal data fosters reflective thinking. AI also promotes openness by recommending new experiences and opportunities, encouraging individuals to broaden their horizons. Though limited in generating humor, AI curates humorous content, contributing to well-being and a balanced perspective.

By evaluating AI systems using the SAWS scale, we can assess how well AI supports these dimensions of wisdom. This analysis highlights AI’s strengths, such as its cognitive capabilities and potential to enhance emotional and reflective aspects of wisdom. It also identifies areas for improvement, guiding the development of AI systems that better align with the multifaceted nature of wisdom. Ultimately, understanding AI’s role in enhancing human wisdom can inform its integration into decision-making processes, promoting wiser and more informed choices.

Monika Ardelt –  Three-Dimensional Wisdom Scale (3D-WS)

The Three-Dimensional Wisdom Scale (3D-WS) breaks down wisdom into three key components: cognitive, reflective, and affective [2]. This multidimensional approach allows for a nuanced understanding of how AI can enhance different aspects of wisdom. In the cognitive domain, AI shines with its ability to process and analyze vast amounts of data, providing insights that help humans make informed decisions. Its analytical prowess complements human cognitive capabilities, enabling more effective problem-solving.

Reflective thinking, another crucial aspect of wisdom, is where AI can also offer significant benefits. AI encourages self-reflection by presenting diverse perspectives and prompting users to reconsider their beliefs and decisions. This helps individuals develop a deeper understanding of themselves and the world around them. On the affective front, while AI does not experience emotions, it supports emotional well-being by offering tools and resources for managing stress and fostering empathy. By addressing these three dimensions, AI has the potential to enrich human wisdom, guiding individuals toward more balanced and thoughtful decision-making.

Ardelt Wisdom Scale

The Ardelt Wisdom Scale measures wisdom through three interconnected dimensions: cognitive, reflective, and affective [2]. This holistic approach provides a comprehensive framework for assessing how AI can enhance wisdom. In the cognitive realm, AI’s ability to process and analyze large amounts of information aligns perfectly with this dimension. AI can offer insights and knowledge that help individuals understand complex issues and make more informed decisions, effectively complementing human intellect.

The reflective dimension of the Ardelt Wisdom Scale focuses on self-awareness and introspection. AI can significantly aid in this area by encouraging individuals to reflect on their past experiences and behaviors. By identifying patterns and providing feedback, AI helps users gain a deeper understanding of themselves, fostering personal growth. In the affective dimension, which involves empathy and emotional regulation, AI can provide support through tools and resources designed to help individuals manage their emotions and develop a more compassionate outlook. While AI itself doesn’t feel emotions, its ability to assist in emotional management can enhance overall well-being and empathy, contributing to a more balanced and wise approach to life.

Comparative Analysis

When we compare AI’s capabilities across the three wisdom scales: Webster’s SAWS, Monika Ardelt’s 3D-WS, and Ardelt’s Wisdom Scale we see a clear picture of how AI aligns with different aspects of wisdom. Each scale highlights AI’s strengths and potential areas for growth. In terms of cognitive abilities, all three scales recognize AI’s exceptional analytical and data-processing skills. This is where AI truly excels, offering comprehensive insights that can enhance human decision-making and problem-solving.

Reflectiveness is another area where AI shows promise. By encouraging individuals to reflect on their experiences and consider multiple perspectives, AI supports the development of deeper self-awareness and understanding. Both the Webster and Ardelt scales emphasize this reflective aspect, which AI can facilitate through data analysis and personalized feedback. However, the affective dimension presents more of a challenge. While AI can provide tools for emotional regulation and suggest strategies for managing emotions, its lack of true emotional experience means it can only indirectly support empathy and emotional intelligence.

From this comparative analysis we can understand that AI can significantly enhance cognitive and reflective aspects of wisdom, with some potential to aid in emotional well-being. This understanding guides the development of more holistic AI systems that better support human wisdom.

Implications for Decision-Making

AI’s integration into decision-making processes can lead to more informed and balanced choices. Its cognitive strengths provide deep insights and data-driven analysis, enhancing our understanding of complex issues. By encouraging reflective thinking, AI helps individuals consider diverse perspectives and learn from past experiences. Additionally, AI’s tools for emotional regulation support better emotional management, contributing to more thoughtful decisions. Overall, leveraging AI in decision-making can foster greater wisdom, leading to more ethical and effective outcomes in both personal and professional contexts.

Conclusion

AI has the potential to significantly enhance human wisdom by aligning with key dimensions of established wisdom scales. It excels in providing cognitive insights, encourages reflective thinking, and supports emotional regulation. While AI cannot fully replicate human emotional experiences, its tools and strategies can still contribute to emotional well-being. By integrating AI into decision-making processes, we can make more informed, balanced, and ethical choices. As AI continues to evolve, its role in augmenting human wisdom will likely grow, offering new opportunities for personal and professional development.

References:

• Webster, J.D. An Exploratory Analysis of a Self-Assessed Wisdom Scale. Journal of Adult Development 10, 13–22 (2003). https://doi.org/10.1023/A:1020782619051
• Ardelt, M. (2003). Empirical assessment of a three-dimensional wisdom scale. Research on Aging, 25(3), 275-324.

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 As A Tool for Enhancing Wisdom: A Comparative Analysis 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.

SwissCognitive Guest Blogger: Dr. Raul V. Rodriguez, Vice President, Woxsen University and Dr. Hemachandran Kannan,  Director AI Research Centre & Professor – AI & ML, Woxsen University – “Rethinking the Future of Singularity State with Critical Thinking”

In a time of swift technological progress, education has never had more opportunity to change. Generative and quantum AI present exciting opportunities for improving student learning outcomes and upending educational paradigms as traditional teaching approaches change. First, we explore the possible uses, advantages, and difficulties of incorporating generative and quantum artificial intelligence (AI) into educational environments, and we end up imagining a future in which these advances push education into new frontiers of brilliance and performance.

Understanding Generative AI

A branch of artificial intelligence called “generative AI” is concerned with producing new content—like literature, graphics, and even music—by using patterns discovered in previously collected data. It functions by producing an output that closely resembles the properties of the input data. Generative AI in education makes content generation, assessment automation, and personalized learning possible. For example, platforms like Google’s AutoML allow teachers to create personalized learning resources, while technologies like OpenAI’s GPT models may create educational materials suited to each student’s needs. These instances show how generative AI encourages creativity and adaptability in teaching methods.

Exploring Quantum AI

Using the ideas of quantum mechanics, quantum artificial intelligence (AI) is able to do calculations that are beyond the reach of classical AI. Quantum artificial intelligence (AI) uses quantum bits, or qubits, which are multi-state entities that can exist concurrently, as opposed to classical AI, which uses binary bits. This enables exponential efficiency in solving complicated issues for Quantum AI. Quantum AI has great potential in education for applications such as scheduling algorithm optimization, molecular structure simulation for chemistry lectures, and complex mathematical problem solving that beyond the capabilities of traditional computing. A greater knowledge of quantum principles in education is made possible, for instance, by IBM’s Quantum Experience platform, which provides instructors and students with opportunity to investigate quantum concepts and algorithms firsthand.

Revolutionizing Education: Case Studies and Examples

1. Real-world examples of educational institutions or initiatives leveraging Generative and Quantum AI

At the end of last year, MIT hosted a symposium as part of their “MIT Generative AI Week” to examine state-of-the-art generative AI initiatives being worked on by the academic institution. These projects include a mobile app that employs AI-assisted observational learning to enhance public speaking abilities and individualized educational chat tutors for quantum physics using generative AI. Another such is the University of Cambridge, which has been investigating how deep learning algorithms for educational applications—like more effective and precise language translation models—can be improved by using quantum computing.

1. Success stories of student performance enhancement through the integration of these technologies

The AI Research Center at Woxsen University in India has developed AI chatbots in the Metaverse for Management courses that help students grasp the material clearly and retain it for the rest of their lives. Students who utilized the chatbot to receive texts regarding assignments, academic support, and course content were more likely to receive a B grade or better. Georgia State University’s artificial intelligence-enhanced chatbot, named “Pounce,” has been shown to improve student performance in classes. Similar to this, at California State Polytechnic, Pomona, students are writing and participating better because of the usage of an AI-powered platform called Packback, which encourages critical thinking and deeper engagement with the course materials.

1. Challenges and limitations faced in implementing Generative and Quantum AI in education

Rather than merely creating technology-driven solutions, a major challenge is to match the development of AI tools and solutions with the changing requirements and complexity of the educational system. In addition to pointing out that technologists have historically found it difficult to create tools that properly meet the demands of educators and students, panelists at the MIT symposium emphasized the significance of comprehending the social and technical systems that comprise contemporary education. Furthermore, the search results indicate that in order to fully realize the potential of these cutting-edge technologies in the classroom, a fundamental rethinking of the educational model will be required, shifting away from traditional instructivist techniques and toward more constructionist, hands-on learning.

Future Implications and Possibilities

The future of learning is expected to be significantly impacted by the integration of Generative and Quantum AI in education as they develop further. The combination of these technologies creates new opportunities for tailored instruction, flexible learning environments, and data-driven understanding of students’ development. Furthermore, a paradigm shift in teaching approaches is predicted given the possibilities for complex problem-solving enabled by Quantum AI and immersive virtual environments powered by Generative AI. By adopting these innovations, educators may look forward to a time when education will be more dynamic, inclusive, and engaging, enabling students to succeed in a world that is getting more complicated and dynamic by the day.

Conclusion

The unparalleled opportunity to transform education is presented by the convergence of Quantum AI and Generative AI. Through the utilization of Generative AI for customized learning and content development, and Quantum AI for addressing intricate issues beyond standard computing, educational establishments have the opportunity to improve student learning results and challenge established ideas. The tangible advantages of these technologies are demonstrated by real-world examples, which range from enhanced student performance to personalized chat instructors. But issues like pedagogical changes and alignment with educational needs need to be addressed. Future learning experiences that are adaptable, immersive, and successful are promised by the integration of generative and quantum artificial intelligence (AI), equipping students for success in a world that is always changing.

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.

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Copyright: bcg.com – “Consumers Know More About AI Than Business Leaders Think”

– Overall, 75% of survey respondents have used ChatGPT or another AI-driven tool. In markets such as India, Brazil, and the United Arab Emirates, AI usage exceeds the levels in so-called mature markets.
– Consumers have a nuanced understanding of AI. Many are excited, but a significant subset see potential downsides if the technology is not “done right.”
– For lifestyle uses of GenAI, respondents expressed a mix of excitement (43%) and concern (29%). Excitement about GenAI in the workplace was higher at 70%, with 15% expressing concern.
– The misinformation-excitement-concern curve shows that, prior to using AI, people have more negative than positive feelings about the technology.
– Leaders should build trust by respecting consumers’ views, countering early misinformation, practicing responsible AI, and tapping into existing pockets of excitement.

It took Spotify some 150 days to garner a million users. Instagram, about 75 days. ChatGPT? Just five days.

Artificial intelligence isn’t new, but broad public interest in it is, particularly as generative AI (GenAI) tools have been released over the past year. ChatGPT, for example, has become a household name. And our recent survey of consumers found that people are more knowledgeable and excited about AI than you might think. (See “About Our Research.”) Don’t underestimate them. Do understand them, deeply.

The facets of AI that excite consumers—and the ones that concern them—as they use AI-driven tools to shop, find information, do their jobs, and more are valuable guides to developing and deploying AI-enabled solutions and transformations.

BCG’s Center for Customer Insight surveyed 21,000 consumers from 21 countries, across continents[…]

Read more: www.bcg.com

Der Beitrag Consumers Know More About AI Than Business Leaders Think erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Bloggers: Bidyut Sarkar, Senior Solution Manager, IBM USA and Rudrendu Kumar Paul – Boston University, Boston, USA – “Conversational AI on Manufacturing Floors With NLP-Enabled Assistants”

Takeaways:

• NLP-enabled assistants are transforming manufacturing by simplifying human-machine interactions.
• Industry giants like Toyota, Boeing, and Shell have witnessed enhanced efficiency and reduced errors through AI integration.
• The future of manufacturing envisions plant floors driven by data-rich, conversational interactions.

The advent of artificial intelligence in the manufacturing sector has brought a transformative era. As industries evolve, the integration of AI technologies becomes not just advantageous but essential. Natural language interfaces are a pivotal innovation among the myriad of advancements. These interfaces, rooted in human language and cognition principles, offer a seamless bridge between intricate machine operations and human understanding. In contemporary production settings, the ability to communicate with machines using everyday language can redefine operational efficiency. Such interfaces eliminate the barriers of complex coding languages, making data queries and command executions more intuitive. The shift towards these natural language interfaces underscores a broader movement in manufacturing: embracing AI not as a mere tool but as a collaborative partner. This partnership, built on the foundation of mutual understanding, promises to reshape the dynamics of production floors, making them more agile, responsive, and intelligent.

For Europe, the anticipated compound annual growth rate (CAGR) for the natural language processing industry from 2023 to 2030 is projected to be 15.19%, leading to an estimated market value of $17.41 billion by the end of the period. (Statista)

Source: Statista

At the same time, the value added by the manufacturing market in Europe is anticipated to reach $3.54 trillion in 2028, with an expected compound annual growth rate (CAGR) of 3.93% from 2023 to 2028. (Statista)

Source: Statista

The Role of Conversational Interfaces in Manufacturing

Conversational interfaces represent a paradigm shift in how humans interact with machines. At their core, these interfaces harness the nuances of human language, enabling a more intuitive communication pathway with technological systems. In the context of manufacturing, this innovation holds profound significance. Historically, interactions with machines required specialized knowledge, often demanding intricate command sequences or coding.

Conversational interfaces, on the other hand, simplify this interaction. They allow operators to engage with systems using natural language, making the process more accessible and less daunting. This shift democratizes access and accelerates response times as the need for translating thoughts into machine-specific commands diminishes.

Comparing conversational interfaces with their traditional counterparts reveals stark contrasts. Standard interfaces, often graphical or command-line-based, necessitate a learning curve and can limit responsiveness. Causal models break these barriers, offering a more fluid, adaptive, and user-centric approach. In essence, the evolution from traditional to conversational interfaces in manufacturing marks a transition from rigid, prescriptive systems to more flexible, understanding, and adaptive ones. This transition holds the potential to redefine the efficiency and adaptability of manufacturing processes. (Swiss Cognitive)

Technological Foundations

The underpinnings of conversational interfaces lie in two pivotal technological advancements: Natural Language Processing (NLP) and neural networks. NLP, a subfield of artificial intelligence, delves into the interaction between computers and human language. Its primary objective is to enable machines to understand, interpret, and generate human language meaningfully and contextually relevantly. This understanding forms the bedrock of any conversational interface, ensuring that interactions are syntactically correct and semantically coherent.

Neural networks, inspired by the structure and function of the human brain, play a complementary role. (IJAIM) These interconnected algorithms process information in layers, allowing for recognizing patterns and relationships in vast datasets. In NLP, neural networks facilitate the deep learning processes that drive language comprehension, sentiment analysis, and response generation.

When NLP and neural networks converge, the result is a conversational interface capable of understanding intricate language patterns, discerning context, and generating appropriate responses. Unlike traditional systems that rely on explicit programming for every possible interaction, these interfaces learn and adapt. They draw from vast linguistic datasets, refining their understanding with each interaction. This continuous learning, underpinned by the combined might of NLP and neural networks, empowers conversational interfaces to be dynamic, adaptive, and increasingly attuned to the nuances of human language. In the manufacturing sector, this translates to responsive and predictive interfaces, heralding a new age of intelligent interaction.

Leading Innovators in the Field

Several trailblazers have emerged in the dynamic landscape of conversational interfaces, each carving a distinct niche with innovative solutions. CoPilot.ai, Sigma, and Arria NLG have garnered significant attention for their pioneering contributions to manufacturing.

CoPilot.ai stands at the forefront of integrating artificial intelligence with human-centric design.

Their platform emphasizes intuitive interactions, ensuring operators can query and command production systems seamlessly. By prioritizing user experience, CoPilot.ai has managed to bridge the gap between sophisticated AI algorithms and the practical needs of manufacturing floors.

Sigma, on the other hand, has taken a data-driven approach. Their platform harnesses the power of big data analytics, combined with NLP, to offer insights and recommendations. This means real-time feedback, predictive maintenance alerts, and actionable insights that can significantly enhance operational efficiency in manufacturing. Sigma’s strength lies in transforming raw data into meaningful, actionable intelligence.

Arria NLG, focusing on the Natural Language Generation, brings a fresh perspective. Instead of merely understanding or interpreting human language, Arria NLG’s solutions excel in generating human-like text based on data. In manufacturing, this capability translates to detailed reports, summaries, and explanations generated on the fly, providing operators with a clear understanding of complex processes and data streams.

These innovators are redefining the boundaries of what’s possible in manufacturing. While varied in approach, their unique solutions share a common goal: to enhance the symbiotic relationship between humans and machines. By doing so, they are not only elevating the capabilities of individual operators but also setting the stage for a more collaborative and intelligent manufacturing future.

Source: Avnet

Real-world Applications and Case Studies

The theoretical promise of AI-powered conversational interfaces is compelling, but it’s in real-world applications where their transformative potential truly shines. Several industry giants have already begun harnessing these technologies, yielding tangible benefits.

Synonymous with automotive excellence, Toyota has integrated AI-powered assistants into its production lines. The primary objective was to combat the perennial challenge of downtime. By leveraging these advanced interfaces, Toyota’s operators can swiftly diagnose issues, receive instant feedback, and implement corrective measures. The result was a significant reduction in unproductive hours, ensuring that assembly lines run smoother and more efficiently.

Boeing, a behemoth in the aerospace sector, has turned to conversational interfaces to streamline its intricate manufacturing processes. Given the complexity of aircraft production, even minor inefficiencies can lead to substantial delays. Boeing’s adoption of these interfaces has enabled its engineers and technicians to access critical data, seek clarifications, and receive guidance without wading through cumbersome manuals or databases. The outcome has marked improved workflow efficiency and reduced production bottlenecks.

Shell, a global leader in the energy sector, faces the daunting task of managing vast and complex operations. The introduction of AI-guided processes has been a game-changer. These systems assist in monitoring equipment, predicting maintenance needs, and even guiding operators in crisis scenarios. The result is a more streamlined operation with a notable decrease in errors, leading to safer and more efficient energy production.

Beyond these industry leaders, several other enterprises have embraced the power of conversational AI. For instance, pharmaceutical companies use these interfaces for precision drug formulation, while textile manufacturers employ them for quality control. The common thread across these applications is straightforward: conversational interfaces, backed by robust AI, are ushering in a new era of enhanced productivity, reduced errors, and more intuitive human-machine collaboration.

Benefits of AI-Powered Production Assistants

Integrating AI-powered production assistants into manufacturing processes has ushered in a series of tangible benefits that are reshaping the industry landscape. One of the most pronounced advantages is the substantial reduction in downtime. By providing real-time diagnostics and predictive insights, these assistants enable swift identification and rectification of issues, ensuring that production lines remain operational and minimizing costly disruptions.

Furthermore, the precision and vigilance of AI assistants have led to a marked decrease in errors and mistakes. Unlike human operators, AI systems maintain consistent accuracy and may overlook anomalies or misinterpret data under pressure. Their ability to process vast amounts of data quickly and identify discrepancies means that potential issues are flagged and addressed before they escalate.

Lastly, the overarching impact of these advancements is the enhancement of overall efficiency and productivity. Production rates improve with streamlined workflows, instant access to data, and the elimination of common bottlenecks. Moreover, operators, freed from routine troubleshooting, can focus on more value-added tasks, driving innovation and quality.

In essence, adopting AI-powered assistants in manufacturing is not just about automating processes; it’s about elevating the entire production ecosystem to new heights of excellence.

The Future of Conversational Plant Floors

The journey through the intricacies of NLP-enabled assistants underscores their transformative potential in reshaping manufacturing dynamics. These advanced interfaces, bridging human intuition with machine precision, promise a future where communication barriers on production floors become relics of the past. As industries evolve, the vision is clear: plant floors will become hubs of data-driven conversations, where machines execute commands and offer insights, fostering a collaborative atmosphere. This synergy between human expertise and AI-driven insights is set to redefine manufacturing, heralding an era where conversational interactions drive innovation, efficiency, and unparalleled growth.

About the Authors:

Bidyut Sarkar, Fellow of the IET (UK) and author of books on AI is an expert in life sciences and industrial manufacturing industry solutions with applied AI/ML experience, having served as a keynote speaker and judge at startup competitions. His professional experience has taken him to various parts of the world, including the USA, Netherlands, Saudi Arabia, Brazil, Australia, and Switzerland.

Rudrendu Kumar Paul is an applied AI and machine learning expert and the author of multiple books on AI, with over a decade of experience in leading data science teams at Fortune 50 companies across industrial high-tech, automation, and e-commerce industries. Rudrendu holds an MBA, an MS in Data Science from Boston University (USA), and a bachelor’s degree in electrical engineering.

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