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

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

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

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

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

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

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

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

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

Der Beitrag Global AI Capital Moves at Full Speed – SwissCognitive AI Investment Radar erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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

A Note to the Reader

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

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

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

Introduction

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

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

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

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

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

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

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

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

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

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

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

Read more: www.patrickhebron.com

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

Copyright: forbes.com – “Emotional Intelligence is More Important Than Ever in the Age of AI”

While most of us accept that artificial intelligence isn’t going to take over the world just yet, there’s a growing recognition that businesses and their employees are going to have to adapt their skills pretty swiftly. According to the 2024 Global CEO Survey from consulting firm PwC, seven out of 10 CEOs believe that AI will significantly change the way their company creates, delivers and captures value over the next three years. On the plus side, 41% believe it will increase revenue. However, those in “AI-exposed” jobs (such as administration and customer service agents) have seen 27% lower job growth, and anticipate a 25% higher skills change rate than those who are not at risk.

In most cases, AI won’t replace entire jobs, but speed up or automate certain aspects of them, often freeing staff up to work on something more satisfying or of higher value. The emotionally intelligent, human side of work is something it is unlikely to be able to replicate, at least in the near future. AI’s power lies in being able to process vast amounts of data with speed and accuracy, but its limitations become apparent when it encounters the complexity of human behaviors. It’s also known for its fallibilities, sometimes producing false responses to prompts or biased outcomes because of the data it’s working on or the way it has been programmed.

I define emotional intelligence as self-awareness, which is a critical skill in this increasingly AI-driven world. Whatever level someone is working at, it’s important that they know how to read the room and adapt how they work with a colleague or client.[…]

Read more: www.forbes.com

Der Beitrag Emotional Intelligence is More Important Than Ever in the Age of AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “AI for Disabilities: Quick Overview, Challenges, and the Road Ahead”

AI has enormous power in improving accessibility and inclusivity for people with disabilities. This power lies in the potential of this technology to bridge gaps that traditional solutions could not address. As we have demonstrated in the series of articles devoted to AI for disabilities, AI-powered products can really change a lot for people with various impairments. Such solutions can allow users to live more independently and get access to things and activities that used to be unavailable to them before. Meanwhile, the integration of AI into public infrastructure, education, and employment holds the promise of creating a more equitable society. These are the reasons that can show us the importance of projects building solutions of this type.

Yes, these projects exist today. And some of them have already made significant progress in achieving their goals. Nevertheless, there are important issues that should be addressed in order to make such projects and their solutions more efficient and let them bring real value to their target audiences. One of them is related to the fact that such solutions are often built by tech experts who have practically no understanding of the actual needs of people with disabilities.

According to the survey conducted in 2023, only 7% of assistive technology users believe that their community is adequately represented in the development of AI products. At the same time, 87% of respondents who are end users of such solutions express their readiness to share their feedback with developers. These are quite important figures to bear in mind for everyone who is engaged in the creation of AI-powered products for disabilities.

In this article, we’d like to talk about the types of products that already exist today, as well as potential barriers and trends in the development of this industry.

Different types of AI solutions for disabilities

In the series of articles devoted to AI for disabilities, we have touched on types of products for people with different states, including visual, hearing, mobility impairments, and mental diseases. Now, let us group these solutions by their purpose.

Communication tools

AI can significantly enhance the communication process for people with speech and hearing impairments.

Speech-to-text and text-to-speech apps enable individuals to communicate by converting spoken words into text or vice versa.

Sign language interpreters powered by AI can translate gestures into spoken or written language. It means that real-time translation from sign to verbal languages can facilitate communication, bridging the gap between people with disabilities and the rest of society.

Moreover, it’s worth mentioning AI-powered hearing aids with noise cancellation. They can improve clarity by filtering out background sounds, enhancing the hearing experience in noisy environments.

Advanced hearing aids may also have sound amplification functionality. If somebody is speaking too quietly, such AI-powered devices can amplify the sound in real time.

Mobility and navigation

AI-driven prosthetics and exoskeletons can enable individuals with mobility impairments to regain movement. Sensors and AI algorithms can adapt to users’ physical needs in real time for more natural, efficient motion. For example, when a person is going to climb the stairs, AI will “know” it and adjust the movement of prosthetics to this activity.

Autonomous wheelchairs often use AI for navigation. They can detect obstacles and take preventive measures. This way users will be able to navigate more independently and safely.

The question of navigation is a pressing one not only with people with limited mobility but also for individuals with visual impairments. AI-powered wearable devices for these users rely on real-time environmental scanning to provide navigation assistance through audio or vibration signals.

Education and workplace accessibility

Some decades ago people with disabilities were fully isolated from society. They didn’t have the possibility to learn together with others, while the range of jobs that could be performed by them was too limited. Let’s be honest, in some regions, the situation is still the same. However, these days we can observe significant progress in this sphere in many countries, which is a very positive trend.

Among the main changes that have made education available to everyone, we should mention the introduction of distance learning and the development of adaptive platforms.

A lot of platforms for remote learning are equipped with real-time captioning and AI virtual assistants. It means that students with disabilities have equal access to online education.

Adaptive learning platforms rely on AI to customize educational experiences to the individual needs of every learner. For students with disabilities, such platforms can offer features like text-to-speech, visual aids, or additional explanations and tasks for memorizing.

In the workplace, AI tools also support inclusion by offering accessibility features. Speech recognition, task automation, and personalized work environments empower employees with disabilities to perform their job responsibilities together with all other co-workers.

Thanks to AI and advanced tools for remote work, the labor market is gradually becoming more accessible for everyone.

Home automation and daily assistance

Independent living is one of the main goals for people with disabilities. And AI can help them reach it.

Smart home technologies with voice or gesture control allow users with physical disabilities to interact with lights, appliances, or thermostats. Systems like Alexa, Google Assistant, and Siri can be integrated with smart devices to enable hands-free operation.

Another type of AI-driven solutions that can be helpful for daily tasks is personal care robots. They can assist with fetching items, preparing meals, or monitoring health metrics. As a rule, they are equipped with sensors and machine learning. This allows them to adapt to individual routines and needs and offer personalized support to their users.

Existing barriers

It would be wrong to say that the development of AI for disabilities is a fully flawless process. As well as any innovation, this technology faces some challenges and barriers that may prevent its implementation and wide adoption. These difficulties are significant but not insurmountable. And with the right multifaceted approach, they can be efficiently addressed.

Lack of universal design principles

One major challenge is the absence of universal design principles in the development of AI tools. Many solutions are built with a narrow scope. As a result, they fail to account for the diverse needs that people with disabilities may have.

For example, tools designed for users with visual impairments may not consider compatibility with existing assistive technologies like screen readers, or they may lack support for colorblind users.

One of the best ways to eliminate this barrier is to engage end users in the design process. Their opinion and real-life experiences are invaluable for such projects.

Limited training datasets for specific AI models

High-quality, comprehensive databases are the cornerstone for efficient AI models. It’s senseless to use fragmented and irrelevant data and hope that your AI system will demonstrate excellent results (“Garbage in, Garbage out” principle in action). AI models require robust datasets to function as they are supposed to.

However, datasets for specific needs, like regional sign language dialects, rare disabilities, or multi-disability use cases are either limited or nonexistent. This results in AI solutions that are less effective or even unusable for significant groups of the disability community.

Is it possible to address this challenge? Certainly! However, it will require time and resources to collect and prepare such data for model training.

High cost of AI projects and limited funding

The development and implementation of AI solutions are usually pretty costly initiatives. Without external support from governments, corporate and individual investors, many projects can’t survive.

This issue is particularly significant for those projects that target niche or less commercially viable applications. This financial barrier discourages innovation and limits the scalability of existing solutions.

Lack of awareness and resistance to adopt new tools

A great number of potential users are either unaware of the capabilities of AI or hesitant to adopt new tools. Due to the lack of relevant information, people have a lot of concerns about the complexity, privacy, or usability of assistant technologies. Some tools may stay just underrated or misunderstood.

Adequate outreach and training programs can help to solve such problems and motivate potential users to learn more about tools that can change their lives for the better.

Regulatory and ethical gaps

The AI industry is one of the youngest and least regulated in the world. The regulatory framework for ensuring accessibility in AI solutions remains underdeveloped. Some aspects of using and implementing AI stay unclear and it is too early to speak about any widely accepted standards that can guide these processes.

Due to any precise guidelines, developers may overlook critical accessibility features. Ethical concerns, such as data privacy and bias in AI models also complicate the adoption and trustworthiness of these technologies.

Such issues slow down the development processes now. But they seem to be just a matter of time.

Future prospects of AI for disabilities: In which direction is the industry heading?

Though the AI for disabilities industry has already made significant progress in its development, there is still a long way ahead. It’s impossible to make any accurate predictions about its future look. However, we can make assumptions based on its current state and needs.

Advances in AI

It is quite logical to expect that the development of AI technologies and tools will continue, which will allow us to leverage new capabilities and features of new solutions. The progress in natural language processing (NLP) and multimodal systems will improve the accessibility of various tools for people with disabilities.

Such systems will better understand human language and respond to diverse inputs like text, voice, and images.

Enhanced real-time adaptability will also enable AI to tailor its responses based on current user behavior and needs. This will ensure more fluid and responsive interactions, which will enhance user experience and autonomy in daily activities for people with disabilities.

Partnerships

Partnerships between tech companies, healthcare providers, authorities, and the disability community are essential for creating AI solutions that meet the real needs of individuals with disabilities. These collaborations will allow for the sharing of expertise and resources that help to create more effective technologies.

By working together, they will ensure that AI tools are not only innovative but also practical and accessible. We can expect that the focus will be on real-world impact and user-centric design.

New solutions

It’s highly likely that in the future the market will see a lot of new solutions that now may seem to be too unrealistic. Nevertheless, even the boldest ideas can come to life with the right technologies.

One of the most promising use cases for AI is its application in neurotechnology for seamless human-computer interaction.

A brain-computer interface (BCI) can enable direct communication between the human brain and external devices by interpreting neural signals related to unspoken speech. It can successfully decode brain activity and convert it into commands for controlling software or hardware.

Such BCIs have a huge potential to assist individuals with speech impairments and paralyzed people.

Wrapping up

As you can see, AI is not only about business efficiency or productivity. It can be also about helping people with different needs to live better lives and change their realities.

Of course, the development and implementation of AI solutions for disabilities are associated with a row of challenges that can be addressed only through close cooperation between tech companies, governments, medical institutions, and potential end users.

Nevertheless, all efforts are likely to pay off.

By overcoming existing barriers and embracing innovation, AI can pave the way for a more accessible and equitable future for all. And those entities and market players who can contribute to the common success in this sphere should definitely do this.

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Der Beitrag AI for Disabilities: Quick Overview, Challenges, and the Road Ahead erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: fortune.com – “Demis Hassabis-James Manyika: AI Will Help Us Understand the Very Fabric of Reality”

If you want to understand the universe, you can start by reading the greats: Feynman, Weinberg, Curie, Hofstadter, Kant, Spinoza, Turing, and all the brilliant scientists and philosophers who advanced the frontiers of human knowledge and on whose shoulders modern civilization stands.

Der Beitrag AI Will Help Us Understand the Very Fabric of Reality erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Advait Avinash Sowale – “Quality Dimensions of Generative AI Applications”

Generative AI the buzz word of today. Everyone is talking about it and using it for different purposes. The advantage of generative AI is not hidden today. Various sectors of society are using Gen AI in different areas like content creation, image designing, audio video creation and many others. People are authoring books using Gen AI.

Being an IT professional, we are usually never amazed with the results provided by Gen AI but look forward to making it better and better. Same as human, Gen AI tries to improve its last performance. We just help it to be there.

There are four distinct types of machine learnings. Supervised, Unsupervised, Semi-Supervised Learning and Reinforcement Learning. Till now we were surfing in the world of Supervised, Unsupervised, Semi-Supervised Learning by using these learning for the development of some smart applications, products and systems but the beauty of Gen AI is, it’s a first sphere towards the universe of Reinforcement Learning.

In many geographic the use of Gen AI is increased heavily. Various domains like Pharma, Education, Retail, Entertainment and many others are getting the solutions from Gen AI.

When we use traditional software, we do rigorous testing of the software. The software needs to pass extreme tests for functionality, performance, security, usability and for many other aspects.

Any AI enabled application minimizes human intervention. It is working on its own and hence it should work seamlessly. When we use any AI enabled application or systems, we minimize our dependency towards the task for which we have deployed the AI and therefore it is important to address the quality of that system.

For all the AI enabled applications rigorous testing is needed and Gen AI is not an exception to it.

Like in traditional testing, Gen AI testing includes Unit Testing, Integration Testing, System Testing, Functional Testing , Non-Functional Testing includes performance, usability and security.

The major difference between traditional and AI application testing with the dimensions of the quality parameters

The dimensions of the Gen AI testing majorly consist of Accuracy, Robustness, Ethics and Compliance. Extreme testing on these dimensions helps in making the Gen AI application a strong Gen AI application.

It is mandatory for the Gen AI to be a quality product because we are now transforming from the era of Weak AI to Strong AI.

When we talk about the quality of AI OR Gen AI application another aspect is the EAST.

So, what is EAST?

EAST stands for Explainability, Accountability, Security and Transparency.

These four aspects are utmost important when we are talking about comprehensive AI or specific to any AI like Gen AI.

The only context would be different. As Gen AI is providing the results for large numbers of types and pattern of data then it is mandatory to check for the explainability as how the outcome has been generated. With which process it is understanding the input, analyze the data and after processing, it is provided the output.

Accountability is another important aspect as there should be some responsible body, authority OR resource behind every output provided by Gen AI model. This can be achieved by maintaining and analyzing the entire process logs. Tracking of how the process is following defined ethical guidelines also helps to manage accountability.

Security has a vast spectrum for the Gen AI. Input, Content, analyzed data, output and learning and other miscellaneous all these factors are coming under security and for that it is needed to define security KPIs at various levels. Examples are Data, Authentication, Authorization, Incident Response, Vulnerability Management, User Behavior, Monitoring and many others. Data protection, model integrity and user privacy are some of the key factors which need to be addressed on the security front.

Finally, Transparency.

The output and the process of output should be transparent. Here the major important part is algorithm transparency. It leads to build the model confidence as transparency in algorithms is useful to understand how the algorithm works on different datasets. Model designing, Decision making process and overall process communication are among other factors which should consider to maintain the Transparency.

Now all these aspects are working to bring the quality of Gen AI model and add to that it is associated with the important factor of ethics. The Gen AI model should be ethically strong. Its output should not show any layer of bias and fairness. The points we have touched above must be considered at the extreme level with their KPIs and methodologies for the testing purpose.

Thus, the contribution of all these approaches and methodologies help to make a quality Gen AI.

About the Author:

Advait Avinash Sowale A Pune-based IT Professional with a decade of diverse expertise. Advait boasts an extensive career spanning over 14+ years, encompassing various domains such as Analysis, Designing, Development, Quality, and Delivery within the IT industry. Throughout his journey, he has contributed his skills and knowledge to renowned IT giants, catering to a global clientele.

Der Beitrag Quality Dimensions of Generative AI Applications 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.

Copyright: forbes.com – “How AI Is Pushing Leaders To Be Better”

There seems to be two areas of thought when it comes to AI. Some people think the technology is going to take their job by replacing them. Others view AI as a tool to enhance their work by automating repetitive tasks. While I typically fall into the second school of thought, I think there’s another perspective worth exploring: how AI is driving humans to improve themselves.

While we give AI prompts and train it to get better, it’s simultaneously improving our ability to give instructions and strengthening our critical thinking skills. But that’s not all. Because AI is a tool that allows people to focus on more creative, strategic, and high-value activities, it’s time to flex those muscles and strengthen them. This is especially true for leaders, who rely on their interpersonal skills and emotional intelligence to navigate the challenges that come from managing other people.

Here are a few ways I think AI is pushing leaders to be the best version of themselves:

1. Enhancing Emotional Intelligence

As you’ve likely already heard, AI is reshaping industries by automating routine tasks. This lets humans focus on more complex parts of their jobs, and this shift means many professionals need to upskill and reskill to stay relevant. When it comes to leadership, one of the most important skills you can develop is emotional intelligence, since this is an area where AI falls short.

Unsurprisingly, EQ is the number one leadership skill for 2024. According to the Word Economic Forum’s Future of Jobs report, qualities associated with EQ are “highly prized” by businesses and, no surprise, they will continue to be for the next few years. (I’d bet indefinitely.) Resilience, curiosity, and self-awareness will always be important.[…]

Read more: www.forbes.com

Der Beitrag How AI Is Pushing Leaders To Be Better erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “Security Of AI Products: How To Address The Existing Risks”

AI-related risks and threats

Once appeared Generative AI tools had a great “Wow” effect for a wide audience. However, upon closer inspection, users started noticing that the quality of content produced by LLMs was not always as high as they expected. The content is often biased and stereotyped and here we do not even mention that it can have a lot of factual mistakes. And while in some cases, it is just an accidental misunderstanding, in others, it can be a well-planned operation.

Let’s have a closer look at the potential threats related to the use of AI tools.

• Data training. What we get from AI is a reflection of what we have fed AI models with. The “Garbage in, garbage out” principle works perfectly well in this situation. If the team behind a particular LLM hasn’t trained this model on the data that should be used for the set task, AI won’t be able to provide an adequate response.

Also, there can be cases of so-called data poisoning attacks. They presuppose tampering with the data that an AI model is trained on in order to produce some specific outcomes that can be desired by bad players (for example, biased or incorrect info about some people or events).

• Prompt injections. This type of attack is quite similar to those related to the manipulations with data. But in this case, attackers work with prompts. They create inputs that can make AI models behave in an unintended way. As a result, they can push AI to reveal some sensitive data or produce potentially dangerous or offensive content.
• Insecure data storage. This point is relevant not only to AI tools but in general to the data that we work with today. The problem is that quite often the storage, as well as the ways of data processing, are not secure enough. Due to the lack of such basic things as encryption and proper access controls, sensitive data can become easy prey for hackers.
• AI misuse. Generative AI can be a very dangerous tool in the hands of malicious actors. Misleading information and deepfakes created with the help of AI can look rather realistic which greatly contributes to the spread of disinformation. Already today there are a lot of cases of using AI to discredit political opponents or undermine one’s reputation.

But one of the most alarming issues here is that the world still doesn’t have reliable AI regulations. There are no strict rules that would be used to bring to justice for the misuse of AI and infringement of intellectual property rights.

One of the latest controversial cases (but not the only one) was the situation with Adobe Stock. The stock photo service was selling AI-generated images “inspired” by the works of the renowned American landscape photographer and environmentalist Ansel Adams. According to the Ansel Adams estate, it was already not the first case when references to Adams’s work appeared in AI-generated listings.

By the time of writing this article, the mentioned AI-generated Ansel Adams-style images had already been deleted from Adobe Stock. However, in a global context, such solutions can’t directly address the problem itself.

How can we deal with the existing security issues?

It’s a well-known fact that AI/ML tools are good at detecting patterns and further defining deviations from these patterns that can be signs of suspicious behaviors, fraudulent activities, possible data leaks, etc.

That’s why such technologies are widely applied for anomaly detection, behavioral analytics, as well as real-time monitoring in various types of solutions. Nevertheless, these tools can cope only with point tasks. As a result, 100% protection is, unfortunately, not guaranteed.

It is also impossible to create a tool that will fully protect people from potential risks associated with the use of AI. For example, we can only make it more difficult for users to get undesirable or potentially harmful information during their interactions with an LLM. But we can’t fully eliminate the risks of accessing such info.

AI hallucinations: Can we fully rely on what AI tells us?

Have you ever noticed that during your communication with ChatGPT (or any other GenAI tool), it offered you something absolutely crazy that had nothing in common with reality? For example, it could mention something that didn’t exist or something that was irrelevant. This notion is known as AI hallucinations.

It happens so because AI itself doesn’t understand what it needs to tell you. It lacks reasoning because it is trained only to predict the next word/character that could be valuable in this or that case.

But sometimes AI can go the wrong way. It can occur because of such factors as:

• Low-quality, insufficient, or obsolete data;
• Unclear prompts (especially when you use slang expressions or idioms);
• Adversarial attacks (these are prompts that are designed to purposely confuse AI).

Unfortunately, AI hallucinations are a significant ethical concern. First of all, they can seriously mislead people. Let’s imagine a very simple situation where a student uses ChatGPT for learning and needs to understand a new topic. What if AI starts hallucinating?

Secondly,  it can cause reputational harm to some companies or individuals. There are already known cases when AI “accused” known people of bribery or harassment while in reality, they had nothing in common with those cases.

Moreover, AI hallucinations can represent safety risks, especially when it comes to such sensitive areas as security or healthcare. Today chatbots that can analyze your symptoms and guess what health problems you have are gaining popularity. Nevertheless, with the risk of incorrect diagnoses, they can bring more harm than benefits. With erroneously chosen operational commands in case of dangerous situations posing risks to health and life, the consequences are unpredictable.

Is it possible to address such issues? Yes, but not with 100% accuracy.

Here are the most important factors that can help to minimize the risks of AI hallucinations:

• High-quality training data;
• The use of data templates;
• Datasets restrictions;
• Specific prompts;
• Human fact-checking.

Actually, verification of what AI has offered you is one of the fundamental things to do, especially when you need to publish the generated content for a wide audience or if AI is applied in sensitive areas.

AI vs Humanity: Will we lose this battle?

While talking about the threats that are associated with the use of AI, it’s impossible not to mention one of the most serious concerns voiced by some people.

Will AI really take the world one day? Maybe governments and businesses should stop investing in this technology right now in order to avoid disastrous consequences in the future? Quick answers to both of these questions should be “No”. AI doesn’t want to rule the world (in reality, it can’t “want” at all). And the investments in its development definitely have more pros than cons.

There are three types of AI:

• Narrow AI with a very limited range of abilities;
• General AI which is on par with human capabilities;
• Super AI which can surpass human intelligence.

LLMs, together with, for example, well-known image recognition systems or predictive maintenance models, are included in the first group of narrow AI solutions. And even this category is not explored well enough. And even the results of the work of narrow AI tools (not to mention general and super AI) are still not perfect. Today engineers know how neurons work and how to reproduce this process. Nevertheless, other processes and capacities of the intelligence are yet to be studied.

We should not humanize AI. AI itself can’t think and can’t make decisions. Unlike a human, it has no intentions. To let AI tools have intentions, first of all, it is still necessary to understand what it is, and why and how it appears.

AI can be compared with a Genie in a bottle or oil lamp that can’t survive without it. And AI can’t survive independently. Any LLM is sitting absolutely quietly till the moment you ask it to offer your ideas for lunch or a structure for your next article. AI tools can fulfill the set tasks that they were trained for. And nothing more than that.

Moreover, an AI solution is just a solution. Decisions are made by a human. And at the moment, we do not have any reliable predictions for the timelines when (and whether at all) AI will be able to make decisions on its own.  It’s worth mentioning that we can’t talk about 100% automation of all the processes which is also a serious barrier for independent AI functioning.

AI can generate thousands of ideas or write poems. But it is not able to create absolutely new genres of music or art, as a human can.

In other words, people should leave all their fears aside. AI is not going to rebel against our supremacy.

AI: Good or bad?

With all the AI-related concerns that we have discussed in this article, is it still worth relying on this technology? Definitely yes, regardless of all the issues (but with diligent attention and caution, of course).

In the previously published articles, we talked a lot about the value of AI for education and for expanding possibilities for people with disabilities. And it is just a small part of its use cases. AI has an enormous power to transform a lot of spheres and processes around us. But it can make our lives much better only with the right approach to its development and application.

About the Author:

In his current position, Artem Pochechuev leads a team of talented engineers. Oversees the development and implementation of data-driven solutions for Sigli’s customers. He is passionate about using the latest technologies and techniques in data science to deliver innovative solutions that drive business value. Outside of work, Artem enjoys cooking, ice-skating, playing piano, and spending time with his family.

Der Beitrag Security Of AI Products: How To Address The Existing Risks erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: intelligencebriefing.substack.com – “The Next Wave In Generative AI Deployment: AI Agents”

The last few weeks have brought several innovations in foundation models, including announcements from OpenAI, Google, and Anthropic. What most of the coverage has been missing: the bigger picture. Yes, these models are another leap forward, especially those that are multi-modal such as OpenAI GPT-4o and Google Gemini. But it’s not about building better chatbots.

It’s rather the answer to “What’s next in Generative AI?” After the initial scenarios, such as generating, summarizing, and translating text (and other types of media), are implemented, the next level of capabilities is just around the corner. And along comes the next level of productivity gains.

It’s just that this time, it won’t be automating clicks (Robotic Process Automation), individual approval steps in a process (Machine Learning), or language tasks (large language models). This next phase is all about using agents to automate problems with limited uncertainty and complexity. So, let’s jump in…

Six Common Types of AI Agents

Agents are software components that can make decisions under uncertainty based on defined objectives and interact with their environment. Agents have existed for decades.

For example, the thermostat in your house is an agent. A sensor measures the current room temperature and if that temperature is outside of a defined threshold during the next measurement (e.g. colder than what you have set it to), the thermostat fires up your heating until the target temperature is reached.

But Generative AI adds unique, new abilities to agents: they use Generative AI models to understand an abstract goal, divide it into subgoals, evaluate possible options for achieving these subgoals, and execute the steps necessary to do so.

Agents are built into applications and available as stand-alone extensible frameworks.[…]

Read more: www.intelligencebriefing.substack.com

Der Beitrag The Next Wave In Generative AI Deployment: AI Agents erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.