SwissCognitive Guest Blogger: Dileep Kumar Pandiya – “AI for Transformative Enterprise Growth: Insights from a Principal Engineer”

You know, it’s amazing to think about. Imagine your sales team closing deals twice as fast. Or your supply chain just adapting on the spot when the market shifts. Honestly, it’s not something from the future—it’s happening now, all thanks to AI.

I have been working in tech for almost 18 years, and I’ve seen how these tools turn ambitious ideas into actual results. I want to show you what that looks like in real life—where AI didn’t just help businesses grow, it completely changed the game.

How AI Unlocks Growth in Enterprises

What if your business could predict customer needs before they even knew them? AI makes this possible. It’s no longer about guesswork or reacting late; it’s about proactive strategies powered by data.
Take a retail chain struggling with overstock issues. By implementing AI to forecast demand using real-time trends, they reduced inventory waste by 20% and increased availability of high-demand items by 15%. It’s a transformation that goes beyond efficiency—it’s about building smarter, more agile businesses.

AI Copilot: Redefining Sales with AI

Sales has always been about timing and relationships. But what if AI could help you focus on the right opportunities at exactly the right moment? That’s the promise of AI Copilot.
When we launched Copilot, the goal was simple: empower sales teams to act smarter and faster. By integrating AI, I built a platform that could analyze millions of data points in seconds to identify high-potential accounts. The result: Sales teams were no longer overwhelmed by data they were driven by insights.
Here’s what stood out most to me: within three months, Copilot wasn’t just saving time—it was generating millions in additional revenue. Seeing the tangible impact on businesses and hearing feedback like “I can’t imagine working without this” made every late night worth it.

Scaling Smarter with AI and Microservices

Think of a system that can process thousands of real-time events every second, with no downtime. That’s what we built with the Phoenix Project, a scalable platform that uses AI and microservices to empower B2B clients.
One client used this platform to optimize marketing campaigns dynamically. Instead of waiting weeks for data analysis, they could adjust strategies on the fly, improving lead quality by 30% and cutting acquisition costs dramatically. It’s proof that scalability isn’t just a technical goal—it’s a business imperative.

Lessons for Enterprises Ready to Embrace AI

Here’s a story I often share: A small business hesitant to invest in AI started with a single pilot project—automating customer inquiries with AI chatbots. Within six months, they expanded the system to handle order tracking, inventory checks, and even personalized product recommendations. Today, they credit AI for a 25% increase in customer retention.
My takeaway is to start small, but think big. AI’s value compounds over time, so even small steps can lead to significant transformations.

Future Trends in AI and Enterprise Growth

The future isn’t just about doing things faster—it’s about doing them smarter. Imagine systems that can explain their decisions clearly or tools that work alongside humans to tackle complex problems.
One trend I’m particularly excited about is real-time decision-making. For example, picture a global logistics company rerouting shipments during a storm, avoiding delays and cutting costs. This kind of agility is becoming the new standard, and businesses that embrace it early will set themselves apart.

Final Thoughts

AI is the foundation for building the future of business. Whether it’s transforming sales strategies, driving efficiency, or enabling agility, the opportunities are immense. My advice: Don’t wait for the perfect moment to start. Take a step, learn, and grow with AI.

About the Author:

Dileep Kumar Pandiya is a globally recognized Principal Engineer with over 18 years of groundbreaking work in AI and enterprise technology. He has pioneered transformative AI-driven platforms and scalable systems, driving innovation for Fortune 500 companies like ZoomInfo, Walmart, and IBM. His leadership has redefined sales technology and digital transformation, earning him prestigious awards and international acclaim for his contributions to business growth and industry advancement. Known for his ability to blend visionary thinking with practical solutions, Dileep continues to shape the future of enterprise technology.

Der Beitrag AI for Transformative Enterprise Growth: Insights from a Principal Engineer 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: thetransmitter.org – “Solving Intelligence Requires New Research and Funding Models”

Our research ecosystem isn’t built to deliver the breakthroughs needed to understand intelligence at scale. We need a dedicated research institution to take up the task.

We stand at the threshold of a new scientific revolution. The convergence of neuroscience, artificial intelligence and computing has created an unprecedented opportunity to understand intelligence itself. Just as deep-learning architectures inspired by neural circuits have revolutionized AI, insights from machine learning are now transforming our understanding of the brain. This virtuous cycle between biological and artificial intelligence is poised to drive rapid progress in both fields—but only if we can coordinate research at sufficient scale.

Neuroscience has never been better positioned to make transformative discoveries about how intelligence emerges from neural circuits. But our intellectual and financial resources remain fragmented. To truly harness them, we need a new research model that can drive systematic breakthroughs. If we continue to rely on traditional research models that weren’t designed for the scale and complexity of intelligence science, we risk squandering this historic opportunity.

The recent mapping of an entire adult fruit fly brain—a watershed achievement that made headlines worldwide—offers a glimpse of what’s possible. But this breakthrough almost didn’t happen. It required the serendipitous alignment of support from three non-traditional funders: Scientists at the Howard Hughes Medical Institute’s Janelia Research Campus imaged the complete fly brain; the Intelligence Advanced Research Projects Activity drove the development of tools for scalable neural-circuit mapping through its MICrONS program; and the National Institutes of Health BRAIN Initiative provided sustained support for data analysis[…]

Read more: www.thetransmitter.org

Der Beitrag Solving Intelligence Requires New Research and Funding Models erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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

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

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

Copyright: cio.com – “Agentic AI: 6 promising use cases for business”

AI agents will play a vital role in software programming and cybersecurity, but they will also change enterprise workflows and business intelligence, experts say.

Agentic AI is having a moment, as proponents see the benefits of using autonomous AI agents to automate manual tasks across organizations.

Agentic AI, which Forrester named a top emerging technology for 2025 in June, takes generative AI a step further by emphasizing operational decision-making rather than content generation. The promise the approach has for impacting business workflows has organizations such as Aflac, Atlantic Health System, Legendary Entertainment, and NASA’s Jet Propulsion Laboratory already pursuing the technology.

CRM leader Salesforce has since centered its strategy around agentic AI, with the announcement of Agentforce. IT service management giant ServiceNow has also added AI agents to its Now Platform. Microsoft and others are also joining the fray.

With AI agents popping up in so many situations and platforms, organizations interested in the technology may find it difficult to know where to start. A handful of use cases have so far risen to the top, according to AI experts.

Agentic AI will integrate smoothly with ERP, CRM, and business intelligence systems to automate workflows, manage data analysis, and generate valuable reports, says Rodrigo Madanes, global innovation AI officer at EY, a consulting and tax services provider. AI agents, unlike some past automation technologies, can make decisions in real-time, making process automation a primary use case.

“AI agents can automate repetitive tasks that previously required human intervention, such as customer service, supply chain management, and IT operations,” Madanes says. “What sets the technology apart is its ability to adapt to changing conditions and handle unexpected inputs without manual oversight.”[…]

Read more: www.cio.com

Der Beitrag Agentic AI: 6 Promising Use Cases for Business erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: forbes.com – “Integrating Artificial Intelligence (AI) Into Your Workforce”

Artificial intelligence (AI), like any disruptive technology, tends to evoke mixed reactions. While some employees embrace the change, others may resist it. Preparing your workforce for this shift is an important yet challenging process that typically involves developing new skills, shifting mindsets and aligning daily tasks with complimentary AI. Let’s take a moment to focus on the “people” element of the People, Process, and Technology Framework and what to consider when encouraging your workforce to embrace AI tools.

The Growth Of AI

Having worked in machine learning since the late 2000s, my initial impressions of AI tools like ChatGPT were lukewarm. Early iterations seemed more like novelty acts than transformative tools. However, I’ve kept track of their rapid progress, and the pace of improvement has been remarkable. AI’s development now presents opportunities that are hard to ignore and even harder to predict, requiring constant adaptation.

Today, AI is automating tasks from data entry to sophisticated data analysis, freeing employees to focus on more meaningful work. Research shows that AI can increase employee productivity by an average of 66%. However, this rapid transformation also stirs anxiety, with employees worrying their jobs may become obsolete.

So where do you start? How can you get the most out of your organization’s AI efforts?

1. Conduct a skills audit.

Preparing your workforce for AI starts with a thorough understanding of the organization’s current capabilities. Evaluate employee preparedness by determining the readiness of each team member for being impacted by AI, including their technical capabilities and essential soft skills such as adaptability, communication and creativity. This will provide insight into the overall training needs for various roles and responsibilities.[…]

Read more: www.forbes.com

Der Beitrag Integrating Artificial Intelligence (AI) Into Your Workforce 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.

Credit: This article with Dalith Steiger-Gablinger has been published in German as “ZHAW-Veranstaltung: Wie KI die Rolle der Bank-Mitarbeitenden verändert” – “How AI Is Changing The Role Of Bank Employees – ZHAW”

The next Finance Circle will take place on 16 September 2024 under the title “Banking Skills in the Age of AI”; organized by the Zurich University of Applied Sciences (ZHAW) and in collaboration with the Zurich Bankers Association (ZBV). finews.ch is a media partner of the ZBV.

Beforehand, artificial intelligence (AI) expert Dalith Steiger-Gablinger addresses the topic in a guest article and talks about the potential changes in banking and what skills bank employees will need in the future to remain successful.

AI takes over data-intensive tasks – but not everything

Everything that is connected to data processing and preparation will be taken over by AI in the near future. AI can provide enormous support, especially in the area of portfolio management and customer advice.

The role of emotional intelligence

Artificial intelligence gives us more time to invest in interpersonal relationships, both with clients and within teams. In a world where technology is becoming increasingly dominant, skills such as empathy and emotional intelligence are more in demand than ever. Accordingly, socially critical and philosophical questions are becoming increasingly central.

Collaboration between humans and machines can only be successful if humans build the emotional bridge between the data analysis provided by AI and the needs of the customer. It’s not just about the data provided by AI, but also about how we can interpret this information in human terms and communicate it to customers.

Key skills in dealing with AI

It is a misconception that AI makes us think less. On the contrary: when dealing with AI, you have to think carefully about the goal you are pursuing and ask the AI the right questions. The result depends heavily on how precisely we formulate the task.

Dealing with ChatGPT is comparable to communication between a boss and a secretary: In the past, bosses had to communicate very clearly what they wanted to say in a letter. If the instructions were unclear, the letter was not what they had in mind. The situation is similar with ChatGPT: the more precise and well thought-out the input, the better the result.

Technological understanding required

Although technical knowledge is not the main focus when dealing with AI, it is still important that bank employees understand the “power of the technology”. It’s similar to a smartphone. You don’t need to know how it works on the inside, but you should understand the possibilities it offers.

Employees don’t need to know the technical details of an AI application, but rather recognize its potential and be able to correctly assess when and how they can use it.

Further training and gut feeling as decisive factors

In the past, stenography and typewriting skills were basic requirements. Today and in the future, it will be essential to master the use of AI applications. Bank employees who find it difficult to use these technologies will find it harder to hold their own in the industry in the future.

Another key point is gut feeling. Even if AI delivers a result that seems logical, we still have to trust our gut feeling. If we sense that an AI result doesn’t suit the customer, even though the numbers are right, we need to listen to that intuition. Humans have the unique ability to evaluate situations in context and this ability remains essential.

Ultimately, it is not about using technology at all costs, but about where it supports us in a meaningful way and where it does not. Just because something is technically possible does not mean that we should do it. Humans must always remain in control and define the framework conditions for how AI can be used in different areas – from medicine to banking.

Conclusion: Humans remain crucial

The development of AI is progressing relentlessly, but humans remain indispensable in many areas. Emotional intelligence, critical thinking and the correct assessment of technologies are examples of the crucial skills needed to survive in the job market of the future.

Register for ZHAW-s free event today and meet Dalith Steiger-Gablinger, and the fellow esteemed participants:

– Dr. Michel Neuhaus, Head AI & Analytics, UBS Switzerland
– Dr. David Schlumpf, Head Learning & Leadership Development, JB Academy, Julius Bär
– Matthias Läubli, Vorsitzender der Bankleitung Raiffeisenbank Zürich
– Mark Dittli, Geschäftsführer und Redaktor, The Market

The event will be conducted in German.

Original article in german.

Der Beitrag How AI Is Changing The Role Of Bank Employees – ZHAW erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data and AI at Sigli – “Future Of AI-Powered Solutions For Disabilities: On The Verge Of Fantasy”

Today, while discussing Artificial Intelligence, we often talk about Generative AI tools, virtual assistants, or recommendations assistance. Such tools are already widely adopted and that’s why it is not surprising that they come to our mind in the first turn.

However, the potential of AI is much higher than that. This technology can be used in some mind-blowing solutions that seem to be absolutely fantastic.

Nevertheless, their introduction can be much closer than we may think.

In this article, we offer you to take a look at the most cutting-edge AI-powered projects that can greatly change the lives of people with disabilities (and not only).

Multimodal LLMs

Let’s start with something that sounds the most realistic – multimodal LLMs. Probably, all of you are already well-familiar with models that can work only with text inputs and provide text outputs.

Multimodal models are able to work with data in different formats. It means that they can deal with text, images, and sounds simultaneously and provide a relevant output. That is exactly what GPT-4o is expected to offer.

Of course, such models can be highly helpful for everyone. But their value will be significantly higher for people with different kinds of disabilities, including those with vision impairment, physiological disorders, and mental diseases.

Multimodal LMS can act as full-scale virtual assistants. Their functionality can offer much more possibilities in comparison to well-known solutions like Siri.

What can multimodal LLMs offer to people who can’t interact with their surroundings in a traditional way? We can say “practically everything” and from some point of view, we even won’t exaggerate.

For example, they will be able to explain everything that is written on the screen or describe what is shown in the picture. Their functionality will allow them to instantly translate and read aloud a text from the PDF file. They will help people to interact with their computers and smartphones. Based on the voice command made by users, they will open different menus, choose the necessary options, or move a pointer to the required line, while for a person with low vision or hand tremors, it can be very challenging to do this.

In the future, such models are expected to process video content as well. This will allow them to recognize films and describe their plots for users. Or they will be able to understand what sports game you will show to them and explain the rules.

Of course, these are just a couple of examples that demonstrate how multimodal LLMs can be used by people with disabilities. The range of their applications can be really wide.

AI-powered prosthetics

For people who were born without some parts of their bodies or who lost them under different circumstances, prostheses can become the best solution. These artificial body parts can restore some of the function and appearance of the lost anatomy. However, everything is not as seamless as we may think. The use of traditional prostheses can be associated with huge discomfort and various limitations, like limitations in dexterity or sensory feedback.

Nevertheless, such issues can be at least partially addressed by AI-powered prosthetics. Yes, AI arms today are not just something from a science fiction book. That’s a reality.

Artificial intelligence can significantly enhance the functionality, adaptability, and user experience of prostheses. In such solutions, ML is applied to teach bionic limbs how to understand movement patterns and how to make predictions based on the behaviours demonstrated previously. Thanks to this, limbs become more dexterous and more “natural”.

Such prostheses, both arms and legs, are non-invasive. But they have sensors that can measure electrical signals to identify the user’s intended movement.

Photo: University of Michigan

Of course, the use of AI-powered limbs is much more convenient in comparison to traditional prostheses. AI can automatically adjust artificial limbs for a better fit and can even make real-time changes based on user movements and activity levels.

The most advanced models can provide feedback on pressure and texture, which allows them to simulate the sense of touch for users.

Nevertheless, the cost of such devices is very high at the moment. This is one of the main factors that prevent them from being widely adopted today.

Bionic eye

Bionic limbs are a cutting-edge technology but what do you think about bionic eyes?

These experimental devices can restore functional vision for people who have partial or even total blindness.

The implantation of the earliest version of the bionic eye took place in 2012. The patient who got this artificial eye suffered from profound vision loss. After the surgery, he was able to see light. However, he couldn’t make distinctions within the environment. Since then, this first eye model has been greatly improved. Some other versions helped people start seeing abstract images. Nevertheless, none of the patients has regained vision.

One of the most widely discussed projects from this category is the Prima system by Pixium Vision. Their bionic vision solutions are being developed to help patients with profound vision loss and improve their independence and mobility.

Photo: https://www.pixium-vision.com

The core of their idea is the use of a 2-by-2-millimeter square implant that should be surgically placed under the retina. This implant should receive infrared data from camera-equipped glasses and further turn it into pulses of electricity which will replace signals generated by photoreceptor rods and cones.

Some early feasibility studies conducted in the US and European Union demonstrated that this system could be potentially effective and safe for people. Nevertheless, the project faced some financial difficulties which resulted in the delay in further research and development.

Rehabilitation robots and exoskeleton

Rehabilitation is a very important process for people with disabilities and patients after injuries. AI-powered robots can greatly help in the process of physical therapy through repetitive and controlled movements. They can offer personalized exercises and continuously monitor the progress to optimize recovery outcomes.

Such robots are often used in targeted therapy for patients with neurological or musculoskeletal impairments, such as stroke, spinal cord injury, or orthopedic injuries.

One of the most well-known robots of this kind is Lokomat which helps individuals relearn walking patterns. It ensures the most physiological movement which can be guaranteed by the individually adjustable patient interface.

Photo: https://www.hocoma.com/us/solutions/lokomat

Another type of solution used in rehabilitation is the exoskeleton. Exoskeletons can be defined as wearable devices that work in conjunction with the user’s movements to enhance or assist physical capabilities.

They can help individuals with mobility impairments to stand, walk, or perform other movements. Moreover, they can be used to enhance the physical abilities of healthy individuals, such as in industrial or military applications.

Over the last several years, we could observe the growing interest in designing innovative tools of this kind that incorporate AI. The obvious benefits of such exoskeletons are their capabilities to analyze data and adjust to the individual user’s needs in real-time.

One such groundbreaking AI-powered exoskeletons was developed by a group of researchers at North Carolina State University and the University of North Carolina at Chapel Hill. This wearable device can ensure great energy savings during human movement, which could lead to great improvements in athletic performance and significantly help individuals with mobility issues.

This exoskeleton is powered by data-driven and physics-informed reinforcement learning. With this approach, wearable robots can become intuitive and predict user’s movements.  This technology can also generate synergistic assistance across different activities, such as walking or stair-climbing. The controller can automatically adapt to various kinematic patterns. It means that the transition between activities can take place without any handcrafted control.

Photo: https://www.foxnews.com/tech/ai-driven-exoskeleton-lightens-your-load-elevates-performance

Though the actual prices of exoskeletons can vary from $50,000 to $120,000, Hao Su, Ph.D., associate professor at North Carolina State University and the University of North Carolina at Chapel Hill, noted that their efficient learning-in-simulation framework allows for rapid design and testing in computer simulations.

This can help to reduce the cost of research and development.

“Looking forward, we plan to make our robots truly affordable and accessible through innovative hardware design, namely low-ratio gears and cost-effective but high-torque electric motors. In about one year, we aim to make our exoskeletons for sale at a price range of $1,500 to $4,000, depending on specific features and manufacturing scale,” he explained.

Elderly care robots/ assistive robots for people with disabilities

While talking about robots, we can’t but mention robots that could fully or at least partially replace nurses, tutors, and caregivers.

In August 2023, the first commercial general-purpose humanoid robot Apollo by Apptronik was presented to the public. At the initial stages of its development, it was planned that it would be used in the manufacturing and warehousing industries. Nevertheless, later the range of its use cases was expanded. It can be also helpful in construction, retail, and elderly care. In the latest case, such robots can handle dozens of household chores and become good companions for people who spend a lot of time in isolation due to their disease or disabilities.

Photo: https://apptronik.com/apollo

However, this project is far not the only one in this group.

For example, there are a lot of reports from Japan revealing that the country is actively investing in the automation of elder care by employing various robots.

Though probably the majority of us start thinking about humanoid devices when asked about care robots, it’s far from being true. They can come in different sizes and shapes. Some of them are intended for physical care. In this case, we are talking about those ones that can help lift patients who can’t get on their own. There are robots that assist people in exercising and moving. Some others can track the physical activity of patients, detect falls, and help them use the toilet or take a bath.

There are also robots that are intended to communicate with elderly people, they can entertain them and even conduct cognitive training.

Robot guide dogs

Guide dogs are known to have been helping people with visual impairments for centuries. They can be great assistants and companions but the use of their services is associated with a row of challenges. The training of a guide dog usually requires significant time and expense. Moreover, let’s not forget about an average dog’s lifespan. This explains why a lot of countries face a shortage of trained guide dogs.

For example, according to the data published by the China Association for the Blind, currently, there are only around 400 trained dogs in the country, while the number of people who may need their help is over 17 million.

Dogs require particular care. They all have their personalities. This also can cause some difficulties for people.

But with the application of modern technologies, such issues can be successfully solved. Especially for those who do not feel like having a furry friend, as they can have a robot friend instead.

Robot guide dogs can efficiently provide real-time navigation services for people with visual disabilities and let them travel independently and safely. Such robots can identify road conditions, obstacles, and surrounding facilities. Moreover, they can work with voice prompts and provide vibration feedback, which makes interaction with them quite simple.

It is known that a group of Chinese researchers have been already conducting field tests of a six-legged guide dog that relies on cameras and sensors for navigation. This robot can successfully recognize traffic light signals, while in the case of real dogs, this “feature” is not available.

Photo: https://edition.cnn.com/2024/07/08/china/chinese-robot-guide-dog-intl-hnk/index.html

Of course, robot dogs require some maintenance but at least users do not need to feed them on a daily basis.

Brain-computer interface

Another technology that we should mention is a brain-computer interface. It can establish a direct communication pathway between the brain and an external device. It is possible thanks to its capability to decode the neural signals associated with attempted but unarticulated speech. In other words, it can translate neuronal information into commands capable of controlling external software or hardware systems.

In a very simplified way, we can explain its work as follows:

1. Collection of brain signals using electrodes or sensors;
2. Signal processing, filtering, and amplifying;
3. Extraction of relevant patterns or features within the signals;
4. Translation of these patterns into commands that can be understood by external devices.

Some BCIs are being developed for entertainment purposes. With their help, players can enjoy more immersive experiences. However, the majority of such projects have healthcare-related goals. For example, they can be used to assist in the in the recovery of motor functions.

In this context, it’s worth recollecting Neuralink. That’s definitely one of the most widely-known projects of this kind. This BCI is fully implantable. It’s invisible. And it can help users to seamlessly control their smartphones and computers. This technology can greatly help people with disabilities who are looking for ways to become more independent. Its efficiency in this aspect has been already proven in the first human trial.

Photo: https://neuralink.com/blog/prime-study-progress-update

In January 2024, Noland Arbaugh, a 30-year-old man paralyzed from the neck down, became the first patient who received the Neuralink device. Though there were some technical challenges during the trial, the general results look quite promising.

Thanks to the Neuralink device, the young man got practically full control of a computer. With the power of his mind, he can play games and browse the web at any moment. Moreover, according to Neuralink, Noland has managed to set the human record for cursor control with a brain-computer interface.

In an interview with journalists, Noland explained that the biggest advantage of using a BCI is the possibility of being independent.

“It’s just made me more independent, and that helps not only me but everyone around me. It makes me feel less helpless and like less of a burden. I love the fact that the people around me don’t have to wait for me so much. Outside of being completely healed, I believe what most quadriplegics want is independence,” he said.

Conclusion

Though today the majority of solutions mentioned in this article haven’t been widely adopted, that’s obvious that they have great potential given their incredible social value.

Moreover, we can say for sure that the real power of technologies, and AI in particular, hasn’t been even fully explored yet.

We still have a lot of things to learn and to do. But one thing is clear: today we are close to the future as never before. And we definitely shouldn’t stop in making life easier and better for everyone with the power of AI.

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 Future Of AI-Powered Solutions For Disabilities: On The Verge Of Fantasy 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.