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.

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

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

The Growing Concern of Cross-Border Data Transfers

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

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

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

AI for Enhanced Data Privacy in Cross-Border Transfers

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

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

Blockchain for Secure and Transparent Data Transfers

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

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

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

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

Conclusion

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

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

References:

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

About the Author:

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

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

SwissCognitive Guest Blogger: Vishal Kumar Sharma – “Saul Robot, A Frontier in Hospital Disinfection”

The Saul Robot uses strong UV (UVC) rays to fight harmful bacteria like Ebola and C. difficile, therefore transforming hospital cleanliness. Saul provides a quick, environmentally friendly, automated disinfection system unlike conventional chemical techniques, therefore lowering infection rates and safeguarding of healthcare professionals. Robots like Saul will set new benchmarks in worldwide healthcare cleanliness as technology develops.

The Concern Before efficient cleaning tools were developed

Hospitals faced enormous challenges controlling the spread of contagious diseases. Usually reliant on chemical agents, conventional disinfection methods had several disadvantages. These methods took time, harmed the environment and human health, and often proved futile against viruses like Ebola and powerful bacteria like Clostridium difficile (C. diff). The focus on hand washing also increases the risk of exposing medical professionals to harmful microorganisms, therefore complicating initiatives at infection control.

The Methodical Approach of Saul Robot in Problem Solving

Designed in concert with the 633rd Medical Group at Joint Base Langley-Eustis, Virginia, Xenex Healthcare Services presents a significant advance in hospital disinfection using the Saul Robot. Originally first presented in 2014, Saul effectively sterilizes hospital rooms and healthcare environments with intense UV (UVC) photons.

The Saul Robot boasts the following clear benefits and features:
25,000 times brighter than fluorescent lights, Saul’s UVC ray pass through bacterial and virus cell walls, therefore neutralizing dangerous diseases. Changing their DNA and RNA helps the UVC rays prevent diseases by stopping germs from growing.

Saul says his virus killing capacity is 99.9%. It rapidly sterilizes a room, therefore reducing the cleaning time required as compared to more traditional methods. This efficiency will help one to keep high rates of patient turnover without compromising safety.
Mercury-based compounds, which are harmful to human health and the environment, can form basis for conventional disinfection methods. Saul offers a green replacement that enables safer and more sustainable healthcare operations by employing UV light.
By automating the disinfection process, Saul can minimize the demand for human cleaning and thereby lower the possibility of healthcare workers getting into touch with harmful germs. This relates to general safety criteria for medical practitioners.

Future Course and Demand

The introduction of the Saul Robot has changed the way hospitals handle cleaning, thereby improving patient outcomes and reducing infection rates. As technology advances, the use of robots such as Saul in healthcare is expected to expand. The following lists some necessities as well as possible future repercussions:
Global Adoption increases as after Covid, hospitals all around aim to improve infection control, the acceptance of advanced disinfection robots like Saul is most likely to increase. These robots will set new standards for medical facility hygiene standards.
Constant advances in robotics and UVC technology will increase the capacity of disinfection robots, therefore enhancing their efficiency and adaptability in the battle against a larger spectrum of illnesses.

Future developments could show integration with hospital systems whereby disinfection robots coupled with hospital management systems for automatic and optimum cleaning schedules could be observed, therefore improving patient safety and efficiency.
Dealing with Emerging Threats as new infectious diseases develop, robots such as Saul will be indispensable in providing quick and effective responses to curtail outbreaks and protect public health.

Conclusion

Apart from being a technical marvel, the Saul Robot shows how creatively able public health can be enhanced. Using UVC radiation, Saul presents a safe, rapid, eco-friendly answer to the crucial issue of hospital disinfection. As we continue facing the challenges presented by infectious diseases, the Saul Robot demonstrates the path to a cleaner, safer, and healthier future in healthcare.

References:

1. Weber, D.J., Rutala, W.A. (2013). Understanding Hospital Disinfection Techniques. American Journal of Infection Control, 41(5), S31-S35.
2. Wilson, A.P., et al. (2018). The Role of Automated UV-C Devices in Infection Control. Journal of Hospital Infection, 99(3), 249-257.
3. Anderson, D.J., et al. (2017). Effectiveness of UV Disinfection in Healthcare Settings. The Lancet, 389(10071), 805-814.

About the Author:

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

Der Beitrag Saul Robot, A Frontier in Hospital Disinfection 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.

Copyright: omfif.org – “AI Is Driving ESG Integration In Emerging Markets”

Developments are providing new opportunities for sustainable investment

Artificial intelligence can help institutional investors and asset managers align their emerging market investment strategies with the United Nations’ sustainable development goals. AI can leapfrog gaps in environmental, social and governance data disclosures and reshape business models to drive economic growth and societal progress.

EMs offer attractive investment opportunities to achieve financial returns and impact. ESG-integrated assets under management are expected to increase from $18.4tn in 2021 to $33.9tn by 2026 in EMs.

Although they still lag developed markets, ESG disclosure requirements have grown exponentially in EMs. For example, of the 62 EMs with sustainable bond issuances in 2023, 51% had ESG and climate disclosure requirements. The International Financial Reporting Standards’ framework on sustainability and climate-related disclosures will help to drive an increase in reporting.

Many EM regulators, like Brazil’s Comissão de Valores Mobiliários, are already signalling their intent to require companies to disclose information according to these requirements. Regulators are also mandating domestic ESG disclosure requirements such as the Securities and Exchange Board of India’s Business Responsibility and Sustainability Reporting framework for the top 1,000 Indian-listed companies to disclose quantitative and standardised ESG information.

Turning to AI

As investment opportunities and disclosures increase, so do the demands of asset managers and industry for solutions that efficiently structure information to measure, track and validate climate and other ESG risks. Unsurprisingly, there is growing interest in the role AI-powered solutions can play to increase the speed and efficiency of the ESG disclosure process, especially in the post-ChatGPT world.

Use cases range from corporate reporting to investor analysis to regulatory oversight. AI-enabled providers span companies that provide ESG data, analytical services and software-as-a-service solutions.[…]

Read more: www.omfif.org

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Copyright: sdxcentral.com – “How Will The Data Center Ecosystem Be Impacted By AI?”

Data centers are set for an artificial intelligence (AI) boost that could see investments surge past $1 trillion dollars over the next several years, with that investment also spilling over into the connectivity tissue linking those locations.

Dell’Oro Group predicts data center capex will surge to a 24% compound annual growth rate (CAGR) by 2028 due to “surging demand in AI-related data center infrastructure.” This could result in a 13-figure market over the next several years.

“AI has the potential to generate more than a trillion dollars in AI-related infrastructure spending in cloud and enterprise data centers over the next five years,” Baron Fung, senior research director at Dell’Oro Group, wrote. “AI infrastructure, which includes servers with GPU or custom accelerators, along with dedicated networking, storage, and facilities, are highly capital-intensive. While the industry continues to assess the potential return on AI-related investments, major efforts have been underway in the ecosystem in achieving long-term sustainable capex growth.”

The research firm noted that U.S.-based hyperscalers like Amazon, Google, Meta, and Microsoft will drive much of this investment, with just those four firms accounting for half of global data center capex as early as 2026.

Those hyperscalers have increasingly hinted at this need to increase their AI-connected investments.

“We are – and we’ve talked about now for quite a few quarters – we are constrained on AI capacity,” Microsoft CFO Amy Hood said during Microsoft’s latest earnings call. “And because of that … we’ve … signed up with third parties to help us as we are behind with some leases on AI capacity. We’ve done that with partners who are happy to help us extend the Azure platform, to be able to serve this Azure AI demand. And you do see us investing quite a bit as we’ve talked about in builds so that we can get back in a more balanced place.”[…]

Read more: www.sdxcentral.com

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Copyright: forbes.com – “Generative AI As A Service: An Emerging Frontier In Small Business Banking”

Small-business owners (SMBs) face many challenges juggling multiple roles—engaging with customers, hiring and retaining employees, closing sales and managing finances. Small-business bankers often ask how they can deepen their relationships with this coveted cohort that allows them to differentiate their offerings and create trusted partnerships additive to their financial solution offerings.

The pace of technology change adds to the challenge these business owners face—particularly with rapidly evolving technology such as generative AI, and this is where banks can lean in with these customers to offer solutions. Unlike larger enterprises, small businesses typically lack the expertise and capital to invest in advanced technologies. This is where banks can help SMBs adopt these technologies.

By leveraging generative AI, banks can offer innovative, specifically tailored tools. While traditional solutions include API-based interfaces into accounting systems, integrated treasury management solutions and even free co-working spaces, the next frontier may be leveraging artificial intelligence as a service (AIaaS).

The Power Of Generative AI For Small Businesses

Generative AI can provide small businesses with a range of powerful tools, from personalized financial advisory services to dynamic financial planning and automated financial management. These solutions can analyze vast datasets to provide tailored advice, create financial models that evolve in real time and automate mundane tasks such as bookkeeping and invoice processing.

Beyond financial solutions, generative AI can enhance business operations by generating high-quality marketing content, handling customer inquiries through chatbots and virtual assistants and developing personalized marketing campaigns based on customer data analysis.

Positioning Banks As Technology Partners

By offering AIaaS, banks can position themselves not just as financial service providers but as essential technology partners for small businesses. This strategic positioning can set them apart from competitors.[…]

Read more: www.forbes.com

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Copyright: allbusiness.com – “AI and Business Development: How AI Can Drive Growth for SMBs”

The year 2023 can aptly be summed up as a year of exploration, with the remarkable debut and early applications of generative artificial intelligence (AI). As this technology continues to rapidly advance, 2024 marks a significant turning point for people and businesses alike, moving beyond the concept of AI and its influence to determining how they can adopt it for the long haul in impactful ways.

For small and medium-sized businesses (SMBs) specifically, this means assessing the technology’s true impact—and SMB owners have already begun to scratch the surface. According to a recent Visa survey, 90% of Generation Z and millennial SMB owners, 91% of Generation X SMB owners, and 70% of baby boomer SMB owners are aware of AI and have gained an understanding of its potential business applications.

While AI can be incredibly valuable for SMBs, it can also raise valid questions and concerns. These challenges potentially stem from the complexity of the AI landscape, coupled with the limited resources and technical expertise that are commonly pervasive within smaller companies.

The survey also revealed a notable gap in the adoption of AI among SMBs. While many expressed positive sentiments, only 26% of SMB leaders have used AI tools like ChatGPT for personal and business purposes, with 45% yet to embrace its potential benefits. Navigating these intricacies can pose a significant barrier to AI implementation for SMBs, which are likely to gain more from the technology than their larger counterparts.

Three ways business can drive growth with AI

AI is reshaping every industry and transforming how businesses operate at an unprecedented rate. Amidst these rapid advances, SMBs stand as the backbone of world economies, making their AI journey particularly valuable to drive growth and efficiency. AI adoption can be a catalyst for streamlining operations, meeting customer demands, and improving productivity. After all, SMBs aren’t strangers to being tasked with doing more with less.

According to the survey, 44% of SMB leaders think AI will be most impactful in customer support or operations, 43% in marketing or accounting, 37% in financial management, 33% in sales, 29% in human resources regarding current employees, and 27% in recruiting and hiring.[…]

Read more: www.allbusiness.com

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Next-Gen AI Innovations And Investments – SwissCognitive AI Investment Radar

In this edition, we embark on a comprehensive journey through the heart of global innovation, from AI-powered startups transforming e-commerce analytics to massive funding rounds setting the pace for future technology.

With AI technologies spearheading unprecedented advancements across industries, we delve into the dynamic shifts marking AI’s significant impact. From the bustling tech hubs of Pittsburgh to the visionary investments of Saudi Arabia, we uncover the pivotal role AI plays in shaping our world. Notable rounds like FundGuard’s $100 million Series C and strategic insights for board members navigating the GenAI boom signal a pivotal moment in technological growth and investment strategies.

As the world tunes into the potentials unlocked by AI in accounting, stock markets, short-haul trucking, and beyond, we explore the blend of innovation, and strategic foresight guiding investments.

Join us on the latest updates of investment and innovation in the AI sphere, spotlighted in this edition of SwissCognitive’s AI Investment Radar.

Previous SwissCognitive AI Investments Radar: Shaping Tomorrow’s Tech Today.

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.

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Copyright: venturebeat.com – “Specialized Models: How AI is Following The Path of Hardware Evolution”

The industry shift towards deploying smaller, more specialized — and therefore more efficient — AI models mirrors a transformation we’ve previously witnessed in the hardware world. Namely, the adoption of graphics processing units (GPUs), tensor processing units (TPUs) and other hardware accelerators as means to more efficient computing.

There’s a simple explanation for both cases, and it comes down to physics.

The CPU tradeoff

CPUs were built as general computing engines designed to execute arbitrary processing tasks — anything from sorting data, to doing calculations, to controlling external devices. They handle a broad range of memory access patterns, compute operations, and control flow.

However, this generality comes at a cost. As CPU hardware components support a broad range of tasks and decisions about what the processor should be doing at any given time — which demands more silicon for circuity, energy to power it and of course, time to execute those operations.

This trade-off, while offering versatility, inherently reduces efficiency.

This directly explains why specialized computing has increasingly become the norm in the past 10-15 years.

GPUs, TPUs, NPUs, oh my

Today you can’t have a conversation about AI without seeing mentions of GPUs, TPUs, NPUs and various forms of AI hardware engines.

These specialized engines are, wait for it, less generalized — meaning they do fewer tasks than a CPU, but because they are less general they are much more efficient. They devote more of their transistors and energy to doing actual computing and data access devoted to the task at hand, with less support devoted to general tasks (and the various decisions associated with what to compute/access at any given time).

Because they are much simpler and economical, a system can afford to have a lot more of those compute engines working in parallel and hence perform more operations per unit of time and unit of energy.[…]

Read more: www.venturebeat.com

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