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

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

Introduction to Self-Healing Systems

Definition and Functionality of Self-Healing Cybersecurity Systems

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

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

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

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

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

Key Features of Self-Healing Systems

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

Advantages Over Traditional Cybersecurity Methods

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

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

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

Challenges and Limitations

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

Real-World Applications of Self-Healing Systems

Financial Institutions

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

Healthcare Industry

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

Government and Defense

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

Future Outlook

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

Conclusion

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

References

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

About the Authors:

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

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

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Copyright: htworld.co.uk – “AI Could Advance Inclusivity by Interpreting Sign Language in Real Time”

The research could play a key role in breaking down communication barriers and ensuring more inclusive interactions.

The researchers developed a custom dataset of 29,820 static images of American Sign Language hand gestures.

Using MediaPipe, each image was annotated with 21 key landmarks on the hand, providing detailed spatial information about its structure and position.

These annotations played a critical role in enhancing the precision of YOLOv8, the deep learning model the researchers trained, by allowing it to better detect subtle differences in hand gestures.

Results of the study reveal that by leveraging this detailed hand pose information, the model achieved a more refined detection process, accurately capturing the complex structure of American Sign Language gestures.

Combining MediaPipe for hand movement tracking with YOLOv8 for training, resulted in a powerful system for recognising American Sign Language alphabet gestures with high accuracy.

First author Bader Alsharif is a Ph.D. candidate in the Florida Atlantic University (FAU) Department of Electrical Engineering and Computer Science.

The researcher said: “Combining MediaPipe and YOLOv8, along with fine-tuning hyperparameters for the best accuracy, represents a groundbreaking and innovative approach.

“This method hasn’t been explored in previous research, making it a new and promising direction for future advancements.”

Findings show that the model performed with an accuracy of 98 per cent, the ability to correctly identify gestures (recall) at 98 per cent, and an overall performance score (F1 score) of 99 per cent.

It also achieved a mean Average Precision (mAP) of 98 per cent and a more detailed mAP50-95 score of 93 per cent, highlighting its strong reliability and precision in recognising American Sign Language gestures.[…]

Read more: www.htworld.co.uk

Der Beitrag AI Could Advance Inclusivity by Interpreting Sign Language in Real Time 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.

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SwissCognitive Guest Blogger: Zachary Amos – “How AI Is Transforming Drone Delivery”

E-commerce has grown exponentially since the COVID-19 pandemic drove people out of stores and into their homes. Old habits die hard as consumers continue relying on their trusty devices to make purchases.

Although business has gone well for online retailers, meeting customer expectations with timely deliveries poses considerable challenges. Fortunately, artificial intelligence (AI) advancements have paved the way for leveraging drone delivery. However, some issues must be addressed before widespread adoption.

The Rise of Drone Delivery

The convenience of e-commerce has evolved shopping, whether for food, clothing, electronics or last-minute gifts. The consumer landscape has set its sights on drones for fast, reliable delivery to fulfill these orders.

Though still in its infancy, drone delivery has the potential to reduce distribution times, improve accuracy, and lower standard shipping costs for businesses and buyers. From 2019 to 2022, customers received over 660,000 drone delivery flights, with even more tests conducted to develop and improve this method.

However, despite drones’ efficiencies, the demand outweighs current capabilities. For instance, 10 years after announcing 30-minute drone deliveries for items under 5 pounds, Amazon only fulfilled 100 shipments using the technology in the United States.

If Amazon can overcome the regulatory and logistics hurdles, its promise of 30-minute deliveries will significantly appeal to consumers. The market value for same-day delivery was $5,795.64 million in 2021, while experts predicted the demand to rise by 12.65% by 2027. Drones can play a critical role in achieving customer satisfaction.

Drone Delivery: An AI Revolution

AI has rapidly infiltrated every industry, with more companies integrating the technology into their business models. The use of AI in drone delivery fleets is a game-changer for e-commerce and has already proven its effectiveness. Here are three ways AI-powered drones have changed how orders get distributed.

1. Improved Navigation

Conventional drones use GPS to navigate, which may be unreliable in some areas. However, AI-backed drones rely on real-time sensors and computer vision to take in their surroundings.

High-resolution cameras and light detection sense various landscapes, synthesizing the information in real time via an AI algorithm. Image and object recognition allows the drone to pinpoint potential obstacles and track different objects to avoid crashing.

LiDAR sensors also use ranging lasers to map the terrain and measure distances — drone features commonly found in the surveying sector.

2. Optimized Routes

AI can process large quantities of traffic information, such as routes, congestion, rerouting, weather conditions and buildings. Drones aren’t restricted to roadways, so the algorithm can plan the appropriate path for optimized delivery of goods with reduced flight duration.

The drone delivery fleet is also more eco-friendly than traditional methods, producing nearly 47 times fewer greenhouse gases and less energy.

3. Enhanced Security

AI’s ability to detect and avoid obstacles protects the drone and its packages. The system can also circumvent unauthorized areas during flight. As a result, in-flight safety is guaranteed and orders are delivered securely.

Companies can also use AI to detect maintenance issues with drones, allowing them to ensure their fleet is up to date and prevent malfunctions.

AI Drones in the Future

Although AI-powered drones are a feat in technological advancements, the method isn’t perfect — a reason widespread adoption has been slow. For instance, AI may have a 74-day shorter breach time frame, saving companies around $3 million more than those who don’t use it. However, drone privacy is still a hotly debated topic.

The Federal Aviation Administration (FAA) requires drones to publicize their locations, meaning anyone can view the destinations and track their flight routes. A privacy breach could lead to unsolicited advertisements and the release of personal information.

Drones are also vulnerable to state and city regulations. Phoenix would be an ideal location to set up drone delivery. However, much of the city has restricted airspace, with several small airports and an Air Force base. The FAA would hesitate to allow drones to fly without a pilot for safety reasons.

Additionally, although rural areas could greatly benefit from drone delivery fleets, densely populated suburban and metropolitan regions might pose more of a problem. AI-powered drones can navigate obstacles, but trees, swimming pools, animals, cars and people may challenge the most advanced software. Inclement weather may also be an issue, as not every area has dry, sunny weather conditions year-round.

AI-Powered Drone Delivery Has Potential

AI-backed drones can improve e-commerce and ensure consumer satisfaction in package delivery. However, industries must overcome regulatory and logistics issues before the world relies on them to do the job correctly.

About the Author:

Zachary Amos is the Features Editor at ReHack, where he writes about artificial intelligence, cybersecurity and other technology-related topics.

Der Beitrag How AI Is Transforming Drone Delivery erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: spectrum.ieee.org – “Will Scaling Solve Robotics?”

Last year’s Conference on Robot Learning (CoRL) was the biggest CoRL yet, with over 900 attendees, 11 workshops, and almost 200 accepted papers. While there were a lot of cool new ideas (see this great set of notes for an overview of technical content), one particular debate seemed to be front and center: Is training a large neural network on a very large dataset a feasible way to solve robotics?

Of course, some version of this question has been on researchers’ minds for a few years now. However, in the aftermath of the unprecedented success of ChatGPT and other large-scale “foundation models” on tasks that were thought to be unsolvable just a few years ago, the question was especially topical at this year’s CoRL. Developing a general-purpose robot, one that can competently and robustly execute a wide variety of tasks of interest in any home or office environment that humans can, has been perhaps the holy grail of robotics since the inception of the field. And given the recent progress of foundation models, it seems possible that scaling existing network architectures by training them on very large datasets might actually be the key to that grail.

Given how timely and significant this debate seems to be, I thought it might be useful to write a post centered around it. My main goal here is to try to present the different sides of the argument as I heard them, without bias towards any side. Almost all the content is taken directly from talks I attended or conversations I had with fellow attendees. My hope is that this serves to deepen people’s understanding around the debate, and maybe even inspire future research ideas and directions.

I want to start by presenting the main arguments I heard in favor of scaling as a solution to robotics.

Why Scaling Might Work

It worked for Computer Vision (CV) and Natural Language Processing (NLP), so why not robotics? This was perhaps the most common argument I heard, and the one that seemed to excite most people given recent models like GPT4-V and SAM. The point here is that training a large model on an extremely large corpus of data has recently led to astounding progress on problems thought to be intractable just 3-4 years ago.[…]

Read more: www.spectrum.ieee.org

This post was originally published on the author’s personal blog.

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Copyright: news.mit.edu – “Looking for a specific action in a video? This AI-based method can find it for you”

The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.

But pinpointing when and where a particular action happens in a long video can be tedious. To streamline the process, scientists are trying to teach computers to perform this task. Ideally, a user could just describe the action they’re looking for, and an AI model would skip to its location in the video.

However, teaching machine-learning models to do this usually requires a great deal of expensive video data that have been painstakingly hand-labeled.

A new, more efficient approach from researchers at MIT and the MIT-IBM Watson AI Lab trains a model to perform this task, known as spatio-temporal grounding, using only videos and their automatically generated transcripts.

The researchers teach a model to understand an unlabeled video in two distinct ways: by looking at small details to figure out where objects are located (spatial information) and looking at the bigger picture to understand when the action occurs (temporal information).

Compared to other AI approaches, their method more accurately identifies actions in longer videos with multiple activities. Interestingly, they found that simultaneously training on spatial and temporal information makes a model better at identifying each individually.

In addition to streamlining online learning and virtual training processes, this technique could also be useful in health care settings by rapidly finding key moments in videos of diagnostic procedures, for example.

“We disentangle the challenge of trying to encode spatial and temporal information all at once and instead think about it like two experts working on their own, which turns out to be a more explicit way to encode the information. Our model, which combines these two separate branches, leads to the best performance,” says Brian Chen, lead author of a paper on this technique.

Chen, a 2023 graduate of Columbia University who conducted this research while a visiting student at the MIT-IBM Watson AI Lab, is joined on the paper by James Glass, senior research scientist, member of the MIT-IBM Watson AI Lab, and head of the Spoken Language Systems Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Hilde Kuehne, a member of the MIT-IBM Watson AI Lab who is also affiliated with Goethe University Frankfurt; and others at MIT, Goethe University, the MIT-IBM Watson AI Lab, and Quality Match GmbH. The research will be presented at the Conference on Computer Vision and Pattern Recognition.[…]

Read more: www.news.mit.edu

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

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

Understanding Generative AI

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

Exploring Quantum AI

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

Revolutionizing Education: Case Studies and Examples

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

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

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

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

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

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

Future Implications and Possibilities

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

Conclusion

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

About the Authors:

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

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

Der Beitrag Reforming Education with Generative and Quantum AI erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dilshad Durani – “How AI Drives Streaming AD Technology?”

Understanding Contextual Advertising

Traditional advertising methods rely on demographic targeting, which aims to reach targeted audiences in segments. However, contextual advertising takes a completely different approach.

Contextual ads use the context of the consumed content. In simple words, ads are tailored to match viewers’ moods, preferences, and themes, providing a more personalized ad experience.

How AI Drives Streaming Ad Technology for Disney?

Walt Disney is harnessing AI to power ad tools that help brands tailor their commercials to fit viewers’ screens within a TV series and movie.

Disney’s innovative approach with “Magic Words” exemplifies how AI is powering contextual advertising in streaming services. By analyzing scenes across its vast library using AI and machine learning, Disney can identify the mood, content, and brands featured in each scene.

Brands can use metadata (descriptive tags) to identify specific moods and personalize messages to match their tone. A OTT streaming script similar to Netflix and Disney are investing in artificial intelligence to provide personalized and improved experiences to viewers.

Disney has invested in streaming ad technology as it moves away from cable TV and broadcast, along with viewers. Hulu’s and Disney’s streaming giants services ad revenue fell almost 3% in the first quarter of 2024.

According to the eMarketer report, Netflix outpaces Disney’s ad revenue by $1.03 billion versus Disney’s $911.9 million.

Source: (eMarketer)

Disney relies on streaming ad technology that powers the linear TV business and Hulu, which helps the company to increase its ad revenues.

Personalized Messaging

AI-driven streaming ad technology enables brands to move beyond demographic targeting and embrace personalized messaging.

By leveraging metadata and AI insights, advertisers can craft tailored messages that resonate with individual viewers based on their viewing preferences and emotional cues. This shift towards audience-centric advertising promises higher engagement and conversion rates.

Beta Testing and Industry Adoption

Disney’s partnership with leading advertising companies like Omnicom, Dentsu, and GroupM underscores the industry’s growing interest in AI-driven streaming ad technology. Beta testing initiatives aim to refine and optimize these new advertising tools, paving the way for widespread adoption across streaming platforms.

Rise of Ad-Supported Streaming Services

As consumers increasingly gravitate towards ad-supported streaming options, platforms like Disney+ and Hulu are seizing the opportunity to monetize through targeted advertising. AI-powered ad technology enables these platforms to deliver relevant ads seamlessly integrated into the viewing experience, balancing user satisfaction with revenue generation.

Future Prospects and Innovations

Looking ahead, AI-driven streaming ad technology holds immense potential for further innovation and refinement. Advancements in computer vision, natural language processing (NLP), and predictive analytics will enable even deeper insights into viewer behavior and preferences, fueling the next wave of personalized advertising solutions.

Conclusion

AI is driving a paradigm shift in streaming ad technology, ushering in an era of contextual advertising and personalized messaging. From Disney’s Magic Words to Netflix’s sophisticated recommendation algorithms, AI-powered solutions transform how brands connect with consumers in the digital age. As the streaming landscape continues to transform, we expect artificial intelligence to remain at the forefront of innovation, shaping the future of advertising in the streaming era.

About the Author:

Dilshad Durani is a seasoned Digital Marketer and Content Creator currently contributing her expertise to the dynamic team at Alphanso Technology, a leading company specializing in Eventbrite clone and event management system in PHP development. She is curious to learn new things and is passionate about helping people understand market trends, changing marketing approaches, business ethics, and more with her writing.

Der Beitrag How AI Drives Streaming AD Technology? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Sandeep Saharan, Assistant Professor, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University, Vice President, Woxsen University and Dr. Hemachandran Kannan, Professor and Director, AI Research Centre, Department of AI and Analytics, School of Business, Woxsen University – “Exploring the Cognitive Psychology of Consumer Behavior in the Age of Artificial Intelligence”

In the dynamic and always changing realm of digital marketing, maintaining a competitive edge is essential for achieving favorable outcomes. With the increasing connectivity and engagement of customers on social media platforms, companies are using data analytics to optimize their marketing tactics. The amalgamation of data analytics with social media marketing is influencing the trajectory of advertising and enabling organizations to execute more precise, customized, and impactful campaigns compared to previous practices. This article aims to examine the interdependent association between data analytics and social media marketing, elucidate the advantages it presents, and elucidate how enterprises might exploit this convergence to stimulate expansion and involvement.

The Rise of Social Media Marketing: The pervasive use of social media has become an indispensable component of our everyday existence. Social media platforms provide as a means of establishing connections with friends, engaging with preferred companies, and exploring novel goods and services. Consequently, social media platforms have undergone significant transformations, becoming as influential marketing tools that provide unique capabilities in terms of audience reach and engagement. The substantial number of people present on social media platforms such as LinkedIn, Twitter, Instagram, Facebook, and TikTok renders them very advantageous environments for companies to establish connections with their intended target demographics. Nevertheless, the primary obstacle is in differentiating oneself within the densely populated digital environment and providing material that effectively connects with people. Data analytics plays a crucial role in this context. The field of data analytics encompasses the processes of gathering, scrutinizing, and interpreting data in order to arrive at well-informed and rational decision-making. Within the realm of social media marketing, the use of data analytics empowers firms to get valuable information pertaining to user behavior, preferences, and prevailing trends. These insights possess significant value in the customization of marketing tactics that are not only efficient but also relevant and current.

The Data-Driven Approach to Social Media Marketing

The era of just depending on intuition or subjective estimations to develop marketing initiatives has become obsolete. The use of data-driven methodologies in social media marketing is significantly reshaping the business. Here’s how it works:

Audience Insights

Data analytics tools provide firms a plethora of information pertaining to their social media audience. This encompasses several factors such as demographic characteristics, geographical location, areas of interest, and even real-time indicators of user involvement. With this acquired information, marketers has the ability to generate content that effectively communicates with their intended target audience.

Content Optimization

The examination of various content formats, such as photographs, videos, infographics, and articles, enables organizations to enhance their content strategy. Data analysis allows marketers to get insights into audience preferences and identify successful strategies, allowing them to concentrate their efforts on effective approaches.

Timing and Frequency

The use of data analytics may facilitate the identification of optimal time periods for content posting, hence maximizing exposure and interaction. Additionally, it may provide valuable information about the optimal frequency of content sharing, ensuring that the audience is neither overwhelmed or alienated.

Competitor Analysis

Businesses may obtain a competitive advantage by closely monitoring the social media activity and engagement metrics of their rivals. Data analytics
technologies have the capability to identify deficiencies in the market, prospects for
distinctiveness, and domains in which rivals are succeeding.

Campaign Performance

The evaluation of marketing campaigns’ effectiveness heavily relies on the monitoring of essential performance metrics, often referred to as key performance indicators (KPIs). These KPIs include conversion rates, click-through rates, and return on investment (ROI). The use of data analytics allows the continuous monitoring and modification of campaigns in real-time, with the aim of optimizing outcomes.

The Tools of the Trade

Data Analytics for Social Media: In order to optimize the use of data analytics within the context of social media marketing, enterprises depend on a diverse array of tools and platforms.

Here are some of the key players in the field:

1. Google Analytics

This multifunctional instrument offers valuable insights on the volume of website traffic that originates from various social media networks. The use of this tool aids organizations in monitoring conversions, analyzing user behavior, and assessing the influence of social media on website efficacy.

2. Facebook Insights

Facebook Insights provides a detailed analysis of page performance, audience demographics, and engagement analytics for companies who have a presence on the Facebook platform. The use of this instrument is crucial for enhancing the effectiveness of Facebook marketing endeavors.

3. Hootsuite

Hootsuite is a comprehensive social media management software that facilitates companies in the scheduling of posts, monitoring of social media discussions, and analysis of performance data across various social networks.

4. Sprout Social

Sprout Social provides a comprehensive range of social media management solutions including analytics, publishing, and interaction functionalities. This analysis offers significant perspectives on the demographics of the audience and their patterns of involvement.

5. Buffer

Buffer streamlines the procedure of arranging and disseminating social media content. Additionally, it provides analytics tools to assess the efficacy of content across several platforms.

6. Google Analytics 360

Google Analytics 360 offers enhanced analytics and data integration functionalities to cater to the needs of bigger organizations, enabling them to get a more thorough assessment of their marketing effectiveness.

Benefits of the Convergence

Why Data Analytics Matters: The amalgamation of data analytics with social media marketing presents several advantageous outcomes for firms:

Targeted Advertising

The use of data analytics enables organizations to effectively segment their audience and implement precise advertising strategies. This practice not only
enhances the relevancy of advertisements
but also optimizes the efficiency of ad expenditure.

Content Personalization

By comprehending user preferences and behavior, organizations have the ability to provide customized content that effectively connects with specific users, hence enhancing user engagement and conversion rates.

Improved ROI

Marketing initiatives that are informed by data are more probable to provide a greater return on investment. Marketers has the ability to enhance resource allocation efficiency and improve campaigns by using real-time data.

Real-time Insights

Data analytics offers timely and valuable information into the success of campaigns. This enables marketers to promptly modify their strategies and take advantage of developing trends or possibilities.

Competitive Advantage

Companies that use data analytics in their social media marketing strategies have a competitive advantage by proactively anticipating industry trends, understanding customer preferences, and adapting to market dynamics.

Leveraging Data for Social Media Success

Best Practices

In order to properly use the potential of data analytics in the realm of social media marketing, organizations are advised to adhere to the following set of recommended practices:

Set Clear Objectives

Establishing clear and well-defined objectives for social media marketing endeavors is crucial. These objectives may include several aims, including but not limited to augmenting brand recognition, generating higher volumes of online visitors, or enhancing sales performance. It is essential that data analytics be aligned with these stated goals.

Choose the Right Metrics

The primary focus should be on the key performance indicators (KPIs) that are most important to your organization. These metrics could consist of engagement rates, conversion rates, click-through rates, and consumer acquisition costs.

Use Multiple Data Sources

Integrate data from many sources, including social media platforms, customer relationship management (CRM) system, and website analytics, to provide a holistic perspective of your target audience and organizational performance.

Invest in Training

It is essential to ensure that the marketing staff has comprehensive training in the proficient use of data analytics solutions. The acquisition of knowledge and the enhancement of skills are vital in the context of this swiftly progressing domain.

A/B Testing

Conduct many experiments using diverse content, posting schedules, and advertising formats in order to ascertain the most effective means of engaging with your target audience. A/B testing enables the refinement of plans via the use of insights derived from data analysis.

Monitor and Adapt

It is important to consistently evaluate the efficacy of one’s social media efforts and remain flexible in response to evolving trends and shifts in audience behavior.

Data Privacy Compliance: Throughout the process of collecting and using client data, it is crucial to safeguard user privacy and comply with data protection standards.

Conclusion

The Future of Social Media Marketing: The combination of data analytics and social media marketing is revolutionizing the way in which businesses establish relationships with their target consumers. In today’s world characterized by an abundance of data, the ability to analyze and respond effectively to data is a valuable skill that can have a significant impact on the success or failure of a marketing endeavor. With the continuous advancement of technology, it is anticipated that more advanced data analytics tools and methodologies would emerge, hence augmenting the capabilities of social media marketing. The future trajectory of marketing is dependent on the ability of individuals to utilize data effectively in order to provide customized, pertinent, and influential information to their designated demographic. By implementing this convergence, businesses can position themselves strategically for success in the era of digitalization, when data is the most important asset for attaining marketing excellence.

About the Authors:

Sandeep Saharan is Assistant Professor at AI Research Center, Department of AI and Analytics, School of Business, Woxsen University. He received his Bachelor of Technology degree in Computer Science and Engineering from M.D. University, and then Master of Engineering degree in Computer Science and Engineering from Thapar University. He is pursuing Ph.D. with the Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology. He has published research articles in reputed international journals as well as in international conferences, such as, IEEE Transactions on Intelligent Transportation Systems, Future Generation Computer Systems, Computer Communications, Applied Mathematics and Information Sciences, and IEEE Globecom. His research interests are in the areas of evolutionary optimization, game theory, and intelligent transportation system. He is an active member of various organizations, such as, IEEE, ACM, and CSI.

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 The Convergence of Data Analytics and Social Media Marketing erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Artem Pochechuev, Head of Data Science at Sigli – “Bridging the Gap: How AI Can Facilitate Social Inclusion Of People With Disabilities”

A recent study conducted by the UK national disability charity revealed that around 60%, which is almost two-thirds, of people with disabilities are chronically lonely. Even a bigger percentage of respondents (70%) admitted that they feel that social isolation has an impact on their mental health, which can also lead to an increase in mental health conditions in combination with the disabilities that they already have.

And here, we can observe a very important dissonance. On the one hand, a lot of people with disabilities can’t feel that they are a part of society due to the restrictions that they have to face every day. So, they do not have the possibility to communicate with other individuals a lot.

On the other hand, many of them can’t live fully independently. Due to their disorders, they can’t handle even routine tasks. As a result, they need to rely on the support of their family members, nurses, or tutors. Consequently, they are obliged to interact with others to deal even with the simplest everyday processes.

Amid all these issues, questions related to social isolation and lack of independence shouldn’t be ignored by organizations that work with people with disabilities.

Given all the new opportunities that emerging technologies like AI open, they can make a huge contribution to such projects. And it is great to see the growing interest of the world’s community and international institutions in the adoption of AI to help all groups of society.

According to the European Network of National Human Rights Institutions), “AI holds significant potential to positively impact the lives of persons with disabilities by addressing various challenges and providing innovative solutions. From speech-to-text applications to vision-enhancing tools, AI can break down barriers and provide new avenues for communication and interaction.”

In this article, we offer you to take a closer look at AI-powered projects and initiatives aimed at breaking social barriers for people with disorders of various types.

AI and communication

Speech-to-text and text-to-speech tools are widely applied to help people with hearing and speech impairments communicate with others. In one of our previously written articles, we’ve already described the power of Artificial Intelligence in enhancing the way people with such disorders interact with the world around them.

Thanks to virtual assistants enriched by such features, people can communicate with others in the traditional way. And this approach presupposes a real-time dialogue with practically no delays. As a result, they can solve a lot of tasks (like making calls to book a table at a restaurant, setting an appointment with a doctor, or ordering a delivery) without waiting for help from others. You may say that such tasks can be performed via sending a message. But let’s be honest. Quite often, a phone call is the fastest way to get what you want. And in this case, they can rely on AI-powered virtual assistants like Siri.

But this functionality is not the only one that can be ensured thanks to Artificial Intelligence. AI can fully change the way society can communicate with those who use sign language. With modern solutions, you do not need to know all the signs and their meaning to communicate with those who can’t express thoughts with the help of verbal means. AI algorithms can recognize signs and transform them into natural languages and vice versa.

The AI-powered era in marketing

And that’s cool that there are projects like Signapse AI, which helps businesses adapt their marketing strategies to the needs of everyone. What does it mean? The offered solution can translate written text into a Sign Language video which businesses can place on their websites, social media accounts, and blogs.

It will be sensible to note that all the information can be delivered to people with hearing disorders via written texts. Yes, it is possible. But such projects allow companies to make their communication with everyone vivid and engaging enough. Moreover, that’s a good way to demonstrate that you respect all your existing and potential and appreciate them regardless of any disabilities that they may have.

Autism and social skills

However, while discussing barriers in communication, we should mention not only speaking or hearing disabilities. In this context, it is also crucial to talk about people with autism spectrum disorder.

For individuals with autism, it can be extremely challenging to participate in various formats of social interactions. And AI-powered tools for social skill training can bring huge value. Such tools can simulate different role-playing real-life scenarios, analyse people’s behaviour in offered situations, and provide real-time feedback. It means that, in this case, AI is applied to create a supportive, controlled, and fully safe environment where users can practice and enhance their social skills.

One of the projects that are working on delivering such solutions is SocialMind. It is a member of the Microsoft for Startups Founders Hub. This platform relies on AI and NLP to improve the social skills of children with autism. Thanks to AI, the social skills training can be personalized for each child based on his/her specific needs and learning style.

But that’s not the only possible AI use case in this domain. AI can let people with autism communicate via alternative means. How is it possible? AI technology can recognize non-verbal or limited verbal means. Then language processing algorithms, predictive text, and voice recognition tools can transform them into natural language. As a result, individuals with autism get more freedom in expressing their thoughts. They can engage in various activities and take part in conversations.

AI and consumption of content of different formats

Books, movies, and art are among those things that, in this or that form, are present in our social life and affect it.

Let us mention a very simple example. When you move to a new country and try to integrate into a new community, sooner or later you may feel that you are a stranger. It can happen even if you perfectly know the local language and have already learned the names of local supermarkets. You may start feeling that you do not know the cultural context. For example, you do not know what songs were popular when people from your generation went to school and what cartoons they liked. As a result, you simply can’t understand the majority of memes and jokes.

But, of course, it is not a problem in comparison to what people with hearing or vision disorders experience when it comes to the cultural context.

However, thanks to AI, we can introduce alternative forms of consuming and producing different content. And it’s not only about social and cultural aspects. Such tools can be also of great use for educational and professional purposes.

Live captioning and screen readers are expected to greatly change the game for people with disabilities. Real-time captioning solutions make the participation of individuals with hearing disorders in various online events possible.  At the same time, image and text readers can be used by those with visual impairments and people with dyslexia.

AI and accessibility of public spaces

It’s amazing to observe that today, public spaces are gradually becoming more accessible thanks to AI. Smart city solutions can rely on systems powered by Artificial Intelligence for sharing real-time data on accessible transportation options (like wheelchair-accessible ramps or buses that can be easily used by people with limited mobility).

Architects and designers can benefit from AI-powered tools for creating so-called disability-friendly urban planning and buildings.

And already today there are apps that use the power of AI to provide accessibility information about various public spaces.

For people with visual disorders, the market can offer smart glasses and AI-powered apps that use smartphone cameras. Special cameras can be placed on a person’s eyeglass frames. They utilize optical character recognition technology. With it, they can transform digital or printed text into real-time auditive feedback. Some other solutions of this kind can also be powered by stereo sound sensors and GPS technology. They can recognize colours, read signs, and provide spoken directions.

Moreover, there are applications like Seeing AI by Microsoft that can safely navigate people with visual impairment. AI algorithms can identify objects and people caught by the device camera and then audibly describe what is happening around them.

While Seeing AI is a universal app that can be used in many situations, similar technologies can be more industry-specific. For example, cameras powered by computer vision can be installed in gyms and can notify people about any threats caused by irregular use of equipment. Such a solution can be of great use for a very wide audience. But it will have the highest importance for people with visual impairments.

AI and independent living

Let’s be honest, the modern world is built in such a way that even a lot of healthy people can stay at home, work from home, order deliveries, communicate with others only via chats, and still feel completely okay about it. Total digitalization (in which AI also takes an important position) makes it possible for everyone. However, even when all these opportunities are available, people still feel that they are a part of society. They can easily leave their homes if they want to do it and go wherever they want. They do not depend on others.

But we can’t say just the same about people with disabilities. Many of them can’t live fully independently due to many reasons which causes a lot of discomfort to them. Nevertheless, AI is here to address such issues.

For example, smart controllers used in smart home systems can greatly increase the safety of people with vision and cognitive disabilities. They can track whether all home appliances are turned off when they are not used, monitor possible water leaks, etc. Assistants with voice control can be used to manage a lot of tasks. Meanwhile, cameras with computer vision can recognize who is standing at the door and inform a person.

Special smart controllers can also be responsible for detecting various threats and dangerous situations related to people’s health. Already today, there are projects that offer to install sensors that will detect falls and the absence of movements during a particular period. Such solutions are highly relevant for families with elderly people living alone. If a dangerous pattern is identified, the system will send notifications to nurses or other authorized individuals who can take the required measures.

For people with limited mobility, there are quite a lot of voice-controlled devices and smart robotic products. But some solutions that are available today are ground-breaking. For example, what about walking simply by thinking about it? Thanks to electronic brain implants, it can be possible. The system is still not widely available and is at an experimental stage. Nevertheless, a 40-year-old Dutch man who was paralyzed after an accident got the possibility to walk again. The electronic implants wirelessly transmit his thoughts to his legs and feet via an implant on his spine.

Neurosurgeon Prof Jocelyne Bloch at Lausanne University, who carried out the surgery and inserted the implants, explained the project’s goal in the following way:

“The important thing for us is not just to have a scientific trial, but eventually to give more access to more people with spinal cord injuries who are used to hearing from doctors that they have to get used to the fact that they will never move again.”

And that’s exactly how we at Sigli (Cortlex) view the mission of introducing tech innovations – to make impossible things possible for everyone.

But even such cutting-edge projects are not at the highest level of what we can expect to see in this field in the near future. Research and experiments are going on.

Conclusion

Have we mentioned all the existing AI-powered projects aimed at facilitating everyday tasks for people with disabilities? Definitely no. This list could be practically endless.

Without any doubt, it is extremely exciting to explore the potential of AI in this aspect. And it is even more exciting to make our contribution to this huge common initiative – ensuring that our world is comfortable for everyone. If you need any technical assistance in the realization of such projects, we would be happy to hear from you.

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 How AI Can Facilitate Social Inclusion Of People With Disabilities erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.