SwissCognitive Guest Blogger: Zachary Amos – “Leveraging AI to Predict and Reduce College Dropout Rates”

Artificial intelligence (AI) is already impacting education in many ways. Some schools are embracing it to serve students better, and many learners use it to help them with research and assignments. One of its more promising uses in this field, though, is reducing dropout rates.

Dropping out of college before finishing a degree may limit students’ opportunities in the future, but it can also be difficult for schools to predict. AI can help all parties involved through several means.

Identifying At-Risk Students

Preventing dropouts starts with recognizing which people are at risk of quitting prematurely. Machine learning is an optimal solution here because it excels at identifying patterns in vast amounts of data. Many factors can lead to dropping out, and each can be difficult to see, but AI can spot these developments before it’s too late.

Studies show early interventions based on warning signs can significantly reduce dropout rates, and AI enables such action. Educators can only intervene when they know it’s necessary to do so, and that level of insight is precisely what AI can provide.

Early examples of this technology have already achieved 96% accuracy in predicting students at risk of dropping out. Combining such predictions with a formal intervention plan could let higher ed facilities ensure more students finish their degrees.

Uncovering Non-Academic Risk Factors

In addition to recognizing known predictors of dropout risks, AI can uncover subtler, non-academic indicators. The causes of dropping out are not always easy to see in classroom performance. For example, over 60% of college students experience at least one mental health issue, which can threaten their education. AI can reveal these relationships.

Over time, AI will be able to highlight which non-tracked factors tend to appear in students with a high risk of dropping out. Once schools understand these non-academic warning signs, they can craft policies and initiatives to address them.
Enabling Personalized Education
AI is also a useful tool for minimizing the risks that lead to quitting school before someone even showcases them. Personalizing educational resources is one of the strongest ways it can do so.

The AI Research Center at Woxsen University in India successfully used chatbots to tailor lessons to individual students. Students utilizing the bot — which offered personalized reminders about classwork — were more likely to receive a B grade or higher. People attending Georgia State University showed similar results when using a chatbot to drive engagement.

Personalized education is effective because people have varying learning styles. AI provides the scale and insight necessary to recognize these differences and adapt resources accordingly, which would be impractical with manual alternatives.

Improving Accessibility

Similarly, AI can drive pupil engagement and prevent stress-related dropout factors by making education more accessible. Many classroom resources and university buildings were not designed with accessibility for all needs in mind. Consequently, they may hinder some students’ success, but AI can address these concerns.

Some AI apps can scan physical texts into digital notes to streamline note-taking for those with impairments limiting their ability to use pens or keyboards. Natural language processing can lead to better text-to-speech algorithms for users with vision impairments. On a larger scale, AI could analyze a campus to highlight areas where some buildings or walkways may need wheelchair ramps or other accessibility improvements.

Responsible AI Usage Can Minimize Dropout Rates

Some applications of AI in education — largely dealing with students’ usage of the technology — have raised concerns. The technology does pose some privacy risks and other ethical considerations, but as these use cases show, its potential for good is also too vast to ignore.

Responsible AI development and use can help universities ensure every student gets the help they need. As a result, dropout rates will fall. Schools will benefit from the higher student success rate, and the student body will benefit by achieving goals that will help them in their future careers.

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 Leveraging AI to Predict and Reduce College Dropout Rates 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 in Cyber Defense: The Rise of Self-Healing Systems for Threat Mitigation”

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

Introduction to Self-Healing Systems

Definition and Functionality of Self-Healing Cybersecurity Systems

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

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

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

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

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

Key Features of Self-Healing Systems

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

Advantages Over Traditional Cybersecurity Methods

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

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

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

Challenges and Limitations

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

Real-World Applications of Self-Healing Systems

Financial Institutions

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

Healthcare Industry

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

Government and Defense

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

Future Outlook

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

Conclusion

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

References

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

About the Authors:

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

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

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

SwissCognitive Guest Blogger: Eleanor Wright, COO at TelWAI – “A New Era of Intelligent Robots”

Imagine a world where humanoid robots cook for you, care for your loved ones, and streamline your workday – all powered by AI smarter than ever before. The global AI in robotics market, projected to surpass $124 Billion by 2030, is set to make this vision a reality. As the capabilities of AI evolve, these machines will become our companions, caregivers, and coworkers, they’ll make mobility more affordable, transform access to services, and redefine the value of human effort.

From Amazon’s fleet of 750,000 warehouse robots to Tesla’s ambitions to build 10,000 humanoid Optimus robots this year, the age of robots is upon us. Dependent on sensors and actuation systems to navigate and interact with the physical environment, this new age of robotics hinges on the developments of AI, designed to mimic and learn from its biological makers. Equipping these robots with intelligence, engineers working across various domains of expertise, utilise AI to enable vision, natural language processing, sound processing, pressure sensing, and more.

Beyond sensing, AI also enables robots to reason, adapt, and learn, using approaches including—but not limited to—reinforcement learning, neural networks, and Bayesian networks. These models and methods enable robots to assess risks and determine actions, and by learning from experience, robots can adapt to new tasks and environments. Thus, AI enables robots to perceive, act, learn, and adapt, allowing them to perform tasks with greater autonomy and precision.

However, integrating AI into robotics isn’t seamless, it comes with hurdles. Robots struggle with real-time processing delays, adapting to messy unpredictable environments, squeezing efficiency from limited hardware, and understanding human quirks like vague commands or gestures. These challenges constrain capabilities and the pace at which robots enter and dominate markets.

So, how can these challenges be addressed?

Some developments in addressing these challenges include:

1. Parallel computing

Parallel computing involves dividing larger tasks into smaller, independent tasks that can be processed simultaneously rather than sequentially. This enables increased computational efficiency, reduced latency, and improved cost efficiency. In robotics, parallel computing allows robots to process inputs from LIDAR, radar, and cameras simultaneously, enabling them to navigate environments more effectively and efficiently.

2. Transfer learning

Transfer learning leverages pre-trained models to solve new, but similar, problems. In this approach, a model trained on one task or dataset is reused and fine-tuned for a related task. For example, in machine vision for defect detection in manufacturing, fine-tuning a pre-trained model on a smaller dataset of images allows it to quickly adapt to detect specific defects, such as cracks or dents, without needing to train a model from scratch.

3. Self-calibrating AI

Self-calibrating refers to AI systems that autonomously adjust their parameters, models, or processes to maintain optimal performance without manual intervention. In robotics, self-calibrating AI enables robots to adapt to changes in their environment, hardware, or tasks, ensuring they operate with optimized accuracy and efficiency over time.

4. Federated learning

Federated learning is a technique that enables AI systems to learn from distributed data sources whilst ensuring privacy and security. It allows AI to collaboratively train a shared model without transferring sensitive data, preserving privacy and reducing reliance on centralised storage. For example, delivery robots use federated learning to optimise pathfinding without sending raw data, such as sensor inputs or location, to a central server. Instead, they locally update their models and share improvements, preserving both privacy and security.

These developments indicate a key focus on efficiency, adaptability, and learning – all of which are essential for the continued evolution of robotics in complex, real-world environments. Additionally, these advancements contribute to a future where robots collaborate with humans, leveraging their ability to learn from experience and improve over time.

So, what’s next for AI in Robotics?

Just as AI agents are taking over the digital realm, they are about to flood robotics too. AI agents embedded in robotics will supercharge the autonomy and flexibility of robots, enabling them to communicate with humans and even interpret intentions by analysing gestures and potentially emotional cues. Crucial to human-robot interactions, AI agents may prove highly effective in assisted care, hospitality, and other service industries.

Additionally, as technologies like federated learning and edge computing evolve, robots will share knowledge without compromising privacy or relying on centralised data. This will improve scalability and efficiency by reducing the need for costly centralised storage and processing, and enable additional robots to integrate rapidly into existing networks.

So, where does this leave us?

Although there are abundant market opportunities for AI in robotics, the pace at which different markets adopt robotics will vary; with AI being a key factor driving this adoption. Crucial for overcoming challenges related to autonomy, adaptability, and decision-making, AI will empower robots to perform tasks once considered too complex or risky for automation. As AI continues to evolve, it will not only raise important concerns about safety, ethics, and integration but help address them; ensuring robots can work seamlessly alongside humans and contribute to a more productive future.

About the Author:

Holding a BA in Marketing and an MSc in Business Management, Eleanor Wright has over eleven years of experience working in the surveillance sector across multiple business roles.

Der Beitrag A New Era of Intelligent Robots – AI and Robotics erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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

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

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

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

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

Different types of AI solutions for disabilities

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

Communication tools

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

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

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

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

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

Mobility and navigation

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

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

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

Education and workplace accessibility

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

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

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

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

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

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

Home automation and daily assistance

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

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

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

Existing barriers

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

Lack of universal design principles

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

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

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

Limited training datasets for specific AI models

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

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

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

High cost of AI projects and limited funding

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

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

Lack of awareness and resistance to adopt new tools

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

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

Regulatory and ethical gaps

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

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

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

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

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

Advances in AI

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

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

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

Partnerships

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

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

New solutions

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

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

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

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

Wrapping up

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

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

Nevertheless, all efforts are likely to pay off.

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

About the Author:

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

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

SwissCognitive Guest Blogger: Ashwin Tambe – “Enabling a Smart Consumer with AI based Search Experience”

A smart consumer search experience empowers shoppers to make informed decisions quickly and confidently. It transcends traditional keyword matching to grasp the deeper intent behind a search query. This includes anticipating needs by suggesting relevant products or services before the consumer even realizes they need them, offering personalized recommendations based on past browsing behavior and purchase history, and seamlessly comparing products and prices across different retailers.

Advances in semantic search, AI, and natural language processing are enabling search engines to better understand user intent, deliver personalized recommendations, and facilitate conversational interactions.

Semantic Understanding and Organized Responses

Traditional keyword-based search is giving way to semantic search, where engines try to understand the intent and context behind a query. Search is getting a major upgrade! Imagine a giant encyclopedia that not only stores information but also understands how different concepts are connected. This is what search engines are building: massive knowledge graphs that link people, places, things, and ideas together. By analyzing these connections, search engines can grasp the deeper meaning of your queries, even if you don’t use the perfect words. For example, if you search for “best running shoes for beginners,” the search engine can understand that you’re not just looking for any running shoes, but for shoes that are specifically designed for people who are new to running. This allows the search engine to deliver more insightful results, such as reviews that focus on comfort and support for new runners, or comparisons that highlight features like shock absorption and breathability.

Personalized Recommendations

Search engines are getting to know you better! By remembering your past searches, location, and other bits of information, they can curate results that fit your interests. Imagine searching for “hiking trails” and seeing suggestions for beginner-friendly paths near your city, based on your previous searches for outdoor activities.

Behind the scenes, powerful algorithms are sifting through mountains of data, like detectives looking for clues. They recognize patterns and make predictions to personalize your experience. This might mean suggesting new cookbooks based on past recipe searches, or recommending movies similar to ones you’ve enjoyed before.

Even chatting with search engines is getting a makeover! Instead of clunky text interfaces, AI-powered assistants are emerging that can have natural conversations. These chatbots can answer your questions and offer help in a more conversational way, just like talking to a friend. Imagine asking “What are the best things to do in Paris?” and having a friendly AI chat back with personalized suggestions based on your interests and travel style.

Unified Experience – One destination Endless possibilities

Imagine a world where interacting with technology feels effortless and intuitive, anticipating your needs and desires before you even express them. This is the vision of Artificial Intelligence (AI) and its role in crafting a unified user search experience.

Traditionally, navigating the digital world has often been a disjointed experience. We juggle between various apps, websites, and devices, each with its own logins, interfaces, and functionalities. AI has the potential to bridge these gaps, creating a seamless and unified experience across all interaction points.

One way AI achieves this is through personalization. By intelligently analyzing our behavior, preferences, and past interactions, AI can tailor the user experience to our individual needs. For instance, an AI-powered virtual assistant might proactively suggest restaurants based on our recent searches and past dining habits. Similarly, an e-commerce platform might curate product recommendations that align with our interests and purchase history. This eliminates the need to endlessly search through countless options, saving us time and frustration.

AI also fosters foresight. AI algorithms can anticipate our needs and provide assistance before we even request it. Imagine a smart home system that automatically adjusts the temperature based on your daily routine or a fitness tracker that prompts you for a workout when you’ve been inactive for too long. This level of anticipation creates a sense of flow and removes the need for constant manual interaction.

Furthermore, AI can break down language barriers. Imagine traveling to a foreign country and being able to have a natural conversation with locals through an AI-powered translator that understands context and subtleties. This removes communication hurdles and opens doors to richer cultural experiences.

The possibilities of a unified experience powered by AI extend far beyond personal use cases. In the healthcare industry, AI can analyze patient data to provide more personalized treatment plans and improve overall health outcomes. In the education sector, AI-powered tutors can adapt to individual learning styles, creating a more effective and engaging learning environment.

However, it’s important to acknowledge the ethical considerations surrounding AI and user experience. Data privacy concerns and the potential for prejudice in algorithms need to be addressed to ensure a truly unified and positive experience for all.

Overall, AI has the potential to revolutionize the way we interact with technology. By creating a unified experience that is personalized, proactive, and removes language barriers, AI can empower us to achieve more and unlock a world of endless possibilities.

About the Author:

Ashwin Tambe, (Delivery Management Google , Retail CPG) is a management professional with expertise in enabling customers with AI adoption and bridging business gaps with use of modern technology. Ashwin actively engages in the public discourse on Large Language Models (LLMs) by sharing his insights through articles published on various digital platforms, exploring their consumption, societal impact, and potential role in shaping the future. Beyond his professional experience, Ashwin actively contributes to the academic community.he is serving as a judge and student mentor in University of Arlington Texas.

Der Beitrag Enabling a Smart Consumer with AI based Search Experience erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Zachary Amos – “How AI Reduces Physician Burnout”

Burnout rates among health care providers have been on the rise since the pandemic, with severe implications for broader public health. Advanced artificial intelligence systems can play a critical role in addressing this challenge, improving work-life balance among physicians for better patient outcomes. Explore the different ways to implement AI and machine learning (ML) to help mitigate physician burnout.

1. Automating Administrative Tasks

In a recent survey of 1,000 medical professionals, 93% said they felt burned out regularly. Nearly 50% considered quitting their jobs or stopping patient consultations altogether because of the pressures associated with the job.

One of the biggest causes of this work fatigue is excessive administrative tasks, such as scheduling, billing and documentation. According to Statista, 62% of U.S. physicians in the United States found these bureaucratic functions increasingly cumbersome, contributing immensely to burnout.

AI can automate routine administrative duties, allowing doctors and nurses to focus more on patient care. For example, AI-powered natural language processing (NLP) tools can transcribe physician-patient conversations into electronic health records, reducing the time spent on data entry. Physicians must carefully review notes before uploading them, but the time saved is still significant and can go a long way in reducing work stress.

2. Addressing Staff Shortages

The health care sector has been coping with a sustained worker deficiency since the pandemic — and things appear to be worsening. Experts estimate the U.S. will face a shortfall of 124,000 physicians and nearly 200,000 nurses by 2030.

As a result, medical staff spend more hours at work, increasing job dissatisfaction. Around 37% of burnout cases stem from this issue, highlighting the need for innovative solutions.

AI tools can effectively bridge gaps in health care worker shortages by automating routine monitoring tasks, allowing doctors to focus on more complex patient needs. For instance, these systems can continuously track vital signs and health metrics, alerting providers only when intervention is necessary. These real-time insights enhance decision-making capabilities for existing staff, enabling them to manage larger patient loads more efficiently.

3. Predictive Analytics for Patient Management

Health care analytics can contribute to stress and burnout by overwhelming doctors with excessive data and complex reports requiring time-consuming analysis. The pressure to quickly interpret vast amounts of information can lead to cognitive overload, reducing job satisfaction.

Additionally, analytics tools that are poorly integrated into workflows can create inefficiencies, forcing physicians to spend more time on non-patient tasks. This imbalance between data demands and actual clinical work can exacerbate feelings of frustration, ultimately leading to increased burnout.

Advanced ML systems can identify patterns and anomalies in patient data much quicker than humans, allowing for proactive care management. For example, AI predictive analytics can enhance early disease detection with up to 95% accuracy, alleviating the workload burden on physicians. These insights can also help forecast which patients are at risk of hospital readmission, enabling early intervention.

4. Bridging Training Gaps

Inadequate training or resources can significantly contribute to burnout. Physicians become frustrated when unprepared for work challenges, whether due to rapid advancements in medical technology, evolving treatment protocols or complex patient cases.

ML-driven platforms can assess individual knowledge gaps and learning preferences to create tailored training programs. These systems can work directly with AI-based virtual reality and augmented reality simulations, allowing health care professionals to practice procedures in a risk-free environment.

This targeted training helps physicians feel more prepared, improving decision-making and reducing anxiety during patient care. Additionally, increasing competence and procedure familiarity can shorten the learning curve, allowing doctors to manage their workloads more effectively and ultimately decreasing burnout.

AI Is Vital to Physician Fulfillment

Integrating AI in health care presents a promising avenue for reducing stress and improving overall career fulfillment. AI and ML systems can improve work-life balance by automating administrative tasks, addressing skilled labor shortages and streamlining clinical processes. As these technologies evolve, their potential impact on physician well-being will likely grow, contributing to improved job satisfaction and patient care quality.

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 Reduces Physician Burnout erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: cio.com – “The Role Of AI In Operational Efficiency: Beyond The Silver Bullet”

AI has transformational power, but for most enterprises, focusing on operational efficiency rather than miracles is far more valuable.

Artificial Intelligence (AI) has earned a reputation as a silver bullet solution to a myriad of modern business challenges across industries. From improving diagnostic care, to revolutionizing the customer experience, there are many industries and organizations that have experienced the true transformational power of AI.

However, that’s not the case for the masses. And organizations that view AI as a fix-all are missing a huge opportunity—and are also likely to encounter significant challenges. When AI is applied in a way that overemphasizes its strengths and downplays its weaknesses, that’s when we run into problems.

While we tend to hear more about innovative, breakthrough AI use cases, the real value of AI lies in its ability to vastly improve operational efficiency. Is it less exciting than AI writing and producing its own songs or creating fine art in a matter of seconds? For sure. But for most businesses, a catchy tune or pretty picture aren’t going to move the needle.

The strengths of AI in modern business

AI’s ability to automate tasks, reduce errors, and make data-driven decisions at scale are its best lauded strengths. From predictive analytics to natural language processing (NLP), AI-powered applications enable faster and more accurate decision-making. In other words, the allure of AI lies in its ability to process vast amounts of data quickly, identify patterns that might be invisible to humans, and adapt to new information in real time.

These capabilities are undeniably valuable. In sectors like finance, healthcare, and manufacturing, AI-driven solutions have already proven their worth by optimizing supply chains, improving risk management, and enhancing customer service.[…]

Read more: www.cio.com

Der Beitrag The Role Of AI In Operational Efficiency: Beyond The Silver Bullet erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

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

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

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

Webster’s Self-Assessed Wisdom Scale (SAWS)

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

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

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

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

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

Ardelt Wisdom Scale

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

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

Comparative Analysis

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

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

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

Implications for Decision-Making

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

Conclusion

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

References:

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

About the Authors:

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

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

Der Beitrag AI As A Tool for Enhancing Wisdom: A Comparative Analysis erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

SwissCognitive Guest Blogger: Dilshad Durani – “How AI-Generated Creative Assets For Ads Help B2B Marketers?”

Do you know that well-known platforms including Google and Linkedin are adapting to AI-generated creative assets for ads?

They are doing so as it helps them provide advanced tools that satisfy B2B marketers’ requirements for improving their advertising strategy. Read this blog to find out more about how AI-generated creative assets for ads help B2B marketers.

Power of AI: How AI-Generated Creative Assets are Proven Game Changers for B2B Marketing?

AI is used by marketers for one or other purposes. According to a Statista report, more than 90% of marketing professionals across 35 countries use AI to automate customer interactions, while 88% of them use it to personalize the customer journey across channels.

Source: Statista

AI-generated creative assets provide various benefits across various aspects of advertising strategies. AI technology usage can help marketers streamline business processes, improve efficiency, and scale campaigns.

Implementing AI optimizes ad spending after analyzing data to target audiences with personalized content which ultimately leads to improved ROI. Moreover, this modern technology ensures consistency in advertising. AI helps to improve productivity and implement innovative approaches to engage customers, helping B2B marketers to grow in today’s competitive market.

LinkedIn’s Innovations with AI in Advertising

LinkedIn has recently introduced several enhancements aimed at empowering B2B marketers through AI-generated creative assets. Wire Programs is one of them, it can be integrated in-stream video ads with premium publisher content. It expands B2B ads’ reach and also ensures the ads are placed in relevant contexts.

Additionally, LinkedIn’s Accelerate tool utilizes AI to streamline campaign management. It offers functionalities like Microsoft Designer integration for creative customization, enhanced targeting capabilities through exclusion lists, and an AI marketing assistant that provides valuable campaign guidance.

Google’s Approach to AI in Advertising

AI-generated creative assets for ads are also introduced by Google which delegates businesses to provide personalized experiences to their targeted audience. The transformation of artificial intelligence helps marketers redefine how the ad campaign is carried out to reap fruitful results.

With Google adopting this change, AI-driven ads are set to change the face of B2B marketing, opening possibilities of budding strong connections with their targeted audience on a deeper level via innovative approaches.

Generative AI use enables brands to automatically create assets that meet their branding guideline. This can prove to be beneficial for painting consistency across all the advertising channels. Besides it also ensures that your ads campaign helps you provide a personalized experience to your targeted audience.

The immersive ad format is also being introduced by Google, these include:

• Virtual try-ons
• 3D visualizations

The above-listed ad format uses AI to provide an improved experience to your targeted audience. This empowers brands to showcase their products and services in more improved and engaging ways which was not possible for them previously.

YouTube’s Innovative Ad Strategies: Spotlight Moments

YouTube pioneers unique AI-powered ad strategies with initiatives like Spotlight Moments. This advanced functionality helps to identify the trendy moments including award shows or sports events, facilitating advertisers to run ads across relevant streaming content. This helps B2B marketers ensure that their ads can create maximum engagement across all YouTube channels.

Two campaigns further import the effectiveness, these include:

• YouTube’s AI-driven Video Reach
• YouTube’s AI-driven Video View

The above-listed approaches have resulted in significant growth in reach. Compared to the traditional approach it results in a per-impression cost decrease. YouTube’s AI-driven ad delivery and targeting helps marketers improve their ad reach and deliver engaging messages.

Incorporating a streaming platform like YouTube into AI-powered advertising strategies allows brands to invest in platform engagement capabilities. This helps to boost the ad’s visibility and ensure that it is visible to viewers at the right time, resulting in rising business profits.

The video-sharing platforms remain at the top as they continuously adapt to change with AI-driven ad solutions like Spotlight Moments. This helps YouTube to provide unparalleled opportunities for marketers to reach their targeted ways in meaningful ways.

Disney’s Implementation of AI in Advertising

Disney has embraced AI in its advertising strategy, particularly in optimizing ad placement and creative development. Rita Frow, Disney’s president of Global Advertising, highlights the use of AI in their advertising technology stack.

AI algorithms are utilized to optimize yield across their tech stack, from ad placement to creative management. This approach allows Disney to enhance targeting precision and optimize ad performance based on real-time data insights.

Disney uses AI in different processes to efficiently manage creative content. It helps video streaming platforms to streamline their workflows and maintain a high standard of streaming content offering. AI integration helps to boost Disney’s ad campaign efficiency to resonate with audience expectations.

Benefits of AI-Generated Creative Assets for B2B Marketers

Don’t you agree that AI has introduced a new era of efficiency in B2B ad campaigns?

Of course, it has more in creative asset development. There are lots of perks waiting on the way for B2B marketers who are using or thinking of using AI-generated creative assets to improve their ads approach and overall business outcomes.

Cost-Effectiveness and Improved ROI

Do you know that AI-powered tools optimize advertising spending?

It analyzes a great amount of information to identify the most effective targeting parameters and content variations. This targeted approach reduces wasted ad spend and improves ROI by ensuring that marketing budgets are allocated towards high-performing strategies.

McKinsey reports that companies using AI for marketing and sales initiatives see an average increase in leads and appointments of more than 50%, and cost reductions of 40-60% in areas such as call center operations and predictive maintenance.

Personalization at Scale

Offering improved and better experience is the backbone of a B2B advertising campaign. AI helps to analyze data and deliver personalized experiences to their segmented audience. It helps them to deliver tailored content that meets their audience’s behaviors.

Real-Time Optimization and Insights

Real-time insight and AI-driven analytics help marketers track customer behavior and their viewing habits. This in the end facilitates marketers to optimize their ad campaigns, driving more engagement and profit for brands.

PwC artificial intelligence study shows that organizations utilizing AI in marketing are 2.6 times more likely to surpass their revenue targets compared to those brands that don’t invest in this modern tech.

Source: PwC

Consistency in Brand Messaging

It becomes essential for brands to maintain brand messages across different channels as it helps to build trust. AI-generated creative asserts brand voice, regardless of the targeted segment or platform brands use.

Future Outlook

AI’s future in B2B marketing is undoubtedly optimistic. Modern technology including NLP, ML, and predictive analytics improves the complexity of AI-generated creative assets. Additionally, AI integration with other technologies like AR and VR opens unexplored possibilities for marketing approaches.

AI-generated creative assets represent a paradigm shift in B2B marketing, providing unparalleled efficiency and personalization capabilities. AI technologies implementation helps B2B marketers streamline their operations and engage their targeted segments, resulting in sustainable growth in today’s competitive market.

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 Soundcloud clone and open-source event ticketing system development. Her insatiable curiosity fuels a relentless pursuit of knowledge, driving her to unravel the intricacies of changing trends, evolving marketing approaches, and ethical business practices.

Der Beitrag How AI-Generated Creative Assets For Ads Help B2B Marketers? 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.

Der Beitrag Will Scaling Solve Robotics? erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.