The journey of Thaer, a Social Media Specialist at Kuality AI, is a testament to the power of curiosity and adaptability. Thaer’s drive to learn and his belief in the transformative power of knowledge has allowed him to build a diverse career that transcends the boundaries of his academic degree in Informatics Engineering.
Beyond the Degree: Thaer’s Exhilarating Journey
Thaer describes his journey as an “exhilarating ride”, filled with opportunities to grow and learn. His academic degree laid the groundwork for his career, but it was his insatiable curiosity that led him to explore various fields and industries. Thaer believes that each role he’s held has served as a stepping stone, enabling him to acquire new skills and broaden his horizons. He finds beauty in this approach, as it continually allows him to learn, adapt, and gain invaluable insights, regardless of the industry or domain. Thaer’s journey underscores his belief that knowledge isn’t confined within the walls of one’s academic degree; it’s a boundless entity that can be acquired from various sources and experiences.
Driving the Mission of Kuality AI: Thaer Mohammad’s Role as a Social Media Specialist
Currently, Thaer is harnessing his versatile skill set at Kuality AI, As a Social Media Specialist, Thaer plays a pivotal role in driving the company’s mission forward. His responsibilities involve creating engaging content and fostering connections with the target audience to increase brand awareness and promote the company’s values. Thaer’s strategic planning and execution contribute to the company’s growth and success, highlighting the significant role he plays in the company.
A Thirst for Knowledge: Thaer’s Motivations and Inspirations
Thaer’s motivation stems from his desire for personal growth and his aspiration to improve the quality of life. He is driven by his innate curiosity and thirst for knowledge, which extends to both literary books and scientific articles. Technology advancements and the challenges posed by climate change particularly engross him. Thaer is also inspired by the idea of enhancing the quality of life for individuals and communities, which fuels his drive for personal growth and knowledge acquisition.
Problem-Solving Approach: A Key to Adaptability and Growth
When faced with challenges, Thaer maintains a composed demeanour, distancing himself from the emotions of the situation to see it objectively. This approach enables him to manage the problem and find viable solutions effectively. He utilises a problem-solving mindset, analyzing the situation from different angles, and seeking input from others when necessary. Thaer’s strategy allows him to navigate difficulties with clarity and resolve, further contributing to his growth and adaptability.
Climate Advocacy and Research: Thaer’s Volunteer Work at the Syrian Climate Pioneers
In addition to his role at Kuality AI, Thaer is also a volunteer at the Syrian Climate Pioneers. He initially joined as a blogger to raise awareness about climate change and responsible consumption. Recently, he assumed leadership of a research group within the foundation. Their team focuses on researching climate change’s impact on various aspects of life. They aim to highlight the challenges faced by communities and industries and explore potential solutions for a sustainable future, demonstrating Thaer’s commitment to climate change advocacy.
Guided by Core Values: How Thaer Navigates Life and Career
Thaer’s journey is marked by his adaptability and willingness to venture into new fields. He holds creativity, empathy, gratitude, and responsibility as his core values. These values guide him in making decisions that align with his principles and contribute positively to society. They drive him to approach challenges with innovative solutions, connect with others on a deeper level, appreciate the opportunities life presents, and take ownership of his actions and their consequences.
Teamwork and Collaboration: Thaer’s Approach to a Healthy Team Environment
In terms of working within a team environment, Thaer believes in the power of collaboration and adaptability. He prioritizes active listening to understand others’ perspectives and contribute meaningfully to discussions. He fosters an environment of growth and mutual respect through giving and receiving constructive feedback. Thaer embraces changing situations by maintaining a flexible mindset, identifying opportunities for learning and improvement, and supporting his team members in their endeavours.
Professional Aspirations: Thaer’s Goals in AI and Climate Change Research
Thaer’s current professional development goals involve enhancing his skills and abilities in working with artificial intelligence software. This would allow him to harness the power of AI more effectively in his role as a Social Media Specialist at Kuality AI. Thaer also aims to continue expanding his knowledge and expertise in the field of climate change research, focusing on finding innovative solutions to mitigate its impact on communities and the environment.
Advice to Peers: Continuous Learning, Strong Work Ethic, and Meaningful Relationships
Thaer offers some advice to his peers, encouraging them to never stop learning and exploring new areas of interest. He believes it’s essential to cultivate a strong work ethic and a proactive mindset, alongside taking initiative, seeking out challenges, and being persistent in pursuing goals. Thaer also emphasizes the importance of building meaningful relationships and networks, collaborating with others, sharing knowledge, and supporting one another. He believes that together, we can create a more prosperous and sustainable future.
Thaer Mohammad’s journey is a reminder that success isn’t confined to professional accomplishments. It’s about the continuous pursuit of knowledge, a willingness to adapt and grow, and a commitment to making a positive impact on society. His story is an inspiration for those who aspire to break the boundaries of their academic degrees, venture into new fields, and contribute to a better future.
Elias Toome, an AI engineer at Kuality AI, shares insights on the essential tools and frameworks for aspiring AI professionals, staying current with advancements in AI, pivotal projects and experiences, and the importance of soft skills in the AI field.
Discovering a Passion for AI Elias’s fascination with AI began during his university days when he stumbled upon a graduation project called “Smart Mirror.” The idea of a simple mirror transforming into an intelligent device capable of recognizing users and providing information intrigued him. This project sparked Elias’s curiosity and led him to explore the world of AI and machine learning (ML) further.
The Allure of Computer Vision and Machine Learning As Elias delved deeper into AI, he found himself particularly drawn to computer vision, the ability of machines to recognize and understand visual information. He was captivated by the potential applications of computer vision in various fields, such as identifying organisms in nature or diagnosing diseases at an early stage. Elias believes that the limitless possibilities for learning and growth in this area are what make it so compelling.
Growing as a Junior AI Engineer at Kuality AI Joining Kuality AI, an Emirati company specializing in AI technologies, allowed Elias to work with a team of experts and learn from their experiences. One of the most significant projects he was involved in was “ThirdEye” which employed computer vision and machine learning algorithms to reduce road accidents. Working on this project exposed Elias to various challenges and helped him develop new problem-solving approaches.
Overcoming Challenges in AI Engineering Elias acknowledges that with every new project comes a set of challenges, especially when it comes to obtaining sufficient data for AI applications. While there are numerous resources for free data, most are only suitable for academic or research purposes. To overcome this obstacle, Elias suggests seeking the help of data scientists or partnering with specialized companies.
Technical challenges in coding and algorithm complexity are also inevitable in the field of AI. Elias recommends turning to experts for advice, as there are always more experienced engineers who may have faced similar challenges. Platforms dedicated to AI discussions can be particularly helpful in this regard.
The Impact of Multidisciplinary Experience Before delving into AI, Elias worked as a webmaster and engaged in front-end and back-end development, This experience taught him the value of adaptability and ambition, as he quickly transitioned from focusing solely on front-end tasks to managing entire websites.
Elias’s multidisciplinary background has not only complemented his AI endeavours but has also fostered a growth mindset that is vital for success in the ever-evolving AI landscape. He believes that there is always something new to learn, and having a limitless ambition for knowledge is essential for thriving in the AI industry.
Essential Tools and Frameworks for Aspiring AI Professionals Elias emphasizes that problem-solving is a crucial skill for any engineer, as their main task is to find solutions to various problems, often under constraints like cost or time. Python is the go-to programming language for AI, thanks to its simplicity and ease of use.
For more advanced learners, Elias recommends familiarizing themselves with TensorFlow, Google’s end-to-end open-source machine learning platform. TensorFlow offers several benefits, including ease of use, compatibility with various devices, the ability to use GPUs for training, and flexibility when working with real-time projects. It also supports devices with lower capabilities, such as mobile phones.
Staying Current with AI Advancements In the fast-paced world of AI, staying updated on the latest tools and frameworks is essential. Elias uses social media, particularly LinkedIn, to stay connected to the AI community. However, he advises caution when reading articles on social media, as not all information is reliable. He considers social media a gateway to more credible sources like Medium.com, which features technical articles written by engineers about their experiences and challenges.
Github.com is another valuable resource, as it contains projects from individuals and companies, showcasing the tools and algorithms they use to solve specific problems or implement real-time features.
Elias also credits his network of experts and mentors for keeping him up to date on the latest AI advancements. These connections enable him to attend global conferences and learn from the best in the field.
Pivotal Projects and Practical AI Knowledge Elias’ first project that combined his theoretical knowledge from university with practical experience was “Dawwar” a university graduation project. Alongside four fellow AI engineering students, they aimed to solve transportation issues and reduce costs through a mobile app that used machine learning algorithms to track users’ routines and match them with others who shared similar patterns. This project taught Elias how to apply AI algorithms and utilize mobile phone sensors in real-world applications.
Soft Skills and Qualities for Success in AI In addition to technical skills, soft skills play a significant role in AI professionals’ success. Patience is vital, as the field is research-oriented and often requires multiple experiments to achieve the desired results. The ability to learn and read is also crucial, as the AI landscape is continually evolving, with new tools and solutions emerging regularly. Since there’s no limit to knowledge in AI, continuous learning is a must.
Advice for Those Considering a Career in AI For individuals feeling overwhelmed or unsure about starting a career in AI, Elias encourages them not to be intimidated. The field of AI is expanding rapidly, with new algorithms and improvements consistently being developed. The industry has room for everyone and welcomes diverse ideas and innovations.
Elias believes that AI will soon become an integral part of our daily lives, much like mobile phones. AI has the potential to save time, money, and energy and even preserve human lives by taking on complex and dangerous tasks. The future of AI promises to be an exciting and rewarding journey for those who choose to pursue it.
Conclusion Elias’s journey in the world of AI and computer vision has been marked by passion, curiosity, and a hunger for knowledge. His experiences at Kuality AI, have allowed him to grow both professionally and personally. By embracing the challenges that come with AI engineering and leveraging his multidisciplinary background, Elias continues to make significant contributions to the field and inspire others to join him on this exciting journey.
The age of artificial intelligence (AI) has revolutionised various aspects of our lives, and one such domain is road safety. With the advent of AI-powered devices, companies are taking the initiative to ensure safer roads and communities. One such breakthrough innovation is ThirdEye, an AI-powered device by Kuality AI. This device aims to decrease road accidents, enhance compliance, and create safer cities. ThirdEye offers three unique solutions: A driver Assistance System, Driver Monitoring System, and Command & Control. In this article, we’ll explore the impact of ThirdEye on delivery company efficiency and driver safety.
Delivery Companies Grapple with Safety Challenges Amid High Accident Rates and Regulatory Compliance
The delivery industry has witnessed a significant boom in recent years, thanks to the growing demand for e-commerce and quick deliveries. As a result, delivery companies are under immense pressure to ensure timely and efficient deliveries. However, the need for speed should not come at the expense of safety. Delivery companies are facing considerable challenges in ensuring the safety of their drivers and the efficiency of their operations. One of the most significant challenges is the high rate of accidents and collisions that occur on the roads. These accidents not only put the lives of drivers at risk but can also result in expensive insurance claims and legal battles. Additionally, delivery companies must also comply with various regulations and standards to ensure the safety of their drivers and the public. This is where ThirdEye comes into play.
How ThirdEye Transforms Delivery Companies
The ThirdEye can greatly improve the safety of delivery drivers and reduce the likelihood of accidents. This device uses advanced AI algorithms and sensors to detect potential collisions and alert drivers in real time. It can also provide coaching and feedback to drivers to help them improve their driving skills and reduce the risk of accidents. Moreover, ThirdEye can collect and analyse data on driver behaviour, route information, and road conditions to help companies identify areas for improvement and optimise their operations.
In addition to improving driver safety, ThirdEye can also increase delivery company efficiency. By providing real-time data on driver behaviour and route information, companies can optimise their routes, reduce delivery times, and improve customer satisfaction. The device can also help companies comply with various regulations and standards, such as the Compliance, Safety, Accountability (CSA) scores, by providing data on driver behaviour and safety.
ThirdEye can also exonerate drivers from expensive insurance claims by providing evidence of their safe driving behaviour. This can help reduce insurance costs and improve driver morale by rewarding safe driving practices. Additionally, the device can be used to coach drivers and help them improve their skills, reducing the risk of accidents and improving overall driver performance.
ThirdEye Solutions for Delivery Companies:
Driver Assistance System
With the help of the Driver Assistance System, ThirdEye provides predictive collision alerts that help drivers anticipate risks caused by other road users such as drivers, cyclists, and pedestrians. The system also keeps track of changing traffic signals and other road conditions. By providing real-time, actionable information, ThirdEye enables delivery drivers to make informed decisions that can prevent accidents and ensure safer roads.
Driver Monitoring System
Delivery drivers often work long hours and cover vast distances, which can lead to fatigue and unsafe driving behaviour. ThirdEye’s Driver Monitoring System uses AI algorithms to analyse facial movements and other physical indicators to detect unsafe driver behaviour in real-time. By detecting drowsiness, distraction, or other potential hazards, the system can alert drivers and managers, ensuring necessary action is taken to prevent accidents.
Command & Control System
ThirdEye’s Command & Control solution offers an effective way to monitor driver safety and performance. It consolidates all driving events and behaviours into one comprehensive score, allowing delivery companies to track drivers’ safe driving habits over time. The system also enables communication with drivers on demand, facilitating coaching and feedback.
The Impact on Delivery Company Efficiency and Driver Safety
The implementation of ThirdEye’s solutions has led to significant improvements in delivery company efficiency and driver safety. Here are some of the notable impacts:
1. Reduced Accident Rates: With real-time alerts and monitoring systems in place, drivers can anticipate risks and take appropriate action, leading to a reduction in accidents.
2. Improved Compliance: By monitoring driver behaviour and ensuring adherence to safety regulations, ThirdEye helps delivery companies maintain their Compliance, Safety, and Accountability (CSA) scores, reducing the risk of penalties and fines.
3. Lower Insurance Costs: By reducing the number of accidents and enhancing compliance, delivery companies can benefit from lower insurance premiums and avoid costly claims.
4. Enhanced Driver Training: The data collected by ThirdEye can be used to coach drivers, helping them improve their driving habits and overall safety on the road.
5. Increased Operational Efficiency: By ensuring drivers are operating safely and efficiently, delivery companies can optimise their resources and reduce delays, leading to increased customer satisfaction and a stronger bottom line.
In conclusion, By implementing AI-powered solutions like ThirdEye, delivery companies can ensure the safety of their drivers and the broader community while maintaining optimal operational efficiency. This innovative approach to road safety is a step forward in creating a safer and more efficient future for the delivery industry.
As the world of Artificial Intelligence expands at an unprecedented pace, Kuality AI has established itself as a prominent player in the industry. At the heart of our company Maher AL-Zehouri, is an accomplished AI engineer with a vast array of experience and knowledge in the field. In this exclusive interview, we have the privilege of exploring Maher’s insights and experiences, delving into the cutting-edge work being done at Kuality AI. Join us as we embark on an exciting journey into the world of AI, guided by one of its foremost experts. Can you tell us about your experience working with AI technologies and their impact on improving safety in organizations?
Maher: Over the last six years, I have worked on various AI projects ranging from basic games and apps using computer vision and augmented reality to more complex projects involving robotics, neural networks, IoT, machine learning, and deep learning. Through my experience, I strongly believe that AI can play a crucial role in enhancing safety in organizations by identifying potential hazards, analyzing data to find patterns, and providing recommendations based on that data.
For instance, AI can be utilized to monitor and detect anomalies in equipment performance or identify hazardous conditions in the workplace. By using AI-powered predictive maintenance systems, organizations can prevent equipment failures and improve overall safety. These systems analyze data from sensors and other sources to predict when equipment is likely to fail and recommend maintenance actions.
Moreover, AI can also aid in risk assessment and management by analyzing data from accident reports, maintenance logs, and inspection records. This information can help identify potential hazards and risks in the workplace, allowing organizations to develop strategies to reduce accidents and improve overall safety.
What are some of the biggest challenges organizations face when implementing AI to improve safety, and how do you address them?
Maher: Implementing AI to improve safety in organizations can be a complex and challenging process. One of the main challenges is ensuring high-quality, consistent, and complete data, which is necessary to effectively use AI models. However, many organizations may not have access to the necessary data or may encounter data that is incomplete or inconsistent, which can make it difficult to develop effective AI models. Another challenge is integrating AI with existing systems and processes, which may require significant changes to workflows and processes to fully leverage the benefits of AI.
In addition to data quality and integration challenges, regulatory compliance can be a significant concern for organizations, particularly in highly regulated industries such as healthcare and finance. Organizations must ensure that their use of AI is compliant with relevant regulations and data privacy laws, which can be complex and constantly evolving.
Finally, organizations may struggle with a lack of AI expertise, making it difficult to develop and implement AI models. Finding and retaining qualified AI professionals can be a challenge, and organizations may need to invest in training and development programs or partner with external experts.
To address these challenges, organizations can take several steps. Firstly, they can invest in data management tools and processes to ensure high-quality and easily accessible data. Secondly, organizations should plan for the integration of AI with existing systems from the outset to minimize disruptions and ensure a seamless implementation. Thirdly, staying informed about relevant regulations and engaging legal or regulatory experts can help organizations remain compliant. Lastly, investing in training and development programs to help employees develop the necessary AI skills or partnering with external experts can address the lack of AI expertise.
How does your company ensure the ethical and responsible use of AI when implementing safety measures?
At our company, we recognize the importance of the ethical and responsible use of AI in improving safety measures. To achieve this goal, we have implemented several practices that ensure the technology is used in a way that benefits society without causing harm. Some of these practices include:
Establishing clear guidelines and policies: We have established clear guidelines and policies that outline the ethical and responsible use of AI in safety measures. These guidelines cover areas such as data privacy, algorithmic transparency, and bias mitigation.
Conducting regular risk assessments: We conduct regular risk assessments to identify potential ethical and legal risks associated with the use of AI in safety measures. These assessments consider factors such as data privacy, algorithmic fairness, and the impact on human rights.
Using diverse and representative data: To avoid bias in AI models, we use diverse and representative data sets when developing and training AI models. This ensures that our models are fair and unbiased.
Ensuring transparency and explainability: We ensure that our AI systems are transparent and explainable, meaning that users can understand how the AI system is making decisions and question and challenge those decisions if necessary.
Engaging stakeholders: We engage with stakeholders, including employees, customers, and communities, to ensure that their concerns and perspectives are considered when developing and implementing AI-based safety measures.
By following these best practices, we can ensure that our use of AI in safety measures is ethical and responsible, benefiting both individuals and society.
Can you discuss a time when your company faced a challenge when implementing AI for safety measures, and how did you overcome it? One of the challenges we faced is when we implemented a face ID authorization system where privacy matters and we can’t store reference images for each face on the server. We overcame this challenge by extracting a custom number of key points and features of the faces images and only storing these encodings as encrypted text on the server which makes it impossible for outsiders to know the allowed persons’ identity.
What are some of the key technical capabilities and tools that AI offers to enhance safety in organizations?
AI offers a multitude of technical capabilities and tools that can significantly enhance safety in organizations. Here are some of them:
Predictive maintenance: AI-powered predictive maintenance systems can analyze data from sensors and other sources to predict when equipment is likely to fail and provide recommendations for maintenance. This can help organizations prevent equipment failures and reduce the likelihood of accidents.
Real-time monitoring: Real-time monitoring using AI can detect anomalies in equipment performance or identify potentially hazardous conditions in the workplace. By continuously monitoring and identifying potential safety issues as they arise, organizations can take corrective action immediately.
Natural Language Processing (NLP): NLP is a branch of AI that focuses on the interaction between humans and computers using natural language. NLP can be used to analyze safety-related data such as incident reports, safety manuals, and inspection records to identify patterns and trends and provide recommendations for safety improvements.
Computer Vision: AI-powered computer vision technology allows computers to interpret and understand visual information from the world around them. This can be utilized for safety inspections, hazard detection, and safety monitoring in industrial settings, construction sites, and other environments.
Autonomous systems: Autonomous systems such as robots and drones can perform hazardous tasks like inspections or repairs without exposing human workers to risks. These systems can be powered by AI technologies such as computer vision, natural language processing, and machine learning.
Simulation and modelling: AI-powered simulation and modelling tools can help organizations simulate safety scenarios, test safety protocols, and identify potential safety hazards before they occur. This can help organizations develop effective safety strategies and minimize risks.
How do you guarantee that your AI systems accurately identify potential safety risks while ensuring their reliability?
Ensuring the reliability and accuracy of AI systems in identifying potential safety risks is critical to their success. Here are some practices we follow:
High-quality data: The accuracy and reliability of an AI system depend on the quality of data used to train and test it. Organizations should ensure that they use relevant, high-quality data to train their AI models.
Ongoing testing and validation: To ensure that our AI systems continue to perform accurately and reliably, we test and validate them regularly. This includes comparing the results to the ground truth and making adjustments if necessary.
Human oversight: While AI can be a powerful tool for identifying potential safety risks, it should not replace human judgment entirely. We ensure that our AI systems have human oversight to validate and verify the results and take corrective action if necessary.
Regular maintenance and updates: We maintain and update our AI systems regularly to ensure that they continue to function accurately and reliably. This involves updating the algorithms, retraining the system on new data, and implementing new features or functionality.
By following these best practices, we can guarantee that our AI systems are reliable and accurate in identifying potential safety risks. This can help prevent accidents, reduce risks, and protect the well-being of employees and customers.
What advice would you give to companies looking to implement AI technologies to improve safety in their organizations?
Here are some pieces of advice for companies looking to implement AI technologies to improve safety in their organizations:
Clearly define the problem: Before implementing any AI technology, companies should clearly define the safety problem they are trying to solve. This includes identifying the scope of the problem, the specific safety risks they are trying to address, and the metrics they will use to measure success.
Start small and iterate: Implementing AI technologies can be complex and challenging. Companies should start with a small pilot project and iterate as they learn more about the technology and how it works in their specific context. This can help companies identify and address any challenges before scaling the technology across the organization.
Invest in high-quality data: The accuracy and reliability of an AI system depend on the quality of the data used to train and test it. Companies should invest in high-quality data that is relevant to the safety risks they want to detect.
Ensure regulatory compliance: Companies should ensure that their AI systems comply with all relevant regulations and standards for safety in their industry. This can include data privacy regulations, safety regulations, and other industry-specific standards.
Focus on ethics and responsibility: AI technologies can have a significant impact on society and individuals. Companies should ensure that their use of AI is ethical, responsible, and aligned with their values and principles. This includes ensuring transparency, accountability, and fairness in the use of AI.
Involve employees: Successful implementation of AI technologies for safety requires the involvement and support of employees. Companies should engage employees in the implementation process, ensure they are trained on how to use the technology, and address any concerns or questions they may have.
Overall, companies should approach AI implementation for safety with caution and care, focusing on clear problem definition, high-quality data, regulatory compliance, ethics and responsibility, and employee engagement. By following these best practices, companies can maximize the benefits of AI technologies for safety while minimizing the risks.
Looking into the future, how do you anticipate Artificial Intelligence (AI) will continue to enhance safety measures in organizations, and what new capabilities do you expect to emerge?
The role of AI in enhancing safety measures is expected to increase significantly in the coming years. One area where we can expect to see advancements is predictive analytics. AI systems are becoming more advanced and capable of identifying safety risks before they happen by analyzing large datasets to detect patterns and trends. This allows organizations to take preventive action and avoid accidents or incidents.
Another exciting development is the rise of autonomous systems. Drones and robots, which are powered by AI, are already being used to perform dangerous tasks and reduce the risk of accidents. As these systems become more sophisticated, they will be able to play an even greater role in enhancing safety within organizations.
Natural language processing is also a rapidly evolving field within AI that enables computers to understand and interpret human language. This technology is expected to enhance safety measures by enabling more effective communication between humans and machines.
Finally, edge computing is a distributed computing paradigm that allows AI systems to process data and make decisions closer to where the data is generated, reducing latency and enabling real-time decision-making. This technology will become increasingly important in enhancing safety measures within organizations.
In conclusion, AI is set to play an even more significant role in enhancing safety measures in organizations in the future. The emergence of new capabilities, including predictive analytics, autonomous systems, natural language processing, and edge computing, will help organizations identify and mitigate safety risks more effectively.
The Importance of School and University Bus Fleet Safety
Ensuring the safety of our students is paramount, and this rings especially true for those who rely on school and university buses to get to and from their classes. Every day, millions of young minds depend on these vehicles, and while school buses are widely considered to be one of the safest modes of transportation, there are still potential risks associated with approaching or leaving a school bus. It’s crucial that everyone involved – drivers, parents, schools, universities, and students – fully understand and adhere to school bus safety protocols.
Thankfully, ThirdEye fleet management solutions have revolutionized the way educational institutions can improve their bus transportation systems and guarantee the safety and security of their students. With an AI-based platform providing near-real-time awareness of the location and operations of every bus in the fleet, schools can streamline their operations and collect up-to-date insights.
In this article, we will explore the critical role of bus fleet safety in ensuring the well-being of our students and how ThirdEye solutions transform the way schools manage their transportation systems
The Limitations of Traditional Bus Safety Measures
When it comes to school and university bus fleet safety, traditional safety measures such as driver training and equipment maintenance are crucial. But, let’s be honest, sometimes they may not be enough to prevent all accidents and ensure student safety. The problem lies in other drivers on the road who may not follow traffic laws, like passing stopped school buses illegally. Additionally, traditional safety measures do not always account for the unpredictable behaviour of young passengers, which can lead to accidents or other grave consequences.
Moreover, any school is responsible for almost 2,000 students every day, shuttling them between two campuses and to various extracurricular activities. And let’s be real, delays and disruptions can be a nightmare for teachers, students, and parents alike. To make matters worse, traditional measures may not provide real-time monitoring and alerts. This can leave operators unaware of issues as they occur and hinder a timely response to incidents. It’s clear that we need new and innovative approaches to ensure the safety and security of students when they’re using school transportation. That’s where ThirdEye fleet management solutions come in handy. They can provide real-time monitoring, and automated driver assistance, among other features, to help overcome the limitations of traditional bus safety measures.
ThirdEye Technology: A Game-Changer for School and University Bus Safety ThirdEye Technology is making waves in school and university bus safety with its groundbreaking AI software. This amazing technology can help prevent collisions and minimize their impact by using the integrated data, which means better Compliance, Safety, and Accountability (CSA) scores and fewer costly insurance claims. The best part is that ThirdEye’s solutions can also help drivers become better by coaching them to improve their skills. ThirdEye’s Driver Assistance System is top-notch, with Predictive Collision Alerts that give drivers the heads-up on potential risks like other drivers, cyclists, pedestrians, and traffic signals. The Driver Monitoring System uses AI to analyse facial movements from internal cameras in real time and detect unsafe driver behaviour. Plus, the Command & Control feature sums up all driving events and behaviours into a simple score, making it easy to track drivers’ safe driving progress. ThirdEye AI offers a dual camera that can detect distracted and drowsy driving and potential risks inside and outside the vehicle. With predictive AI monitoring the driver’s face, it can detect distractions, cell phone usage, smoking, noise, no seatbelt, and more. The best part? ThirdEye’s AI-powered alerts can predict collisions before they happen, giving drivers more reaction time to avoid them. And don’t worry about privacy – the software only records collisions and high-risk events.
Results: a safer, more efficient bus fleet
Since the introduction of ThirdEye, schools and universities have been able to significantly improve the safety and efficiency of their bus fleets. Fleet managers now have complete visibility over their entire vehicle fleet, giving them peace of mind that they can keep track of their buses and students. And when it comes to driver behaviour, any problems can be identified and resolved before they become serious issues, thanks to ThirdEye’s full situational awareness. The system also provides real-time data monitoring, ensuring that any issues with the buses are immediately identified and addressed. For instance, when a bus suffered a spark plug failure during a field trip, ThirdEye’s diagnostics helped quickly identify and fix the problem, keeping the trip on schedule. With ThirdEye, fleet managers are not just running a bus fleet, they’re keeping kids safe and making parents’ lives easier.
Looking towards the future, the continued development and improvement of the ThirdEye system promise a brighter tomorrow for school and university bus fleets. The system’s ability to monitor drivers’ behaviour and detect drowsiness and distractions will undoubtedly play a crucial role in enhancing road safety. Furthermore, the adoption of this technology can potentially reduce the cost of insurance and maintenance for bus fleets, making it a cost-effective investment in the long run. Overall, the implementation of the ThirdEye system in school and university bus fleets has proven to be a revolutionary step and a testament to our continuous efforts towards improving safety on our roads.
Fleet management plays a crucial role in helping businesses optimize their fleet operations by reducing costs, improving efficiency, and ensuring compliance with regulatory requirements. This involves a range of activities, including vehicle maintenance and repair, fuel management, route planning, driver safety monitoring, and more. Fleet management systems have been a game-changer for fleet managers, providing them with real-time data and insights to make informed decisions and streamline their operations. These systems have evolved significantly over the years, leveraging the latest technologies such as GPS tracking, telematics, and artificial intelligence to deliver even greater value to businesses. Despite their complexity, fleet management systems are designed to make fleet management easier and more efficient, ultimately helping businesses achieve their goals and stay ahead of the competition.
How The Future of Fleet Management Will be Shaped by Artificial Intelligence
Looking ahead, it’s clear that artificial intelligence (AI) is transforming the way we live and work. While some may be hesitant, supporters of AI have high hopes for its capabilities. Essentially, AI works to simulate cognitive intelligence in computing systems, and its impact has been nothing short of remarkable.
Industries and businesses of all kinds are feeling the effects of AI, including fleet management. Fleet managers face the challenge of prioritizing driver safety while still maintaining cost efficiency, and AI-powered solutions like GPS fleet trackers are making this possible. Telematics solutions and smartphones are providing drivers with real-time information, helping them make informed decisions and enhance their overall experience.
Thanks to AI algorithms, fleets can now plan routes, predict vehicle performance, and manage on-road risks like never before. Scalable routing algorithms, predictive vehicle performance models, and traffic data analytics work together seamlessly to provide optimal routes in real time. AI algorithms and GPS technology have also personalized the user experience, making the journey easier with OBD-II trackers and traffic applications.
But the benefits of AI-powered systems go beyond route recommendations and personalized experiences. They can also analyze on-road risk management data and train drivers to perform their jobs safely. With accuracy, convenience, efficiency, and ease of operation, AI is making our lives simpler in ways we never thought possible.
Ultimately, AI-powered fleet management systems are game-changers that bring a new level of safety, efficiency, and cost-effectiveness. As fleet managers and drivers alike seek to stay ahead of the curve, AI will undoubtedly play a critical role in shaping the future of transportation. After all, at the heart of every fleet are human beings, and anything that can help ensure their safety and well-being is truly invaluable.
A Basic Explanation of Our AI-Based Solution (Third Eye)
At the heart of fleet management is the priority of driver safety and compliance. AI-based technology can provide assistance in ensuring that drivers stay safe on the road. By leveraging AI, fleet managers can streamline their operations and eliminate human error from all processes. AI-powered solutions can make recommendations that lead to better decision-making and improved long-term fleet performance while still allowing drivers to retain autonomy during each transport cycle. One example of such a solution is ThirdEye, which not only prevents collisions but also reduces their severity. It can use integrated data to improve CSA scores, enhance compliance, and even exonerate drivers from expensive insurance claims. Additionally, ThirdEye can be used to coach drivers and help them improve their driving skills.
Ways ThirdEye Can Enhance Driver Safety and Compliance in Fleet Management
Driver Assistance System Anticipating potential risks on the road is a crucial aspect of ensuring driver safety, and that’s where predictive collision alerts come in. By leveraging AI technology, these alerts can help drivers anticipate risks caused by other vehicles, pedestrians, cyclists, changing lights, and other potential hazards on the road. But that’s just the beginning of the safety features that ThirdEye, our AI-based fleet management solution, has to offer. With features like pedestrian collision warnings, forward collision warnings, safety distance alarms, speed limit recognition, road sign detection, traffic light detection, lane departure warnings, and verbal alerts, ThirdEye provides a comprehensive safety net for drivers. And with real-time GPS tracking that sends location data to the Command & Control system, fleet managers can monitor the safety and compliance of their drivers, ensuring that everyone on the road is operating at the highest levels of safety and efficiency
Driver Monitoring System: ThirdEye, our AI-based fleet management solution, offers a comprehensive driver monitoring system that ensures driver safety and compliance. With the help of AI-powered internal cameras, the system analyzes facial movements in real-time to detect unsafe driver behaviour. The Face ID feature uses advanced face recognition algorithms to detect and recognize the driver. In case of any misbehavior, the driver receives verbal alerts based on each behavior, and a notification is sent to the Command & Control system to describe the type and time of the misbehavior. The system also offers on-demand video calls with the driver to discuss any misbehavior.
ThirdEye’s Driver Monitoring System offers driver risk scoring, which allows drivers to see their risk score for the day, week, and month. The system uses machine learning algorithms to detect driver distraction and classify behavior, including talking to a passenger for an extended period, operating the radio with noisy high sound or switching channels for an extended period, reaching behind the passenger seat for an extended period, texting or calling while driving, eating, drinking, smoking, having loud noises in the vehicle, and drowsiness. The system even detects when a driver is not fastening the seatbelt while driving above 10 km/h. The system provides real-time GPS tracking, allowing the Command & Control system to monitor driver safety and compliance. With ThirdEye, fleet managers can ensure that their drivers are operating at the highest levels of safety and efficiency
Command & Control System: Our Command & Control System is a powerful tool for fleet management, providing a simple and intuitive way to keep track of drivers’ safe driving behaviors over time. With the ability to communicate with drivers on demand, the system ensures that everyone is on the same page when it comes to safety. Access control is built-in, and the system can integrate with Windows Active Directory for user management. Admins can manage users, vehicles, and drivers and track the location of each vehicle in real-time on a full map view. Notifications and alerts are received with details of the misbehavior type and date, and vehicle markers turn red in case of driver issues. All driver and vehicle details are collected and audited for 12 months, and admins can download violation reports. The system automatically calculates driver scores, which are updated whenever a violation occurs. Admins can choose to accept violations and update driver scores accordingly. Our Command & Control System is built with the latest web technologies, using.Net Core and Angular, and the data is stored in either SQL Server or MySQL databases, with the ability to switch between them.
At the end of the day, creating a culture of safety within your trucking fleet is crucial, and the best way to ensure safety on the road is by promoting good driving habits. And what better way to do so than by utilizing an AI-powered fleet dash cam like the ThirdEye? ThirdEye’s comprehensive solutions are designed to enhance the safety and efficiency of your fleet while providing you with valuable insights and analytics. Contact us today to learn more about how we can help you keep your drivers safe and your business running smoothly
Road safety has been a major concern in the world for many years, but recent developments suggest that things are starting to change for the better. According to a recent report, the number of accidents and injuries on European roads has decreased significantly since the turn of the century. This is due in large part to the efforts of various organizations and government agencies to improve road safety through the use of active safety technologies. However, since 2010, progress in reducing accidents and casualties has stagnated. It aims to achieve zero fatalities in road traffic by 2050, and while gradual automation in cars could contribute to achieving this goal, it also comes with new safety risks.
Let us investigate how the automation industry could manage these risks
Many new cars on the road today have systems designed to make driving easier, such as maintaining the speed limit and keeping a certain distance from other vehicles, staying in the middle lane, and intervening independently with an emergency braking system in case of an imminent collision. However, drivers are putting too much trust in these systems. Several recent incidents have shown that this trust is not always justified. For example, a car with adaptive cruise control and auto steer engaged collided with the rear of a sudden merging truck, and another one drove straight across a roundabout and collided with a pole because the car’s automated systems did not recognize the roundabout. So blindly relying on automated systems does not always work, and car drivers must be ready to intervene at any time if technology fails, making driving more difficult. From a legal point of view, these systems are intended only to provide support, but the disclaimers of manufacturers and governments that the driver is always responsible do not adequately address the issue at hand. It is often unclear to the driver what the limitations of the technology are or how it works.
Currently, advanced driver assistance systems are like a black box for the government, and the police struggle to interpret relevant data after accidents. Additionally, manufacturers do not share their experiences in automation with each other, which means that some companies could create improved and safer cars through software updates while other car companies still lag behind. Therefore, adopting responsible innovation practices would benefit the industry as a whole, promoting greater transparency and collaboration. Moreover, automotive manufacturers should provide car drivers with more and clearer information about what their cars can do and, most importantly, what they cannot do.
Advanced driver assistance systems have the potential to improve road safety, but adjustments are necessary to utilize this potential to the fullest.
Let’s take a deeper journey in the driver assistance systems realm:
Do you ever worry about making mistakes while driving? Adas, or Advanced Driver Assistance Systems, is here to help! These advanced systems can actually prevent most accidents caused by human errors using all kinds of safety features, both passive and active, that work together to eliminate errors and provide 360-degree vision near and far. It consists of sensors, systems on a chip, and a powerful computer processor that integrates all the data With fancy technologies like radar and cameras, which enables it to sense what’s going on around your vehicle and either give you information or take action to keep you safe, This makes the drivers more confident and comfortable behind the wheel! Plus, as Adas technology continues to improve, we’re getting closer and closer to fully autonomous vehicles. Who knows, maybe one day you won’t even need to drive at all!
These systems are equipped with an array of advanced sensors that work together to enhance the driver’s senses and decision-making abilities. Using Sensor Fusion technology, which is similar to how the human brain processes information, Adas combines data from various sensors such as ultrasound, lidar, and radar. What this means is that Adas can physically respond faster than a human driver and can “see” things that might be difficult for humans to detect, like in the dark or in all directions at once. Ada’s vehicles categorize different technical features based on the amount of automation and scale, ranging from level 0 (where the driver is entirely responsible) to level 4 (where the vehicle can operate without a driver and is restricted to specific geographic boundaries). So whether you’re someone who wants a little extra help staying safe on the road or you’re excited about the possibilities of fully autonomous vehicles, Adas is definitely worth exploring!
Level 5 vehicles are the ultimate goal of autonomous driving, and they’re pretty exciting! Imagine being able to sit back and relax while your car handles all the driving tasks without needing any input from you. It’s like having your own personal chauffeur. But how does it work? Well, the vehicle uses different advanced driver-assistance systems (ADAS) to ensure safety and efficiency on the road.
One of the most impressive ADAS systems is adaptive cruise control. This system helps maintain a safe following distance and speed limit, making it ideal for long highway trips. With adaptive cruise control, the car can adjust its speed and even stop if necessary based on other objects’ actions in the area. This takes a lot of the stress out of driving on busy roads and highways, allowing you to sit back and enjoy the ride.
All of these ADAS systems work together seamlessly to ensure the vehicle can perform all driving tasks under any condition. This means that you don’t have to worry about anything while you’re on the road, and we can’t wait to see what other advancements are in store!
Crosswind Stabilization is designed to help the driver remain in their lane by detecting track offset caused by strong crosswinds and automatically correcting the vehicle’s course at a speed of 50 miles per hour. This system distributed the wheel load according to the velocity and direction of the crosswind and was first featured in a 2009 Mercedes-Benz S-Class.
The Traction Control System helps prevent traction loss in vehicles, preventing them from turning over on sharp curves and turns. The system detects if a loss of traction occurs among the car’s wheels and automatically applies the brakes or cuts down the car’s engine power to the slipping wheel. These systems use the same wheel speed sensors as the anti-lock braking systems, and individual wheel braking systems are deployed through TCS to control when one tire spins faster than the others.
Electronic Stability Control helps prevent loss of control in curves and emergency steering maneuvers by stabilizing the car when it begins to veer off its intended path. The system can lessen the car’s speed and activate individual brakes to prevent understeer and oversteer, working automatically to help the driver maintain control of the car during hard steering maneuvers.
Parking sensors, whether electromagnetic or ultrasonic, alert drivers of obstacles while parking by scanning the vehicle’s surroundings for objects. Audio warnings notify the driver of the distance between the vehicle and its surrounding objects, and the faster the audio warnings are issued, the closer the vehicle gets to the object. Automatic Parking Assist controls parking functions, including steering, braking, and acceleration, to assist drivers in parking. This technology uses sensors, radars, and cameras to take autonomous control of parking tasks, helping drivers safely and securely store their vehicles without damaging them or other cars parked nearby.
Driver Emergency Stop Assist facilitates emergency counteract measures if the driver falls asleep or does not perform any driving actions for a long period of time. The system will send audio, visual, and physical signals to the driver. If the driver does not wake up after these signals, the system will stop safely, position the vehicle away from oncoming traffic, and turn on the hazard warning lights.
Hill Descent Control is a driver assistance system that helps maintain a safe speed when driving down a hill and allows a controlled hill descent in rough terrain without any brake input from the driver. This system works by pulsing the braking system and controlling each wheel independently to maintain traction down the descent.
Lane Centering Assistance is currently the highest level of Lane Monitoring technology and proactively keeps the vehicle centered within the lane it is traveling in. It utilizes automatic steering functionality to make constant adjustments based on road marking information from the front-mounted camera. The Lane Departure Warning System warns the driver when the vehicle begins to move out of its lane on freeways and arterial roads by using cameras to monitor lane markings. The system sends an audio or visual alert to the driver but does not take control of the vehicle to help sway the car back into the safety zone.
Blind Change Assistance informs the driver of potential hazards when changing lanes on roads and highways with several lanes. The vehicle will notify the driver through an audio or visual alert when a car is approaching from behind or is in the vehicle’s blind spot. Rain sensors detect water and automatically trigger electrical actions such as the raising of open windows and the closing of open convertible tops. A range sensor can also take in the frequency of rain droplets
The technology of traffic sign recognition enables vehicles to identify the various signs on the road, such as speed limit, turn ahead, or stop. This is achieved by analyzing the sign’s shape, such as hexagons and rectangles, as well as its color, to determine its meaning for the driver. However, factors such as poor lighting conditions, extreme weather, and partial obstructions can negatively impact the system’s accuracy.
Vehicle communication systems are computer networks that allow vehicles and roadside units to exchange information, such as safety warnings and traffic updates. These systems come in three forms: vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-everything. Vehicle-to-vehicle communication enables the wireless exchange of information about speed, location, and heading, while vehicle-to-infrastructure communication allows wireless data exchange between vehicles and road infrastructure. Vehicle-to-everything (V2X) communication refers to the parsing of information between a vehicle and any entity that may impact the vehicle and vice versa.
Automotive night vision systems use various technologies, such as infrared sensors, GPS, LIDAR, and radar, to enable drivers to see obstacles and pedestrians in low-visibility situations, such as at night or during heavy weather. There are two categories of night vision implementations: active systems that project infrared light and passive systems that rely on thermal energy. Some premium vehicles offer night vision systems as optional equipment.
The rearview camera provides real-time video information about the vehicle’s surroundings, helping drivers navigate when reversing. The camera, located in the rear of the car, is connected to a display screen that shows what is happening in the area behind the vehicle.
Omniview technology provides a 360-degree view of a vehicle’s surroundings through a video display generated by four wide-field cameras located in the front, back, left rear view mirror, and right outside mirror of the vehicle. This technology uses bird’s-eye views to create a composite 3D model of the vehicle’s surroundings.
Blind spot monitoring involves cameras that monitor the driver’s blind spots and notify the driver if any obstacles come close to the vehicle. The system uses a sensor device to detect other vehicles to the driver’s side and rear, and the warnings can be visual, audible, or vibrating.
Driver drowsiness detection aims to prevent collisions caused by driver fatigue. The vehicle obtains information such as facial patterns, steering movement, driving habits, turn signal use, and driving velocity to determine if the driver is exhibiting signs of drowsy driving. If drowsy driving is suspected, the vehicle will typically sound an alert and may vibrate the driver’s seat.
Intelligent speed adaptation assists drivers in adhering to the speed limit by using GPS to detect the vehicle’s location and link it to a speed zone database, allowing the vehicle to know the speed limit on the road. Some systems adjust the vehicle’s speed to the relative speed limit, while others only warn the driver when they are going over the speed limit.
Adaptive light control systems automatically adjust headlights based on the vehicle’s direction, swiveling to illuminate the road ahead. These systems also automatically dim the headlights to a lower beam when oncoming traffic approaches and brighten them once the traffic has passed.
Automatic emergency braking systems use sensors to detect an imminent forward collision and apply the brakes without waiting for the driver to react. Some emergency braking systems also take preventive safety measures, such as tightening seat belts, reducing speed, and engaging adaptive steering to avoid a collision.
So, where is the future of car technology headed?
It’s easy to get lost in the realm of science fiction, but to truly understand where we’re headed,. We need to focus on the innovations that are already here. From better infotainment and improved safety to enhanced sustainability and a more comfortable driving experience, the future of car technology is all about refining the familiar.
Advanced Driver Assistance Systems (ADAS) is already experiencing a major transformation. These cutting-edge systems are revolutionizing the way we drive, and major car manufacturers have already integrated them into their vehicles. While the full impact of ADAS on road safety is yet to be realized, we’re confident that staying ahead of the curve in this field will be crucial to the driver’s legal and financial well-being.
By harnessing the power of ADAS and other advanced technologies, we can make driving safer, more sustainable, and more enjoyable for everyone. So let’s embrace the future with open arms and steer ourselves towards a brighter tomorrow on the roads.
Computer vision is a rapidly growing field in the world of artificial intelligence that focuses on enabling computers to process and understand visual data in the same way that humans do. It’s not a new invention, but rather the result of several decades of work in the field. With the advancements in computer vision in recent years, it has been applied to various industries and has changed the way we approach certain tasks. In this article, we will discuss multiple real-world applications of computer vision.
Self-Driving Cars
Self-driving cars have been a topic of interest for nearly 100 years, but with the rapid advancements in computer vision in the last 10 years, many major automotive manufacturers are now testing autonomous vehicle systems.
Computer vision algorithms are used to track objects around the car and provide inputs for the vehicle to react to its driving environment, which provides safer roads, lower transportation costs, and reduced air pollution and greenhouse gas emissions. Although the exact timeline for the widespread availability of self-driving cars is unclear, it’s only a matter of time before fully autonomous vehicles become a reality.
Waste Management and Recycling
Computer vision technologies, such as AI-based waste recognition systems, are being used in the waste management and recycling industries to identify, check, and analyze waste composition.
The latest systems can sort waste more efficiently and reliably than human workers, from identifying recyclable materials in waste bins to monitoring facilities and trucks, which leads to optimizing the waste management and recycling processes.
Agriculture
Computer vision is used to automate various tasks in agriculture, including plant disease detection, crop monitoring, and soil analysis.
Drones equipped with cameras capture aerial imagery and provide detailed information about the condition of the soil and crops. The data collected from the drones is fed into smart systems that analyze the data and provide reports that enable farmers to adopt more efficient growing methods.
Real-Time Surveillance
With the rise in security concerns, constant surveillance of public places and private organizations has become necessary.
Thanks to computer vision, a single location can be equipped with hundreds of sensors and cameras that are monitored by sophisticated computer vision systems in real-time. The systems can analyze the vast amount of data generated by these devices and send out an alert to the security team as soon as they detect any unusual activity.
Ball Tracking Systems in Sports
Computer vision algorithms have been used for the past 15 years to track the precise trajectories of tennis, cricket, and badminton balls. The systems analyze multiple objects in an image and build a three-dimensional trajectory of the ball’s movement frame by frame, which is crucial for fair refereeing, and computer vision algorithms are capable of building predictions of ball trajectories in real time.
Manufacturing industry
The manufacturing industry has embraced the power of computer vision in its automation technologies to enhance safety, increase productivity, and improve efficiency. One of the most significant applications of computer vision in this field is defect detection. Gone are the days when trained workers would manually inspect items for flaws in certain batches. Now, computer vision can spot even the tiniest defects, as small as 0.05mm, such as metal cracks, paint defects, and incorrect printing. This is made possible by the use of vision cameras that employ algorithms that act as an “intelligent brain.” These algorithms are trained with images of both defective and defect-free items to ensure they are specifically tailored to each application.
Another important application of computer vision in the manufacturing industry is barcode reading. Optical Character Recognition (OCR) technology, a component of computer vision, can be used to automatically identify, validate, convert, and translate barcodes into legible text. This is useful, as most items have barcodes on their packaging. Labels or boxes that have been photographed can have their text retrieved and cross-referenced with databases using OCR. This process helps in detecting items with incorrect labels, providing expiration date information, publishing product quantity information, and tracking packages throughout the entire product creation process.
Construction Industry
Computer vision (CV) plays a vital role in the construction industry, helping businesses and workers maintain equipment, reduce downtime, and ensure safety. With predictive maintenance, CV can notify staff of potential equipment issues, enabling them to fix them before it’s too late. In addition, CV can also provide PPE detection to ensure that workers are wearing the necessary protective gear. CV also monitors machinery for potential issues, detecting flaws or changes and alerting human operators. With deep learning, CV can recognize protective equipment in different settings, promoting safety and quick identification and response to accidents.
As we’ve seen, CVis a rapidly growing field that has made a significant impact across various industries. By automating repetitive tasks, increasing crop production, and ensuring safety, CV is truly changing the game. With more and more companies embracing the AI revolution, it’s clear that computer vision will continue to be a major driving force in the transformation of industries everywhere.
Have you ever wondered about the hype surrounding AI technology? Many people have the misconception that AI is like the evil computer in Hollywood movies that tries to take over the world and subjugate humankind. However, the reality is far from this. Actually, AI is already integrated into many aspects of our daily lives. For example, it is used in Google search to predict what you’re looking for, in online shopping to suggest products, and in albums and apps to recognize faces. AI integration into our lives makes things easier and provides us with more free time to pursue our goals. Companies are racing to take advantage of this technology, but they need to be aware of how it’s being used and to what extent it can be integrated into their systems in order to maximize the ROI of adopting AI and stay competitive in the market.
How to know when AI is the right solution
Many large companies have already adopted artificial intelligence, especially those that aren’t at risk of falling behind. A recent survey by McKinsey found that 55% of companies are using AI in at least one area, and 27% of earnings before interest and taxes are attributed to AI. But what about small businesses? which may lack the financial and technical resources to adopt it?
Well, on the one hand, not adopting AI could mean the difference between a thriving business and one that struggles to grow.
AI can automate repetitive tasks, freeing up time and resources for small businesses, which can be especially valuable in those cases where time is a precious commodity. Additionally, early adopters will have a competitive advantage, particularly in areas such as marketing and lead generation, and will also be able to spend less time on customer support and fine-tuning campaigns, which will prompt various industries, leading vendors, and businesses to look for ways to implement it.
On the other hand, not all businesses can benefit from AI, and using it inappropriately can lead to wasted resources and create confusion among employees, customers, and leads. So businesses need to be aware of the challenges that may arise with AI and make sure they are using it wisely and efficiently before embarking on an AI project, and it is important to consider whether it meets the criteria of having business value, having access to sufficient trained data, and having a culture that is open to change. Evaluating these factors can help ensure that your AI project will not be a wasted investment.
Before determining if your organization is prepared to adopt AI, various questions need to be considered.
Do we have enough data? To be able to develop and automate your business with AI, it is crucial to have enough data. The amount of data required will vary depending on the complexity of the problem and the learning algorithm. Utilizing good-quality, unique, and original data is the key factor for AI algorithms to perform well. Therefore, organizations can consider AI implementation if they have a substantial amount of data at their disposal.
Do we have quality data? One of the major challenges in AI is ensuring the use of high-quality data. Data obtained from the internet is not tailored to a specific business and its operations. To guarantee accurate outcomes from the AI algorithm, it is essential to have specific data for the business and its processes. This can be achieved by regularly updating, adding comments to any modifications, discarding outdated or irrelevant data, creating backups, filling in any missing data, monitoring any changes made, and using standardized data formats. It is vital to identify and address any deficiencies in the system before implementing AI.
Which processes are we considering for automation? When considering automation in business, identifying processes that are best suited for AI automation is crucial. Automating the processes that typically require many employees, are repetitive, and take a lot of time to complete manually can save both time and money for any type of business. To identify the processes and routines that can be automated in an organization, there are quite a number of factors to consider beforehand, such as data requirements, availability of data, common requests, time-consuming tasks, costly manual processes, and the ability to shift employees to other high-priority tasks.
In what areas do we require assistance with decision-making?
AI is a powerful tool for analytics and decision-making, and many leading organizations are using it to gain valuable insights and make better decisions. In the field of marketing, for example, AI can help gather real-time data on customer behavior, create forecasting, and predict trends to make more informed decisions on product placement, marketing strategy, and more. However, before adopting AI, it’s important to understand the decision-making capabilities of AI and identify the areas in which it can be most beneficial.
Is our workforce equipped with the necessary skills and qualifications? Before adopting AI, it’s important to ensure that you have skilled, trained, and experienced employees to manage the technology. Without the right employees, AI adoption may not be successful. To overcome this, you can provide online classes to existing employees, create a plan to hire professionals, and invest in training for long-term success. Remember, AI depends on human input and data to function properly, and it’s important to have a team in place that can handle AI and automation tasks.
Is investing in AI worthwhile? To determine if your organization is ready for AI, perform a cost-benefit analysis. Once you have identified how you want to use AI, consider the following points: Create a checklist of your goals, research industry data, understand the cost of the specific AI technology, consider secondary factors such as licensing, salaries, and risk, and calculate the cost of the current manual process. This will give a clear vision of whether AI will be worth the investment or not. AI can save money in the long run when implemented successfully.
Maximize Your ROI: Here is a set of precautions to consider Before Investing
Begin with the most basic solution available Zack Fragoso, data science and AI manager at Domino’s Pizza, suggests that data scientists often have a tendency to lean towards an AI-first approach, but AI is not always the best solution. The company has been adopting change during the pandemic, with customers now having 13 digital ways to order pizzas. Domino’s generated over 70% of sales through digital ordering channels in 2020, which has created an opportunity for AI. However, the key to applying AI at Domino’s has been to start with the simplest solution possible. This way, the solution runs faster and performs better, and it is also easier to explain to business partners. The approach is to first look at the simplest, most traditional solution to a business problem, then determine if there is a value-add in the performance of the model if AI is applied.
Use historical data as a basis for AI predictions
Using past data as a basis for predicting future outcomes is a key aspect of AI. However, it’s important to note that AI’s predictions are limited by the quality and relevance of the historical data used. Additionally, external factors that cannot be predicted or controlled can greatly impact the accuracy of AI predictions. It’s also important to consider if the implementation of AI will change the behavior of the system being analyzed. Therefore, it’s crucial to carefully assess the specific problem and potential limitations before committing significant resources to an AI solution. The COVID-19 pandemic has demonstrated how unforeseen events can greatly impact a company’s revenue, as seen in McKinsey’s state of AI survey, where there was a decline in revenues in various areas. AI can be useful in situations where history is likely to repeat itself, but its usefulness diminishes when there are unpredictable factors or changes in behavior as a result of its implementation. One example is a consumer goods conglomerate that tried to use AI to forecast financial metrics, but the predictions were inaccurate due to biased data and assumptions. In this case, a simpler solution, such as a financial dashboard, provides the necessary insights without the need for extensive AI implementation.
Obtaining data for your AI projects: The challenge ahead One of the main challenges of AI projects is having enough high-quality data that is properly labeled and without biases. Collecting this data can be time-consuming and expensive, and companies need to consider whether the data can be reused for other projects. Additionally, businesses need to be clear about what decisions they want to make with their data and ensure that the data collected is representative and captures the questions they want to answer. In some cases, a rules-based system or traditional formulas may be a more efficient solution than using AI. It is important to consider if the improved performance provided by AI is necessary for the project and if it will provide a good return on investment.
AI Recommendations could be worth $300 Million in losses. The real estate company Zillow has learned the hard way that AI predictions could come with a high cost, having to write down $304 million worth of homes it purchased based on the recommendation of its AI-powered Zillow Offers service. The company may also have to write down another $240 to $265 million next quarter, in addition to laying off a quarter of its workforce. The CEO of Zillow, Rich Barton, explained that they have been unable to accurately forecast future home prices due to the impact of the pandemic and the supply-demand imbalance that led to a rise in home prices at an unprecedented rate. This serves as a reminder that AI predictions can be affected by unforeseen events and that it’s important to understand the limitations of AI.
In summary, AI can bring many benefits to your business by increasing productivity, reducing workloads, and driving growth. However, it’s important to keep in mind that it’s not a universal solution for everything and should not be considered a magic solution for increasing profits; it’s based on math, and it does require a large amount of data to function properly. If a company does not produce a lot of data, it’s unlikely that AI will bring significant benefits to the business. To adopt AI successfully, a company must have structured data, well-defined business problems, and a flexible strategy in place.
“Transforming business with the power of Computer Vision”
The human ability to interpret and respond to visual information has always been taken for granted. However, it turned out that replicating it into machines is a challenging endeavor that took decades of research to overcome. This is due to the vast amount of information in the visual world and the fact that we still have much to learn about how human vision works and how the brain processes visual information.
Although computer vision CV still isn’t as complicated as human vision, it has made significant progress and has become practical for various business applications.
In this article, we will explore the intricacies of CV and its use cases in the business world
What is Computer vision?
Computer vision is the branch of computer science that allows computers to interpret and understand visual data from around the world to replicate and, in some cases, exceed human capabilities.
Recently, there has been significant progress in this field due to the use of neural networks and the increase in computing power, data storage, and inexpensive high-quality cameras.
This technology works by analyzing the pixel data from cameras and using specialized algorithms to identify patterns and objects. These algorithms are trained using large amounts of sample images, and with progress in machine learning and cloud computing, this process has become increasingly automated, resulting in highly accurate computer vision models.
Computer vision technology is now able to perform a wide range of advanced tasks, such as asset monitoring, predictive maintenance, inventory management, disease prevention, and initiating actions or alerts based on input, allowing humans to focus on more valuable tasks.
The process of computer vision can be broken down into three stages:
Capturing the image: where digital cameras produce a digital file of binary data
Analyzing the image: where algorithms are used to identify the basic geometric elements of the image
Understanding the image: where high-level algorithms make decisions based on the analyzed image.
Why is computer vision important?
Computer vision technology is becoming more and more popular as AI and IoT are implemented across different industries. These technologies allow data to be extracted from the environment through the use of various sensors that provide feedback on different data points such as temperature, proximity, vibration, and pressure. While other sensors like laser measurement and radar, LiDAR, and infrared systems have their specific advantages, computer vision can give more detailed and nuanced information about the surrounding environment. This includes the ability to identify, classify, and react to various conditions, as well as infer data from obscured or hidden objects. This can be particularly useful in situations like warehouse inventory management where a camera may only have a limited view, but with the help of computer vision’s 3D modeling capability based on product size and shelf depth, the system calculates the total number of items in a given space. Additionally, computer vision can be used in combination with other sensors to gain a more thorough understanding and deeper insights in the context of product inspection, where the system that uses computer vision can not only detect defects but also trigger further diagnostic analysis using the sensors to pinpoint and locate the source of the malfunction.
“Why the Present is Ripe for Adoption of Computer Vision”
In recent years, there has been an upsurge in the number of AI and computer vision-related products, specifically those that utilize cloud-based technologies, frameworks, and microservices. This has made it simpler for data scientists with minimal experience to build and maintain machine learning models. In addition, advances in edge devices have made it possible for these models to operate without the need for cloud-based resources. Which resulted in more efficient, accurate, and cost-effective models. Moreover, the widespread turmoil caused by the COVID-19 pandemic in 2020 accelerated the pace of digital transformation across various industries and led to an obvious shift in perceptions about the importance of AI, automation, and IoT, resulting in an expansion in investments in these areas.
Computer Vision use cases in business
Use cases in energy and resources
The energy industry is heavily investing in computer vision technology as it has the potential to save time and money, according to research from Insight and IDG. One of the main use cases for this technology is employee safety, with 88% of those investing in or planning to invest in computer vision exploring how it can be used for this purpose. By automating certain processes, computer vision can reduce human exposure to dangerous environments, such as inspecting pipelines or wind turbines. This can help to lower costs, reduce risk and human error, and enable early repairs of equipment. Computer vision has also been used to improve efficiency in tasks such as land surveys and equipment maintenance in mining operations. Additionally, in the quest to improve energy efficiency, computer vision is being utilized to analyze satellite imagery, monitor weather conditions, and improve the accuracy of power requirement estimates by region.
Use cases in manufacturing
Computer vision is becoming increasingly popular among manufacturing and production companies, with 78% of them investing in or planning to invest in it. It provides many benefits, such as reducing downtime, improving employee safety, reducing theft, and improving customer outcomes. In addition, it can be used to pull out employees from remote or high-risk environments and decrease the potential for human error. Moreover, it can help improve predictive maintenance and create a safer working environment. All in all, computer vision is proving to be a valuable asset for manufacturers and production companies.
Use cases in retail
Retailers are turning to computer vision technology to maximize their inventory and reduce expenditures. By correlating inventory data with ERP systems, discrepancies can be identified, and future purchasing decisions can be made with confidence. Moreover, shrinkage can be decreased by identifying valuable items and linking pricing to POS machines. Thermal cameras are also being employed to reduce losses and enhance food safety. Furthermore, computer vision can be utilized to notify staff of product spills, lengthy checkout lines, and curbside pickups, allowing them to act quickly and prioritize customer satisfaction. By establishing computer vision solutions, retailers are able to boost their profitability, product availability, and customer experience
Use cases in healthcare
Computer vision technology presents a variety of opportunities for healthcare, particularly in medical diagnostics for conditions such as cancer and heart disease. However, the potential harm caused by a misdiagnosis is a significant concern, making it necessary for stricter protocols to be put in place, such as more thorough training, tighter margins of error, and greater human involvement. To mitigate this risk, healthcare providers are exploring alternative, lower-risk applications of the technology to optimize processes and enhance patient care. One popular example is utilizing optical character recognition (OCR) to automate document processing, which can reduce administrative burdens and decrease errors while also allowing healthcare providers to spend more time with patients. Additionally, computer vision can be utilized to improve inventory management and guarantee that medical supplies are easily accessible. Moreover, it can also be used to enhance security by monitoring pharmaceuticals and controlling the spread of COVID-19. With the ongoing pandemic, computer vision has become increasingly valuable in detecting fever symptoms and promoting good hygiene practices.
Use cases in Agriculture
Computer vision technology can be used in the agriculture industry to improve crop production and reduce the use of herbicides. A demonstration at CES 2019 featured a semi-autonomous combine harvester that used AI and computer vision to analyze grain quality and find the best route through the crops. Additionally, computer vision can be used to identify weeds, allowing herbicides to be targeted directly at them, which could potentially reduce herbicide usage by 90%.
Use cases in transportation
Autonomous vehicles: Computer vision technology plays a vital role in the functioning of autonomous vehicles, as it allows the vehicle to perceive and understand its surroundings. Automotive companies such as Tesla, BMW, Volvo, and Audi make use of a combination of cameras, lidar, radar, and ultrasonic sensors to gather images and data from the environment. These tools help the vehicle identify objects, lane markings, traffic signs, and signals, which in turn enable the vehicle to navigate safely on the road. Parking and Traffic: Computer vision can improve parking operations by using Automatic Number Plate Recognition (ANPR) technology to grant access to specific or all vehicles in a ticketless car park. Additionally, it can keep track of parking occupancy and identify how long vehicles stay in certain parking spaces in real-time, speeding up payment transactions and applying different pricing within the parking lot. Furthermore, it can detect stolen or uninsured vehicles and prevent criminal activity. Additionally, computer vision can help manage traffic by monitoring and analyzing density in different areas and reducing safety risks by assessing road conditions and detecting defects.
“How to Select the Right Use Case for Your Business Needs”
When considering implementing computer vision to address business challenges, it’s essential to choose the right use case. Computer vision has a wide range of potential applications; however, to ensure maximum benefits and minimize potential harm, it’s important to pick a use case that has clear business value, is relatively simple and specific, has high-quality labeled data, and has strong executive support for responsible AI usage. It’s essential to have all four of these criteria met for a project to be successful. Lacking any one of these factors could lead to difficulties in delivering results and even a negative impact on human outcomes. To identify the best use case for computer vision in an organization, we should ask questions like:
Is there value in the proposed use case?
Is there enough accessible data?
Is there enough support and sponsorship?
Is it responsible for implementing this use case?
By choosing the right use case, we will ensure a steady flow of visible benefits and encourage future AI investments while expanding expertise and reusable methods. Conversely, projects that fail to deliver value will miss potential benefits and discourage future investment in AI.
In summary, Many organizations have come to realize the value that computer vision can bring to their business. Over 90% of organizations have acknowledged the potential benefits of computer vision technology. Even though there are some factors to consider when investing in this technology, the benefits of a successful implementation outweigh the costs. By using best practices and utilizing computer vision, organizations can improve their processes, increase their revenue, and enhance the experiences of both employees and customers. Adopting computer vision early can give organizations a competitive edge as this technology continues to influence the marketplace.
