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.
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.
