Information Science Technology (IST) Department Chair Amaury Lendasse, Ph.D., is a Technology Division professor and researcher whose published works have just surpassed 10,000 lifetime citations. We sat down with him to mark the occasion and learn a bit more about his experiences with machine learning, data science and neural networks.
How do you like to explain your research and its importance to people who may be unfamiliar with your field?
Data science is like being a detective, but instead of solving crimes, you solve puzzles using data. Imagine you have a giant jigsaw puzzle, and each piece is a bit of information from the world around us - like sales in a store, weather patterns, or posts on social media. Data scientists put these pieces together to find patterns and insights. They use these insights to make predictions, like which product will be the next big hit, or how the weather will change.
Machine learning is like training a really smart assistant. Imagine you're teaching someone to recognize patterns and make decisions, but this someone is a computer. This computer can sift through mountains of data — like a million photos or a year's worth of weather information — in just moments. It learns from this data, getting smarter over time. It's like showing it thousands of cat photos until it learns to recognize cats in any new picture. This ability to learn and improve makes machine learning a powerful tool. It's used for things like suggesting which movie you should watch next, helping self-driving cars navigate, or even assisting doctors in diagnosing diseases. It's all about teaching computers to help us by finding meaningful patterns in the vast sea of data.
What are some common, everyday examples of machine learning and the importance of data science? Are there any examples that people might find surprising?
Machine learning, a field of artificial intelligence, is increasingly integrated into our everyday lives, often in ways we might not immediately recognize. Common examples include personalized recommendations from services like Netflix and Spotify, which analyze your viewing or listening habits to suggest new content. Email services like Gmail use machine learning to filter spam and organize your inbox.
Voice assistants like Siri and Alexa are also prime examples, as they learn from your speech patterns and preferences to improve their responses. Navigation apps such as Google Maps use machine learning to analyze traffic data and suggest the best routes.
Another significant application is in financial services, where machine learning algorithms are used to determine the approval of credit cards or mortgages by analyzing an individual's financial history and patterns to assess creditworthiness.
Beyond these familiar uses, there are surprising applications as well. In agriculture, machine learning helps farmers optimize crop yields and monitor field conditions. In the medical field, algorithms can assist in diagnosing diseases, often with higher accuracy than traditional methods. Even in finance, apart from credit assessments, machine learning is used for sophisticated stock market predictions and automated trading. These examples highlight how machine learning isn't just a futuristic concept but a present reality, subtly enhancing many aspects of our daily lives.
What do you think the next few years will bring in terms of technology like neural networks?
The continued progress in machine learning and neural networks is vital due to their potential to fundamentally change our interaction with technology and information processing. Large Language Models (LLMs) like GPT-4 are prime examples of this progress. They have greatly enhanced natural language understanding and generation, opening new possibilities in human-computer interaction and making technology more user-friendly and accessible. In the coming years, we can anticipate further advancements in these technologies. Neural networks are likely to become more sophisticated in processing complex information, leading to AI systems that are more effective in a range of tasks and applications.
The application of LLMs is expected to expand across various sectors, including education, healthcare, and more, potentially offering more tailored and efficient services. Furthermore, there is likely to be an increased focus on the development of ethical AI, emphasizing the creation of technologies that are transparent, fair, and secure. Another significant trend could be the democratization of AI, making these powerful technologies more accessible to a wider range of users and businesses. This would enable a broader spectrum of society to benefit from the advancements in machine learning and neural networks.
Your work has now been cited in other researchers' publications more than 10,000 times. How does it feel to be reaching this milestone?
Reaching the milestone of 10,000 citations is an incredibly proud moment for me, something I could never have dreamed of when I began my academic journey. In the world of academia, the adage 'publish or perish' often looms large, emphasizing the importance of regularly contributing research and findings to our fields. However, the true impact of our work isn't just in its publication, but in its utility and necessity, as reflected in how often it is cited by others.
Each citation represents a fellow researcher or academic finding value and relevance in my work, applying it to their own studies or using it to build upon existing knowledge. This is more than just a number; it's a testament to the relevance and importance of my research within the academic community. It signifies a recognition by my peers that the approach and findings I have contributed are not only valid but crucial to the ongoing dialogue and development in our field.
In essence, reaching 10,000 citations is not just a personal achievement, but a validation of my work's significance and its role in shaping our understanding and knowledge. It's a humbling reminder of the impact one's work can have, and it serves as a powerful motivator to continue pushing the boundaries of research, exploration, and discovery.
My approach to research has always been grounded in addressing real-world problems. While my work is applied in one sense, it firmly remains within the realm of theoretical research. This approach has allowed my work to transcend traditional academic boundaries, fostering rich, interdisciplinary collaborations across a diverse array of fields. This includes Biology, Telecommunications, Chemistry, Marine Biology, Cyber Security, Genetics, Mechanical Engineering, Nursing, Medicine, and various other specialized domains. Each citation of my work is a nod to its practical applicability and its contribution to theoretical foundations across these varied disciplines.