We are excited to announce at a new strategic partnership with Cleveland Clinic's Hwang Lab. xLab and Hwang Lab will collaborate to work on training workshops, seminars, and client projects combining design thinking and digital business competencies of xLab with data analytics and AI competencies of Hwang Lab.
Our next xLab Quarterly Roundtable Meeting for members will feature a presentation by Dr. Hwang, followed by an in-depth discussion on how companies can build their AI strategy and roadmap. In preparation of our quarterly meeting, we sat down with Dr. Tae-Hyun Hwang for a short interview.
Question: Tell us a bit about your lab at Cleveland Clinic
Hwang: Our lab is translational machine learning AI lab. Our members are from backgrounds in computer science, electrical engineering, financial engineering, medical degree from 18 years old and up.
We utilize large, complex, sparse (and noisy) data from image to genomic, electronic health records, wearable devices to anything that we can feed to the algorithms. We are focused on developing algorithms that can be readily useful in the clinical setting to ultimately help patients with lethal disease.
Question: In your mind, what is AI? In today's world, if it is an exciting application of computers and data, people tend to call it AI. But I worry that if everything is AI, then nothing is AI. What are some of the defining characteristics of AI that are different from other computing technologies?
Hwang: This is a tough and complicated question. At least, from my point of view, AI is an algorithm that can learn patterns from the data. The patterns related to recognizing your face from a selfie you took, predicting the weather forecast, or what would be your traffic for your commute.
Question: Where is the field of AI going? What in your mind are the most exciting technological developments in AI?
Hwang: I think everyone would admit that there is still a hype about AI, and no longer any doubt about whether AI is driving and impacting many industries or even lives. The field of AI, and many people, are starting to think about building more rigorous, accurate and ethical AI models (no longer simply claiming that AI can solve any problems) and how to educate people to responsibly use AI.. In particular, people are more cautious and careful to claim the capability and feasibility of the AI system they are developing.
The most exciting technologies would be the AI in healthcare, of course. Sorry I am biased to the healthcare industry since I work in this area, but isn't it amazing that the AI system could help care for your loved ones?
Question: What are the 2-3 most significant applications of AI in business today?
Hwang: There are too many applications. Internet of Things (IoT) that you use every day, your phone, speaker, voice command, etc. Anomaly detection in finance, such as fraud detection, identity theft, and fake news in financial media outlets. The personalized advertising industry based on an average consumer spending a significant amount of time with all devices. Also, using virtual reality in the fashion and leisure industry.. Lastly, the healthcare industry with better patient care, wellbeing management with a wearable device and drug development!
Question: What are the 2-3 mistakes or misconceptions that managers have about AI?
Hwang: The biggest misconception is we can build the AI system with our data; I mean any data we have. The key to developing rigorous and reliable AI systems are mainly relying on the quality of the data, not the quantity. Zillions of data points from your data are not going to help to build better the AI system. Likewise, even relatively small datasets could help to build the AI system.
Once you have the AI system, you consistently feed new data to make it better and more rigorous. That means you need feedback from your employees on evaluating which areas the AI systems are doing well and performing poorly. To do that, you need to educate your employees on how to use the AI system, how to build a better AI system, etc.
Bio
Tae Hyun Hwang received his PhD in Computer Science (Machine Learning, Data Mining and Computational Biology) at the University of Minnesota Twin-Cites at 2011. He and his research group lead machine learning and AI research at Cleveland Clinic. Prior his appointment at the Cleveland Clinic, he was a Research Associate in the Department of Computational Biology and Bioinformatics at Genentech and was a tenure-track faculty at the University of Texas Southwestern Medical Center where he led a team of computational scientists for cancer research. He currently serves as a bioinformatics core director for NASA Specialized Centers of Research (NSCOR) as well as committees of various Machine Learning, Data Mining, and AI conferences.
His lab is working on developing novel machine learning and AI algorithms that are readily applicable in the clinical setting to help patients with a lethal disease