Deep Learning Summit Deep Learning Summit schedule

Presentation: Explainable AI for Addressing Bias and Improving User Trust

Explainable Artificial Intelligence (or XAI) has a long history of research, which has re-emerged as an active area in Deep Learning. With the growing popularity and success of Machine Learning and, especially, Deep Learning techniques, the community has been striving to open these “black boxes”. This session will show you how visual explanations can help expose harmful biases encoded by humans in the training data or model design.

Kai Xin Thia works at the intersection of data and product innovation. Over the last ten years, he has led data teams to develop machine learning products such as deep learning sentiment models, knowledge graphs, recommender systems, segmentation & targeting across industries like finance, media, jobs, eCommerce, and healthcare.

He holds a master's degree in computer science, specializing in interactive intelligence, designing systems where artificial intelligence and human intelligence can coexist harmoniously and thrive. He also co-founded DataScience SG, a data community with 10,000+ members that held 80+ meetups over the last eight years, and AI Professionals Association (AIP) for engineers and professionals working in AI-related roles.

Janet is passionate in helping clients create value for their businesses. She does this by combining her expertise in research, consultancy, advanced analytics and IT technologies. In the course of her career, Janet has provided expert advice on data science, big data analytics, artificial intelligence, and digital transformations for companies in various industries such as telecommunications, retail, smart cities, fintech, insurance, oil & gas, and manufacturing in Asia, Central America, and Africa.

Janet started her career in Globe Telecom, a telecommunications company in the Philippines, where she participated in projects ranging from quality management system implementation to new product development.

Case Study: Digital Transformation in Health Systems

Telemedicine, AI-enabled medical devices, and blockchain electronic health records are a few examples of recent innovations in healthcare. - What is the impact of the speed of these changes? - How do you maintain this evolution to deliver better patient outcomes? - Why is it important to enable organisations to innovate at scale?

Nicholas Lim is experienced in data analysis & processing, with a background in ML model training and deployment. He is interested in how to leverage the power of AI, Machine Learning and Deep Learning to solve challenging problems.

Nancy F. Chen received her Ph.D. from MIT and Harvard in 2011. She worked at MIT Lincoln Laboratory on her Ph.D. research in multilingual speech processing. She is currently leading research efforts in conversational AI and natural language generation with applications related to education, healthcare, journalism, and defense at the Institute for Infocomm Research (I2R), A*STAR. Speech evaluation technology developed by her team has been deployed at the Ministry of Education in Singapore to support home-based learning during the COVID-19 pandemic. Dr. Chen also led a cross-continent team for low-resource spoken language processing, which was one of the top performers in the NIST Open Keyword Search Evaluations (2013-2016), funded by the IARPA Babel program.

Dr. Chen has received numerous awards, including Singapore 100 Women in Tech (2021), Young Scientist Award at MICCAI 2021, Best Paper Award at SIGDIAL 2021, the 2020 P&G Connect + Develop Open Innovation Award, the 2019 L’Oréal Singapore For Women in Science National Fellowship, Best Paper at APSIPA ASC (2016), the Singapore MOE (Ministry of Education) Outstanding Mentor Award (2012), the Microsoft-sponsored IEEE Spoken Language Processing Grant (2011), and the NIH (National Institute of Health) Ruth L. Kirschstein National Research Award (2004-2008).

Dr. Chen is currently serving on the ISCA (International Speech Communication Association) Board (2021-2025) a senior IEEE member, an elected member of the IEEE Speech and Language Technical Committee (2016-2018, 2019-2021), senior area editor of Signal Processing Letters (2021-2022), associate editor of IEEE/ACM Transactions on Audio, Speech, and Language Processing (2020-2023), Neurocomputing (2020-2021), and IEEE Signal Processing Letters (2019-2021) and was the guest editor for the special issue of “End-to-End Speech and Language Processing” in the IEEE Journal of Selected Topics in Signal Processing (2017).

In addition to her academic endeavors, Dr. Chen has also consulted for various companies ranging from startups to multinational corporations in the areas of climate change (social impact startup normal), emotional intelligence (Cogito Health), EdTech (Novo Learning), speech recognition (Vlingo, acquired by Nuance), and defense and aerospace (BAE Systems).

For more info, please see http://alum.mit.edu/www/nancychen

Presentation: The Many Ways to Explain AI

Are you struggling to explain how AI and machine learning work? AI is a complex and often invisible technology. An explanation and understanding of AI is beneficial to many, but also brings many challenges. Hear how to explain AI to consumers, why it's hard to explain, and look at where an increased understanding of AI would be beneficial.

Case Study: Neuro-Symbolic Learning Algorithms Why And What For?

Neuro-Symbolic AI algorithms help incorporate common sense reasoning and domain knowledge into deep learning. The session will address: • The challenges using instructible neuro-symbolic reasoning systems • How systems can be directly instructed by humans in natural language, resulting in sample-efficient learning in data-sparse scenarios

Experienced Research & Development leader specializing in cutting-edge Data Science and Big Data technologies. He has experience of more than 10 years in software development and technology innovation. He has led the teams consisting of engineers and researchers in two silicon valley companies (eHealthInsurance.com/PayPal). He has successfully led several research innovations and delivered production-ready solutions to impact business. His team was the global winner (1st place) of the global innovation competition of PayPal 2012. He has two patents filed with Microsoft Research Asia and eBay Inc. His data science team currently focuses on delivering large-scale machine learning solutions for e-commerce/payment industry. He has the unique capability and experience of managing a joint team of engineers and researchers to conduct applied research and convert them to production-ready solutions/innovations to impact business. As a computer scientist, he also maintained a successful track record in academic research. He has published 1 book chapter and over 40 scientific papers in the flagship international computer science journals/conferences, i.e. TPAMI/TIP/TMM, CVPR/ICCV/ECCV, etc. His papers have been cited in 1500+ papers in international scientific publications (http://scholar.google.com/citations?user=gIHCye8AAAAJ\​&hl=fr). He is a data hacker in different data science competitions. He is one of few master players in Kaggle from Singapore. Kindly check his record in Kaggle: https://www.kaggle.com/codingneo.

Case Study: Tools for Speech Recognition

There are many advantages of deep learning for speech recognition stems. Mainly the flexibility and predicting power of deep neural networks that have recently become more accessible. Why you should consider: - Combining speech processing and NLP - Uncovering new applications in sectors like healthcare.

David McKeague is a serial tech entrepreneur with over 20 years of experience building advanced systems and technology businesses. Before Curious Thing, David was the Co-founder of Incoming Media, a mobile-embedded Machine Learning media solution backed by Intel, Warner Brothers, and Warner Music. It was exited to OVO in 2017. David was also a founding team member of Nitero, a semiconductor and wireless VR solution exited to AMD.

Case Study: Efficient Hyperparameter Optimization for Deep Learning Algorithms.

Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Join the session to learn more about how to implement and what use case is best for its execution.

Ilija is working on developing novel optimization methods for non-convex problems where gradients are unavailable or uninformative. His background is in machine learning (PhD, 2018) and software engineering (MSc, 2014). His main interests lie in solving real-world problems using machine learning and optimization. In the past, he has worked on FX portfolio construction and optimization, interpretable deep learning for finance, image question answering, discourse analysis, movie and news recommender systems, and building complex city models from satellite images and census data.

Case Study: Digital Transformation in Health Systems

Telemedicine, AI-enabled medical devices, and blockchain electronic health records are a few examples of recent innovations in healthcare. - What is the impact of the speed of these changes? - How do you maintain this evolution to deliver better patient outcomes? - Why is it important to enable organisations to innovate at scale?

Nicholas Lim is experienced in data analysis & processing, with a background in ML model training and deployment. He is interested in how to leverage the power of AI, Machine Learning and Deep Learning to solve challenging problems.

Ranjith has 20+ years of experience in delivering Leadership, Innovation, Business and Technology Strategy, Architecture, Consulting, and hands-on Development.

Experience and expertise in diverse SAP Technologies, Cloud Computing, Analytics & Machine Learning, Microservices, API Management, and Blockchain, with proven delivery of solutions in Healthcare, Transportation & Supply Chain applications.

Expertise in synthesizing business and technology strategies to bring out the best value.

Andy Chun is a seasoned senior executive and a broad technologist with over 30 years of experience in a wide range of industries, including finance, insurance, health, transportation, and education. He is widely recognized as a top IT leader and a pioneer in AI and emerging technologies. Chun has a diverse background in academia, industry, consulting, and start-up operations. He has a proven track record of leveraging technology to successfully transform businesses and create value while improving customer experience. AI systems he created have contributed to improving the quality of life and benefiting millions of citizens daily.