Most of the kind of AI that gets the spotlight these days revolves around machine learning. But there actually are many approaches, or tribes, in AI. What is the role of knowledge representation and reasoning, or symbolic AI, today?
How can more traditional, knowledge-based approaches connect with, and contribute to, machine learning for better AI? And what’s graph got to do with it? This panel will discuss how to bridge the gap among the AI tribes.
Director of Technical Services, Communication and Strategy, Wolfram Research Europe
With over 25 years of experience working with Wolfram Technologies, Jon has helped in directing software development, system design, technical marketing, corporate policy, business strategies and much more.
Jon gives regular keynote appearances and media interviews on topics such as the Future of AI, Enterprise Computation Strategies and Education Reform, across multiple fields including healthcare, fintech and data science.
Jon holds a degree in mathematics from the University of Durham. Jon is also Co-founder and Director of Development for computerbasedmath.org, an organisation dedicated to a fundamental reform of maths education and the introduction of computational thinking.
Researcher at Facebook AI Research
Sebastian is a Researcher at Facebook AI Research. He is also a Professor at University College London and an Allen Distinguished Investigator, leading the Machine Reading Lab. He has also worked in research with Andrew McCallum at UMass Amherst, Tsujii Junichi at Tokyo University and DBCLS, and Ewan Klein at the University of Edinburgh.
Sebastian works on teaching machines how to read and reason, in the intersection of Natural Language Processing (NLP) and Machine Learning. Recently, he has been tackling these problems via forms of end-to-end differentiable program interpreters as well as adversarial regularisation. He is generally interested in deep learning, good old graphical models as well as old-school symbolic AI.
Senior ML Engineer, Data Reply UK
Christos is a Senior ML-Engineer at Data Reply UK. He works with Big Data Frameworks, and develops ML-models and end-to-end big data pipelines. He is also a founding member of the Apache Flink meetup community in London.
Christos holds a PhD in Computer Science and is a visiting researcher at King’s College London, where he works on the development of efficient document clustering algorithms.
For his PhD thesis, he worked on knowledge representation and AI reasoning, touched on the field of logics, and closely studied and applied a variety of machine learning and graph analytics techniques.
Neural Networks and Graph AI Researcher, Octavian
Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products.
Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks.
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