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[SAIF 2019] Day 2: Symbolic Logic meets Machine Learning: Towards Reliable AI – Vaishak Belle

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Artificial Intelligence (AI) provides many opportunities to improve private and public life, and it has enjoyed significant investment. Indeed, discovering patterns and structures in large troves of data in an automated manner is a core component of data science. Machine learning currently drives applications in computational biology, natural language processing and robotics. However, such a highly positive impact is coupled with a significant challenge: when can we convincingly deploy these methods in our workplace? For example, can we provide prior knowledge and suggestions to the learning modules? Can we learn interpretable symbolic structures from data? In this talk, we look at the fundamental problem of unifying reasoning and learning, and how this enables a systematic way to integrate human knowledge and data-driven learning methods. We will then briefly consider how that unification may help us take steps towards a commonsensical, transparent and responsible AI.
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