The AI Core hosted Microsoft’s Lee Stott and Bianca Fortuna at Imperial College London on 19 February on machine learning and AI application.
The event focused on bias and diversity in machine learning and artificial intelligence. Students from Imperial and other London universities attended to listen to presentations, engage in a discussion, and network over refreshments.
The attendees heard from Lee Stott, a Senior Program Manager within the Academic Ecosystems team which sits in the Microsoft Cloud + AI organisation, and Bianca Furtuna, an Applied Machine Learning Scientist at Microsoft who is passionate about all things data.
The discussion covered a range of topics. Firstly, Stott and Furtuna showed how Microsoft provides technology, resource, and expertise to empower those working to solve humanitarian issues and create a more sustainable and accessible world. This includes, for example, protecting biodiversity, helping farmers to increase sustainable food production, protecting complex eco-systems and fighting species extinction.
They presented many cases of companies using machine learning and AI to deal with any type of business problems were presented: from how inventory management is improved with Azure AI and how Spotify uses cognitive services to transform customer experience. Then there was a practical demo about how an automated bot can replace customer’s industry. Finally, the discussion moved to the speakers’ journeys into AI, and the importance of diversity in AI – for example how lack of diversity can introduce bias.
“The AI Core aims to raise awareness around the global issues that can be faced with machine learning and AI – to help us all understand the issues of bias and diversity in machine learning in the practical world.”
The AI Core aims to attract attendees from a range of backgrounds and get them involved with all of its upcoming workshops, which cover both theory and code implementation of state of the art machine learning techniques. Their events in 2020 include deep learning and deep reinforcement learning.
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