A Common Language for Intelligence
Question Answering at Scale
Bloomsbury AI aims to unlock this expertise and enable its seamless communication by combining Machine Reading (MR), Deep Learning and Data Augmentation to provide experts with AI-driven, guided systems allowing them to transfer and share their knowledge more accurately and efficiently than ever before.
We present some insights from our research into transfer learning for open-domain question answering, together with a demonstration of the technology and a brief discussion of some ideas for further work.
Max Bartolo
Originally from a mechanical engineering background, Max followed up on his passion for machine learning and AI with a Masters from UCL. He now works on solving challenging problems focused on making the world's expertise more accessible.
Shared principles and concepts for the artificial and human. In pursuit of a common language for intelligence
We will be looking at interesting principles and concepts on the algorithmic level of description (Marr) that has emerged in the collision of the neural and cognitive approaches to psychological science, that could be of particular interest to translate and implement in AI. The talk will mainly focus on the role of artificial curiosity, internal models and action.
Marco Lin
Marco comes from a background in cognitive neuroscience, which is where the neural and cognitive approaches in psychological sciences collide. His main interest is specifically consciousness and the fundamental nature of neurocognition, the relation between structure and function. He believes that in this collision has come forth a perspective that also provides a point of intersection for human and artificial intelligence, and is on a journey to further explore these bridges.
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