Shakir Mohamed, Research Director, Google DeepMind & Co-Founder
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Shakir Mohamed, Research Director, Google DeepMind & Co-Founder

Dr Shakir Mohamed works on technical and sociotechnical questions in machine learning and artificial intelligence research, aspiring to make contributions to machine learning principles, applied problems in healthcare and environment, and ethics and diversity. Shakir is a research scientist and lead at DeepMind in London, an Associate Fellow at the Leverhulme Centre for the Future of Intelligence, and a Honorary Professor of University College London.
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Shakir works as a research scientist at London’s DeepMind. Having worked at DeepMind for more than five years, he has seen the company evolve from a little startup to the premier hub for artificial intelligence and its applications worldwide. In particular, generative models, variational inference, and unsupervised learning are the focus of Shakir’s research, which examines the nexus of probabilistic reasoning, deep learning, and reinforcement learning. Shakir received a Junior Research Fellowship from the Canadian Institute for Advanced Research (CIFAR) at the University of British Columbia after completing a PhD in statistical machine learning from the University of Cambridge. Shakir, a proud native of Johannesburg, studied engineering at the University of the Witwatersrand in Johannesburg for both his undergraduate and master’s degrees.

Shakir contributes to the Deep Learning Indaba’s initiatives pertaining to community development, leadership, recognition, and policy, all of which support the organization’s goal of enhancing artificial intelligence and machine learning in Africa. The largest gathering in the world devoted to mentoring, discussion, and instruction at that level of artificial intelligence will take place in South Africa in 2018.


2023

An AI research team at Google’s DeepMind project has created a particular kind of AI system that may exhibit social learning skills. The team details how they created an artificial intelligence program that demonstrated its capacity to pick up new abilities in a simulated environment by imitating the behaviors of an implanted “expert” in their publication published in the journal Nature Communications.

The team’s initial task was to create GoalCycle3D, a virtual environment with uneven terrain, different barriers, and multicolored spheres. Next, artificial intelligence (AI) agents were included. Their goal was to navigate the virtual environment by dodging barriers and entering circles. All that was provided to the agents in terms of knowledge about the world they would live in were learning modules. They used reinforcement learning to learn how to go forward.


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