This week we announced the renewal and expansion of our scholarship programme with the University College London. Four more DeepMind graduate scholarships for students wishing to pursue a master’s degree in the Department of Computer Science will be available for students starting courses in 2020–21. But UCL is just one example. We also work with numerous other universities, such as Oxford, Queen Mary University London, the University of Cambridge and NYU, to broaden participation in AI and computer science.
I’ve seen many examples of the impact that diverse perspectives can have in practice. Take Shaquille Momoh, one of our DeepMind scholars, who was inspired to research protein folding prediction while studying at UCL. Nearly every function our body performs—contracting muscles, sensing light, or turning food into energy—can be traced back to proteins and how they move and change. Predicting their structure is fundamental to understanding the body, as well as diagnosing and treating diseases believed to be caused by misfolded proteins. Shaquille had a specific motivation for studying protein folding. He wanted to better understand sickle-cell anaemia, a painful inherited condition much more prevalent in black communities – and for which there is no current cure.
Supporting universities
To ensure the next generation of researchers reach their full potential, protecting and strengthening the research and teaching capacity of our academic institutions is vital too.
DeepMind partners with a range of world-leading universities with the aim of extending research excellence and teaching capacity. We’ve established academic chairs in machine learning at the University of Alberta, University College London, and the University of Cambridge, offering unrestricted funding for world-renowned researchers to freely pursue their academic interests. These chairs will be supported in their research and teaching efforts by PhDs students. And many of our researchers hold dual affiliations, allowing them to continue teaching or supervising students at Cambridge, Oxford, Imperial, MIT, McGill and elsewhere (you can access some of these courses on YouTube).
Investing in the ecosystem
Only by investing the right way across the ecosystem will we able to ensure the highest quality AI research that benefits everyone. It’s also why we partner with charities like Chess in Schools and Communities and In2Science, and have become founding partners of the Deep Learning Indaba in Africa, Khipu AI in South America, the Eastern European Machine Learning Summer School, the Southeast Asia Machine Learning School (SEAMLS), and the AI4Good Summer Lab in Canada.
And to research how the lack of diversity affects the development of AI – how companies work, what products get built, and who benefits – last month we announced a research fellowship with the Partnership on AI to explore the pervasive challenge of developing AI for the benefit of people and society.
History has shown us that hard problems are best solved with collective effort. Innovation happens when people with different experiences, knowledge, and backgrounds join together, break down boundaries, openly share ideas and collaborate for a common goal. Building advanced AI responsibly may be one of the hardest scientific challenges to solve. If our sector can provide the right support for researchers and foster an open, collaborative and diverse academic culture, the impact could be truly transformative.
Source: https://deepmind.com/blog/article/strengthening-the-ai-community
