Women in AI Dinner
London

Babylon's app has been downloaded over a million times and it allows people in UK, Ireland, and Rwanda to ask a chatbot a series of questions about their condition without having to visit a GP.

Scientists have developed a new test that can pick out women at high risk of relapsing from breast cancer within 10 years of diagnosis. Their study looked for immune cell ‘hotspots’ in and around tumours, and found that women who had a high number of hotspots were more likely to relapse than those with lower numbers. The new test could help more accurately assess the risk of cancer returning.

Take a look at Noor's blog, where she discusses her research. Topics include applying Deep Neural Networks in the field of video games and experience-driven content generation.

Facebook have been working on a new technique to use multilingual embeddings to help them scale to more languages, help AI-powered products ship to new languages faster, and ultimately give people a better Facebook experience. Read on to understand more about the challenges faced for NLP across languages, and how multilingual word embeddings are trained and used for text classification.

The global AI revolution has just shot way ahead of the curve thanks to PROWLER.io – a Business Weekly Killer50 company and reigning Startup of the Year – following a high-powered launch in California. The decision making system includes technologies that can implement optimal decisions across multiple policies or strategies.

At the BBC, we want to bring better and more journalism to under-served audiences – so we’re asking questions such as: “how do we encourage more young women to read political news?”. Those are really exciting problems for a data scientist and for a technologist to be solving.

Since joining DeepMind two years ago, Jane's non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. “It’s exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks,” she shares.

Mounia Lalmas leads an interdisciplinary team of research scientists in Boston, London, New York and San Francisco, working on personalization and discovery. Mounia also holds an honorary professorship at University College London. Her work focuses on studying user engagement in areas such as native advertising, digital media, social media, search, and now music.