My background is primarily in artificial intelligence (AI), which I have been working with for almost 30 years. Since the late 1990s, I have worked on a wide range of innovative projects instrumental in driving the transformation of Elsevier from an established scientific print publisher through to its transition into a predominantly digital information analytics company. In 2009, I was responsible for developing Elsevier’s first semantic databases for therapeutic drug design and, in 2014, as education director in our health solutions division I introduced new digital business models and launched adaptive learning courses into the NHS in the UK. Since 2016, as consulting director for text and data analytics, my work has focused on making these information analytics tools truly productive for customers in commercial R&D, and now my work is focusing on stewardship of data resources and validation and auditing of machine learning. In 2018, I launched Elsevier’s new AI and semantic data platform (entellect.com) and organised a datathon challenge to investigate repurposing drugs to treat rare diseases using the new platform in collaboration with the pharma pre-competitive group, the Pistoia Alliance, along with not-for-profit organisations, CuresWithinReach and Mission-Cure. In 2019, I will extend this work to cancer research.
Throughout my career, it has always been important to have a positive impact on society. In 2018, I had responsibility for the launch of Elsevier’s AI and semantic data platform (entellect.com) which is a culmination of the last couple of decades work at Elsevier in refining science information analytics. The launch of the platform was through a datathon challenge and it is great to see people coming together to work on problems in the environment and seeing positive life-enhancing outcomes as a result. We hope to see benefits brought to patients suffering from chronic pancreatitis as a result of the work.
It is vital to get a comprehensive understanding of the philosophical underpinnings of AI as well as the engineering principles. I am grateful to Professor Margaret Boden for the broad foundation in AI she gave me as an undergraduate at Sussex University and I would recommend reading her new book!
My prediction for 2018 was to see AI techniques becoming productive and this has certainly been the case. At Elsevier, I see this with both the corporate and research customers we support and with the developments our operations teams are driving in analytical technology, from creating deep learning-ready data sets to developing intuitive user experiences that make gaining insights more accessible. Last December, Elsevier published a free-report on the industry, “Artificial intelligence: How knowledge is created, transferred and used,” which gives some great examples of how these approaches are being used across a wide range of industries and research fields.
In 2019, we will see increased importance placed on data stewardship and the ethical use of AI, which is a key theme for the new UK Government Office for AI and the sector deal. This places great importance on avoiding or managing bias in machine learning models and emphasises the importance of data quality for AI. Part of these developments will center on and the use of the FAIR data protocols for data aggregation and integration to support use of AI for research and development. Supporting these developments in data stewardship is something we at Elsevier are focused on.
Being able to work with great people in diverse, multi-disciplinary teams with cutting-edge technologies at the same time as fulfilling your idealistic dreams of having a positive impact on society is a powerful story. It is one we can tell about working at Elsevier. Last year, I worked on a short animated film, showcasing our work and telling this story about data science at Elsevier. The film was called “The loneliest dataset” and it is shortlisted for the radawards 2019 best single use video (radawards.com), which was a great way to get the message out and boost recruitment.
These tools can amplify the work done by researchers to address the challenges our society faces, such as in climate change and antibiotic resistance. I am pleased to think that data and analytics can support innovation to address these challenges and make the world a better place.