We started Mantis with a clear idea of what sort of company we wanted it to be, and the sort of work that we wanted to do.
Experiments should be easily reproducible. This encourages more trust in the results, enables better collaboration within a team, and reduces duplication of work.
Open source software and practices have made, among other things, the AI revolution possible. They accelerate progress, and encourage collaboration between people and organisations. We use open source by default, and contribute to the open source community where possible.
Data reflect human biases. AI and as an extension Natural Language Processing applications mimic those biases and can cause unintended harm. Detecting and reducing ethical risks is the responsibility of all data scientists.
It’s our responsibility to combat climate change to support a more sustainable way of life. Training large deep learning models for Natural Language Processing tasks can have enormous carbon footprints so we avoid using such models where possible, and use computing resources responsibly and efficiently.
AI and NLP is not a neutral technology. Applying Natural Language Processing tools for military, political or marketing purposes has an impact on our democracy and free will. We think carefully about the impact our work might have, and are selective about the industries and clients we work with.
Matt is an environmental scientist by background with a PhD in forestry. He left academia in 2015 and has worked as a data scientist in a number of UK Government departments, startups, and NGOs. Matt has experience of working in the Legal, Health, and Government sectors, where his work focussed on developing reproducible and transparent ways of working and natural language problems.
Nick has studied computer science at Imperial College where his thesis was about detecting lies from video. He has been working as a data scientist for more than 8 years in various startups and lately at the Wellcome Trust. Since his transition to the industry he switched focus from Computer vision to NLP and has experienced working with datasets from a few hundreds to millions of examples and thousands of classes. He has a strong interest in reproducible, reusable and open source code.
Daniel is a software developer who transitioned to Machine Learning in 2018 out of passion for the field. He's interested in Kaggle competitions, and anything that involves Natural Language Processing and AI assistants. His main motivation is making an impact and creating wonderful and innovative products that help others and deliver the best value.
Chris is a Physicist with a PhD in solid state physics. After completing his PhD and a PostDoc in the area of novel nanostructures for photovoltaics, he moved to industry in 2016. Since then, he has worked in different roles: as (big) data engineer, data scientist, engineering manager, product and program manager; in Berlin, Germany and then Limassol, Cyprus; in different fields: mobility, fintech and health.