We invited 5 experts to discuss their thoughts on the impact of Machine Learning so far, its challenges and the opportunities it can offer.
The panel consisted of:
Toby Cockayne, Co-Founder & Head of Product, Sportr
Sportr is an athletic recruitment network dedicated to connecting athletes to agents and fans worldwide. It uses natural language processing to curate and generate unique real-time sports content for a variety of businesses. Toby was awarded LSE Entrepreneur of the Year and Social Entrepreneur of the Year 2015.
Dr. Sybil Wong, Data Scientist & Head of Marketing and Communications, Sparrho
Sparrho is a startup combining human and machine intelligence to disseminate scientific knowledge by facilitating for students to search through the thousands of research papers and texts online. They have so far searched through 48million articles and started building a comprehensive database, and they are mostly interested in the data generated by the users of their site. Dr. Wong said: “Author demarkation is a complete mess in the academic world. If you search Dr. S. Wong on PubMed, it would come up with about 235 publications – I can tell you I’ve only published five.”
Naré Vardanyan, Founder, MindBin Technologies
Mindbin technologies build artificially intelligent solutions for emotional well-being. Naré is a Master of Politics, Security and Integration and started the company this year at the age of 25. She created Mindbin to create a repository of human emotions and personality types for improving human experience and human/machine interaction, and suggests ‘Canine Empathy’ as a way to do so. ‘Canine Empathy’ is the theory that dogs imitate humans’ behaviours in order to bond easily with humans and thus improve chances of survival. Naré thinks we are a long way off building an AI with ‘Human-level empathy’, so that a more realistic goal is an AI that can at least mimic their human user’ behaviours and characteristics.
Jorge Cardoso, Quantitative Neuroradiology Lecturer, UCL
Jorge works in the Translational Imaging Group (TIG) of UCL Centre for Medical image Computing (CMIC) and the Dementia Research Centre as a Lecturer, developing new methods for medical image analysis. He discusses using Machine Learning to analyse images of the human brain in order to identify diseases.
There is a large margin for human error when analysing brain images and trying to identify diseases (e.g. the onset of alzheimer’s), so they are training an algorithm to better process, analyse and compare brain images therefore improving the likelihood of identifying diseases at an earlier stage.
Giovanni Charles, Data Scientist, Mendelian
Mendelian are introducing a search engine able to understand patients’ clinical features and infer their causative genes to identify rare diseases in people’s genes through unsupervised bioinformatic techniques and analysing data from around the world.
Let us know what you think in the comments.