12 Jul 2020

Daniel Neil

We are pleased to announce that BenevolentAI is sponsoring the virtual ICML 2020 conference from the 12th to 18th of July. You can find our team on the EXPO day and Women in Machine Learning workshop - we hope to meet you there.

ICML Expo Day | 12 July

[Timings and joining instructions will be shared by the ICML organisers on ICML website. Please ensure you sign up for tickets to the conference to be able to attend our talks.]

Keynote: How we leverage machine learning and AI to develop life-changing medicines - a case study with COVID-19.

The current model for drug discovery and development is failing patients. It is expensive and high risk, with long research and development cycles. This has a societal cost, with 9,000 diseases being untreated - in addition to the disappointing reality that the top ten best-selling drugs only are effective in 30-50% of patients. Tackling this challenge is very complex. While many companies focus on one component of the drug discovery process, BenevolentAI chooses to apply data and machine-learning driven methods across drug discovery, from the processing of scientific literature, to knowledge completion, to precision medicine, to chemistry optimisation, each leveraging domain expert knowledge and state-of-the-art research.

In this talk, we will discuss the peculiarities of machine learning for the drug discovery domain.  In this field, there exist many unique challenges, including tradeoffs between novelty and accuracy; questions of quality and reliability, both in extracted data and in the underlying ground-truth; how best to learn from small volumes of data; and methods to best combine human experts and ML methods.  As we discuss the tools and methods that BenevolentAI has developed, we will explore these themes and walk through approaches.

Finally, to give a real example of how we apply machine learning and AI in our day-to-day work, we will showcase the application of our technology to repurpose existing drugs, using our tools and internal clinical experts, as a potential treatment for COVID-19. Baracitinib, the top drug we identified is currently being investigated in a Phase 3 clinical trial.

Speakers: Daniel Neil, VP Artificial Intelligence, Sia Togia, AI Lead for Knowledge (NLP & Knowledge Graph), Olly Oechsle, Lead Application Engineer and Aylin Cakiroglu, Senior AI Scientist at BenevolentAI

Panel | Machine Learning in Health: What’s next?

[Timings and joining instructions will be shared by the ICML organisers on ICML website. Please ensure you sign up for tickets to the conference to be able to attend our talks.]

This year has been an extraordinary year that has both emphasised the vulnerability of the world to health risks and the rapid generation and availability  of data .  Within weeks of the outbreak of COVID-19, the genome sequence of the virus was available, full-document datasets covering the disease were released, and online citizen science communities sprung up to help identify possible new approaches to treat the disease.  What questions should machine learning researchers, new to the field, pursue to best further the identification of new therapies?  What are some of the hard, unsolved problems in discovering new treatments?  How has the rapid development of new data modalities affected the kinds of research needed? This panel will explore these themes.

Speakers: Daniel Neil, VP Artificial Intelligence, Alix Lacoste, VP Data Science, JB Michel, Scientific Advisor and founder at Patch.io, Páidí Creed, Director AI Science and Ana Leite, Lead Bioinformatics Data Scientist at BenevolentAI

Women in Machine Learning Un-Workshop | 13 July, 10:25am BST

[Timings and joining instructions will be shared by the ICML organisers on ICML website. Please ensure you sign up for tickets to the conference to be able to attend our talks.]

Breakout Session: How will data diversity become a requirement for training AI models

It is well documented that the lack of representation in biomedical research is leading to a data gap that can no longer be overlooked if we are to avoid exacerbating existing health inequalities in the age of digital health and precision medicine [1] [2] [3]. Advances in machine learning (ML) techniques are allowing the scientific community to unlock the potential of biomedical data and extract valuable insights like never before. Yet amidst the hope sits a certain uncomfortable reality: not everyone is set to benefit from these advances. At the heart of innovation in healthcare lie the datasets used to train the algorithms, such as data from scientific literature, clinical trials, omics, and patient real-world data. These datasets are the lifeblood of new technologies, and are extremely meaningful. Yet, they have significant shortcomings, since the majority of medical research is conducted on white and predominantly male populations of European descent [4]. This lack of diversity in data has serious consequences for medical care, as the products discovered through the use of these data may not benefit everyone. For example , as covid-19 is already disproportionately affecting people of color [5] [6], working with data sets that do not include that population equally could further exacerbate the health disparities.

Speakers: Adepeju Oshisanya, Data Diversity Advocate and Clinical Drug Development Leader BenevolentAI, Allison Gardner, Programme Director Data Science Degree Apprenticeship at Keele University / Co-Founder of Women Leading in AI, Simone Larsson, AI / ML Commercial Product Lead at Digital Catapult, Aylin Cakiroglu, Senior AI Scientist at BenevolentAI.


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