Entrusting our health to AI?

Entrusting our health to AI?

Entrusting our health to AI?

event Saturday, September 23, 2023 schedule 11.30am - 12.30pm BST
Past event
Past event
event Saturday, September 23, 2023 schedule 11.30am - 12.30pm BST
  • Image of a robot looking an x-ray
In-person | Free
Open to: 
Alumni and guests
Location: 
Richard Eden Suite | View details

One of the first areas of AI to impact healthcare has been image processing, leading to the controversial suggestion by one prominent scientist in 2016 that "we should stop training radiologists now", on the grounds that AI would be doing their job within five years. (Note: radiologists are still hard at work). AI can also contribute in another way by helping to predict the effectiveness of a treatment for an individual patient, rather than on average for a population. That could improve quality of life and save resources -- indeed one prominent UK physician said in 2018 that "AI may be the thing that saves the NHS".  Another big opportunity is in biochemistry and the discovery of new drugs, where AI is contributing new insights into cells and how particular chemicals affect them. And as always with AI, there are the ever-present questions about whether and how far we can trust it. Our discussion will touch on each of these exciting and important issues.

Speakers

Andrew Blake (Professorial Fellow, Clare Hall)

Andrew Blake

Andrew Blake, PhD, FREng, FRS, is a pioneer in the development of the theory and algorithms that make it possible for computers to behave as seeing machines. His interests are primarily in image processing and segmentation as optimisation, on visual tracking as probabilistic inference, and on real-time, 3D vision.

Professor Blake has been Director of Microsoft Research in Europe (2010-15), inaugural Director of the Alan Turing Institute (2015-18), and Chair of Samsung AI in Cambridge (2018-22). Currently he is a consultant in Artificial Intelligence: Scientific Adviser to the FiveAI autonomous driving company, recently acquired by Bosch, and Chief Scientific Adviser at Mantle Labs. Recently he has been consulting for Samsung, Siemens and the UK Stock Exchange.
 

Alicia Curth

Alicia Curth

Alicia Curth is a final-year PhD student at the University of Cambridge, working in the area of Machine Learning (ML) for healthcare. Most of her research focuses on how to best use ML to estimate personalized causal effects of treatments, but she is broadly interested in many topics relating to causality, missing data, distribution shifts and statistical machine learning more generally.

Although Alicia is a ML researcher today, she is an applied statistician at heart and most of her research therefore reflects her desire to connect ideas from applied statistics with machine learning. She holds a BSc in Econometrics and Operations Research (summa cum laude) and a BSc in Economics and Business Economics (summa cum laude) with specialisation in Policy Economics from the Erasmus University Rotterdam, and a MSc in Statistical Science (with distinction and a prize for the top performance in the cohort) from the University of Oxford. She also briefly worked as a data scientist evaluating the effectiveness of marketing campaigns, and as a research intern at Pacmed, a Dutch start-up bringing ML solutions to clinical practice.
 

Srijit Seal

Srijit Seal

Srijit Seal is a researcher specializing in chemoinformatics. His research is centred on using machine learning techniques, particularly modelling and interpretation of the Cell Painting assay, to predict drug bioactivity, safety, and toxicity. Seal actively engages in academic outreach, promoting the understanding of Artificial Intelligence and delivering seminars on its applications in drug discovery.

He completed his BSc at St. Stephen's College, University of Delhi, and his MPhil in Chemistry at the University of Cambridge. He is currently a final-year PhD student in Chemistry at the University of Cambridge.
 

Stefan Heimersheim

Stefan Heimersheim

Stefan is a SERIMATS scholar researching Mechanistic Interpretability of Transformers and AI Alignment. He completed his Master’s in Physics at RWTH Aachen University. He completed the AGI Safety Fundamentals Programme at Effective Altruism Cambridge in 2021 and the advanced track for Machine Learning Safety Scholars (MLSS) at the Center for AI Safety in 2022. He also spent time working as a Machine Learning Alignment Theory Scholar at the Stanford Existential Risks Initiative in early 2023. He is the Society Events Officer at the Cambridge Existential Risks Initiative and is currently a final-year PhD student in Astronomy at the University of Cambridge.

Booking information

It is free to attend this event. Cambridge alumni can bring along a maximum of 2 guests. Please click on the book button

Tea, coffee and biscuits will be served from 11:00 a.m. onwards. Please ensure to arrive at least 10 minutes before the start of the event. 

Bookings for this event are handled externally. Please contact the event organiser if you have any questions.

 

Booking for this event is now closed.

Location

Richard Eden Suite
West Court, Clare Hall
Cambridge
CB3 9AL
United Kingdom
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