Remember back at school when you were taught that correlation doesn’t mean causation, that increased ice cream sales are correlated with sunnier weather but don’t cause the clouds to part? Peter Tennant, a fellow of the Alan Turing Institute based at Leeds Institute for Data Analytics explains why it’s important for scientists to become more confident in talking about causation, how "causal inference" methods are transforming the field of epidemiology and why AI isn’t typically best placed to make sensible assumptions about complex data. This episode was recorded before the Covid-19 lockdown began in the UK, but the topics discussed couldn’t be more relevant!
Mapping the UK's Solar Power
Robert Winston on science & the public in the Covid era
AlphaFold & Beyond: How AI and Data Science are Revolutionizing Biology
The Dark Triad: Modelling Psychopathy
The Privacy Collective
Project Odysseus: Capturing city activity to help exit lockdown
Reproducible data science: How hard can it be?
Digital Identity: Can we trust it?
Being an Epidemiologist in 2020
Data journalism in the Covid19 era
Antibody Certificates for COVID-19?
The Future of Tech
Amsterdam's 3D printed steel bridge and it's digital twin
AIrsenal: The Fantasy Football AI
Superbug evolution: understanding the spread of antimicrobial resistance
Image analysis in neurodegenerative disease
Astrophysics in the age of big data
Data trusts: Power to the people in the digital age
Tracking the Pandemic
Create your
podcast in
minutes
It is Free
DNA Today: A Genetics Podcast
Museum of the Missing
Strange by Nature Podcast
Sasquatch Chronicles
Hidden Brain