Modelling socio-spatial dynamics
The near-pervasive adoption of mobile devices and the growing use of sensors embedded in physical environments are enabling a new generation of models for studying human-environment interactions.
COVID-19: Different Strokes
This is a part of a series of posts analysing the data being published about the spread of COVID-19. The focus is on the cumulative number of deaths reported. If you find such data upsetting, please do not view this post.
COVID-19: Daily Updates
This post provided a daily update of COVID-19 data shared by John Hopkins University. It shows the trending lines for countries with the highest number of reported deaths and summarises key data changes up to 1 May 2020
COVID-19: 28 days later…
This article contains visuals regarding the global impact of the COVID-19 virus during its initial spread.
How to create a better AI
How can we create a better artificial intelligence? By being aware of the flaws in the data, theories and objectives we are using to build current AIs…
Is the use of AI dehumanising behaviour?
Is the use of machines to evaluate and predict human behaviour a de-humanising act? Should a birth-to-death record of our actions be used in judgement throughout life? How about through generations…?
Machine and Deep Learning Limits
If decisions are to be delegated to artificially intelligent machines, we need to appreciate the limits of intelligence without cognition
How sensor data gets smart
Talk delivered at The Things Conference. Discussing the good, the bad, the ugly and the beautiful ways in which data traces from digitised physical interactions can be converted into actionable insights…
Digitally sensing people-place interactions
Introducing recent research and development in urban cognitive analytics to sense population dynamics and people-place interactions
Do not trust cognitive algorithms
Algorithms based on training data will incorporate any biases in that training data, including not accounting for the missing data
