AI and automation vs. the COVID-19 pandemic: Trading liberty for safety
Reports on the use of AI to respond to COVID-19 may have been greatly exaggerated. But does the rush to pandemic-fighting solutions like thermal scanners, face recognition and immunity passports signal the normalization of surveillance technologies?
Read More →Explainable AI: From the peak of inflated expectations to the pitfalls of interpreting machine learning models
We have reached peak hype for explainable AI. But what does this actually mean, and what will it take to get there?
Read More →Explainable AI: A guide for making black box machine learning models explainable
In the future, AI will explain itself, and interpretability could boost machine intelligence research. Getting started with the basics is a good way to get there, and Christoph Molnar's book is a good place to start.
Read More →Data governance and context for evidence-based medicine: Transparency and bias in COVID-19 times
In the early 90s, evidence-based medicine emerged to make medicine more data-driven. Three decades later, we have more data, but not enough context, or transparency.
Read More →Garbage in, garbage out: Data science, meet evidence-based medicine
Did you ever wonder how data is used in the medical industry? The picture that emerges by talking to the experts leaves a lot to be desired.
Read More →Scientific fact-checking using AI language models: COVID-19 research and beyond
Fact or fiction? That's not always an easy question to answer. Incomplete knowledge, context and bias typically come into play. In the nascent domain of scientific fact checking, things are complicated.
Read More →AI chips in 2020: Nvidia and the challengers
Now that the dust from Nvidia's unveiling of its new Ampere AI chip has settled, let's take a look at the AI chip market behind the scenes and away from the spotlight
Read More →Data science vs the COVID-19 pandemic: Flattening the curve — but how?
Whether they are epidemiologists or not, a few people have attempted to use data and predictive models to model the COVID-19 pandemic. Let's look at the models, the data, and the assumptions and implications that come with them
Read More →Machine learning vs payment fraud: Transparency and humans in the loop to minimize customer insults
What are customer insults, and what does machine learning have to do with it?
Read More →Data science vs social media disinformation: the case of climate change and the Australian bushfires
While a newly released World Weather Attribution study ties the Australian bushfires to anthropogenic climate change, disinformation on social media abounds
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