Getting there: Structured data, semantics, robotics, and the future of AI
Leveraging structure in data is key to making progress in AI, says AI prodigy Gary Marcus. A forward-looking view on Software 2.0, AI chips, robotics, and the future of AI
Read More →Rebooting AI: Deep learning, meet knowledge graphs
Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there.
Read More →Own your newsfeed, own your data
A guide to keeping all your news sources and items in one place We all have things we care about and follow. Whether it’s sports, arts, technology, from the mainstream to the obscure, we gravitate around them. Over time, we tend to both specialise, accumulating knowledge in specific sub-domains, and expand, jumping to adjacent topics […]
Read More →What’s next for AI: Gary Marcus talks about the journey toward robust artificial intelligence
Gary Marcus is one of the more prominent, and controversial, figures in AI. Going beyond his critique on Deep Learning, which is what many people know him for, Marcus puts forward a well-rounded proposal for robust AI
Read More →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.
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