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 →The state of AI in 2020: Biology and healthcare’s AI moment, ethics, predictions, and graph neural networks
Research and industry breakthroughs, ethics, and predictions. This is what AI looks like today, and what it's likely to look like tomorrow.
Read More →The state of AI in 2020: Democratization, industrialization, and the way to artificial general intelligence
From fit for purpose development to pie in the sky research, this is what AI looks like in 2020.
Read More →Hybrid AI through data, space, time, and industrial applications: Beyond Limits scores $113M Series C to scale up
Machine learning approaches to AI alone don't cut it. Good old-fashioned AI alone does not cut it either. Beyond Limits takes a page from the AI vision book and combines different approaches for large-scale industrial applications
Read More →Nvidia’s acquisition of Arm strengthens its ecosystem, brings economies of scale to the cloud, expansion to the edge
Nvidia is after a double bottom line in the AI chip market: Better performance and better economics. Arm's acquisition helps with the economies of scale in the data center and expands Nvidia's footprint to the edge
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.
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