Adrian Colyer was CTO of SpringSource, then CTO for Apps at VMware and subsequently Pivotal. He is now a Venture Partner at Accel Partners in London, working with early stage and startup companies across Europe. If you’re working on an interesting technology-related business he would love to hear from you: you can reach him at acolyer at accel dot com.
Vega and Vega-lite follow in a long line of work that can trace its roots back to Wilkinson’s ‘The Grammar of Graphics.’ Since then VegaLite has come into existence, bringing high-level specification of interactive visualisations to the Vega-Lite world.
This is a summary of a recent paper on an age-old topic: what visualisation should I use? No prizes for guessing “it depends!” Is this the paper to finally settle the age-old debate surrounding pie-charts??
The two main takeaways from this paper: firstly, a sharpening of my understanding of the difference between explainability and interpretability, and why the former may be problematic; and secondly some great pointers to techniques for creating truly interpretable models.
Today we’re looking at a more general fake news problem: detecting fake news that is being spread on a social network. This is a summary of a recent paper which demonstrates why we should also look at the social context: the publishers and the users spreading the information!
TensorFlow.js brings TensorFlow and Keras to the the JavaScript ecosystem, supporting both Node.js and browser-based applications. Read a summary of the paper which describes the design, API, and implementation of TensorFlow.js.
This article summarizes a paper which presents us with a broad sweep of the graph neural network landscape. It’s a survey paper, so you’ll find details on the key approaches and representative papers, as well as information on commonly used datasets and benchmark performance on them.
What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
What if instead of hand designing an optimising algorithm (function) we learn it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!
Read this engaging overview of a report from the Stanford University 100 year study of Artificial Intelligence, “a long-term investigation of the field of Artificial Intelligence (AI) and its influences on people, their communities, and society.”
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Understanding Deep Learning Requires Re-thinking Generalization