by Daniel Jeffries | May 8, 2020 | Practical-Ethics
Making machine learning algorithms fair is a tricky business. What does “fair” even mean? You might think it’s easy to define but what’s fair to you might not be fair to someone else. Even defining the word is a slippery slope filled with...
by Daniel Jeffries | Apr 2, 2020 | Datasets
We all know deep learning isn’t very deep. Over the decades, through AI spring and winter, teams have tried to deliver higher order reasoning to give human like intelligence to machines. Now researchers have come up with a new dataset to show researchers just...
by Daniel Jeffries | Feb 25, 2020 | Language, NLP
Practical AI 78: NLP for the world's 7000+ languages – Listen on Changelog.com AI that can synthesize and process language with ease has made rapid strides in the last few years with NLP teams delivering key breakthroughs like Wavenet that powers the Google...
by Daniel Jeffries | Feb 4, 2020 | Explainable-AI
In many ways, AIs are alien intelligences. They make decisions in a “black box” and we can’t really understand why they made the choices they made. Explainable frameworks usually look like the answer. They turn AIs in “glass box”...
by Daniel Jeffries | Jan 21, 2020 | Reproducibility
In this talk (slides here) by data scientist Suneeta Mall at Kubeflow Con, she talks about why reproducbility needs to gets much stronger in the coming years of AI development. Without understanding, auditing and reproducibility, we got zero chance of fixing the wild...
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