Peering under the hood of fake-news detectors
Study uncovers language patterns that AI models link to factual and false articles; underscores need for further testing.
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Engineers program marine robots to take calculated risks
Algorithm could help autonomous underwater vehicles explore risky but scientifically-rewarding environments.
Continue ReadingIdentifying artificial intelligence “blind spots”
Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.
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School of Engineering welcomes new faculty
Eleven new professors join the MIT community.
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Neural Ordinary Differential Equations
Neural ordinary differential equations Chen et al., NeurIPS’18 ‘Neural Ordinary Differential Equations’ won a best paper award at NeurIPS last month. It’s not an easy piece (at least not for me!), but in the spirit of ‘deliberate practice’ that doesn’t mean there isn’t something to be gained from trying to understand as much as possible. […]
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The tradeoffs of large scale learning
The tradeoffs of large scale learning Bottou & Bousquet, NIPS’07 Welcome to another year of The Morning Paper. As usual we’ll be looking at a broad cross-section of computer science research (I have over 40 conferences/workshops on my list to keep an eye on as a start!). I’ve no idea yet what papers we’ll stumble […]
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How Deep Learning Is Aiding Preservation of Seneca and Other Endangered Languages
Linguists estimate that at least half of the world’s estimated 7,000 spoken languages will become extinct by the century’s end, due to forces ranging from globalization to cultural assimilation. Part of the challenge of documenting and revitalizing endangered languages is a lack of texts and speech recordings to work with. Seneca, a language of one […]
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Deep-learning technique reveals “invisible” objects in the dark
Method could illuminate features of biological tissues in low-exposure images.
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LEMNA: explaining deep learning based security applications
LEMNA: explaining deep learning based security applications Guo et al., CCS’18 Understanding why a deep learning model produces the outputs it does is an important part of gaining trust in the model, and in some situations being able to explain decisions is a strong requirement. Today’s paper shows that by carefully considering the architectural features […]
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Uncertainty propagation in data processing systems
Uncertainty propagation in data processing systems Manousakis et al., SoCC’18 When I’m writing an edition of The Morning Paper, I often imagine a conversation with a hypothetical reader sat in a coffee shop somewhere at the start of their day. There are three levels of takeaway from today’s paper choice: If you’re downing a quick […]
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