Artificial Intelligence

AI Research News

Discover the latest AI research & find out how AI, Machine Learning and advanced algorithms impact our lives, our jobs and the economy, all thanks to expert articles that include discussion on the potential, limits and consequences of AI.

Top news of the week: 03.02.2022.

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Research

@lawrennd shared
On Feb 1, 2022
RT @Michael_J_Black: I get a lot of reviews that say my work is not novel and I bet I'm not alone. It's always frustrating because I see novelty where the reviewer doesn't. Rather than rebut every critique, I've written a blog post to help reviewers think about novelty. https://t.co/UXLabOkYcn
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Novelty in Science

Novelty in Science

Reviewers have strong ideas about what makes a paper acceptable in top conferences like CVPR. They know that getting into such conferences is hard and that getting a paper in is ...

@etzioni shared
On Jan 31, 2022
RT @allen_ai: Zillow's AI fell victim to adversarial machine learning coupled with hurried decision making. AI2 CEO @etzioni shares what we can learn in a piece on @geekwire today: https://t.co/Te9CtXyHQo
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Commentary: How homeowners defeated Zillow’s AI, which led to Zillow Offers’ demise

Commentary: How homeowners defeated Zillow’s AI, which led to Zillow Offers’ demise

Now that the dust has settled, we can ask how could the smart and savvy team at Zillow Group, armed with cutting-edge AI methods and mountains of data, lose half a billion dollars buying ...

@syhw shared
On Jan 31, 2022
RT @vineelk: We just posted a preprint on Star Temporal Classification - an algorithm which can learn from partially labeled and unsegmented sequential data. Preprint here: https://t.co/DzmMiIcRhO Reference Implementation: https://t.co/xIwovKPbBy Joint work with @awnihannun, @syhw, Ronan.
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Star Temporal Classification: Sequence Classification with Partially Labeled Data

Star Temporal Classification: Sequence Classification with Partially Labeled Data

We develop an algorithm which can learn from partially labeled and unsegmented sequential data. Most sequential loss functions, such as Connectionist Temporal Classification (CTC), break ...

@yoavgo shared
On Jan 31, 2022
https://t.co/T4K0deUdBB
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Introducing Semantic Reader

Introducing Semantic Reader

An AI-powered augmented scientific reading application from the Semantic Scholar team at the Allen Institute for AI, UC Berkeley, and the University of Washington, and supported in part by ...

@kchonyc shared
On Jan 28, 2022
🤯 "Central to the .. understanding of the thermofluidic processes .. is the ... extraction of .. physical descriptors from the highly transient droplet population" ... "deep learning .. can .. harness .. descriptors and quantify thermal performance" https://t.co/afe4befQVu
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A Deep Learning Perspective on Dropwise Condensation

A Deep Learning Perspective on Dropwise Condensation

A vision-based framework utilizing artificial intelligence is proposed to meet the challenges in acquiring physical descriptors of dropwise condensation. Using this framework, the study ...

@_rockt shared
On Jan 30, 2022
RT @HeinrichKuttler: moolib is also the engine behind @BIT_silence et al's recent (excellent) RLMeta library. Check it out! https://t.co/RazHKI6fm8
Open
RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research.

RLMeta is a light-weight flexible framework for Distributed Reinforcement Learning Research. - GitHub - facebookresearch/rlmeta: RLMeta is a light-weight flexible framework for Distributed ...