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: 08.06.2022.

The Domain, Sydney
Approximation
Mozilla
N-gram
Java
Problem solving

@abursuc shared
On Jun 4, 2022
Cool new AD dataset in town 👉 SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation by @JanisPostels @MattiaSegu @DrFisherYu @fedassa et al. https://t.co/CYfMv8tRlA 1/
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SHIFT: A Synthetic Driving Dataset

SHIFT: A Synthetic Driving Dataset

We introduce the largest synthetic dataset for autonomous driving to study continuous multi-task domain adaptation.

@petewarden shared
On Jun 2, 2022
RT @BrianHauerTSO: This is fantastic, @mozilla. Thank you so much for emphasizing local-first software. I hope other software firms learn from your example. It's cool to translate pages without relying on external services. https://t.co/mj2hGneS8Z
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Mozilla releases local machine translation tools as part of Project Bergamot

Mozilla releases local machine translation tools as part of Project Bergamot

In January of 2019, Mozilla joined the University of Edinburgh, Charles University, University of Sheffield and University of Tartu as part of a project fu

@ylecun shared
On Jun 2, 2022
@davidwhogg Answer: no. https://t.co/yLpUqghU6Y
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The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink

Machine Learning (ML) workloads have rapidly grown in importance, but raised concerns about their carbon footprint. Four best practices can reduce ML training energy by up to 100x and CO2 ...

@jeremyphoward shared
On Jun 2, 2022
RT @OuterboundsHQ: 📣 Announcing https://t.co/YngTfx2tAW: a set of guides, tutorials, and examples on how to deploy a wide range of ML products with Metaflow. We believe high-quality documentation is key to making ML infra accessible to all. https://t.co/lP1vnWRDiw Let’s take a tour👇🧵 1/8
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Introducing Metaflow Resources for Data Science and Engineering

Introducing Metaflow Resources for Data Science and Engineering

A knowledge base of free, practical data science and machine learning materials, including how to set up a modern data science infrastructure.

@lawrennd shared
On Jun 3, 2022
With @CambridgeSpark we've created a Python for Science e-learning module for researchers wanting to learn about python and Pandas as part of @AccelerateSci Details here: https://t.co/b9tke0X6rX
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New Python for Science course will help researchers learn programming skills

New Python for Science course will help researchers learn programming skills

A new self-learning module is being offered to help researchers across the University of Cambridge learn programming in Python. Meanwhile, applications have also just opened for the next ...

@stanfordnlp shared
On Jun 6, 2022
RT @StanfordAILab: Hop, hop! In new work published at #ACL2022, @michiyasunaga @jure and @percyliang demonstrate how explicitly modeling document relations during LM pretraining significantly improves multi-hop reasoning across documents. Check it out! https://t.co/LxrdSBcno4
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LinkBERT: Improving Language Model Training with Document Link

LinkBERT: Improving Language Model Training with Document Link

Language Model Pretraining Language models (LMs), like BERT 1 and the GPT series 2, achieve remarkable performance on many natural language processing (NLP) tasks. They are now the ...

@glouppe shared
On Jun 1, 2022
RT @dorigo: The preprint "Simulation-Based Inference with WALDO" is available in the arXiv, and has been submitted to NeurIPS. This is great work and I am sure it will become an important source for frequentist statistic in fundamental science! https://t.co/qHMO1gWw20
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Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm

Simulation-Based Inference with WALDO: Perfectly Calibrated Confidence Regions Using Any Prediction or Posterior Estimation Algorithm

The vast majority of modern machine learning targets prediction problems, with algorithms such as Deep Neural Networks revolutionizing the accuracy of point predictions for high-dimensional ...