A survey on active simultaneous localization and map**: State of the art and new frontiers
Active simultaneous localization and map** (SLAM) is the problem of planning and
controlling the motion of a robot to build the most accurate and complete model of the …
controlling the motion of a robot to build the most accurate and complete model of the …
Trustworthy AI: From principles to practices
The rapid development of Artificial Intelligence (AI) technology has enabled the deployment
of various systems based on it. However, many current AI systems are found vulnerable to …
of various systems based on it. However, many current AI systems are found vulnerable to …
[HTML][HTML] Leakage and the reproducibility crisis in machine-learning-based science
Machine-learning (ML) methods have gained prominence in the quantitative sciences.
However, there are many known methodological pitfalls, including data leakage, in ML …
However, there are many known methodological pitfalls, including data leakage, in ML …
The fallacy of AI functionality
Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …
On the opportunities and risks of foundation models
AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …
Deep reinforcement learning at the edge of the statistical precipice
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing
their relative performance on a large suite of tasks. Most published results on deep RL …
their relative performance on a large suite of tasks. Most published results on deep RL …
[PDF][PDF] The computational limits of deep learning
Deep learning's recent history has been one of achievement: from triumphing over humans
in the game of Go to world-leading performance in image classification, voice recognition …
in the game of Go to world-leading performance in image classification, voice recognition …
Avalanche: an end-to-end library for continual learning
Learning continually from non-stationary data streams is a long-standing goal and a
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
challenging problem in machine learning. Recently, we have witnessed a renewed and fast …
The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies
This editorial aims to contribute to the current debate about the quality of studies that apply
machine learning (ML) methodologies to medical data to extract value from them and …
machine learning (ML) methodologies to medical data to extract value from them and …
Artificial intelligence and machine learning for improving glycemic control in diabetes: Best practices, pitfalls, and opportunities
Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …