Stanford University
Taming Spurious Correlations: New Technique Helps AI Avoid Classification Mistakes
When a neural network learns image labels, it may confuse a background item for the labeled object. New research avoids such mistakes.
Stanford University
Taming Spurious Correlations: New Technique Helps AI Avoid Classification Mistakes
When a neural network learns image labels, it may confuse a background item for the labeled object. New research avoids such mistakes.
Stanford University
Bad Machine Learning Makes Bad Science: Is Machine Learning Driving a Scientific Reproducibility Crisis?
A recent workshop highlighted the impact of poorly designed AI models in medicine, security, software engineering, and other disciplines.
Stanford University
Bad Machine Learning Makes Bad Science: Is Machine Learning Driving a Scientific Reproducibility Crisis?
A recent workshop highlighted the impact of poorly designed AI models in medicine, security, software engineering, and other disciplines.
Stanford University
Decision Trees: From Root to Leaves — Decision Trees for Machine Learning Explained
What kind of beast was Aristotle? The philosopher's follower Porphyry, who lived in Syria during the third century, came up with a logical way to answer the question...
Stanford University
Decision Trees: From Root to Leaves — Decision Trees for Machine Learning Explained
What kind of beast was Aristotle? The philosopher's follower Porphyry, who lived in Syria during the third century, came up with a logical way to answer the question...
Stanford University
AI Progress Report: Stanford University's fifth annual AI Report for 2022
A new study showcases AI’s growing importance worldwide. What’s new: The fifth annual AI Index from Stanford University’s Institute for Human-Centered AI documents rises in funding, regulation, and performance.
Stanford University
AI Progress Report: Stanford University's fifth annual AI Report for 2022
A new study showcases AI’s growing importance worldwide. What’s new: The fifth annual AI Index from Stanford University’s Institute for Human-Centered AI documents rises in funding, regulation, and performance.
Stanford University
Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.
While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive
Stanford University
Multimodal AI Takes Off: Multimodal Models, such as CLIP and DALL·E, are taking over AI.
While models like GPT-3 and EfficientNet, which work on text and images respectively, are responsible for some of deep learning’s highest-profile successes, approaches that find relationships between text and images made impressive
Stanford University
New Models Inherit Old Flaws: Foundation models can pass along biases to their fine-tuned progeny.
Is AI becoming inbred? The fear: The best models increasingly are fine-tuned versions of a small number of so-called foundation models that were pretrained on immense quantities of data scraped from the web.
Stanford University
New Models Inherit Old Flaws: Foundation models can pass along biases to their fine-tuned progeny.
Is AI becoming inbred? The fear: The best models increasingly are fine-tuned versions of a small number of so-called foundation models that were pretrained on immense quantities of data scraped from the web.
Stanford University
Deep Unlearning: AI Researchers Teach Models to Unlearn Data
Privacy advocates want deep learning systems to forget what they’ve learned. What’s new: Researchers are seeking ways to remove the influence of particular training examples, such as an individual’s personal information, from a trained model without affecting its performance, Wired reported.
Stanford University
Deep Unlearning: AI Researchers Teach Models to Unlearn Data
Privacy advocates want deep learning systems to forget what they’ve learned. What’s new: Researchers are seeking ways to remove the influence of particular training examples, such as an individual’s personal information, from a trained model without affecting its performance, Wired reported.
Stanford University
Weak Foundations Make Weak Models: Foundation AI Models Pass Flaws to Fine-Tuned Variants
A new study examines a major strain of recent research: huge models pretrained on immense quantities of uncurated, unlabeled data and then fine-tuned on a smaller, curated corpus.
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