Screen capture showing how Diffbot works
Stanford University

The Internet in a Knowledge Graph: How DiffBot is building the world's largest knowledge graph

An ambitious company is using deep learning to extract and find associations from all the information on the internet — and it isn’t Google. Diffbot built a system that reads web code, parses text, classifies images, and assembles them into what it says is the world’s largest knowledge graph.
Information and examples of CheXbert, a network that labels chest X-rays
Stanford University

Human-Level X-Ray Diagnosis: A research summary of CheXbert for labeling chest x-rays

Like nurses who can’t decipher a doctor’s handwriting, machine learning models can’t decipher medical scans — without labels. Conveniently, natural language models can read medical records to extract labels for X-ray images.
Road sign with the word "trust"
Stanford University

Toward AI We Can Count On: Public trust recommendations from AI researchers

A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public’s trust. Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy.
Graphs related to double descent
Stanford University

Moderating the ML Roller Coaster: A technique to avoid double descent in AI

Wait a minute — we added training data, and our model’s performance got worse?! New research offers a way to avoid so-called double descent.
Screen capture of online conference called Covid-19 and AI
Stanford University

Online Conference Goes Antiviral: A conference explored how AI was deployed against Covid-19.

AI experts convened to discuss how to combat the coronavirus crisis. An online conference hosted by Stanford University’s Institute for Human-Centered AI explored how machine learning is being deployed to address this pandemic — and prepare for the next one.
Chatbot asking for Covid-19 symptoms
Stanford University

Chatbots Disagree on Covid-19: Medical chatbots offered conflicting Covid advice.

Chatbots designed to recognize Covid-19 symptoms dispense alarmingly inconsistent recommendations. Given the same symptoms, eight high-profile medical bots responded with divergent, often conflicting advice.
Women in AI in academia and industry chart
Stanford University

AI’s Gender Imbalance: The data behind deep learning's gender gap

Women continue to be severely underrepresented in AI. A meta-analysis of research conducted by Synced Review for Women’s History Month found that female participation in various aspects of AI typically hovers between 10 and 20 percent.
Information and images related to 6D-Pose Anchor-based Category-level Keypoint-tracker (6-PACK)
Stanford University

Deep Learning for Object Tracking: AI for six-dimensional object tracking for robotics

AI is good at tracking objects in two dimensions. A new model processes video from a camera with a depth sensor to predict how objects move through space.
Results of a technique that interprets reflected light to reveal objects outside the line of sight
Stanford University

Periscope Vision: Researchers used deep learning to see around corners.

Wouldn’t it be great to see around corners? Deep learning researchers are working on it. Researchers developed deep-inverse correlography, a technique that interprets reflected light to reveal objects outside the line of sight.
Information related to the kNN-LM algorithm
Stanford University

Helpful Neighbors: A research summary of the kNN-LM language model

School teachers may not like to hear this, but sometimes you get the best answer by peeking at your neighbor’s paper. A new language model framework peeks at the training data for context when making a prediction.
Excerpt from 2019 Artificial Intelligence Index
Stanford University

Tracking AI’s Global Growth: The 2019 AI Index tracks the industry's worldwide growth.

Which countries are ahead in AI? Many, in one way or another, and not always the ones you might expect. The Stanford Institute for Human-Centered Artificial Intelligence published its 2019 Artificial Intelligence Index, detailing when, where, and how AI is on the rise.
ImageNet face recognition labels on a picture
Stanford University

ImageNet Gets a Makeover: The effort to remove bias from ImageNet

Computer scientists are struggling to purge bias from one of AI’s most important datasets. ImageNet’s 14 million photos are a go-to collection for training computer-vision systems, yet their descriptive labels have been rife with derogatory and stereotyped attitudes toward race, gender, and sex.
Chelsea Finn
Stanford University

Chelsea Finn — Robots That Generalize: Generalization for robotics through reinforcement learning

Many people in the AI community focus on achieving flashy results, like building an agent that can win at Go or Jeopardy. This kind of work is impressive in terms of complexity.
Information related to Implicit Reinforcement without Interaction at Scale (IRIS)
Stanford University

Different Skills From Different Demos: Implicit reinforcement without interaction at scale, explained

Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. But what if one doctor is handier with a scalpel while another excels at suturing?
Information related to Bias-Resilient Neural Network (BR-Net)
Stanford University

Bias Fighter: A neural network for countering bias variables in data

Sophisticated models trained on biased data can learn discriminatory patterns, which leads to skewed decisions. A new solution aims to prevent neural networks from making decisions based on common biases.

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