Landing Ai

Andrew Yan-Tak Ng

Kai Yang

Dongyan Wang

Yiwen Rong

Mark William Sabini

Yu Qing Zhou

Daniel Bibireata

Pingyang He

Gopi Prashanth Gopal

Sanjeev Satheesh

Dillon Laird

Greg Frederick Diamos

Shankaranand Jagadeesan

Sebastian Rodriguez

Quinn Killough

Yuxiang Zhang

Bo Tan

Karthikeyan Thiruppathisamy Nathillvar

Rustam Hashimov

Dillon Anthony Laird

Alejandro Betancourt

Beom Sik Kim

Timothy Viatcheslavovich Rosenflanz

Tian Wu

Yan Liu

Francisco Matias Cuenca-Acuna

Ankur Rawat

Aldrin Leal

Whitney Blodgett

Sanjay Boddhu

Gregory Diamos

Whit Blodgett

Sanjay Bodhu

Cristobal Silva

Juan Camilo Fernancez

Camilo Zapata

Ramesh Sampath

Junjie Guan

Mingrui Zhang

Zeqi Qiu

Camilo Iral

Leela Seshu Reddy Cheedepudi

Yazhou Cao

YuQing Zhou

A system or method for identifying mislabels in a training dataset. The labeled dataset includes multiple labeled samples, each of which is labeled with a ground-truth label. The labeled samples are divided into multiple training subset and hold-out test subset. For each training subset and corresponding hold-out test subset, a machine learning model is trained using the training subset, and the trained machine learning model is applied to the hold-out test subset to generate prediction labels for samples in the corresponding hold-out test subset. Each prediction label has a confidence score indicating a likelihood of the prediction label is correct. A pair of prediction label and ground truth label corresponding to a same sample is compared to determine whether there is a candidate mislabel. The system determines whether the candidate mislabel is a mislabel based in part on a confidence score of the prediction label.

Data centric mislabel detection

A model management system adaptively refines a training dataset for more effective visual inspection. The system trains a machine learning model using the initial training dataset and sends the trained model to a client for deployment. The deployment process generates outputs that are sent back to the system. The system determines that performance of predictions for noisy data points are inadequate and determines a cause of failure based on a mapping of the noisy data point to a distribution generated for the training dataset across multiple dimensions. The system determines a cause of failure based on an attribute of the noisy datapoint that deviates from the distribution of the training dataset and performs refinement towards the training dataset based on the identified cause of failure. The system retrains the machine learning model with the refined training dataset and sends the retrained machine learning model back to the client for re-deployment.

Model management system for improving training data through machine learning deployment

A model management system performs error analysis on results predicted by a machine learning model. The model management system identifies an incorrectly classified image outputted from a machine learning model and identifies using the Neural Template Matching (NTM) algorithm, an additional image correlated to the selected image. The system outputs correlated images based on a given image and a selection by a user through a user interface of a region of interest (ROI) of the given image. The region is defined by a bounding polygon input and the correlated images include features correlated to the features within the ROI. The system prompts a task associated with the additional image. The system receives a response that includes an indication that the additional image is incorrectly labeled and including a replacement label and instruct that the machine learning model be retrained using an updated training dataset that includes the replacement label.

Guided workflow for deep learning error analysis

Systems and methods are disclosed herein for detecting impurities of harvested plants in a receptacle of a harvester. In an embodiment, a harvester controller receives, from a camera facing the contents of the receptacle, an image of the contents. The harvester controller applies the image as input to a machine learning model. The harvester controller receives, as output from the machine learning model, an identification of an impurity of the harvested plants. The harvester controller transmits a control signal based on the impurity.

AI-optimized harvester configured to maximize yield and minimize impurities

A process and a system for creating a visual guide for developing training data for a classification of image, where the training data includes images tagged with labels for the classification of the images. A processor may prompt a user to define a framework for the classification. For an initial set of images within the training data, qualified human classifiers are prompted to locate the images within the framework and to tag the images with labels. The processor determines whether the tagged images have consistent labels, and, if so, the processor adds images to the training data. The processor may add the images by providing a visual guide, the visual guide including tagged images arranged according to their locations within the framework their labels, and prompting human classifiers to tag the additional images with labels for the classification, according to the visual guide.

User-generated visual guide for the classification of images

Systems and methods are disclosed herein for optimizing harvester yield. In an embodiment, a controller receives a pre-harvest image from a front-facing camera of a harvester. The controller inputs the pre-harvest image into a model, and receives as output from the model a predicted harvest yield. The controller receives, from an interior camera of the harvester, a post-harvest image including the plants as harvested. The controller inputs the post-harvest image into a second model and receives, as output, an actual harvest yield of the plants as-harvested. The controller determines that the predicted harvest yield does not match the actual harvest yield, and outputs a control signal.

Growing community of inventors

Los Altos, CA, United States of America

Andrew Yan-Tak Ng