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Dataloop Drives Labeling Into the DataOps Pipeline

#artificialintelligence

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).


Global Big Data Conference

#artificialintelligence

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).


Dataloop raises $11M Series A round for its AI data management platform – TechCrunch

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Dataloop, a Tel Aviv-based startup that specializes in helping businesses manage the entire data lifecycle for their AI projects, including helping them annotate their datasets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed. The Series A round was led by Amiti Ventures with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures. "Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real time validation that can only be achieved with human input into the system," said Dataloop CEO Eran Shlomo. "With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets." For the most part, Dataloop specializes in helping businesses manage and annotate their visual data.


Anolytics – Data Annotation Service For Machine Learning AI Directory - Global Artificial Intelligence Directory

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Anolytics offers a low-cost annotation service for machine learning and artificial intelligence model developments. It is providing the precisely annotated data in the form of text, images and videos using the various annotation techniques while ensuring the accuracy and quality. It is specialized in Image Annotation, Video Annotation and Text Annotation with best accuracy. Anolytics is providing all leading types of data annotation service used as a data training in machine learning and deep learning. It offers Bounding Boxes, Semantic Segmentation, 3D Point Cloud Annotation for fields like healthcare, autonomous driving or drone falying, retail, security surveillance and agriculture.


A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation

arXiv.org Machine Learning

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for document modeling. In this work, we show how to successfully apply and extend this model to the context of visual scene modeling. Specifically, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the hidden topic features by incorporating label information into the training objective of the model. We also describe how to leverage information about the spatial position of the visual words and how to embed additional image annotations, so as to simultaneously perform image classification and annotation. We test our model on the Scene15, LabelMe and UIUC-Sports datasets and show that it compares favorably to other topic models such as the supervised variant of LDA.