Encord

?? Boost AI performance with RLHF You can now perform Reinforcement Learning Human Feedback workflows using Encord’s multimodal annotation interface. Seamlessly compare text, audio, video and image samples in one single interface. Accurately rank and determine the preferred sample based on specified criteria—perfect for fine-tuning AI models ? ?In the demo below, see how Leander Angst uses RLHF to fine-tune an Audio AI application. Ready to learn more? Check out the link in the comments. ?? #MachineLearning #AudioAnnotation #DataAnnotation #RLHF

Turn up the volume on your audio AI data workflows with consolidated data curation and annotation. ?? We’re excited to announce that AI teams building multimodal and voice AI applications can now use Encord to accurately manage, curate and annotate audio data at scale. ?? You can now classify multiple different sections of an audio file with fully customizable labels or categorize whole audio files. ?? Efficiently label speakers, emotions, sound events, languages, sentiment and more with extreme precision and flexibility down to the millisecond. Join Will Caplan to label exactly when different instruments play throughout a track. ?? Learn more here: https://encord.com/audio/ #AudioAnnotation #DataAnnotation #MachineLearning?

AI hallucinations aren’t bugs—they’re a feature of today’s systems. The key isn’t eliminating them but managing them effectively. Encord’s Ulrik Stig Hansen explores how we tackle this in a new article written for Inside AI News. With high-quality, diverse datasets, advanced data curation, and human-in-the-loop workflows, Encord keeps hallucinations in check. By mastering data and workflows, we can master AI hallucinations. What’s your team doing to address AI reliability??Read Ulrik’s contribution at the link in the comments below to learn more. ??

Training and fine-tuning AI models on highly dynamic and complex scenarios such as road traffic, warehouses, shop activity, surgeries and sporting events requires accurate image and video annotation across a number of different angles and perspectives. Manually stitching multiple images and videos together to annotate multiple angles of such scenarios can take hours, if not days. That's exactly why we built our multi-file annotation interface—designed to provide full context for complex labeling tasks. ?? Imagine needing to determine if front or rear airbags have deployed in a crash test video, yet only the front view or rear view video can be annotated at one time. ?? Join Shivant Maharaj to learn how Encord’s multi-file annotation interface enables teams to simultaneously view and annotate multiple views of the same scenario, such as a crash test video set! Learn more here: encord.com/multimodal #DataAnnotation #MachineLearning #Multimodal

The future of AI is multimodal – are you ready? ? AI is evolving beyond text, integrating documents, images, and audio, for richer, human-like understanding. But managing such diverse data is a challenge. A new article from HackerNoon illustrates Encord’s mission to simplify multimodal AI development with a unified platform for curating and annotating all data types – from videos to medical images. Learn how our tool streamlines workflows, enhances data quality, and integrates cutting-edge models for automation in the article below. Visit the link in the comments. ?? #MultimodalAI #DataManagement?

Imagine building an AI assistant that can work with someone wearing smart VR glasses to accurately follow a cooking recipe. This model has computer vision, LLM and voice AI components, allowing it to visually identify ingredients, interpret written cooking instructions, and respond to your voice commands in real time.? Building a sophisticated AI application like this requires a lot of high-quality, well-curated multimodal data. Join ML Lead, {{linkedin_mention(urn:li:person:DDxs9nLK9-|Frederik Hvilsh?j)}} ?? for a demo to learn how to achieve this on Encord. Encord’s multimodal AI data platform seamlessly integrates video, audio, images, and PDFs, enabling you to: ?? Organize and search data with natural language and metadata filters ?? Orchestrate multimodal data annotation workflows Use Encord to accelerate and consolidate data management and annotation workflows for any AI project, including computer vision, voice AI, LLMs, medical AI and more. Learn more at the link in the comments ?? #AI #DataAnnotation #Multimodal #MachineLearning

Seamlessly label text, tables, and visuals within documents to build and deploy high-performing LLMs to production, fast. We’re excited to announce the launch of our document and text annotation platform— enabling teams to precisely and efficiently manage and annotate complex documents like medical reports, legal files, and more. ??Curate and annotate documents at scale. ??? Embeddings-based data visualization and semantic search to quickly find relevant content across hundreds of millions of files. ?? Robust annotation functionality including intuitive text highlighting to accurately capture NER, sentiment analysis, text classification as well as labeling key multimodal content within files. ?? Read our blog to learn more about our latest release: https://lnkd.in/eSRYVdiW #LLM #DocumentAI #MachineLearning?

?? When it comes to understanding and labeling medical data, like the CT scan below, it’s crucial to examine it in the context of other key patient information. Encord facilitates this correlation with the first-of-its-kind multimodal annotation platform: - Annotate across different file types seamlessly—from 2D medical scans to detailed PDF reports. - Label with precision and accuracy to capture every structure and point of interest, at the speed your projects demand. - Combine visual data with text-based notes to gain a comprehensive understanding of complex medical cases. - View in 3D and annotate various sections, building a model that truly comprehends all aspects of a patient’s medical journey. Curious to see it in action? Check out our demo below with Alexandre Bonnet on PDF and DICOM multimodal annotation :rocket: Want to learn more? Visit the link in the comments. #MachineLearning #HealthcareAI #MultimodalAnnotation #DataAnnotation

High quality data is the single most important factor impacting AI model performance. But finding and preparing well curated, balanced datasets can take months. Introducing Encord Index - our multimodal data management and curation platform, enabling AI teams to unify and visualize billions of unstructured files, in seconds. ??Automatically detect errors to improve data quality ?? Embeddings-based natural language search to find exactly what you need in seconds ?? Leverage AI-powered similarity search to locate related data across vast datasets ?? Filter by metadata for precise, customized views of your data Join Justin Sharps to learn more ?? #DataCentricAI #DataCuration #MachineLearning #DataManagement

?? We’re thrilled to see ZDNET spotlight our latest milestone: the announcement of our fully multimodal platform. We empower AI teams with the tools they need to build better, faster, and more efficient models. With the launch of our multimodal AI data development platform, we’re addressing one of the biggest challenges in AI today: unifying siloed data types into a single, seamless interface. By helping our customers work with 35% smaller datasets to achieve 20% more accurate models, we’re driving real-world impact in across industries, from like healthcare to video production, and more. ?? Check out the ZDNET article below #MultimodalAI #DataAnnotation