How Ford Uses AI to Develop Better AI
Explore Ford's AI development lifecycle, focusing on data extraction, annotation, and augmentation for training. Learn about iteration's role in enhancing AI systems at Ford Motor Company.
An Inside Look at NASA's SpaceML Worldview Search
Explore NASA's SpaceML Worldview Search with insights from Machine Learning Lead Anirudh Koul, covering innovative applications in space exploration and data analysis.
Data-Centric AI in Comcast's Voice and Conversational Interfaces
Explore Comcast's data-centric approach to AI voice and conversational interfaces, powering innovative technology solutions.
How Cleanlab Uses AI to Correct Errors in Any Dataset
Discover AI-powered tools for cleansing datasets, enhancing model training accuracy. Explore Cleanlab's innovative approach to data-centric AI and its practical applications.
Can Data-Centric AI Help Us Develop Better Drugs?
Explore how machine learning and high-quality data can revolutionize drug development, predicting outcomes and designing safer, more effective therapies faster and at lower costs.
Why AI Needs New Data Benchmarks and Quality Metrics
Explore DataPerf, a benchmark suite for evaluating ML data quality and optimization algorithms. Learn about the need for data-centric AI benchmarks to advance research and improve real-world applications.
What Landing AI Learned Through Democratizing AI
Explore Landing AI's journey in democratizing AI, focusing on manufacturing challenges, data-centric techniques, and the evolution of Landing Lens for faster iteration and improved performance.
Why Detecting Distributional Shift is So Hard and So Important
Explore challenges and opportunities in detecting data distributional shift, a crucial aspect of AI robustness and reliability in real-world applications.
Why Fixing Your Data Can Fix Your Model in Machine Learning
Explore how addressing data issues can improve machine learning models, focusing on cost-effective solutions and identifying crucial data problems to optimize resources.
Democratizing Machine Learning: MLCommons' Role in Public Datasets and Benchmarks
Explore MLCommons' mission to transform machine learning through benchmarks, best practices, and data. Learn about public datasets' importance and the organization's tools for dataset creation.
How Data Powers Stable Diffusion and Generative AI
Explore the crucial role of data in developing Stable Diffusion and Generative AI with insights from Stability AI's CEO on leveraging data-driven approaches to drive innovation and shape AI's future.
How Comcast Powers AI Speech Recognition with Weak Supervision
Explore Comcast's innovative approach to AI speech recognition using weak supervision and efficient inference techniques, achieving improved accuracy and speed for voice-enabled smart TVs.
Retooling AI Training Sets for Improved Model Performance
Explore DataComp, a benchmark for innovating AI training datasets. Learn how improved data curation outperforms model tweaks, enhancing multimodal AI performance with less compute.
Improving Data and Modeling for Computer Vision Applications
Explore advancements in computer vision for data-centric image classification. Learn about visual prompts, parameter-efficient models, and adapting representations for improved performance and efficiency.
The Future is Neurosymbolic - Insights on AI and Language Models
Explore AI's future with Yoav Shoham: task-specific models, product-algorithm fit, and the fusion of neural and symbolic approaches in language models and AI applications.
How Insurance Competes in the AI Age
Explore AI's impact on insurance: operational effectiveness, new business models, and partnerships. Learn about data-centric approaches for risk capacity matching and policy ingestion.
How Wayfair Uses AI to Build Better E-Commerce Experiences
Explore Wayfair's ML strategies for enhancing e-commerce, from customer understanding to catalog enrichment. Learn about data-centric approaches and SME-DS collaboration for improved product discovery.
Tackling Advanced Classification Using Snorkel Flow
Explore advanced classification techniques using Snorkel Flow, addressing challenges in label schema, high cardinality, and multi-label problems while leveraging foundation models for production-ready solutions.
How to Improve LLM Factual Accuracy and Reliability
Explore techniques for improving LLM factual accuracy, including DSP framework and MLflow LLMOps tools, to create reliable AI applications for production use.
The Making of RedPajama and the Future of Open-Source AI
Explore the creation of RedPajama, an open-source AI project, and its impact on the future of AI. Learn about data collection and training processes for building leading open-source models.
Digging Into RAG and the Future of AI - With Douwe Keila
Explore RAG applications, AI's enterprise future, and the role of data in improving AI systems with industry experts Alex Ratner and Douwe Keila.