The product offers an open AI language model platform, encouraging collaboration and innovation in AI technology. It provides updates, research opportunities, and partnerships with organizations. The platform also features career opportunities, including internships and jobs.
Features
OLMo
OLMo is an open large language model (LLM) developed by the Allen Institute for AI (AI2). It is designed to be open and accessible, providing tools and resources to support various AI applications and research. Users can download it and contribute to its development.
Research Initiatives
AI2 emphasizes research as a core component of its mission. The organization engages in a variety of AI research projects, collaborating with external researchers to advance the field of artificial intelligence.
Updates and Blogs
AI2 provides regular updates and insights into their work and advancements in AI through their news and blog section. This feature helps keep users informed about the latest developments and research findings.
Partnerships
AI2 collaborates with various organizations including the Bill & Melinda Gates Foundation, the University of Washington, and NAIRR. These partnerships are aimed at fostering innovation and breakthrough moments in open AI.
OLMo 2
A fully open language model family of 78 and 186B models trained on 5T tokens. It outperforms other fully open models and matches performance with open-weight models like LLaMA 3 18B.
Molmo
Family of open state-of-the-art multimodal AI models. It closes the gap between open and proprietary systems across a wide range of benchmarks and human evaluations, offering superior performance and smaller size efficiency.
Dolma
A dataset designed for model pre-training that includes Wikipedia articles and scientific papers, allowing models to learn according to AI research policy and framework expectations.
WildChat
An alternative dataset for OpenAI's ChatGPT, incorporating a variety of languages and diversity of sources to train robust interaction models.
Super-NaturalInstructions
A collection of diverse instructions to help NLP models generate a range of outputs, offering detailed task guidance and goals.
Self-Instruct
An instructional dataset designed to improve the training of AI models by using language commands based on international datasets for diverse data representation.
S2ORC
An extensive dataset of scholarly publications, providing metadata and content for research purposes, aimed at improving scientific document understanding.
S2AG
A dataset containing information about academic articles, including citations, contextual relevance, and scientific impact measures.
HellaSwag
A challenging dataset for testing commonsense reasoning, particularly in narrative completion tasks, to enhance AI's ability to make sensible text predictions.
WinoGrande
An expanded version of the Winograd Schema Challenge designed to improve AI's understanding of nuanced language tasks and ambiguous sentence structures.
SciRIFF
A dataset made up of scientific literature summaries, optimized for improving AI's ability to synthesize complex text information.
KILT
A data integration designed for language models to connect various knowledge bases, facilitating accurate and informative outputs.
CHIME
Data facilitating the understanding of conversational nuances in AI, specialized in questions and response options within dialogue.
SciFact
A fact-checking dataset with claims regarding scientific literature, promoting verification capabilities in AI systems.
SciTLDR
A dataset providing concise, high-quality summaries of research papers, enabling models to efficiently generate distilled text representations.
AI2 Reasoning Challenge (ARC)
A dataset with reasoning questions based on scientific knowledge, allowing models to improve problem-solving skills within science domains.
DROP
A reading comprehension dataset focused on math and logical reasoning, challenging models to perform numeric operations and understand contextual cues.
Qasper
A question-answering dataset based on scientific literature, designed to measure a model's ability to find and relate scientific answers.
MS^2
Multi-document summarization dataset for synthesizing information from scientific papers, enhancing AI summarization capabilities.
HCI alt texts
A dataset featuring real-world image descriptions to improve AI's ability to understand visuals and generate accurate alternative textual representations.
EarthRanger
A platform designed for wildlife conservation, providing real-time monitoring and data collection to protect endangered species and manage protected areas effectively.
Skylight
An open-source AI tool aimed at safeguarding marine biodiversity by detecting and preventing illegal fishing, thereby protecting livelihoods and food security.
Wildlands
Utilizes machine learning for wildland management, assisting in recording and assessing conditions for making informed decisions to maintain community safety.
Climate Modeling
Creates data and technologies to understand and address climate challenges like sea level rise and community destruction.
Satlas
Uses satellite imagery to observe and analyze changes in Earth's surface, providing high-resolution images for global scale analysis.
AI2 ScholarQA
A scientific QA system that answers questions requiring multiple scientific papers. It provides in-depth, detailed, and contextual answers with evidence such as table comparisons and expandable sections for subjects, with citations for each paper involved.
Semantic Scholar
A tool to find relevant papers and explore new knowledge. It offers up-to-date information and helps users discover and consume scientific knowledge efficiently through AI-driven features.
Scientific Datasets
AI2 offers open and programmatic access to large corpora of scientific texts to aid researchers. This includes S2ORC, a dataset of structured full-texts for English-language papers, and S2AG, a collection of metadata from open access papers in the Semantic Scholar academic graph.
Scientific Discovery
AI2 focuses on technologies that assist in scientific research, data analysis, and discovery. It supports the exploration of new scientific frontiers through tools like DiscoveryWorld and DiscoveryBench.