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Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–Snorkel AI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! --- Snorkel Evaluate is our new data-centric agentic...

50,043 просмотров • 1 год назад •via X (Twitter)

Комментарии: 12

Фото профиля Alex Ratner
Alex Ratner1 год назад

📽️ For a deeper dive into Snorkel Evaluate & Expert Data-as-a-Service- check out our walkthrough of building an enterprise agentic AI system in insurance here:

Фото профиля Alex Ratner
Alex Ratner1 год назад

📅 Join Snorkel AI and innovators from @Accenture, @Comcast, @Stanford , @QBE, @UWMadison, and more on June 26:

Фото профиля Alex Ratner
Alex Ratner1 год назад

📊 Check out the open sourced benchmark dataset preview:

Фото профиля Alex Ratner
Alex Ratner1 год назад

📜 And for more detail, read more about the announcement and what it means for the future of agentic AI:

Фото профиля Alex Ratner
Alex Ratner1 год назад

🚀 Finally: Thank you to our investors, both existing and new - Addition, @GreylockVC, @Lightspeedvp @qbeventures, @bnyglobal, @prosperity7VC, and others - and launch partners @rox__ai @anthropicAI @awscloud!!

Фото профиля Greg Caplan 🚀
Greg Caplan 🚀2 лет назад

Stop wasting time following up with leads. Let our AI agents do it for you.

Фото профиля Sen Wu
Sen Wu1 год назад

@SnorkelAI Congratulations, Alex and Snorkel team!

Фото профиля Stefano Ermon
Stefano Ermon1 год назад

@SnorkelAI Congrats!

Фото профиля Farooq | zo.me
Farooq | zo.me1 год назад

@SnorkelAI Agentic AI’s potential hinges on trust and trust starts with data. This move by Snorkel nails that foundation.

Фото профиля Khaled Saab
Khaled Saab1 год назад

@SnorkelAI Much needed, especially in the medical domain! Congrats!

Фото профиля David Hendrickson
David Hendrickson1 год назад

@SnorkelAI Do you generate more business cleaning data for users or building custom data agents? My clients prefer to learn how to clean their own data and build (& maintain) their own agents.

Фото профиля Alex Ratner
Alex Ratner1 год назад

@SnorkelAI Our work is a mix but we love supporting customers in being self-serve via our platform- agree that's a powerful mode to be in!

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Scale alone is not enough for AI data. Quality and complexity are equally critical. Excited to support all of these for LLM developers with Snorkel AI Data-as-a-Service, and to share our new leaderboard! — Our decade-plus of research and work in AI data has a simple point: scale alone is not enough. AI success is all about the quality, complexity, and distribution of data—in addition to volume. We’re excited to be powering leading LLM developers with Snorkel AI Expert Data-as-a-Service, our white glove service for custom, expert-level AI datasets—and to now preview some of what we’re building via our new Expert Data Leaderboard (🔗 in 🧵) + upcoming OSS dataset releases! Snorkel Expert Data-as-a-Service is built to meet the rapidly evolving data needs of the agentic AI world—where success is built on the quality, complexity, and distribution of datasets, in addition to size and scale. This kind of high-quality, frontier AI data can only come from a union of technology and human expertise. With Snorkel Expert Data-as-a-Service, we’re powering frontier LLM developers across agentic, expert knowledge, reasoning, coding, multi-modal, and other task types via the combination of these two key components: - (1) The Snorkel Expert Network: A global team of subject matter experts focused wholly on specialized knowledge–spanning thousands of topics in STEM/academic, vertical/professional, and consumer/lifestyle domains. - (2) Snorkel AI Data Development Platform: Our unique programmatic data curation and quality control platform, accelerating and improving expert authoring and review through principled techniques developed over the last decade of R&D. Now: we’re incredibly excited to showcase some of the power of Snorkel Expert Data-as-a-Service via the new Snorkel Leaderboard—putting frontier models to the test in complex, agentic, and reasoning settings inspired by real industry scenarios (not esoteric puzzles)! We’ll be releasing new leaderboards and accompanying expert-verified open source datasets (coming soon!) regularly. To start, we’re sharing three initial ones in preview: - SnorkelFinance: Q&A over financial documents requiring agentic tool-calling and reasoning - SnorkelUnderwrite: Agentic insurance tasks requiring industry-specific reasoning and tool use - SnorkelSequences: Mathematical tasks requiring compositional multi-step reasoning

Alex Ratner

495,851 просмотров • 1 год назад

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 11 месяцев назад