惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Blog — PlanetScale
Blog — PlanetScale
博客园_首页
WordPress大学
WordPress大学
博客园 - 聂微东
P
Privacy International News Feed
Forbes - Security
Forbes - Security
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
Last Week in AI
Last Week in AI
C
CERT Recently Published Vulnerability Notes
月光博客
月光博客
NISL@THU
NISL@THU
美团技术团队
T
Tailwind CSS Blog
Jina AI
Jina AI
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Apple Machine Learning Research
Apple Machine Learning Research
C
Cisco Blogs
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
The Hacker News
The Hacker News
B
Blog
P
Palo Alto Networks Blog
L
Lohrmann on Cybersecurity
有赞技术团队
有赞技术团队
The Register - Security
The Register - Security
S
Securelist
A
Arctic Wolf
MyScale Blog
MyScale Blog
H
Help Net Security
N
Netflix TechBlog - Medium
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
T
Threatpost
Recent Commits to openclaw:main
Recent Commits to openclaw:main
Security Latest
Security Latest
T
Tor Project blog
V
Vulnerabilities – Threatpost
V
V2EX
AI
AI
Hugging Face - Blog
Hugging Face - Blog
大猫的无限游戏
大猫的无限游戏
博客园 - Franky
Simon Willison's Weblog
Simon Willison's Weblog
小众软件
小众软件
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Troy Hunt's Blog
Schneier on Security
Schneier on Security
cs.AI updates on arXiv.org
cs.AI updates on arXiv.org
H
Heimdal Security Blog
Google Online Security Blog
Google Online Security Blog
Know Your Adversary
Know Your Adversary

Snorkel AI

Building AI-Native Systems for Federal Infrastructure: A Conversation with Rezaur Rahman Code World Models and AutoHarness for LLM Agents Benchtalks #1: Alex Shaw (Terminal-Bench, Harbor) – Building the Benchmark Factory Building FinQA: An Open RL Environment for Financial Reasoning Agents How Tool Discipline Let a 4B Model Outsmart a 235B Giant on Financial Tasks Coding agents don’t need to be perfect, they need to recover Closing the Evaluation Gap in Agentic AI SlopCodeBench: Measuring Code Erosion as Agents Iterate Introducing the Snorkel Agentic Coding Benchmark 2026: The year of environments Part V: Future Direction and Emerging Trends in Rubric-Based AI Evaluation The self-critique paradox: Why AI verification fails where it’s needed most Chat With the Terminal-Bench Team | Snorkel AI Intelligence per watt: A new metric for AI’s future Terminal-Bench 2.0: Raising the bar for AI agent evaluation Snorkeling in RL environments Introducing SnorkelSpatial: A Benchmark for LLM Spatial Reasoning Scaling Trust: Rubrics in Snorkel's Quality Process Evaluating Multi-Agent Systems in Enterprise Tool Use Evaluating Coding Agents with Terminal-Bench 2.0 Parsing isn’t neutral: why evaluation choices matter The science of rubric design The right tool for the job: An A-Z of rubrics Data quality and rubrics: how to build trust in your models Building the benchmark: inside our agentic insurance underwriting dataset Evaluating AI agents for insurance underwriting LLM observability: key practices, tools, and challenges Anthropic Claude + AWS: revolutionizing pharma data analytics with Snorkel AI Data-centric development of an enterprise AI agent with Snorkel Building the data development platform for specialized AI LLM-as-a-judge for enterprises: evaluate model alignment at scale Why GenAI evaluation requires SME-in-the-loop for validation and trust Research spotlight: is long chain-of-thought structure all that matters when it comes to LLM reasoning distillation? Why enterprise GenAI evaluation requires fine-grained metrics to be insightful What is specialized GenAI evaluation, and why is it so critical to enterprise AI? LLM alignment techniques: 4 post-training approaches Research spotlight: Is intent analysis the key to unlocking more accurate LLM question answering? Why enterprises should embrace LLM distillation Retrieval-augmented generation (RAG) failure modes and how to fix them What is large language model (LLM) alignment? Databricks + Snorkel Flow: integrated, streamlined AI development How LLM evaluation drives better models in Snorkel Flow Unlock proprietary data with Snorkel Flow and Amazon SageMaker LLM evaluation in enterprise applications: a new era in ML Snorkel AI joins the AWS ISV Accelerate Program and launches Snorkel Flow Availability in AWS Marketplace AI data development: a guide for data science projects SnorkelCon 2024: Inaugural Snorkel AI user conference gathers leaders from 30+ Fortune 500 companies Snorkel Flow 2024.R3: Supercharge your AI development with enhanced data-centric workflows Explore the new GenAI Evaluation Suite: Snorkel 2024.R3 New NLP features in Snorkel Flow 2024.R3 Enterprise data compliance and security review: Snorkel Flow 2024.R3 How a global financial services company built a specialized AI copilot accurate enough for production Task Me Anything: innovating multimodal model benchmarks Alfred: Data labeling with foundation models and weak supervision RAG: LLM performance boost with retrieval-augmented generation Call center AI for customer experience management: a case study New GenAI features, data annotation: Snorkel Flow 2024.R2 How data slices transform enterprise LLM evaluation Meta’s Llama 3.1 405B is the new Mr. Miyagi, now what? Meta’s new Llama 3.1 models are here! Are you ready for it? Data-centric AI with Snorkel and MinIO Weak supervision for non-categorical applications + superalignment Snorkel AI signs strategic collaboration agreement with AWS to help enterprises cross the demo-to-production chasm AI alignment made simple: innovative solutions for businesses How does the Snorkel Flow label model work? Vision language models: how LLMs boost image classification Long context models in the enterprise: benchmarks and beyond How to build production-grade RAG retrieval with Snorkel Flow How Bonito helps fine-tune specialized LLMs faster than ever Walking safely before building flying saucer seatbelts: introducing Enterprise Alignment Role-based access controls in Snorkel Flow secure enterprise data Accelerating AI development in manufacturing with Snorkel Flow and AWS SageMaker How ROBOSHOT boosts zero-shot foundation model performance Discover what’s new in Snorkel Flow: Flexible data and LLM connectivity, secure data controls, and more! Faster than ever document intelligence with new Snorkel Flow FM-first workflow The art of data development for Enterprise LLMs Crossing the demo-to-production chasm with Snorkel Custom How Snorkel topped the AlpacaEval leaderboard (and why we're not there anymore) CRFM's HELM and enterprise LLM evaluation beyond accuracy How we achieved 89% accuracy on contract question answering Five sessions not to miss at Google Cloud Next 24 Content filtering breakthrough: Snorkel client reaches 96% recall in 3 days Here's how Snorkel Flow + Google AI built an enterprise-ready model in a day How Skill-it! enables faster, better LLM training Fine-tuned representation models boost LLM systems. Here's how Enterprise GenAI to surge in 2024: survey results Large language model training: how three training phases shape LLMs LoRA: Low-Rank Adaptation for LLMs LLM distillation demystified: a complete guide Enterprises must shift their focus from models to data in AI development Insurance’s GenAI revolution: a business perspective Scaling human preferences in AI: Snorkel's programmatic approach Building better enterprise AI: incorporating expert feedback in system development “Fall in love with your data”—Snorkel AI’s Enterprise LLM Summit Why QBE Ventures invested in Snorkel AI New benchmark results demonstrate value of Snorkel AI approach to LLM alignment Retrieval augmented generation (RAG): a conversation with its creator Snorkel Flow 2023.R4: enhanced UI + PDF and Databricks tools How Snorkel Flow users can register custom models to Databricks Stanford professor discusses exciting advances in foundation model evaluation
Snorkel teams with Microsoft to showcase new AI research at NVIDIA GTC
Doug Kelly (Microsoft), Friea Berg (Snorkel AI) · 2024-03-19 · via Snorkel AI

Snorkel AI researchers work on the cutting edge of AI innovation to help expand the boundaries of AI knowledge.

That’s a bold statement, but accurate.

As part of a research-first culture, the Snorkel team has contributed to over 150 academic papers on topics covering LLM data curation, LLM evaluation, model distillation, and more. Snorkel AI founders and researchers present regularly at distinguished AI conferences such as NeurIPS, where the team was recognized with a Best Paper award for “Low-Resource Languages Jailbreak GPT-4. A recent co-presentation on MedAlign, a curated open-source benchmark dataset for the evaluation of LLMs for EHR data retrieval, was awarded Best Findings Paper in GenAI for Health.

Snorkel’s partnership with Microsoft plays a critical role in equipping its research team to experiment with new techniques and approaches. Snorkel is a member of the Microsoft for Startups Pegasus Program, Microsoft’s flagship go-to-market program. Through the Pegasus program, Snorkel has access to premier sales resources and technical assets to accelerate AI workloads including early access to Azure AI services, leading models from OpenAI and Mistral, and accelerated high-performance compute. The ability for Snorkel’s Research team to execute its most demanding projects on Azure AI infrastructure powered by NVIDIA GPUs has been a game changer. 

Snorkel’s recent top tier ranking on the AlpacaEval 2.0 LLM leaderboard would not have been possible without the program’s dedicated startups GPU cluster benefit. Access to state-of-the-art Azure NDm NVIDIA A100 instances via a seamless Azure experience has empowered Snorkel to drive cutting-edge research in programmatic alignment/DPO in a quick & efficient manner. Azure AI Infrastructure VMs, which come pre-configured with InfiniBand and NVLINK for optimized scale-out and scale-up, allow Snorkel researchers to run quick experiments from small projects to large-scale distributed jobs on multiple GPUs reliably and with full monitoring mechanisms.

The value of this research extends far beyond academic papers and benchmark results. The Snorkel Flow data development platform was intentionally designed with flexible abstractions and extensible interfaces that allow for the continual integration of the latest and most remarkable technologies from academic collaborations to create an ever more powerful tool for users. Ultimately, Snorkel’s cutting-edge research plays a key role help enterprises successfully move AI projects from prototype to production.

Today, Snorkel is proud to congratulate Microsoft on the new generally available Azure ​​NC H100 v5 VMs, which are tailored to accelerate large-scale AI model training and batch inference. As Microsoft advances the state-of-the-art with optimized AI GPU VMs leveraging the latest NVIDIA technologies, the Snorkel research team can launch increasingly ambitious and challenging projects.  

To learn more, we invite you to read the Microsoft blog post “Microsoft and NVIDIA partnership continues to deliver on the promise of AI” and visit us at NVIDIA GTC.

Learn more in person at NVIDIA GTC

Visit the Microsoft booth at NVIDIA GTC to learn more about the Snorkel research that resulted in a top tier ranking on the AlpacaEval 2.0 LLM leaderboard. Plus, Snorkel will share how designing projects on Azure AI infrastructure powered by NVIDIA GPUs helps our researchers deliver value for our customers and the OSS community even faster.

Topic: Snorkel AI research leverages Azure AI Infrastructure powered by NVIDIA GPUs for cutting-edge AI/ML.
Location: Microsoft booth #1108
Timing: March 19, 3:40-4pm

This blog post is a collaboration between Microsoft for Startups Senior AI Advisor Doug Kelly and Snorkel Head of Partnerships Friea Berg.