NLP

Snorkel AI’s Jan. 25 Enterprise LLM Summit focused on one theme: AI data development drives enterprise AI success.

Snorkel researchers’ state-of-the-art methods created a 7B LLM that ranked 2nd, behind only GPT-4 Turbo, on AlpacaEval 2.0 leaderboard.

Snorkel CEO Alex Ratner spoke with Douwe Keila, an author of the original paper about retrieval augmented generation (RAG).

The Databricks Model Registry integration equips Snorkel Flow users to automatically register custom, use case-specific models.

Snorkel CEO Alex Ratner chatted with Stanford Professor Percy Liang about evaluation in machine learning and in AI generally.

LLMs have claimed the spotlight since the debut of ChatGPT, but BERT models quietly handle most enterprise production NLP tasks.

When done right, advanced classification applications cultivate business value and automation, unlock new business lines, and reduce costs.

A brief guide on how financial institutions could use Google Dialogflow with Snorkel Flow to build better chatbots for retail banking

A proof-of-concept project that combines predictive AI + generative AI to minimize LLM’s risks while keeping their advantages.

Data scientists can fine-tune Llama 2 to adapt it to specific tasks. The Snorkel Flow data development platform makes it easy to do so.

Large language models open many new opportunities for data science teams, but enterprise LLM challenges persist—and customization is key.

LLMs have a broad but shallow knowledge, but fall short on specialized tasks. For best performance, enterprises must fine tune their LLMs.

The Biden administration issued an executive order that creates new AI standards and challenges. AI data development can help.

The Snorkel AI team will present 18 research papers and talks at the 2023 Neural Information Processing Systems (NeurIPS) conference from December 10-16. The Snorkel papers cover a broad range of topics including fairness, semi-supervised learning, large language models (LLMs), and domain-specific models. Snorkel AI is proud of its roots in the research community and endeavors to remain at the forefront

Distillation techniques allow enterprises to access the full predictive power of large language models at a tiny fraction of their cost.

Snorkel AI’s Enterprise LLM Virtual Summit drew 1,000 attendees with speakers from Contextual AI, Google, Meta, Stanford, and Together AI.

Insurance claims processing has long required a lot of tedious and expensive human labor, but artificial intelligence (AI) can help.

Gideon Mann, head of ML Product and Research at Bloomberg LP, chatted with Snorkel CEO Alex Ratner about building BloombergGPT.

Snorkel Flow makes it easy to fine tune LLMs like GPT-3.5 Turbo to work better for specific domain and enterprise requirements.

Sessions at the Future of Data-Centric AI covered LLMs, gen AI, and more. All recordings are now publicly available. See them here!

Most data science leaders expect to customize LLMS, but the process of making LLMs work for your business is still a fresh challenge.

Ai and ML offer new avenues for credit scoring solutions and could usher in a new era of fairness, efficiency, and risk management.

Data labeling remains a core requirement for machine learning projects—especially in the age of genAI and LLMs. Here’s a handy guide.

Regulators and compliance officers face a constantly evolving landscape of financial markets. Rule-based systems struggle where AI succeeds.

The medical industry is exploding with data. Manually labeling data for clinical trials is a challenge. Fortunately, AI can help.

Professionals in the data science space often debate whether RAG or fine-tuning yields the better result. The answer is “both.”

Past U.S. Chief Data Scientist DJ Patil talked with Snorkel AI CEO Alex Ratner on topics including the origin of the title “data scientist.”

The surest way to improve foundation models is through more and better data, but Snorkel researchers showed FMs can learn from themselves.

GPT-3 unlocked additional capacity by automating first drafts of internal updates—including blog summaries and sample tweets.

Handling complaints effectively and efficiently with AI is essential to maintain customer satisfaction and protect the bank’s reputation.