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Amazing keynote by Juho Kim Juho Kim (?) At #NeurIPS . I love the skeptical view of natural language interfaces and their trade-offs. But also, might I propose "outcome centric AI". Being at NeurIPS is a reminder how much the community is still trapped in a model-centric view.

Is implementing #AIObservability on your list for next year? ✅ Sage Elliott workshop on Jan 17th will cover how to use validation and monitoring techniques to implement your own AI observability solution from start to finish! bit.ly/3BYCTic #ML #MachineLearning

Don’t forget to register for Sage Elliott first rsqrdai podcast of 2023 with Jason Koo from Neo4j on Graph Query Language (#GQL) and graph databases! 👉 Register now: bit.ly/3v6z3j7 #GraphQL #ML #Community

You have $7 to spend on the perfect NeurIPS submission: $700: SOTA results $350: theoretical guarantees $200: polished figures $100: well written $7: "GPT-4 is great at X" $7: "GPT-4 is terrible at X" $0: "all you need" as part of the title

🚀🚀🚀It's time to make your #LLM applications safe and responsible! WhyLabs' most powerful LLM monitoring solution is now integration into the most powerful LLM app building library LangChain! #HallucinateResponsibly #LLMs #ResponsibleAI

It's always #techweek for AI ModelOps startups. Stoked to dig into trends in the space with Palak Goel and Alessya Visnjic live on the 14th. pitchbook.com/webinars/explo…

🤓Woke up with #LLMs on your mind? Join me, PitchBook's Brendan Burke and Palak Goel for a discussion about trends and opportunities in #GenerativeAI! Hint: #foundationmodels , #llmops, #opensource, #goldrush... pitchbook.com/webinars/explo…

“😓 LLMs hallucinate, what can we do about it?” - everyone gave WhyLabs this feedback on #LLM #Observability needs. LangKit is a solution to assess hallucinations in a #datacentric way. Thanks Michael Nuñez & VentureBeat for the thoughtful overview: bit.ly/3PecIeP

Transformer model erasing LSTM history… tsk tsk tsk 🤣

# On the "hallucination problem" I always struggle a bit with I'm asked about the "hallucination problem" in LLMs. Because, in some sense, hallucination is all LLMs do. They are dream machines. We direct their dreams with prompts. The prompts start the dream, and based on the

Well said! Hallucination is a feature of LLMs. We need to build LLM applications with the awareness of this feature. #HallucinateResponsibly

best swag of neurips thanks to François Fleuret

🔴 𝐋𝐢𝐯𝐞 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐋𝐋𝐌 𝐁𝐨𝐨𝐭𝐜𝐚𝐦𝐩 🔴 Bernease Herman Herman, Data Scientist at WhyLabs is presenting on an important topic, #LLMOps: Observability & Evaluation. 📢 What will she present? She will guide the participants through setting up thresholds and benchmarks

I appreciate your argument and I fully understand your frustration, but whether the pod bay doors should be opened or closed is a complex and nuanced issue.

So much for emergent magic. If VCs understood the significance of this paper, they would make radically different choices. It really is driverless cars all over again.

Let’s face it… Just because you don’t know what’s in your training data, you can not just call it zero-shot 🤷‍♀️ 1) We just relabeled the old concept of zero-shot learning from attributes to “I have not checked my training data “ resp. “ I have not seen the dataset” (and even

AI teams need a way to control GenAI applications in real time. #GenAI is unique & it’s making #AIObservability platforms obsolete! Learn why & how WhyLabs helps tackle the challenges of moving #AI applications from prototype to production. bit.ly/49RgjGg

Huge win for the Seattle AI community!

It is *incredibly* easy to game the LLM benchmarks. Training on test set is for the rookies. Here're some tricks to practice magic at home: 1. Train on paraphrased examples of the test set. "LLM-decontaminator" paper from LMSys found that you can beat GPT-4 with a 13B model (!!)

Us: “Super productive week, I’m fully caught up on email and almost done with my big presentation!” Elon: