正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

AIP Evolve — our new product for making agents more efficient and cost effective. See how Chad and Colton used it to autonomously swap models, tune prompts, validate outputs, and find structured ontology data that eliminated 2 LLM calls; cutting compute costs while improving accuracy and reliability in production.

Palantir

426,306 subscribers

116,689 次观看 • 1 个月前 •via X (Twitter)

科学技术财经

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Thousands of financial analysts spend countless hours extracting data from PDFs, where a single error could cost millions. Learn how TWG is partnering with Palantir and xAI to integrate Grok with Palantir AIP and the Ontology to automate data extraction, saving time and boosting accuracy.

Thousands of financial analysts spend countless hours extracting data from PDFs, where a single error could cost millions. Learn how TWG is partnering with Palantir and xAI to integrate Grok with Palantir AIP and the Ontology to automate data extraction, saving time and boosting accuracy.

Palantir

57,979 次观看 • 1 年前

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,703 次观看 • 1 年前

👨🏻‍💻 LLM Engineer Toolkit - Collection of 120+ LLM Libraries Category Wise LLM Engineer Toolkit repository contains a curated list of 120+ LLM libraries category wise. 🚀 LLM Training 🧱 LLM Application Development 🩸LLM RAG 🟩 LLM Inference 🚧 LLM Serving 📤 LLM Data Extraction 🌠 LLM Data Generation 💎 LLM Agents ⚖️ LLM Evaluation 🔍 LLM Monitoring 📅 LLM Prompts 📝 LLM Structured Outputs 🛑 LLM Safety and Security 💠 LLM Embedding Models ❇️ Others Repo -

👨🏻‍💻 LLM Engineer Toolkit - Collection of 120+ LLM Libraries Category Wise LLM Engineer Toolkit repository contains a curated list of 120+ LLM libraries category wise. 🚀 LLM Training 🧱 LLM Application Development 🩸LLM RAG 🟩 LLM Inference 🚧 LLM Serving 📤 LLM Data Extraction 🌠 LLM Data Generation 💎 LLM Agents ⚖️ LLM Evaluation 🔍 LLM Monitoring 📅 LLM Prompts 📝 LLM Structured Outputs 🛑 LLM Safety and Security 💠 LLM Embedding Models ❇️ Others Repo -

Kalyan KS

16,643 次观看 • 1 年前

Developers are coming to Avalanche for AI; it offers customizable compute and data structures, plus a robust ecosystem across finance, gaming, payments, and more. Listen to KITE AI talk about how this synergy is powering new AI agents and models for every sector. For more: 👇

Developers are coming to Avalanche for AI; it offers customizable compute and data structures, plus a robust ecosystem across finance, gaming, payments, and more. Listen to KITE AI talk about how this synergy is powering new AI agents and models for every sector. For more: 👇

Avalanche🔺

22,979 次观看 • 1 年前

We’re expanding our strategic partnership with Microsoft to: ⚡ Deliver OpenAI’s state-of-the-art models in Snowflake Cortex AI on Microsoft Microsoft Azure. Customers will soon be able to build AI-powered data agents to run analytical workflows on structured and unstructured data using OpenAI’s models. ⚡ Make Snowflake Cortex Agents available in Microsoft 365 Copilot and Microsoft 365 apps, ensuring AI insights become more accessible for users and enabling better decision-making across the enterprise. Learn more about how we’re bringing easy, efficient, and trusted AI to enterprises around the world:

We’re expanding our strategic partnership with Microsoft to: ⚡ Deliver OpenAI’s state-of-the-art models in Snowflake Cortex AI on Microsoft Microsoft Azure. Customers will soon be able to build AI-powered data agents to run analytical workflows on structured and unstructured data using OpenAI’s models. ⚡ Make Snowflake Cortex Agents available in Microsoft 365 Copilot and Microsoft 365 apps, ensuring AI insights become more accessible for users and enabling better decision-making across the enterprise. Learn more about how we’re bringing easy, efficient, and trusted AI to enterprises around the world:

Snowflake

11,011 次观看 • 1 年前

Welcome to Agentforce: a powerful data platform and an exceptional agent builder. The key to making agents truly effective is data. Salesforce agents deliver greater accuracy thanks to our integrated & comprehensive data & metadata. Without data, you're left with just a "dumb" LLM. No agent platform will gain traction without integrating data and metadata at its core. Get Agentforce at Dreamforce. ❤️

Welcome to Agentforce: a powerful data platform and an exceptional agent builder. The key to making agents truly effective is data. Salesforce agents deliver greater accuracy thanks to our integrated & comprehensive data & metadata. Without data, you're left with just a "dumb" LLM. No agent platform will gain traction without integrating data and metadata at its core. Get Agentforce at Dreamforce. ❤️

Marc Benioff

49,456 次观看 • 1 年前

Cardinal is a document intelligence platform that turns the trickiest PDFs and scans into structured, LLM-ready data. Most of the world’s data is still locked in PDFs, and after trying every solution and finding none that worked, Harvard + MIT alums Devi and Jianna Liu set out to deliver the high-accuracy outputs LLMs need.

Cardinal is a document intelligence platform that turns the trickiest PDFs and scans into structured, LLM-ready data. Most of the world’s data is still locked in PDFs, and after trying every solution and finding none that worked, Harvard + MIT alums Devi and Jianna Liu set out to deliver the high-accuracy outputs LLMs need.

Y Combinator

43,450 次观看 • 10 个月前

The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

The Chairman and CEO of AIG spent over a minute talking about how critical the Ontology is to deploying LLMs in the enterprise 💪 “Ontology is critical for deploying large language models. It brings together the relevant data sets that define the components of our insurance business, integrates and sequences them and then models how they relate to one another. Our ontology will create a clear record of any actions taken, which will inform business logic and provide the ability to audit agents activities.”

Chad Wahlquist

23,869 次观看 • 10 个月前

✅ Agentic prioritization of supply chain risks ✅ Data and processes unified in Ontology ✅ Proactive alerting for Human + AI teams Chris Dimoff shows Chad Wahlquist how Palantir AIP can drive savings through compliance, recover incorrect tariff charges, and enable businesses to adapt to new regulation in real time.

✅ Agentic prioritization of supply chain risks ✅ Data and processes unified in Ontology ✅ Proactive alerting for Human + AI teams Chris Dimoff shows Chad Wahlquist how Palantir AIP can drive savings through compliance, recover incorrect tariff charges, and enable businesses to adapt to new regulation in real time.

Palantir

27,399 次观看 • 9 个月前

Watch our introduction of Fluid compute—a faster and more cost efficient way to build dynamic applications.

Watch our introduction of Fluid compute—a faster and more cost efficient way to build dynamic applications.

Vercel

27,563 次观看 • 1 年前

.@Tensorfuse (YC W24) is a testing and evaluation platform for LLM apps. They make it easy for developers to experiment with various prompts, models, and retrieval methods to find the most effective one. Congrats on the launch agam & samagra14!

.@Tensorfuse (YC W24) is a testing and evaluation platform for LLM apps. They make it easy for developers to experiment with various prompts, models, and retrieval methods to find the most effective one. Congrats on the launch agam & samagra14!

Y Combinator

14,141 次观看 • 2 年前

New tech is cutting delays and improving care for millions of NHS cancer patients. @dranishapatel1 explains how Cancer 360 will bring together data so clinicians can see patients quicker, track progress and reduce waiting times. Find out more:

New tech is cutting delays and improving care for millions of NHS cancer patients. @dranishapatel1 explains how Cancer 360 will bring together data so clinicians can see patients quicker, track progress and reduce waiting times. Find out more:

Department of Health and Social Care

66,991 次观看 • 1 年前

Structured Outputs: The Building Blocks for Reliable AI! 🏗️ I am SUPER EXCITED to publish our newest Weaviate Podcast featuring Will Kurt (Will Kurt) and Cameron Pfiffer (Cameron) from .txt! 🎙️🎉 Dottxt is the company behind Outlines, reshaping how we control LLM outputs with constrained decoding! 🚀 This podcast dives into all sorts of details from new applications in metadata and information extraction, such as self-expanding knowledge graphs, to structured reasoning, report generation, and more! We also dove into the details of how this leverages Automata Theory and Finite State Machines to achieve this, how it is packaged with inference engines such as vLLM, and a rebuttal to Let Me Speak Freely, discussing how Structured Generation actually improve the quality of LLM outputs, in addition to speeding up inference, and of course, making it more reliable! I hope you find the podcast interesting, this was a super fun one! Links below!

Structured Outputs: The Building Blocks for Reliable AI! 🏗️ I am SUPER EXCITED to publish our newest Weaviate Podcast featuring Will Kurt (Will Kurt) and Cameron Pfiffer (Cameron) from .txt! 🎙️🎉 Dottxt is the company behind Outlines, reshaping how we control LLM outputs with constrained decoding! 🚀 This podcast dives into all sorts of details from new applications in metadata and information extraction, such as self-expanding knowledge graphs, to structured reasoning, report generation, and more! We also dove into the details of how this leverages Automata Theory and Finite State Machines to achieve this, how it is packaged with inference engines such as vLLM, and a rebuttal to Let Me Speak Freely, discussing how Structured Generation actually improve the quality of LLM outputs, in addition to speeding up inference, and of course, making it more reliable! I hope you find the podcast interesting, this was a super fun one! Links below!

Connor Shorten

12,742 次观看 • 1 年前

Our migrate and modernize program is more than lift and shift. See how Varonis is using it to cut costs, support customers, and improve their product.

Our migrate and modernize program is more than lift and shift. See how Varonis is using it to cut costs, support customers, and improve their product.

Microsoft Azure

11,431 次观看 • 1 个月前

AI Agents execute decisions for us rather than just advising us. It’s critical that they are accurate, and transparent in their decision making. Using our on-chain data platform for AI our agents are able to provide accuracy and real citations rather than making them up.

AI Agents execute decisions for us rather than just advising us. It’s critical that they are accurate, and transparent in their decision making. Using our on-chain data platform for AI our agents are able to provide accuracy and real citations rather than making them up.

Immutable AI Labs

23,710 次观看 • 1 年前

Learn a development pattern to systematically improve the accuracy and reliability of LLM applications in our new short course, Improving Accuracy of LLM Applications, built in partnership with Lamini and Meta, and taught by Lamini’s CEO Sharon Zhou, and Meta’s Senior Director of Partner Engineering, Amit Sangani. (Disclosure: I am an investor in Lamini.) The path to tuning an LLM application can be complex. In this course, you'll learn a systematic sequence of steps for improving accuracy by reducing hallucinations: - Create an evaluation dataset to measure model accuracy - Add prompt engineering and self-reflection - Fine-tune your model including "memory-tuning" which is a new method of embedding facts in an LLM Using the Llama 3-8B parameter model, you will: - Build a text-to-SQL agent with a custom schema and simulate situations where it hallucinates - Understand the difference between instruction fine-tuning, which gives pre-trained LLMs instructions to follow, and memory fine-tuning - See how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further I appreciate Meta releasing the Llama's family of open models -- this course gives an example of the unique type of work that developers can do with such models. Please sign up here:

Learn a development pattern to systematically improve the accuracy and reliability of LLM applications in our new short course, Improving Accuracy of LLM Applications, built in partnership with Lamini and Meta, and taught by Lamini’s CEO Sharon Zhou, and Meta’s Senior Director of Partner Engineering, Amit Sangani. (Disclosure: I am an investor in Lamini.) The path to tuning an LLM application can be complex. In this course, you'll learn a systematic sequence of steps for improving accuracy by reducing hallucinations: - Create an evaluation dataset to measure model accuracy - Add prompt engineering and self-reflection - Fine-tune your model including "memory-tuning" which is a new method of embedding facts in an LLM Using the Llama 3-8B parameter model, you will: - Build a text-to-SQL agent with a custom schema and simulate situations where it hallucinates - Understand the difference between instruction fine-tuning, which gives pre-trained LLMs instructions to follow, and memory fine-tuning - See how Performance-Efficient Fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) reduce training time by 100x and Mixture of Memory Experts (MoME) reduces it even further I appreciate Meta releasing the Llama's family of open models -- this course gives an example of the unique type of work that developers can do with such models. Please sign up here:

Andrew Ng

66,407 次观看 • 1 年前

Defend the enterprise. Security Forge, our new cyber security offering for source code vulnerability detection that moves at machine speed. See how Chad and George used it to autonomously scan a codebase, compress 109 flags down to 10 actionable findings, and do it for $78 — so security teams know exactly what to fix and why.

Defend the enterprise. Security Forge, our new cyber security offering for source code vulnerability detection that moves at machine speed. See how Chad and George used it to autonomously scan a codebase, compress 109 flags down to 10 actionable findings, and do it for $78 — so security teams know exactly what to fix and why.

Palantir

2,290,878 次观看 • 13 天前

Multi-agent systems offer incredible potential and unprecedented risks. How do you solve for observability, failure mode analysis, and guardrailing in the era of agents? Today, we’re announcing our Agent Reliability platform to observe, evaluate, guardrail, and improve agents at scale. You can get started with the complete platform for trustworthy agentic AI today for free, and here’s how we’re solving some of the biggest challenges in agent reliability: - Observability redesigned for agents Trace views collapse under complex workflows, so we created the Graph View, Timeline View, and Conversation View to offer rich, intuitive visualizations of agent decisions, tool calls, and conversation flows. This multi-dimensional approach enables teams to pinpoint exactly where and why agents deviate or fail. - Automated Failure Mode Analysis with our new Insights Engine Our Insights Engine ingests your logs, metrics, and agent code to automatically surface nuanced failure modes and their root causes. But knowing the problem is not enough; you need to know how to fix it. Insights Engine delivers actionable fixes and can even apply them automatically. With adaptive learning, your insights become smarter and more relevant as your agents evolve. - Evaluating Agents Across Multiple Dimensions Agentic systems interact across complex pathways, and evaluating their performance requires new metrics that reflect this increasing complexity. To deliver comprehensive agentic measurements, we’ve added more out-of-the-box agent metrics like flow adherence, agent flow, agent efficiency, and more. For specialized domains and unique workflows, custom metrics powered by our new Luna-2 small language models can be rapidly designed and fine-tuned for your specific use case. - Real-Time Guardrails Powered by Luna-2 As AI agents become more autonomous and complex, failures like hallucinations or unsafe actions increase dramatically. Without real-time guardrails, these errors will hurt your user experience and brand reputation. Our Luna-2 family of small language models is purpose-built to provide low-latency, cost-effective guardrails that actively stop agent errors before they happen. With support for out-of-the-box and custom metrics, Luna-2 enables enterprises to enforce safety, compliance, and reliability at scale. Enterprises running hundreds of agents and processing hundreds of millions of queries daily already rely on Galileo’s Agent Reliability platform to protect their users, safeguard brand trust, and accelerate innovation. Agent Reliability is available starting today. Try it for free and experience the new standard in AI reliability. Learn more below 👇

Galileo

1,276,298 次观看 • 11 个月前

In Georgia, we’ve been making government more efficient, balancing our budget, and cutting regulations for a long time.

In Georgia, we’ve been making government more efficient, balancing our budget, and cutting regulations for a long time.

Brian Kemp

26,124 次观看 • 1 年前