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。

Automation that stands the test of time will be software defined. At DevCon 2, New Product Development Engineer Ed Barmettler shared how AT&T leveraged Palantir AIP to transform unstructured data from 70k support tickets and years of institutional knowledge into searchable knowledge-based articles.

Palantir

435,086 subscribers

25,683 次观看 • 1 年前 •via X (Twitter)

教育科学技术新闻政治

Anya Rossi• Live Now

Private livecam show

4 条评论

Ben Rainville 的头像

Ben Rainville1 年前

@ATT Palantir is doing everything right to have a lasting long future of dominance in the industry.

NICE 的头像

NICE1 年前

Stay competitive by balancing cutting-edge AI with automation tools. Forrester shows how.

Jeremy Stankiewicz 的头像

Jeremy Stankiewicz1 年前

@ATT 👊🏻

Truth in Accounting 的头像

Truth in Accounting1 年前

@ATT This type of innovation is coming to government financial data!

相关视频

Human-agent teaming requires a human-agent developer experience. At DevCon 5, Palantir Group Lead John Ayad showcases the future of pro-code development in Foundry and AIP — introducing a new CLI that enables developers and agents to create pro-code monorepos for end-to-end applications, including data pipelines, Ontology primitives, and application artifacts.

Human-agent teaming requires a human-agent developer experience. At DevCon 5, Palantir Group Lead John Ayad showcases the future of pro-code development in Foundry and AIP — introducing a new CLI that enables developers and agents to create pro-code monorepos for end-to-end applications, including data pipelines, Ontology primitives, and application artifacts.

Palantir

47,098 次观看 • 5 个月前

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

In the next 2-3 years, the skills you need to build an ETL process will fundamentally differ from anything we've done in the last decade. The EXTRACT and LOAD steps of an ETL pipeline have been solved forever. They are boring, and I suspect they will continue to be boring. But the TRANSFORM step is where the money is at (and where innovation will continue happening!) An ETL pipeline that can use Large Language Models effectively to transform data is a whole new ballgame. I recorded a video to talk about this. And more importantly, I talk about how you can build ETL pipelines to transform UNSTRUCTURED data (the kind of data that's really hard to handle).

Santiago

56,412 次观看 • 1 年前

Palantir CTO Shyam Sankar on the software stock sell-off: "Modestly— we're the only company that has the knowledge to turn AI into economic value." "It speaks to the amount of fear that exists." "People are looking around at the software they've bought historically and are saying, 'maybe it's not actually that valuable.'" "If you look at Palantir— we've had the 10th quarter of accelerating growth on a very substantial basis."

Palantir CTO Shyam Sankar on the software stock sell-off: "Modestly— we're the only company that has the knowledge to turn AI into economic value." "It speaks to the amount of fear that exists." "People are looking around at the software they've bought historically and are saying, 'maybe it's not actually that valuable.'" "If you look at Palantir— we've had the 10th quarter of accelerating growth on a very substantial basis."

Jawwwn

93,306 次观看 • 6 个月前

“By having a quick, iterative way of operating with Palantir, it fits very well into the model of trying to support our supply chain and improve day by day to address our biggest problems.” At AIPCon 7’s Backstage Pass, Alison Jenkins, Senior Technical Product Manager at GE Aerospace, tells us how her team have leveraged Palantir to accelerate production while operating lean, efficient teams - bringing disparate systems into a single, accessible and actionable data layer.

“By having a quick, iterative way of operating with Palantir, it fits very well into the model of trying to support our supply chain and improve day by day to address our biggest problems.” At AIPCon 7’s Backstage Pass, Alison Jenkins, Senior Technical Product Manager at GE Aerospace, tells us how her team have leveraged Palantir to accelerate production while operating lean, efficient teams - bringing disparate systems into a single, accessible and actionable data layer.

Palantir

26,294 次观看 • 1 年前

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.

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 次观看 • 11 个月前

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

168,153 次观看 • 11 个月前

“Our Black People are Rising Up. And we are happy to Rise into the knowledge of self and others. The Rise Of The Dead will be like the blast of a trumpet pretty soon! The Black People will come into the knowledge of themselves so fast that it will look like The Rise Of The Dead has taken place at once! I just love to look at my Black People now. All of them are Rising. And listening to them talk you see how much different they are!”-The Honorable Elijah Muhammad

“Our Black People are Rising Up. And we are happy to Rise into the knowledge of self and others. The Rise Of The Dead will be like the blast of a trumpet pretty soon! The Black People will come into the knowledge of themselves so fast that it will look like The Rise Of The Dead has taken place at once! I just love to look at my Black People now. All of them are Rising. And listening to them talk you see how much different they are!”-The Honorable Elijah Muhammad

GEN

87,435 次观看 • 2 个月前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,403 次观看 • 2 年前

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."

.Aaron Levie says AI agents may take years to diffuse from engineering into the rest of knowledge work. "The models are really good at code, and the work is verifiable." "You have 5 or 10 things that make agents work in an enterprise context for engineering." "The users are less technical, the data is much more fragmented, the systems are much more legacy." "That's why it's going to be a number of years for this diffusion to roll from what we're seeing in Silicon Valley, into the rest of knowledge work."

MTS

13,046 次观看 • 3 个月前

NEW: DevRev raises a $100M Series A to build enterprise AI apps & agents - Valued at $1.1B 🦄 The core of their platform is an enterprise knowledge graph that powers AI apps & agents for customer service, product, software engineering, etc. Here's how DevRev works:

NEW: DevRev raises a $100M Series A to build enterprise AI apps & agents - Valued at $1.1B 🦄 The core of their platform is an enterprise knowledge graph that powers AI apps & agents for customer service, product, software engineering, etc. Here's how DevRev works:

Chief AI Officer

11,222 次观看 • 1 年前

🚨 NEW: Jensen Huang, CEO of Nvidia, discusses the definition of consciousness in AI and states that AI will generate the majority of the worlds future knowledge “In the future, maybe in a couple of years, 90% of the world’s knowledge will likely be generated by AI. And that’s fine. What difference does it make to me that I’m learning from a textbook that was generated by a bunch of people I don’t know? Versus knowledge from computers that are simulating all of these things? I don’t think there’s much of a difference. We still have to fact check it and make sure it’s based on first principles, just like we do today.”

🚨 NEW: Jensen Huang, CEO of Nvidia, discusses the definition of consciousness in AI and states that AI will generate the majority of the worlds future knowledge “In the future, maybe in a couple of years, 90% of the world’s knowledge will likely be generated by AI. And that’s fine. What difference does it make to me that I’m learning from a textbook that was generated by a bunch of people I don’t know? Versus knowledge from computers that are simulating all of these things? I don’t think there’s much of a difference. We still have to fact check it and make sure it’s based on first principles, just like we do today.”

Autism Capital 🧩

47,929 次观看 • 8 个月前

"A lot of the solutions will come from getting our data onchain with Chainlink and figuring out where the standards are going to be set."—Kristen Mierzwa, Head of Digital Assets, FTSE Russell At SmartCon 2025, leaders from FTSE Russell, Tradeweb, Deutsche Börse, Digital Asset, and Chainlink Labs explored how onchain data, institutional interoperability, and AI-driven automation are converging to reshape global finance ↓

"A lot of the solutions will come from getting our data onchain with Chainlink and figuring out where the standards are going to be set."—Kristen Mierzwa, Head of Digital Assets, FTSE Russell At SmartCon 2025, leaders from FTSE Russell, Tradeweb, Deutsche Börse, Digital Asset, and Chainlink Labs explored how onchain data, institutional interoperability, and AI-driven automation are converging to reshape global finance ↓

Chainlink

44,896 次观看 • 9 个月前

🧵THREAD: In 2009, a whistleblower released emails showing how climate academia was manipulating data and blocking articles that didn’t support their global warming agenda. Be prepared to delve into the murky depths of one of the biggest scandals of our time? #ClimateGate

🧵THREAD: In 2009, a whistleblower released emails showing how climate academia was manipulating data and blocking articles that didn’t support their global warming agenda. Be prepared to delve into the murky depths of one of the biggest scandals of our time? #ClimateGate

Dr. Simon

1,055,312 次观看 • 3 年前

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Daniel San

230,185 次观看 • 1 年前

See how Palantir Software Engineer Colton Rusch uses AIP Evolve to prove that cheaper AI doesn't have to mean worse AI: "Evolve screened a whole suite of models across providers, then used evals and expert feedback to rule out certain options and keep others for further optimization." "We got to the point where evolve produced a vastly cheaper version — 68% lower compute costs." "We got lower costs and better quality at the same time."

See how Palantir Software Engineer Colton Rusch uses AIP Evolve to prove that cheaper AI doesn't have to mean worse AI: "Evolve screened a whole suite of models across providers, then used evals and expert feedback to rule out certain options and keep others for further optimization." "We got to the point where evolve produced a vastly cheaper version — 68% lower compute costs." "We got lower costs and better quality at the same time."

Palantir

13,673 次观看 • 26 天前

“Companies that are surging ahead aren’t running pilots. They build their foundations early and are scaling enterprise wide. The gap is widening.” Advanced AI Delivery Lead Tracy Venable reveals how Accenture and Palantir are working together to transform enterprise consulting—operationalizing decades of expertise through AI agents that compress ERP migrations from months to weeks, deliver business value from day one, and enable every engagement to build on institutional knowledge accumulated across thousands of deployments.

“Companies that are surging ahead aren’t running pilots. They build their foundations early and are scaling enterprise wide. The gap is widening.” Advanced AI Delivery Lead Tracy Venable reveals how Accenture and Palantir are working together to transform enterprise consulting—operationalizing decades of expertise through AI agents that compress ERP migrations from months to weeks, deliver business value from day one, and enable every engagement to build on institutional knowledge accumulated across thousands of deployments.

Palantir

183,663 次观看 • 5 个月前

.David Deutsch: “There is an inherent conflict in the human condition—both individually and in society—between the need to preserve existing knowledge and the need to create new knowledge. For most of human history, preserving existing knowledge trumped any kind of attempt to improve knowledge. Because attempts to improve knowledge risk error. Or rather, to be exact, it risks error that has never occurred before. There were plenty of errors that had occurred before. Plenty of kings got overthrown because they couldn’t solve the problem of how to fend off the enemy. But they were in familiar territory conceptually, and they were afraid of changing culture. This fear of changing culture was built into culture itself. And this conflict exists to this day. The most extreme example known to me in the West, anyway, is the conflict in education between preserving existing knowledge and creating new knowledge. Even today, education—education theory—is conceived as the theory of how to decant existing knowledge from the brains that already know it to the brains that do not yet know it. And that’s what Popper calls the ‘bucket theory of the mind’—that the mind is a bucket and knowledge is a fluid. What he stressed, and what I always also want to stress, is that not only is that a mischaracterization of knowledge and of the use of knowledge, but it is the wrong way around. As a practical matter, even right back two million years ago, the transmission of cultural knowledge was not something done by the transmitter; it was something done by the receiver.”

.David Deutsch: “There is an inherent conflict in the human condition—both individually and in society—between the need to preserve existing knowledge and the need to create new knowledge. For most of human history, preserving existing knowledge trumped any kind of attempt to improve knowledge. Because attempts to improve knowledge risk error. Or rather, to be exact, it risks error that has never occurred before. There were plenty of errors that had occurred before. Plenty of kings got overthrown because they couldn’t solve the problem of how to fend off the enemy. But they were in familiar territory conceptually, and they were afraid of changing culture. This fear of changing culture was built into culture itself. And this conflict exists to this day. The most extreme example known to me in the West, anyway, is the conflict in education between preserving existing knowledge and creating new knowledge. Even today, education—education theory—is conceived as the theory of how to decant existing knowledge from the brains that already know it to the brains that do not yet know it. And that’s what Popper calls the ‘bucket theory of the mind’—that the mind is a bucket and knowledge is a fluid. What he stressed, and what I always also want to stress, is that not only is that a mischaracterization of knowledge and of the use of knowledge, but it is the wrong way around. As a practical matter, even right back two million years ago, the transmission of cultural knowledge was not something done by the transmitter; it was something done by the receiver.”

Arjun Khemani

10,523 次观看 • 1 年前

Agents in Microsoft 365 Copilot work with you and on your behalf, breaking down silos across your business applications. And it's fantastic to see how our partners like Adobe, SAP, ServiceNow, and more are building agents to bring their unique knowledge right into the flow of your work and transform every business process and function, from HR and finance to sales. The notion that you have go to siloed, bespoke business applications one at a time will be a thing of the past.

Agents in Microsoft 365 Copilot work with you and on your behalf, breaking down silos across your business applications. And it's fantastic to see how our partners like Adobe, SAP, ServiceNow, and more are building agents to bring their unique knowledge right into the flow of your work and transform every business process and function, from HR and finance to sales. The notion that you have go to siloed, bespoke business applications one at a time will be a thing of the past.

Satya Nadella

66,377 次观看 • 1 年前

Jensen Huang says that in just 2–3 years, AI could be responsible for generating up to 90% of the world’s knowledge! In his view, this won’t be “fake data,” but distilled intelligence: systems that absorb the entire corpus of human science, run massive simulations, and then produce new research at a speed no human team could match. Every day I look forward to the future more and more.

Jensen Huang says that in just 2–3 years, AI could be responsible for generating up to 90% of the world’s knowledge! In his view, this won’t be “fake data,” but distilled intelligence: systems that absorb the entire corpus of human science, run massive simulations, and then produce new research at a speed no human team could match. Every day I look forward to the future more and more.

Chubby♨️

21,776 次观看 • 8 个月前

$PLTR PALANTIR WENT PUBLIC 5 YEARS AGO TODAY. They did a direct public offering at $10 a share. The company's market capitalization has 18x'ed since going public. CEO Alex Karp, 5 years ago: "We are going to deliver the world's best software with the world's most efficient way of delivering it. Investors will decide what's that worth and I think you'll find in a number of years that there will be a consensus Palantir is a truly special software company that is arguably the most important software company in the world."

$PLTR PALANTIR WENT PUBLIC 5 YEARS AGO TODAY. They did a direct public offering at $10 a share. The company's market capitalization has 18x'ed since going public. CEO Alex Karp, 5 years ago: "We are going to deliver the world's best software with the world's most efficient way of delivering it. Investors will decide what's that worth and I think you'll find in a number of years that there will be a consensus Palantir is a truly special software company that is arguably the most important software company in the world."

amit

400,926 次观看 • 10 个月前