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Very special Training Data drops today: Making Cities Awesome ☺️ We haven’t featured an application company on Training Data in a while. The diffusion of AI is the Most Important Thing, and the highest-stakes application category of all is the one sitting near the base of Maslow’s hierarchy: safety....

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Sonya Huang 🐥

29,292 subscribers

32,995 次观看 • 14 天前 •via X (Twitter)

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Anya Rossi• Live Now

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4 条评论

Sonya Huang 🐥 的头像
Sonya Huang 🐥14 天前

Listen to the full episode: Apple: Spotify: YouTube:

Jonathan Sandhu 的头像
Jonathan Sandhu14 天前

What exactly becomes better for a city besides the pitch deck?

Dave Mellish 的头像
Dave Mellish14 天前

It’s becoming extremely difficult not to focus on 🐥’s taste in fashion, and the confidence with which she pulls it off. Pay attention kids. S-tier.

Sonya Huang 🐥 的头像
Sonya Huang 🐥14 天前

ha!! merci ☺️

相关视频

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”
1:33

David Friedberg: Michael Burry’s Datacenter Math is Wrong “I actually think Michael Burberry's got this wrong.” “What Michael Burry is saying is that all of these hyperscalers have extended their depreciation schedule or the useful life of their data centers by roughly 2x, which cuts the operating costs in half when they report it in earnings. And so it's making their earnings inflate.” “So he's claiming they're cooking the books. Google first made this change in Q1 of 2021, where they said the servers are now going from 3 to 4 years. Separately in 2021, Google took networking equipment from 3 to 5 years. And then in 2023, they took it from 5 to 6 years.” “And so this is a result of this effort where they went in and did an analysis. So what happened?” “What happened in the data centers is that the data centers transitioned from being primarily data storage and data transfer systems, where you would use hard drives and RAM and memory to store data and then transmit it back out, to being data processing centers because of the AI boom.” “So as AI became more important in the data center, more of the dollars that are going into data centers were allocated towards chips from data storage, which initially was hard drives.” “And then suddenly, when you put these processors in to process the data to do AI, the majority of the spend and the majority of the energy is going towards the processors.” “I made some calls and I checked around with some other friends, and everyone says the same thing: that these 7-8 year old TPUs and GPUs that are sitting in the data centers are still being used and they're being used at 100% utilization.” “So that actually justifies and validates the depreciation schedule being much longer versus shorter.”

The All-In Podcast

304,450 次观看 • 10 个月前

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.
0:43

Right now the main paradigm that we think of agents in is chatting back and forth, but the biggest use of tokens will come from agents that are just always on running in the background doing work for us, or ones triggered from a workflow. Agents will be working 24/7 in our workflows processing data, reviewing and generating documents, moving data between systems, writing code, accelerating decision making steps, and more. In Claude's new Managed Agents feature, in a couple minutes you can wire up an agent that can read contracts when they come into Box to review them, and then assign a task in Linear with the critical information from the contract. But this could have been any workflow, like reviewing documents for client onboarding, invoice processing, M&A due-diligence, data extraction pipelines, and millions of other use-cases. And integrating data across any system. This is only possible when you can have long-running agents that can complete real work in the background, accurately. Agents have the ability to execute code safely, leverage tools, access a compute sandbox, and connect across systems is clearly the architecture of the future. The industry is now making it easier and easier for enterprises to build and deploy these agents.

Aaron Levie

17,543 次观看 • 5 个月前

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.
0:48

every question in this clip is an ownership question, especially data because when everyone has access to the same intelligence, the only input that separates your business from the next one is the data you own your data is decades of decisions, customers, mistakes, and edge cases that exist nowhere else on earth that is the moat and enterprises are waking up to the fact that they've been leaking it one api call at a time my prediction is this: data ownership is going to absolutely matter in the world we're going into and whoever's building the infrastructure for it is going to have a very good decade.

Adeniyi.sui

13,834 次观看 • 2 个月前

We need the data centers. We have to win the AI race. My guest for episode 5 of Counterweight is Manhattan Institute's Judge Glock. This spring he wrote about Loudoun County, VA in City Journal. Loudoun, one of the richest counties in America, with a median household income of around $170,000, has become the unlikely center of a new data center-based industrial revolution. And it's not just Loudoun. Data center construction is up about twenty times nationwide in three years, leading to an enormous political backlash. New York State imposed a moratorium on data center construction, and even Republican Greg Abbott of Texas has put a temporary pause in place. Meanwhile, speaking for myself, I’ve seen a ton of fear-mongering about them, with very little in the way of clear explanation of what the costs and benefits are and what they mean for the localities that might bring them in. I've brought Judge on to walk through in granular detail how data centers work, how Loudoun County became ground zero for the data center boom, and the recent pushback against data centers. We also talk about the unfortunate habit that AI CEOs have of seeming like the gloomiest people in the world in relation to the products they are trying to sell.
40:20

We need the data centers. We have to win the AI race. My guest for episode 5 of Counterweight is Manhattan Institute's Judge Glock. This spring he wrote about Loudoun County, VA in City Journal. Loudoun, one of the richest counties in America, with a median household income of around $170,000, has become the unlikely center of a new data center-based industrial revolution. And it's not just Loudoun. Data center construction is up about twenty times nationwide in three years, leading to an enormous political backlash. New York State imposed a moratorium on data center construction, and even Republican Greg Abbott of Texas has put a temporary pause in place. Meanwhile, speaking for myself, I’ve seen a ton of fear-mongering about them, with very little in the way of clear explanation of what the costs and benefits are and what they mean for the localities that might bring them in. I've brought Judge on to walk through in granular detail how data centers work, how Loudoun County became ground zero for the data center boom, and the recent pushback against data centers. We also talk about the unfortunate habit that AI CEOs have of seeming like the gloomiest people in the world in relation to the products they are trying to sell.

Will Chamberlain

21,345 次观看 • 11 天前

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”
2:15

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 次观看 • 9 个月前

.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."
1:27

.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 • 4 个月前

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.
1:21

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 • 1 年前

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."
1:27

Chris Camillo explains how Bloom Energy is solving AI data center energy usage problems… “ $BE is one of the only companies in the world that has a method to essentially bring almost an infinite amount of energy to these data centers." And we won't get into all the technology, but they are now working with hyperscalers and they are a resource for energy that will allow a data center to get ramped up quicker. "So, even though it costs a little bit more money today to put an energy cell from Bloom, an energy box from Bloom, which are energy cells, it essentially takes things like natural gas, but instead of burning the natural gas, there is a chemical process that turns that natural gas into energy for the data center."

The Iced Coffee Hour

277,703 次观看 • 4 个月前

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.
16:29

I don't think people understand that one of the biggest real-world use cases for Crypto it is happening - right now. FedEx, just sat down with Mance Harmon of $HBAR and explained why they chose Hedera to track millions of packages a day, to 220 countries. Dale Christie, Blockchain Strategist at FedEx said in 2018, FedEx tried building its own blockchain... But it wouldn’t scale so they chose Hedera. Why? Every package generates hundreds - sometimes thousands - of data points, and Hedera enables the key facts to move through customs at the speed of data, not paper. “When you are a trusted shipper and I am a trusted receiver… that data can move through customs at the speed of data and keep it moving.” One of the largest logistics companies in the world just publicly said Hedera is the foundation they’re building on. Hedera is the most undervalued token imo. and this is one of the biggest use cases Crypto's ever seen.

Mark

44,717 次观看 • 6 个月前

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇
1:12

In the second episode of Scenius Studio's mini-series "The Use-Case", I sit down with Andrej Co-Founder of touch grass. Grass gives users the ability to earn ownership in the Grass network by supplying the protocol with their unused internet bandwidth for data scraping purposes (something that is already happening to most of us and we don’t get paid!). The grass protocol packages this scraped web data and sells it to AI companies who have insufficient data to further develop their models. With over 3 millions users and millions of annualized revenue, Grass is a real commercial business with a roadmap that makes it one of the most exciting projects at the intersection of crypto x AI and data. In this episode we discuss: ➔ Andrej’s background in physics, finance, and sports betting ➔ Big companies using your IP address without your knowledge or permission ➔ How the Grass protocol puts a toll booth on your internet bandwidth highway ➔ Packaging web scraped data and selling it to AI companies building Multi-Modal models ➔ Dynamics between the Grass Protocol and the labs entity developing Grass’ IP ➔ Protocol design decisions to ensure that all tokenholders (VCs, team, and community) are aligned ➔ Why Grass needed to be built on crypto rails to maximize its potential ➔ The future of LLMs and how they will search for context and information Hope you enjoy this episode of Scenius Studio's "The Use-Case". Links to listen in bio or below👇

Ben Jacobs

22,468 次观看 • 1 年前

Handshake AI CSO Sahil Bhaiwala reveals how they build simulated white-collar work environments to train frontier AI agents: "An environment consists of a few elements. The first is the software tools the agent needs access to. If I'm an investment banker, I have access to Excel, SEC EDGAR where all the financial reports are, and PowerPoint. Agents need the same access." "We build simulated versions because of infrastructure and access constraints. Labs are querying that tool thousands of times in a given second." "The second thing you need is data that populates the environment. If I'm building a financial model, I need the data room with the messy files from the company I'm evaluating." "The final piece is tasks and verifiers. What are the requests of the agent that would reflect what a real investment banker might ask. We try to find tasks complex and realistic enough that the models fail. If the model can't perform, we know it needs more training data." "What we send to labs is essentially a Docker file, a full containerized task. It has the tools the model has access to, the full data that populates the world, and the task and verifier. We'll send tens of thousands of these to a given lab for just one domain." Handshake Garrett Lord
3:53

Handshake AI CSO Sahil Bhaiwala reveals how they build simulated white-collar work environments to train frontier AI agents: "An environment consists of a few elements. The first is the software tools the agent needs access to. If I'm an investment banker, I have access to Excel, SEC EDGAR where all the financial reports are, and PowerPoint. Agents need the same access." "We build simulated versions because of infrastructure and access constraints. Labs are querying that tool thousands of times in a given second." "The second thing you need is data that populates the environment. If I'm building a financial model, I need the data room with the messy files from the company I'm evaluating." "The final piece is tasks and verifiers. What are the requests of the agent that would reflect what a real investment banker might ask. We try to find tasks complex and realistic enough that the models fail. If the model can't perform, we know it needs more training data." "What we send to labs is essentially a Docker file, a full containerized task. It has the tools the model has access to, the full data that populates the world, and the task and verifier. We'll send tens of thousands of these to a given lab for just one domain." Handshake Garrett Lord

MTS

16,158 次观看 • 22 天前

Maajid Nawaz from Italy on the Pope's encyclical address warning about Ai: “Peter Thiel’s Palantir as a company, using the UK as a case study, already has, due to Prime Minister Keir Starmer, already has contracts to manage the nation’s, all of the nation’s health data unified on one platform and that health data being interpreted as it is by Palantir’s autonomous AI, making life and death decisions on the nation’s health. Palatir also separately has a contract with the Metropolitan Police using a combination of not just facial recognition but predictive programming to determine crime and to set the police’s priorities. This is something that, they already have this deal with the Metropolitan Police, but in addition, if you add Palantir’s contracts with the Ministry of Defence in the UK, like it has in the Department of Defence in the United States of America, you can see how a combination of health data, domestic crime surveillance and foreign war-mongering, being all under the auspices of one private company called Palantir, in the UK and in the US, it’s the same, it is a particularly worrying scenario.”
1:12

Maajid Nawaz from Italy on the Pope's encyclical address warning about Ai: “Peter Thiel’s Palantir as a company, using the UK as a case study, already has, due to Prime Minister Keir Starmer, already has contracts to manage the nation’s, all of the nation’s health data unified on one platform and that health data being interpreted as it is by Palantir’s autonomous AI, making life and death decisions on the nation’s health. Palatir also separately has a contract with the Metropolitan Police using a combination of not just facial recognition but predictive programming to determine crime and to set the police’s priorities. This is something that, they already have this deal with the Metropolitan Police, but in addition, if you add Palantir’s contracts with the Ministry of Defence in the UK, like it has in the Department of Defence in the United States of America, you can see how a combination of health data, domestic crime surveillance and foreign war-mongering, being all under the auspices of one private company called Palantir, in the UK and in the US, it’s the same, it is a particularly worrying scenario.”

أبو عمّار

24,409 次观看 • 3 个月前

🚨 THEY FOUND A LEGAL WAY TO SPY ON EVERY AMERICAN — AND THE 4TH AMENDMENT CAN'T STOP IT Most Americans believe the government needs a warrant to monitor them, track them, or collect detailed information about their private lives. But unfortunately, they found a loophole. The government may not be allowed to directly collect certain information on Americans, but private companies collect enormous amounts of it every single day through smartphones, apps, websites, search engines, location services, online purchases, and countless other digital tools most people use without a second thought. That means your location history, browsing habits, purchases, interests, movements, and daily routines are already being recorded, stored, and traded by an entire industry that most Americans have never even heard of. The loophole is that while the government may not be allowed to collect certain information directly without a warrant, it can reportedly purchase data that private companies have already collected. The information may be gathered by private companies. The data may be sold by data brokers. And the government may still end up with access to it. For years, there was one major limitation. There was simply too much data. Even if someone wanted to analyze billions of data points across millions of people, it would have required an impossible number of human analysts. Then AI arrived. Suddenly, information that would have taken years to organize can be processed, searched, categorized, connected, and analyzed in a fraction of the time. People are warning that the combination of artificial intelligence and the data broker loophole could fundamentally change what surveillance looks like in America. For the first time in history, technology may finally exist that can sift through enormous amounts of personal information at a scale that was previously impossible. The question isn't whether the data exists. The question is who has access to it. Because once your location, habits, purchases, interests, relationships, and daily routines can all be analyzed by machines, the line between convenience and surveillance starts getting harder to see. The most alarming part? Most Americans have no idea this conversation is even happening. Do you trust the government with more information about your life than your own family knows?
2:39

🚨 THEY FOUND A LEGAL WAY TO SPY ON EVERY AMERICAN — AND THE 4TH AMENDMENT CAN'T STOP IT Most Americans believe the government needs a warrant to monitor them, track them, or collect detailed information about their private lives. But unfortunately, they found a loophole. The government may not be allowed to directly collect certain information on Americans, but private companies collect enormous amounts of it every single day through smartphones, apps, websites, search engines, location services, online purchases, and countless other digital tools most people use without a second thought. That means your location history, browsing habits, purchases, interests, movements, and daily routines are already being recorded, stored, and traded by an entire industry that most Americans have never even heard of. The loophole is that while the government may not be allowed to collect certain information directly without a warrant, it can reportedly purchase data that private companies have already collected. The information may be gathered by private companies. The data may be sold by data brokers. And the government may still end up with access to it. For years, there was one major limitation. There was simply too much data. Even if someone wanted to analyze billions of data points across millions of people, it would have required an impossible number of human analysts. Then AI arrived. Suddenly, information that would have taken years to organize can be processed, searched, categorized, connected, and analyzed in a fraction of the time. People are warning that the combination of artificial intelligence and the data broker loophole could fundamentally change what surveillance looks like in America. For the first time in history, technology may finally exist that can sift through enormous amounts of personal information at a scale that was previously impossible. The question isn't whether the data exists. The question is who has access to it. Because once your location, habits, purchases, interests, relationships, and daily routines can all be analyzed by machines, the line between convenience and surveillance starts getting harder to see. The most alarming part? Most Americans have no idea this conversation is even happening. Do you trust the government with more information about your life than your own family knows?

HustleBitch

23,930 次观看 • 2 个月前

New lecture for the book! Nominally about synthetic data, but mostly is a walk through of the distillation literature from the Hinton 2015 paper to multi-teach on-policy distillation of today! At 7.4 hours of video in my post-training brain dump and counting :) It was fun to stare at the math long enough and talk through the 3-4 core changes that needed to be made to the original formulation to have on-policy distillation be ready for the mainstream like it is today (and in RL frameworks). Otherwise, I include a bit of a history lesson for how synthetic data generally slowly took over all post-training data research (it wasn't always the case)! Then I do some 101 review on constitutional AI, rubrics, and other popular methods. 00:00 The emergence of synthetic data 10:50 Background on teacher-student knowledge-distillation 24:47: On-policy distillation (OPD, MOPD, and OPSD) 37:11 Constitutional AI & AI Feedback 45:50 Rubrics as rewards & conclusions Ofc, watch on YouTube etc.
49:40

New lecture for the book! Nominally about synthetic data, but mostly is a walk through of the distillation literature from the Hinton 2015 paper to multi-teach on-policy distillation of today! At 7.4 hours of video in my post-training brain dump and counting :) It was fun to stare at the math long enough and talk through the 3-4 core changes that needed to be made to the original formulation to have on-policy distillation be ready for the mainstream like it is today (and in RL frameworks). Otherwise, I include a bit of a history lesson for how synthetic data generally slowly took over all post-training data research (it wasn't always the case)! Then I do some 101 review on constitutional AI, rubrics, and other popular methods. 00:00 The emergence of synthetic data 10:50 Background on teacher-student knowledge-distillation 24:47: On-policy distillation (OPD, MOPD, and OPSD) 37:11 Constitutional AI & AI Feedback 45:50 Rubrics as rewards & conclusions Ofc, watch on YouTube etc.

Nathan Lambert

93,111 次观看 • 2 个月前

Catherine Austin Fitts: "I think Palantir is in partnership with the Netanyahu syndicate... [and] they have the treasury data, the IRS data, [and] they have the Social Security data... [and they're] building a complete biometric surveillance [grid] of the entire population." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from an interview with Gary Heasley, et al. of The Conservative Voice (The Conservative Voice) posted to X on July 8, 2025. ---------------Partial transcription of clip--------------- "I think Palantir is in partnership with the Netanyahu syndicate and the breakaways. I don't, you know. But they have contracts with Health and Human Services. They have contracts. The government pays them massive amounts. Massive amounts of money. Massive amounts of money. "And so if you look at the Doge theft, what you've done is you've moved between the government contracts. You know, there's a new sole source ICE contract on the way to Palantir as well. That's just announced. I just found that last night. So you've got, you know, you. Basically, they have the treasury data, they have the IRS data, they have the Social Security data. [And now] they're going to have all the ICE data. "And then, Trump has announced he wants to privatize Freddie and Fannie, but Palantir is going to underwrite all the packages before they come into Freddie and Fannie. So they're going to have all the housing data. And we know HHS has said they're organizing all the health, public and private health data, so I'm assuming that's going in as well. And basically you're talking about that data being managed and, and I think at this point, basically privatized into AI. "So the Doge operation, if I'm right, and this is my hypothesis, if the Doge operation did what I think, they transferred all the data. And, and, and they did it when an XAI announced a partnership with Palantir. And then the government turned around and Palantir had a lot of contracts with those agencies anyway. But, but then they gave them additional contracts, and so they have plenty of money, especially the ICE is going to be sole source, so you can just do pretty much whatever you want. So they have plenty of money to manage that data. And yes, I think they have it on a private basis. "And what's interesting, I have somebody, a professional that I work with who has for, for his business, has to do income verification. And, magically, two weeks after, Doge got the Treasury, Social Security and irs, suddenly the income verification service he uses announces they have 100% of Americans now complete data on everyone. Magically. "[Also,] the ICE story is complicated because there are multiple things going on, but there's no doubt the primary thing going on is building a complete biometric surveillance of the entire population."
2:59

Catherine Austin Fitts: "I think Palantir is in partnership with the Netanyahu syndicate... [and] they have the treasury data, the IRS data, [and] they have the Social Security data... [and they're] building a complete biometric surveillance [grid] of the entire population." This clip of Fitts, a former Assistant Secretary of Housing and Urban Development, investment banker, and founder of the Solari Report (The Solari Report | Catherine Austin Fitts), is taken from an interview with Gary Heasley, et al. of The Conservative Voice (The Conservative Voice) posted to X on July 8, 2025. ---------------Partial transcription of clip--------------- "I think Palantir is in partnership with the Netanyahu syndicate and the breakaways. I don't, you know. But they have contracts with Health and Human Services. They have contracts. The government pays them massive amounts. Massive amounts of money. Massive amounts of money. "And so if you look at the Doge theft, what you've done is you've moved between the government contracts. You know, there's a new sole source ICE contract on the way to Palantir as well. That's just announced. I just found that last night. So you've got, you know, you. Basically, they have the treasury data, they have the IRS data, they have the Social Security data. [And now] they're going to have all the ICE data. "And then, Trump has announced he wants to privatize Freddie and Fannie, but Palantir is going to underwrite all the packages before they come into Freddie and Fannie. So they're going to have all the housing data. And we know HHS has said they're organizing all the health, public and private health data, so I'm assuming that's going in as well. And basically you're talking about that data being managed and, and I think at this point, basically privatized into AI. "So the Doge operation, if I'm right, and this is my hypothesis, if the Doge operation did what I think, they transferred all the data. And, and, and they did it when an XAI announced a partnership with Palantir. And then the government turned around and Palantir had a lot of contracts with those agencies anyway. But, but then they gave them additional contracts, and so they have plenty of money, especially the ICE is going to be sole source, so you can just do pretty much whatever you want. So they have plenty of money to manage that data. And yes, I think they have it on a private basis. "And what's interesting, I have somebody, a professional that I work with who has for, for his business, has to do income verification. And, magically, two weeks after, Doge got the Treasury, Social Security and irs, suddenly the income verification service he uses announces they have 100% of Americans now complete data on everyone. Magically. "[Also,] the ICE story is complicated because there are multiple things going on, but there's no doubt the primary thing going on is building a complete biometric surveillance of the entire population."

Sense Receptor

123,301 次观看 • 1 年前

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."
3:20

#BudgetSession2025 | Lok Sabha LoP and Congress MP Rahul Gandhi says "People talk about AI, but it's important to understand that AI on its own is absolutely meaningless because AI operates on top of data. Without data, AI means nothing. And if we look at data today, there is one thing which is very clear. Every single piece of data that comes out of the production system in the world. The data that was used to make this phone, the data that is used to make electric cars. The data that is used to make basically all electronics on the planet today is owned by China. And the consumption data is owned by the United States... China has at least a 10 year lead on India in this space. China has been working on batteries, robots, motors, optics for the last 10 years and we are behind...We would ensure that our banking system is not captured by 2-3 companies that basically do not allow you to build a production system. But our banking system is open, dynamic and accessible to small and medium businesses and to millions and millions of entrepreneurs who want to take part in this revolution. Our foreign policy would take into account this revolution. When we talk to the United States, we would not send our foreign minister to invite our Prime Minister to his coronation...Because if we had a production system and if we were working on these technologies, the American President would come here and invite the Prime Minister."

ANI

163,581 次观看 • 1 年前

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 次观看 • 10 个月前

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.
5:13

From Eric Vishria on how the top AI founders are building products completely opposite of the SaaS era: "One of the things that is really different in the AI world versus the SaaS world, is that in the SaaS world, over and over again, you had people who really understood the customer. And the problem. And then they understood a domain. They understood what the technology was more or less capable of. But it wasn't a real question of if you could build something or not. For example, take Salesforce, Workday, and ServiceNow. CRM existed before Salesforce. HR management existed before Workday. Same thing with ServiceNow. So in every case, Salesforce followed Siebel. Workday followed Peoplesoft. ServiceNow followed Peregrine and Remedy, and others. So they were just kind of, cloud SaaS versions of the prior generation product. They just understood the customers. They understood the problem. And they were just like, here's a better version. And that evolved a little bit over time in SaaS land. But that's what it is. And so product development in that way was done by people who really understood the customer and the problems. And then just took advantage of the next wave. And this is almost diametrically opposite of product development in the AI era. When I look at the teams that are having the most success today, they have intimate knowledge of the models. They are right on the frontier of understanding which models are better at what, and why, and when. And what they're going to be good at and what they're not going to be good at. And what they're spending their time on, is figuring out how do I apply this capability of this model to this domain or to this user. So they're actually working inside out or technology out, versus customer problem in. And of course, they understand the customer problem. And a lot of times they have firsthand knowledge of it. But they're really close to the metal and capability, and they're applying it. And I think this is a really different way to develop products than in SaaS. I started my career as a product manager a long time ago, and it's almost the complete opposite of everything you learned. "Listen to the customer, understand it, then bring it back to the engineering and product teams." If you did that right now, ask a bunch of customers what they want out of AI, and you brought it back, for the most part, it may not be possible today with today's technology. Whereas the teams that are winning right now really understand the technology and are applying it out. And so I think this reversal matters. I think it's a big difference in terms of how companies are getting built. And maybe even the types of entrepreneurs that will be successful. I'm not sure. You're seeing some real change there. Look at the Bret Taylor's at Sierra. That's a super, super technical founder who really gets it. Brett and Clay really get it. You look at Michael and his co-founders at Cursor. They're super technical founders and they get it. They all really understand what these things can and can't do. And that's a pretty different dynamic relative to the way the best SaaS companies got built." Link in bio for the full conversation going deep on the current class of startups going from zero to $100m+ in ARR within 12 months.

The Peel

209,752 次观看 • 1 年前

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
3:21

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 次观看 • 2 个月前