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I am the only AI engineer that is using primarily off-line data to train AI. It ain’t easy and maybe that’s why they don’t. But it is vital to preserve the data but the context of humanity in each era. This 1 hour VHS was likely only seen a... show more

Brian Roemmele

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19,364 görüntüleme • 9 ay önce •via X (Twitter)

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Of course I have 100s and 1000s of hours of fire, police, air traffic radio saved from over a decade to train AI models on for high protein non-Reddit data. I also am the only AI engineer using CB radio. This data is more complex, not high order, but needed. Dull example:

Of course I have 100s and 1000s of hours of fire, police, air traffic radio saved from over a decade to train AI models on for high protein non-Reddit data. I also am the only AI engineer using CB radio. This data is more complex, not high order, but needed. Dull example:

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Data is the foundation of any AI training. All of the GPUs in the world can't train an AI model if they don't have data to train it on. Don't forget: Grass is the data layer of AI.

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The reality of owning your own AI is spreading. Astute folks accept that one will never, ever trust their private context to a cloud, nor should any astute company ask of this. It ain’t easy. It ain’t as profitable. It is the—only way. It was just me, now it is many more…

The reality of owning your own AI is spreading. Astute folks accept that one will never, ever trust their private context to a cloud, nor should any astute company ask of this. It ain’t easy. It ain’t as profitable. It is the—only way. It was just me, now it is many more…

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you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

you might've heard about context engineering, but nobody has explained what it really is... that's why I made a full practical walkthrough on how to use it to make your AI outputs personalized and consistent in this walkthrough, I go over: – what context engineering actually is (without the jargon) – how to train AI on your own data and preferences – how to get outputs that sound like you wrote them (not generic AI) reply "context" and I'll DM it to you (must be following)

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Meta's AI investments have been incredibly successful, but investors refuse to see it. The company posted 33% advertising revenue growth last quarter on $55B in revenue. Meanwhile Google's was 19%, and Amazon, which is much smaller, was 24%. Eric Seufert: "I was at this dinner [with hedge fund people]. I was talking about why these investments that Meta's making *right now* are bearing fruit *right now*." "[Meta posted] 33% advertising revenue growth last quarter on $55B in revenue. That's incredible. Google Search was 19 percent. Amazon, which is much smaller, was 24%. They're outgrowing everyone except for AppLovin and Reddit. And people don't believe it." "And I asked somebody, 'What would it take to convince you that Meta's AI investments are actually productive in this moment in time?' And they said, '40%.'" "40% growth on $55B-$60B in revenue? Where did that number come from? This is pulled out of thin air." "This is what Meta needs to do. They need to be able to point to something and say, 'You see that ad? That was created by our AI investments.'" "Gem is Meta's foundation model that they trained for ranking. That's important. But you can't see it." "A lot of the research analysts, they're really smart people. But they operate in this paradigm of like, 'Spreadsheet says this. I get it. I can understand why ranking investments would actually be really beneficial for the company. But I put a number in the spreadsheet, and it spits out something. That's the tool I have to work with.'" "These are really smart people. I think they conceptually get why these are good investments. But they have nothing to tie it to that's quantitative." "I think when you can point to the ad that's created, and you can say, 'Look, this ad was created with data that only we have. It's the foundation image model that we built that is trained — not fine-tuned, but trained — on our own data. Only we can do that.'" "If they can point to the output and say, 'That ad that you saw in your IG feed was created only as a result of our ability to train on this data that only we have, then I think you can make the case [for the AI investments]."

Meta's AI investments have been incredibly successful, but investors refuse to see it. The company posted 33% advertising revenue growth last quarter on $55B in revenue. Meanwhile Google's was 19%, and Amazon, which is much smaller, was 24%. Eric Seufert: "I was at this dinner [with hedge fund people]. I was talking about why these investments that Meta's making right now are bearing fruit right now." "[Meta posted] 33% advertising revenue growth last quarter on $55B in revenue. That's incredible. Google Search was 19 percent. Amazon, which is much smaller, was 24%. They're outgrowing everyone except for AppLovin and Reddit. And people don't believe it." "And I asked somebody, 'What would it take to convince you that Meta's AI investments are actually productive in this moment in time?' And they said, '40%.'" "40% growth on $55B-$60B in revenue? Where did that number come from? This is pulled out of thin air." "This is what Meta needs to do. They need to be able to point to something and say, 'You see that ad? That was created by our AI investments.'" "Gem is Meta's foundation model that they trained for ranking. That's important. But you can't see it." "A lot of the research analysts, they're really smart people. But they operate in this paradigm of like, 'Spreadsheet says this. I get it. I can understand why ranking investments would actually be really beneficial for the company. But I put a number in the spreadsheet, and it spits out something. That's the tool I have to work with.'" "These are really smart people. I think they conceptually get why these are good investments. But they have nothing to tie it to that's quantitative." "I think when you can point to the ad that's created, and you can say, 'Look, this ad was created with data that only we have. It's the foundation image model that we built that is trained — not fine-tuned, but trained — on our own data. Only we can do that.'" "If they can point to the output and say, 'That ad that you saw in your IG feed was created only as a result of our ability to train on this data that only we have, then I think you can make the case [for the AI investments]."

TBPN

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I just found out that Luel AI, the company that completely copied my company Kled's entire website, is misrepresenting their compliance practices, using Delve to outsource it, fabricating their website user numbers, and pulling in massive amounts of fraud data from exclusively fraud oriented regions. Skip to 1:15 if you want to skip context. You truly cannot make this up. This is egregious, disgusting behavior from everyone involved. This is people's data. It cannot be taken lightly. The only way a company like this can truly succeed is by investing the time and resources to commit 100% to the trust and compliance of their users' data. Luel AI is doing the exact opposite. Disgusting behavior for a company trusted with people's data.

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ChatGPT connecting to external data sources offers a glimpse into what AI interoperability looks like. Ask any question in ChatGPT and it can now pull data from Box. The future of AI is AI Agents that can access your systems and data to complete any task for you.

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This is how 1000s of cartoons, game shows and movies built a soundtrack. You have likely heard it many times, but very few have seen it, even fewer can play it, 4. Only 12 are known to exist and only 3 are known to still work. This is magic…

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Pixar but make it AI? Using only AI tools we made a full Pixar Style Short. And it was so easy we decided to share the sauce 👀

Pixar but make it AI? Using only AI tools we made a full Pixar Style Short. And it was so easy we decided to share the sauce 👀

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🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

🚨 Jensen Huang says everyone panicked about the AI data when MOST training data was never REAL to begin with. Ilya Sutskever told the industry pre-training was over. "Ilya said, 'We're out of data,' or something like that. 'Pre-training is over,' or something like that," Huang says. "The industry panicked, you know, that this is the end of AI." "And of course, of course that's obviously not true. We're gonna keep on scaling the amount of data that we have to train with." "A lot of that data is probably gonna be synthetic." That's where the panic came from — synthetic data sounds like cheating. "Most of the data that we are training, that we teach each other with, inform each other with, is synthetic." "It's synthetic because it didn't come out of nature." "You created it. I'm consuming it. I modify it, augment it, I regenerate it, somebody else consumes it." The textbook in your hand is synthetic. The post you're reading is synthetic. The lecture you took is synthetic. Nature didn't make any of it. Humans did. AI just learned to do the same thing — faster. "Training is now limited by compute," Huang says. "Data is now limited by compute." The data wall wasn't a wall. It was a mirror. If you're new here, follow @AiEvolutio for the latest on ChatGPT, Claude, and the AI tools shaping how we work and create. — Jensen Huang ( NVIDIA ), NVIDIA CEO, on Lex Fridman's ( Lex Fridman ) podcast

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BREAKING: you can now build a real-time, interactive, multi-asset crypto bloomberg without writing a line of code you’ve probably seen others make the claim but they're using hardcoded data that’s STILL inaccurate this is real. it uses live onchain data & links to explorers so you can verify don't believe me? watch the demo

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Big Tech didn’t build AI for everyone, they built it for themselves. AI should benefit everyone, but today the power, data, and profits stay locked at the top. That’s why individuals feel shut out. However, that can change when people build and own together. Action Model is creating a community-owned AI ecosystem, where intelligence comes from real contributors and value flows back to the network, not a few corporations. AI should serve the people who train it. Do you agree?

Big Tech didn’t build AI for everyone, they built it for themselves. AI should benefit everyone, but today the power, data, and profits stay locked at the top. That’s why individuals feel shut out. However, that can change when people build and own together. Action Model is creating a community-owned AI ecosystem, where intelligence comes from real contributors and value flows back to the network, not a few corporations. AI should serve the people who train it. Do you agree?

Action Model

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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.

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 görüntüleme • 7 ay önce

AI only exists today because it is trained on real data. The better the data input, The better the AI output. We capture incredibly high-quality raw data of real humans, in 3D, from all angles. You do the Math ✍🏻 Our data can vastly improve and expand the capabilities of AI

AI only exists today because it is trained on real data. The better the data input, The better the AI output. We capture incredibly high-quality raw data of real humans, in 3D, from all angles. You do the Math ✍🏻 Our data can vastly improve and expand the capabilities of AI

Metahero.io

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