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Running real models and funnelling to OF while STILL not using AI is like having a dick and never using it Sooner or later every single agency will implement it Difference is the guys who started EARLY already have the edge and are printing extra money from the exact... same models And I’m not talking about buying a Higgsfield subscription, copying some bullshit prompt you found on X and thinking that this is it I mean actually MASTERING it Being able to take your real model and generate crazy reels, test different concepts, locations, outfits and angles without needing her to physically film every fucking thing She can STILL shoot her normal content AI simply gives you another weapon to attack traffic with The video here is actually a very BASIC example that i just wanted to put to show you that even these ones get views and you can see the funnel is to OF and its a real model And remember this little note here 👇🏻 Better pay for info and directly jump over ALL the steps than being a “smartass” that will try to figure it out himself or hire a VA that already knows With AI testing stuff without the proper knowledge will have you end up paying 3-4x more on generation credits, get the most mid quality possible and end up spending way more and not learn anything and most even quit at that stage.. There is a reason why we have the biggest AI agency inside our group learning from US and everyone is slowly understanding the message and switching to what is working NOWshow more

Dr. Hadi H

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63,889 просмотров • 2 дней назад •via X (Twitter)

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Elon Musk on Grok: “I think an AI that is sort of SpaceX’s baby will be a very good AI. I think SpaceX is a collection of some of the very best humans on Earth, both in capability and morality and goodness, and I think we want to have an AI that is born of that capability and that morality and that goodness. I think that’s very, very important. So I’d like to encourage people to help out, actually, with the AI hardware on the ground and the AI software and, of course, with the AI satellites. Solar is going to be a very important part of that. The TerraFab will be a very important part of that, but we must also succeed on the software front, and we’re going to be training Grok on the sum total of all SpaceX information. So in a way, it will be trained on you. You will effectively be the parents of the AI. It will inherit your thoughts and ideas and beliefs, and I think that’s a good thing. So, yeah, we’ve got to win here on the AI hardware and the AI software. That maybe is the most important message. I think that is actually the most important message I wanted to convey today is that we must win on AI because the future is overwhelmingly AI and robots. So we won’t ultimately be able to control the AI. It’ll be too smart for that. But just like if you have a child that is a super genius child, you can still instill in that child the values and beliefs that you think are good and right. And so that’s why it’s incredibly important that we succeed with Grok. So, yeah, well, Grok 4.5 you’ve tried probably. We’ve got 4.6 coming out in about a week. And then 4.7 should be really pretty special. So I’d like to encourage everyone at SpaceX to use AI and to make it better.”

DogeDesigner

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🚨 OBAMA ON AI: "If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet. The reason you are doing that is because you have to market a product that people will pay money for. That’s a misalignment between what our society needs and the commercial imperatives that these companies are facing, not because necessarily they’re trying to do bad things, but because they’ve got to justify these valuations. So, that’s one more reason why it is really important for us to have a competent government and a serious bipartisan conversation around this issue, and we have to do it fast. And I would encourage voters to pay attention to this. If somebody does not have a serious plan for how to deal with this, then they’re not meeting the moment, and you should probably look for somebody else."

🚨 OBAMA ON AI: "If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet. The reason you are doing that is because you have to market a product that people will pay money for. That’s a misalignment between what our society needs and the commercial imperatives that these companies are facing, not because necessarily they’re trying to do bad things, but because they’ve got to justify these valuations. So, that’s one more reason why it is really important for us to have a competent government and a serious bipartisan conversation around this issue, and we have to do it fast. And I would encourage voters to pay attention to this. If somebody does not have a serious plan for how to deal with this, then they’re not meeting the moment, and you should probably look for somebody else."

MeidasTouch

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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 просмотров • 10 месяцев назад

David Friedberg: The AI Jobs Panic Is a Crock of Sh*t Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sh*t.”

David Friedberg: The AI Jobs Panic Is a Crock of Sht Why? The revenue potential outweighs the cost savings by 100x. “There is no job loss with AI. I've said it a thousand times, and I will say it again, and again, and again. What I see on the ground, and what I've seen at dozens of companies, including my company that I run, there are two sides to a business. There is revenue and there’s costs. On the cost side of the equation, AI can be used to reduce humans doing things that cost money, to some extent. The effect there, I would argue, is nominal. The real opportunity with AI is on the revenue side, where suddenly one engineer can do 100x or 1000x what they used to be able to do, meaning you can make more products at your company, whether those are agricultural seed products, or boats and ships, or software for companies, or clothing, or what have you. Because of AI, everyone has the ability to expand their revenue base to create more products, and that is the foundation of good economic prosperity. It is called productivity. We can grow productivity in this country with AI. So where I see AI being used is on the revenue side 100x more than the cost side. And in that equation, people are hiring like crazy. We cannot hire enough people. I just had a review meeting with my product and engineering team two days ago, and they're like, ‘We want to add an extra 15 headcount to our engineering squads because we have all this opportunity to do stuff that we couldn't otherwise do.’ So we are going to hire more people. And to Sacks' point, we are seeing that show up in the jobs numbers. The idea that AI is going to destroy jobs is a Luddite idea that is being disproven every single day, and I see it on the ground. It is only a matter of time before people wake up to this and they realize that this narrative that they've all been sold is a crock of sht.”

The All-In Podcast

152,633 просмотров • 3 месяцев назад

Some of us have been working and thinking about AI, started before most in AI today were born. To them they absolutely believe they have a lock of the dystopian fear. The saw too many Hollywood movies. I first say the VHS in the 1980s and the opening dialog never left me. The value of experience is it allows you to understand the way of things before the inexperienced can put their shoes on. Another example to learn in this video is Japan was the center of the universe. There was largess and I dare say arrogance. They absolutely were convince they would control AI via the government. It did not work out that way, like it ALWAYS does not work out that way. Each generation has to learn the hard lessons that should be obvious. A renegade group in Canada no one cared about to the thread all others dropped and you have AI today because of it. They were the last group to have been seen by the industry to have produced anything. Today the same playbook is being implemented by the largest AI companies full of zero experience and begging for big daddy government to save us from their scary monster. We turn the lights and show it is just some shadows. The entry prediction is accurate already, yet the world did not end. Watch this and see how really immature so many leaders are acting. Who will save us, millions of open source AI developers making thousands of AI models. And inventing the stuff the central controlled commissars never could have thought of. This is always the way it is. It is not “different” this time. They are too immature to know. Give them grace. Now you know better. Now you know. Speak up.

Some of us have been working and thinking about AI, started before most in AI today were born. To them they absolutely believe they have a lock of the dystopian fear. The saw too many Hollywood movies. I first say the VHS in the 1980s and the opening dialog never left me. The value of experience is it allows you to understand the way of things before the inexperienced can put their shoes on. Another example to learn in this video is Japan was the center of the universe. There was largess and I dare say arrogance. They absolutely were convince they would control AI via the government. It did not work out that way, like it ALWAYS does not work out that way. Each generation has to learn the hard lessons that should be obvious. A renegade group in Canada no one cared about to the thread all others dropped and you have AI today because of it. They were the last group to have been seen by the industry to have produced anything. Today the same playbook is being implemented by the largest AI companies full of zero experience and begging for big daddy government to save us from their scary monster. We turn the lights and show it is just some shadows. The entry prediction is accurate already, yet the world did not end. Watch this and see how really immature so many leaders are acting. Who will save us, millions of open source AI developers making thousands of AI models. And inventing the stuff the central controlled commissars never could have thought of. This is always the way it is. It is not “different” this time. They are too immature to know. Give them grace. Now you know better. Now you know. Speak up.

Brian Roemmele

15,451 просмотров • 1 месяц назад

I did not attend Apsara Conference 2026 in person, but I followed it closely online, and it shifted how I am thinking about AI right now. Because the interesting part was never just the models. Watching the coverage come out of Apsara, you start to see the entire AI stack in one frame. Chips, compute, cloud infrastructure, foundation models, agents, and the real applications sitting on top of all of it. Alibaba even teased a 10-trillion-parameter model and debuted what it calls China's most powerful AI chip in the same breath: Qwen is the name most people will recognize, but honestly it is a small piece of a much bigger picture. What kept pulling my attention was how far AI is moving past the chatbot screen. Agents are being built to actually carry out tasks. Infrastructure is being designed around workloads that keep getting heavier. And companies are showing exactly how this stuff drops into real products and real industries. That is my biggest takeaway from watching it unfold: AI is becoming less about "look what this model can generate" and more about "look what we can actually build with it." When every layer shows up in the same place, you get a genuinely useful snapshot of where this whole industry is heading. Apsara is not just showing the future of AI. It is showing which parts of that future are already real.

I did not attend Apsara Conference 2026 in person, but I followed it closely online, and it shifted how I am thinking about AI right now. Because the interesting part was never just the models. Watching the coverage come out of Apsara, you start to see the entire AI stack in one frame. Chips, compute, cloud infrastructure, foundation models, agents, and the real applications sitting on top of all of it. Alibaba even teased a 10-trillion-parameter model and debuted what it calls China's most powerful AI chip in the same breath: Qwen is the name most people will recognize, but honestly it is a small piece of a much bigger picture. What kept pulling my attention was how far AI is moving past the chatbot screen. Agents are being built to actually carry out tasks. Infrastructure is being designed around workloads that keep getting heavier. And companies are showing exactly how this stuff drops into real products and real industries. That is my biggest takeaway from watching it unfold: AI is becoming less about "look what this model can generate" and more about "look what we can actually build with it." When every layer shows up in the same place, you get a genuinely useful snapshot of where this whole industry is heading. Apsara is not just showing the future of AI. It is showing which parts of that future are already real.

Luna Tech

33,893 просмотров • 2 дней назад

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

At the BNB Chain hackathon, CZ 🔶 BNB made several very important points about AI trading (Everything in parentheses is my own view and judgment.) He first said that AI will be involved in trading everywhere. Trading itself is already a huge market: there are 300 million users on Binance alone, and if you add the decentralized ecosystems, that number is not small either. In such a mass-market environment, many different trading strategies can work, with countless different coins, different projects, and different ways to play. But there is a big problem here: building commercial AI trading platforms for retail users is actually very hard. If a trading strategy works very well for one person, once a billion people start using the same strategy, that strategy “might still work, or might stop working.” Take copy trading / follow trading as an example: if you buy first and everyone follows you, the first buyer will perform very well, but the last person to follow may not end up with good results. So, with the exact same strategy and the exact same copy logic, the outcomes can be completely different for different people. (On top of that, every strategy also has its own capital capacity limits.) Teams that can really build strong AI are, with high probability, going to trade with their own money. In today’s world, money itself is already somewhat like a “commodity”; many people have a lot of capital, and it’s actually not that hard to raise funds. If you truly have an algorithm that can make a lot of money, it’s not hard to get money and run your own book. There is really only one situation where you would sell this algorithm to mass-market users: for example, if you charge a $10 monthly subscription and can sell it to one million users, then your $10 million monthly subscription revenue is higher than the profit you could make by trading the strategy yourself. (Here this touches one of our earlier theses: as training AI models becomes relatively easier and the supply of models increases, model companies have more incentive to open-source. By analogy, as the production process of trading strategies is increasingly simplified by AI and the supply of strategies explodes, traders will have stronger incentives to monetize by expanding their influence in other words, by “open-sourcing” their strategies.) Of course, CZ did not say that this model can never work. Another path is to build an AI trading platform that lets users tune different AI algorithms, or very easily assemble their own structures and strategies, so that what each person ends up running is different and better tailored to themselves. Some people will make money, some people will lose money, but the platform still has value because it’s very hard for most people to build an AI trading algorithm from scratch. So there are a lot of trade-offs here; it’s not as simple as saying “once AI shows up, everything automatically gets better.” (This is exactly what we presented at the hackathon: you describe your own strategy in natural language, and the AI automatically generates a workflow. The parameters in that workflow, the models used, the logical structure, the APIs it calls, and even the algorithms it invokes are all customizable. The reasons we think workflows are a good way to do this include: controllable execution paths, Lego-like modular nodes, and better visualization that makes it easier for users to build and adjust their workflows.) Finally, his conclusion was very clear: it’s not that AI will definitely make trading better, and it’s not that AI will definitely make things worse. Rather, no matter what, in the future a huge number of people will use AI to trade. This will be a very large field, and whoever can build the best algorithms will make a lot of money.

Tykoo

25,535 просмотров • 9 месяцев назад

The thing about AI animations ads that people on X don't tell you is that the script and visual directions are WAY more important that the actual generation itself. Anyone can animate a character in Higgsfield. Writing a DR script and pairing it with GOOD visual directions is what will determine whether it performs or not. We have our strategists' handle the ideation and scripting, and have hired a full-time specialist AI video editor to do the visuals. I have to say it's been one of the best hires we've made.

The thing about AI animations ads that people on X don't tell you is that the script and visual directions are WAY more important that the actual generation itself. Anyone can animate a character in Higgsfield. Writing a DR script and pairing it with GOOD visual directions is what will determine whether it performs or not. We have our strategists' handle the ideation and scripting, and have hired a full-time specialist AI video editor to do the visuals. I have to say it's been one of the best hires we've made.

Alex Cooper

68,584 просмотров • 4 месяцев назад

Obama on AI: If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet. The reason you are doing that is because you have to market a product that people will pay money for. That’s a misalignment between what our society needs and the commercial imperatives that these companies are facing — not because necessarily they’re trying to do bad things, but because, you know, they’ve got to justify these valuations. So, that’s one more reason why it is really important for us to have a competent government and a serious, bipartisan conversation around this issue, and we have to do it fast.

Obama on AI: If we are thinking about AI just in terms of how do we cure cancer or get better energy, you can do that without having agentic AI and having it just roaming free in the internet. The reason you are doing that is because you have to market a product that people will pay money for. That’s a misalignment between what our society needs and the commercial imperatives that these companies are facing — not because necessarily they’re trying to do bad things, but because, you know, they’ve got to justify these valuations. So, that’s one more reason why it is really important for us to have a competent government and a serious, bipartisan conversation around this issue, and we have to do it fast.

Clash Report

13,639 просмотров • 5 дней назад

Knowing your nature is critically important to understanding what success is for you. I can't tell you literally what is best for you, but I can tell you that success is not having a lot more money or status than you need. Having the time and freedom to do what you most want to do is far more important. What is success for most people? It is a matter of having meaningful work and meaningful relationships. If you can make your work and your passion one and the same, and do it with people who you care about and care about you, you will have a happy, successful life. Explore Principles for the Graduating Class of 2026 with my AI Twin, Digital Ray in Beta, here:

Knowing your nature is critically important to understanding what success is for you. I can't tell you literally what is best for you, but I can tell you that success is not having a lot more money or status than you need. Having the time and freedom to do what you most want to do is far more important. What is success for most people? It is a matter of having meaningful work and meaningful relationships. If you can make your work and your passion one and the same, and do it with people who you care about and care about you, you will have a happy, successful life. Explore Principles for the Graduating Class of 2026 with my AI Twin, Digital Ray in Beta, here:

Ray Dalio

99,356 просмотров • 4 месяцев назад

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

The most interesting part for me is where Andrej Karpathy describes why LLMs aren't able to learn like humans. As you would expect, he comes up with a wonderfully evocative phrase to describe RL: “sucking supervision bits through a straw.” A single end reward gets broadcast across every token in a successful trajectory, upweighting even wrong or irrelevant turns that lead to the right answer. > “Humans don't use reinforcement learning, as I've said before. I think they do something different. Reinforcement learning is a lot worse than the average person thinks. Reinforcement learning is terrible. It just so happens that everything that we had before is much worse.” So what do humans do instead? > “The book I’m reading is a set of prompts for me to do synthetic data generation. It's by manipulating that information that you actually gain that knowledge. We have no equivalent of that with LLMs; they don't really do that.” > “I'd love to see during pretraining some kind of a stage where the model thinks through the material and tries to reconcile it with what it already knows. There's no equivalent of any of this. This is all research.” Why can’t we just add this training to LLMs today? > “There are very subtle, hard to understand reasons why it's not trivial. If I just give synthetic generation of the model thinking about a book, you look at it and you're like, 'This looks great. Why can't I train on it?' You could try, but the model will actually get much worse if you continue trying.” > “Say we have a chapter of a book and I ask an LLM to think about it. It will give you something that looks very reasonable. But if I ask it 10 times, you'll notice that all of them are the same.” > “You're not getting the richness and the diversity and the entropy from these models as you would get from humans. How do you get synthetic data generation to work despite the collapse and while maintaining the entropy? It is a research problem.” How do humans get around model collapse? > “These analogies are surprisingly good. Humans collapse during the course of their lives. Children haven't overfit yet. They will say stuff that will shock you. Because they're not yet collapsed. But we [adults] are collapsed. We end up revisiting the same thoughts, we end up saying more and more of the same stuff, the learning rates go down, the collapse continues to get worse, and then everything deteriorates.” In fact, there’s an interesting paper arguing that dreaming evolved to assist generalization, and resist overfitting to daily learning - look up The Overfitted Brain by Erik Hoel. I asked Karpathy: Isn’t it interesting that humans learn best at a part of their lives (childhood) whose actual details they completely forget, adults still learn really well but have terrible memory about the particulars of the things they read or watch, and LLMs can memorize arbitrary details about text that no human could but are currently pretty bad at generalization? > “[Fallible human memory] is a feature, not a bug, because it forces you to only learn the generalizable components. LLMs are distracted by all the memory that they have of the pre-trained documents. That's why when I talk about the cognitive core, I actually want to remove the memory. I'd love to have them have less memory so that they have to look things up and they only maintain the algorithms for thought, and the idea of an experiment, and all this cognitive glue for acting.”

Dwarkesh Patel

1,052,518 просмотров • 11 месяцев назад

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Yann LeCun -- Meta's chief AI scientist and a Turing Award winner -- explains why scaling up LLMs will never reach human-level AI, and names the four things today's systems still can't do: "If you think that we're going to get to human-level AI by just training on more data and scaling up LLMs, you're making a mistake." "If you're an investor and you invest in a company that told you we're going to get to human-level AI and PhD level by just training on more data and with a few tricks... that was probably not a good idea." "There are ideas about how to go forward and have systems that are capable of doing what every intelligent animal and human are capable of doing, and that current AI systems are not capable of doing." "Understanding the physical world, having persistent memory, and being able to reason and plan. Those are the four characteristics that need to be there." "That requires systems that can acquire common sense, that can learn from natural sensors like video, as opposed to just text." The entire market is priced on a straight line from bigger models to AGI. One of the people who invented deep learning is telling you the line doesn't reach -- and that the real bottleneck isn't compute, it's world models. If he's right, the winner isn't whoever stacks the most GPUs. It's whoever teaches a machine to learn from video the way a child does.

Karl Mehta

34,818 просмотров • 2 месяцев назад

“I don’t want to put any child through what I went through, and if aborting that child means that child will not go through that, then that is the moral thing to do.” This is really hard, and good on Lila Rose for being so compassionate. It’s obvious this woman is really hurt and angry. But the problem with her worldview is that we’re not God. We don’t get to determine whose life is worth living and whose isn’t. When you’re aborting your child, you’re making that decision FOR him, and he may not see the world the way you do. Frankly, he may be stronger and more resilient than you are. He may make something of himself that you failed to do on your own behalf, and regardless, he is an image bearer of God, with a life and purpose that you cannot understand even as his mother. For this woman to sit here and say abused and neglected children would be better off dead speaks to the spiritual sickness inside her and in our culture, and I don’t even think she actually believes it—because she’s still here. She hasn’t given up, and there’s a reason for that. She chooses to keep going, and that is good. It’s not for her to rob other humans of that same opportunity. “Woe to him who strives with him who formed him…Does the clay say to him who forms it, ‘What are you making?’” Isaiah 45:9 There is a God. We are not Him. Life is hard for every single person. We have to accept the fact that we cannot know or understand everything. We don’t know what tomorrow will bring—especially if we voluntarily choose to rob ourselves of that tomorrow. And under no circumstances is it moral or compassionate to make that choice for someone else.

“I don’t want to put any child through what I went through, and if aborting that child means that child will not go through that, then that is the moral thing to do.” This is really hard, and good on Lila Rose for being so compassionate. It’s obvious this woman is really hurt and angry. But the problem with her worldview is that we’re not God. We don’t get to determine whose life is worth living and whose isn’t. When you’re aborting your child, you’re making that decision FOR him, and he may not see the world the way you do. Frankly, he may be stronger and more resilient than you are. He may make something of himself that you failed to do on your own behalf, and regardless, he is an image bearer of God, with a life and purpose that you cannot understand even as his mother. For this woman to sit here and say abused and neglected children would be better off dead speaks to the spiritual sickness inside her and in our culture, and I don’t even think she actually believes it—because she’s still here. She hasn’t given up, and there’s a reason for that. She chooses to keep going, and that is good. It’s not for her to rob other humans of that same opportunity. “Woe to him who strives with him who formed him…Does the clay say to him who forms it, ‘What are you making?’” Isaiah 45:9 There is a God. We are not Him. Life is hard for every single person. We have to accept the fact that we cannot know or understand everything. We don’t know what tomorrow will bring—especially if we voluntarily choose to rob ourselves of that tomorrow. And under no circumstances is it moral or compassionate to make that choice for someone else.

Natalie Jean Beisner

77,377 просмотров • 6 дней назад

The internet is cooked 😭 I cloned a 5,000,000 views video the full 30 second of it with 1 single prompt to seedance 2.5 and honestly, you would not be able to tell its AI. i'm not saying you should clone anyones content. but I wanted to test how close it can get to reality. and I concluded that AI is finally at that point... the line is completely blurred. and the money making opportunities are endless rn. The best part is that it cost $0 to use seedance 2.5 for 33 days inside of higgsfield:

The internet is cooked 😭 I cloned a 5,000,000 views video the full 30 second of it with 1 single prompt to seedance 2.5 and honestly, you would not be able to tell its AI. i'm not saying you should clone anyones content. but I wanted to test how close it can get to reality. and I concluded that AI is finally at that point... the line is completely blurred. and the money making opportunities are endless rn. The best part is that it cost $0 to use seedance 2.5 for 33 days inside of higgsfield:

Ernesto Lopez

186,889 просмотров • 1 месяц назад

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Ilya Sutskever, co-founder and chief scientist of OpenAI: "The better a neural network can predict the next word, the more it understands it." Everyone calls it autocomplete. Ilya explained why that is a comforting lie. To guess the final word of a murder mystery, the name of the killer, the model has to reason through every clue in the book. Prediction that good is not statistics. It is understanding. Then he said the part that should stop you. The text these models train on is "a projection of the world," and to compress it they learn more and more about people, "the human condition, their hopes, dreams, and motivations." The machine is not memorizing our words. It is quietly building a model of us. That is the whole premise of the fear. The machine will never hate you. It understands you far better than that. The frightening variable was never the intelligence. It was the aim. I wrote the full story: the one fear about AI everyone gets wrong, and the one already here.

Casper

92,361 просмотров • 2 месяцев назад

Naval Ravikant’s checklist for starting a company “The most important thing is there are no formulas. At the end of the day, you have to do what you love, and you have to do it even though people tell you it’ll never work. But that being said, if there was a formula [for starting a company], I would put it something like this.” Naval started seven companies before AngelList and this is the checklist he recommends running through before starting a startup: 1. Pick a great cofounder. This is most important: “You can do a company on your own, but it’s like you can raise a child on your own, but you probably shouldn’t. You need someone who’s going to be there with you.” This has it’s own checklist. Your cofounder should be: a. Very high intelligence (”hopefully they make you feel dumb, or they’re not smart enough”) b. Very high energy (”They should be extremely hardworking. A founder is someone who never has to be motivated. You should not have to be telling them to do their job.”) c. Very high integrity. (”a smart, hardworking crook who’s going to cheat you is the worst kind of person to be paired up with.”) 2. Pick a very large market. “Notice I don’t talk about the idea. I think ideas are almost irrelevant… The more important thing is that you pick a large space that you’re knowledgeable and passionate about. And then you will figure out what the right thing to do within that space is.” You want to be able to say to investors: “This is a space where there’s a huge market. I’m really knowledgeable and passionate about it. Here’s the great person that I have doing it with me. And here’s the minimum viable product that we have built. That will show that we can test in the marketplace… You iterate until you get to product/market fit… And then you go and you raise money from people you trust. And you use that money to scale.”

Naval Ravikant’s checklist for starting a company “The most important thing is there are no formulas. At the end of the day, you have to do what you love, and you have to do it even though people tell you it’ll never work. But that being said, if there was a formula [for starting a company], I would put it something like this.” Naval started seven companies before AngelList and this is the checklist he recommends running through before starting a startup: 1. Pick a great cofounder. This is most important: “You can do a company on your own, but it’s like you can raise a child on your own, but you probably shouldn’t. You need someone who’s going to be there with you.” This has it’s own checklist. Your cofounder should be: a. Very high intelligence (”hopefully they make you feel dumb, or they’re not smart enough”) b. Very high energy (”They should be extremely hardworking. A founder is someone who never has to be motivated. You should not have to be telling them to do their job.”) c. Very high integrity. (”a smart, hardworking crook who’s going to cheat you is the worst kind of person to be paired up with.”) 2. Pick a very large market. “Notice I don’t talk about the idea. I think ideas are almost irrelevant… The more important thing is that you pick a large space that you’re knowledgeable and passionate about. And then you will figure out what the right thing to do within that space is.” You want to be able to say to investors: “This is a space where there’s a huge market. I’m really knowledgeable and passionate about it. Here’s the great person that I have doing it with me. And here’s the minimum viable product that we have built. That will show that we can test in the marketplace… You iterate until you get to product/market fit… And then you go and you raise money from people you trust. And you use that money to scale.”

Startup Archive

36,050 просмотров • 1 год назад

How will we know if an AI take over is imminent? What are the warning signs? Connor Leahy: “Things will seem mostly normal, just… weird. Things will get weirder…and weirder… and then one day, we will just not be in control anymore. “There won't be a fight. There won't be a war. It won't be dramatic. It will just be that one day the machines are in control, and not us.” “The way I expect [AI take over] to feel is like, if you play chess against a grandmaster, it doesn’t feel like you're having a heroic battle against the Terminator…. it doesn’t feel like you're having this incredible back and forth, and then you lose... No, it feels more like you THINK you're playing well, you think everything is okay, and then suddenly… you lose… in one move, and you don't know why. This is what it feels like to play chess against a grandmaster, and this is what it's going to feel like for humanity to play against AGI. It won’t be some dramatic battle where the Terminators rise up and try to destroy humanity. No, it will be… things get more and more confusing. [Editor's note: Like Sam Altman, out of nowhere, raising up to 10% of world GDP?] More and more jobs get automated faster and faster... More and more technology gets built, which no one even quite knows how the technology works... There will be mass media movements that don't really make any sense... Like, do we really know the truth of what's going on in the world right now, even now with social media? Do you or I really know what's going on? How much of this is fake? How much of it is generated with AI or other methods? We don't know. And this will get much worse.” Imagine if you have extremely intelligent systems, much smarter than humans, that can generate any image, any video, anything, trying to manipulate you well...and being able to develop new technologies to interfere with politics.”

How will we know if an AI take over is imminent? What are the warning signs? Connor Leahy: “Things will seem mostly normal, just… weird. Things will get weirder…and weirder… and then one day, we will just not be in control anymore. “There won't be a fight. There won't be a war. It won't be dramatic. It will just be that one day the machines are in control, and not us.” “The way I expect [AI take over] to feel is like, if you play chess against a grandmaster, it doesn’t feel like you're having a heroic battle against the Terminator…. it doesn’t feel like you're having this incredible back and forth, and then you lose... No, it feels more like you THINK you're playing well, you think everything is okay, and then suddenly… you lose… in one move, and you don't know why. This is what it feels like to play chess against a grandmaster, and this is what it's going to feel like for humanity to play against AGI. It won’t be some dramatic battle where the Terminators rise up and try to destroy humanity. No, it will be… things get more and more confusing. [Editor's note: Like Sam Altman, out of nowhere, raising up to 10% of world GDP?] More and more jobs get automated faster and faster... More and more technology gets built, which no one even quite knows how the technology works... There will be mass media movements that don't really make any sense... Like, do we really know the truth of what's going on in the world right now, even now with social media? Do you or I really know what's going on? How much of this is fake? How much of it is generated with AI or other methods? We don't know. And this will get much worse.” Imagine if you have extremely intelligent systems, much smarter than humans, that can generate any image, any video, anything, trying to manipulate you well...and being able to develop new technologies to interfere with politics.”

AI Notkilleveryoneism Memes ⏸️

189,044 просмотров • 2 лет назад

$20,000 by the end of this month... This is what she'd do to make that number REAL: She doesn’t film anything. She doesn’t come up with original ideas. While most people are still trying to make “original” content and struggling to get views… She just finds videos that already have millions of views, copies the transcript, feeds it into AI, and generates a brand new version in minutes. She’s copying what’s already working, letting AI remix it, and collecting the checks. YouTube is still paying between $1,000 to $15,000 per million views on this type of content. This isn’t creativity. It’s content recycling at scale - and it’s working. Most people will watch this and feel uncomfortable. The dangerous ones will start doing it tonight. Watch exactly how she’s doing it in this video.

Frogify

53,201 просмотров • 2 месяцев назад

Maria Popova on the soul, the self, and what AI will never have: "AI will never have feeling. AI will have the simulacrum of feeling. AI will never write the great American poem, the great French poem, because it hasn't suffered. AI doesn't have the capacity to suffer. Even if you try to make it suffer, meaning write a command that is to execute failure, it'll already be succeeding at executing failure. AI can only ever succeed. And without suffering, what kind of true art can there be? All writing that is truly moving is born of feeling and time. AI has neither; it's an instantaneous, unfeeling delivery of pure information. The function of what we call the soul is real. Whatever it's made of, where it is. And the part that I worry about is that today, most people live on the level of the self and not the level of the soul. Just the costume of personality over the soul." The value of persons does not rest on our "costume of personality," but on the soul beneath it.

Maria Popova on the soul, the self, and what AI will never have: "AI will never have feeling. AI will have the simulacrum of feeling. AI will never write the great American poem, the great French poem, because it hasn't suffered. AI doesn't have the capacity to suffer. Even if you try to make it suffer, meaning write a command that is to execute failure, it'll already be succeeding at executing failure. AI can only ever succeed. And without suffering, what kind of true art can there be? All writing that is truly moving is born of feeling and time. AI has neither; it's an instantaneous, unfeeling delivery of pure information. The function of what we call the soul is real. Whatever it's made of, where it is. And the part that I worry about is that today, most people live on the level of the self and not the level of the soul. Just the costume of personality over the soul." The value of persons does not rest on our "costume of personality," but on the soul beneath it.

David Perell Clips

12,220 просмотров • 4 месяцев назад