正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

"The enterprise's ability to leverage this depends on its ability to create training data as fast as it can. Not enough people are focused on training data. I need to get into the brains of the people who solve these problems, extract that knowledge, and codify it so I... show more

Harry Stebbings

614,131 subscribers

31,538 次观看 • 1 个月前 •via X (Twitter)

科学技术教育新闻政治

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Why data is a trillion-dollar market "The bigger models scale, the more data you need. Data is a very durable need. If you believe in the scaling of models, then absolutely you should believe in the data market. I believe it's going to be at least a $100BN by 2030, if not $1TRN." Anastasios Nikolas Angelopoulos Do you have to scale into enterprise and mid-market to get this revenue scale or could you do it on frontier lab spend alone? Brendan (can/do) Garrett Lord Jonathan Siddharth Ali Ansari

Why data is a trillion-dollar market "The bigger models scale, the more data you need. Data is a very durable need. If you believe in the scaling of models, then absolutely you should believe in the data market. I believe it's going to be at least a $100BN by 2030, if not $1TRN." Anastasios Nikolas Angelopoulos Do you have to scale into enterprise and mid-market to get this revenue scale or could you do it on frontier lab spend alone? Brendan (can/do) Garrett Lord Jonathan Siddharth Ali Ansari

Harry Stebbings

21,443 次观看 • 1 个月前

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

"What is P vs NP and how does it relate to human knowledge?" Avi Wigderson (Turing Award & Abel Prize winner): "In a very basic way, all the problems we want to solve are really NP problems. A subset of it is the problems we can solve. So the question of P versus NP is whether we can solve all the problems we want to solve, whether we can know everything we want to know. This is basically about the limits of our knowledge. You can think there is no limit to what we may want to know but I claim that essentially in any human endeavor, what people are searching for have the property that when a solution is found, it is easily recognized. NP are all problems that we can honestly say we really want to solve and P are those that we can solve. Whether they are equal is just the very fact that a solution can be easily recognized would imply that it can be efficiently found. That means that we can know everything we ever want to know, it's a fundamental question about human knowledge."

Ryan Peterman

20,069 次观看 • 3 个月前

One day, we need to talk about this idea that “someone must already have a skill before they can join certain units to serve God.” What do you mean I have to know a lot about music before I can join the choir? I’m not even asking for a microphone. I just want the opportunity to learn, grow, and faithfully attend rehearsals. And before anyone says, “Go to music school,” it doesn’t work that way. Should I leave medical school just to study music? I think the most important thing is having the passion and willingness to learn that craft so you can serve the Lord well. I’ve seen people join the media unit with little or no prior knowledge, receive consistent training, and today they’re doing amazingly well and serving effectively. Not every willing servant starts as an expert. Sometimes the ministry should be where the training begins.

One day, we need to talk about this idea that “someone must already have a skill before they can join certain units to serve God.” What do you mean I have to know a lot about music before I can join the choir? I’m not even asking for a microphone. I just want the opportunity to learn, grow, and faithfully attend rehearsals. And before anyone says, “Go to music school,” it doesn’t work that way. Should I leave medical school just to study music? I think the most important thing is having the passion and willingness to learn that craft so you can serve the Lord well. I’ve seen people join the media unit with little or no prior knowledge, receive consistent training, and today they’re doing amazingly well and serving effectively. Not every willing servant starts as an expert. Sometimes the ministry should be where the training begins.

Tife of CCI🥰❤️

54,920 次观看 • 1 个月前

David Friedberg: Frontier Models are Training on Your Novel Insights as “De-Identified Data” @jason: “Should they trust any of these LLMs with their proprietary knowledge for fear of having it cribbed into a core LLM?” david friedberg: “I have had experiences where we've asked some fairly novel scientific questions, and (the AI model) identifies it as a novel insight. It's like, ‘Oh, never thought about that, interesting, blah, blah, blah.’ And then using a different account, asking the next version (of the model) later, I've now experienced this. It's like, ‘Oh, well, you could do this,’ and it actually just describes this exact thing that we had in our chat in the previous version. Now, these are a handful of anecdotal experiences, but I know the domain that we work in, and the niche of it, and the ideation of this stuff, and the novelty of this stuff, and the lack of papers being published, and so on. So I know that there isn't some new corpus of information out there that's training the new model. So all I can say at that point is that my conversation or our analyses have been used for training.” David Sacks: “Okay, this does raise a really good question. What does it mean that the model is allowed to train on unidentifiable data?” Friedberg: “Well, that's my point. So it doesn't use any of my personal information, but it can use an insight derived from our chat, which it can then say is some training data that is unrelated. But the truth is, it's actually a piece of IP that's our organization’s IP, and our engagement back and forth. We don't have any NDA or confidentiality provisions or protections with them being a service provider back to us. This is why I care a lot about open source because I don't want them having my chat logs because they can use it for training to create an IP advantage that is now diffused to the rest of the market.”

David Friedberg: Frontier Models are Training on Your Novel Insights as “De-Identified Data” @jason: “Should they trust any of these LLMs with their proprietary knowledge for fear of having it cribbed into a core LLM?” david friedberg: “I have had experiences where we've asked some fairly novel scientific questions, and (the AI model) identifies it as a novel insight. It's like, ‘Oh, never thought about that, interesting, blah, blah, blah.’ And then using a different account, asking the next version (of the model) later, I've now experienced this. It's like, ‘Oh, well, you could do this,’ and it actually just describes this exact thing that we had in our chat in the previous version. Now, these are a handful of anecdotal experiences, but I know the domain that we work in, and the niche of it, and the ideation of this stuff, and the novelty of this stuff, and the lack of papers being published, and so on. So I know that there isn't some new corpus of information out there that's training the new model. So all I can say at that point is that my conversation or our analyses have been used for training.” David Sacks: “Okay, this does raise a really good question. What does it mean that the model is allowed to train on unidentifiable data?” Friedberg: “Well, that's my point. So it doesn't use any of my personal information, but it can use an insight derived from our chat, which it can then say is some training data that is unrelated. But the truth is, it's actually a piece of IP that's our organization’s IP, and our engagement back and forth. We don't have any NDA or confidentiality provisions or protections with them being a service provider back to us. This is why I care a lot about open source because I don't want them having my chat logs because they can use it for training to create an IP advantage that is now diffused to the rest of the market.”

The All-In Podcast

54,050 次观看 • 11 天前

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

"If you're an analytical SaaS company, it's over. It's over." - Palo Alto Networks CEO Nikesh Arora "What is an analytical SaaS company? It is a company that says, “I'm going to collect a lot of data for you and analyze it for you.” I don't need you to analyze it for me. I can run models against the data and analyze it myself. Think about it: almost every SaaS company has a marketplace. You can buy applications from the Salesforce Marketplace. What do those applications say? “You have Salesforce data. I'm a marketplace app. Use me, and I'll help you analyze that data.” But I don't need to do that. You don't need those applications anymore. I can simply run an LLM against the data. Therefore, all the additional functionality that has traditionally been sold to us as incremental software modules may no longer need to be sold separately. I would much rather have an LLM run against the data." ---- From "All-In Podcast" YouTube channel, (full video link in comment)

Rohan Paul

52,065 次观看 • 8 天前

Liam Coen says analytics should inform decisions, never make them “We leverage analytics. Absolutely. We leverage it for the data and the information to make a decision, not to make the decision. That’s not how it goes. Ever.” “You have the data, AI, analytics, it is unreal. What are the true decisions that lie in the moment, in the now? What are the facts that you need to know? That’s great to leverage.” “We have game management. We have an analytics department to gather that information. But we are not making decisions solely based on those numbers. Ultimately, it has to come from the gut and from yourself.” “Is this the best decision for the Jacksonville Jaguars right now? Because that’s all you can live in. You’re living in every single play.” “I’m going to lean aggressive offensively, especially in year one when you’re trying to instill confidence. You’re trying to instill a mindset of, ‘Yeah, we are good.’ I believe we can go get fourth-and-two. We can.” “We talk about momentum a lot as a team. You can feel that. You can feel momentum. You know when you got it and you know when you don’t. I feed off that as a coach and I feed off the player’s energy on the grass and when they have confidence and momentum I want to roll with it, I want to keep it.” “When you have it you wanna stomp on it, you want to make them feel it.. That’s where the aggression comes in, you want to hold onto that and put your foot on the pedal. And as you grow as a coach there are times where you might be like, ‘Alright, may need to kick here. It’s going to be one of those kind of games.’” “So you have to be prepared, know your gut, go with your gut, feel this, all that’s going on around you, and make the best educated decision.”

Liam Coen says analytics should inform decisions, never make them “We leverage analytics. Absolutely. We leverage it for the data and the information to make a decision, not to make the decision. That’s not how it goes. Ever.” “You have the data, AI, analytics, it is unreal. What are the true decisions that lie in the moment, in the now? What are the facts that you need to know? That’s great to leverage.” “We have game management. We have an analytics department to gather that information. But we are not making decisions solely based on those numbers. Ultimately, it has to come from the gut and from yourself.” “Is this the best decision for the Jacksonville Jaguars right now? Because that’s all you can live in. You’re living in every single play.” “I’m going to lean aggressive offensively, especially in year one when you’re trying to instill confidence. You’re trying to instill a mindset of, ‘Yeah, we are good.’ I believe we can go get fourth-and-two. We can.” “We talk about momentum a lot as a team. You can feel that. You can feel momentum. You know when you got it and you know when you don’t. I feed off that as a coach and I feed off the player’s energy on the grass and when they have confidence and momentum I want to roll with it, I want to keep it.” “When you have it you wanna stomp on it, you want to make them feel it.. That’s where the aggression comes in, you want to hold onto that and put your foot on the pedal. And as you grow as a coach there are times where you might be like, ‘Alright, may need to kick here. It’s going to be one of those kind of games.’” “So you have to be prepared, know your gut, go with your gut, feel this, all that’s going on around you, and make the best educated decision.”

Josh Chambers

10,496 次观看 • 1 个月前

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)

Coinbase CEO, Brian Armstrong: Some great insights on how they are using internally hosted AI Agents. "It’s connected to every Slack message, every Google Doc, and every Salesforce data confluence. Now, this is all linked up and the data is all aggregated, so you can ask these agents questions. Every team is using it—legal, finance, everything. It’s like the "Oracle of Coinbase." I’ve started to ask it things that go beyond just simple prompting, like "Hey, can you write this kind of memo for me?" I’m asking these AI agents now, as CEO, "What should I be aware of in the company that I might not be aware of?" It will tell me, "Did you know that there’s actually disagreement on this team about the strategy?" I realized I didn't know that, but the AI does because it can read every Slack message and every Google Doc. Tobi, who is on my board, calls this "reverse prompting." Instead of telling the AI agent what you want to do, you ask it what you should be thinking more about." --- From The All-In Podcast YT channel (link in comment)

Rohan Paul

174,977 次观看 • 6 个月前

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

"One of the very confusing things about the models right now: how to reconcile the fact that they are doing so well on evals. And you look at the evals and you go, 'Those are pretty hard evals.' But the economic impact seems to be dramatically behind. There is [a possible] explanation. Back when people were doing pre-training, the question of what data to train on was answered, because that answer was everything. So you don't have to think if it's going to be this data or that data. When people do RL training, they say, 'Okay, we want to have this kind of RL training for this thing and that kind of RL training for that thing.' You say, 'Hey, I would love our model to do really well when we release it. I want the evals to look great. What would be RL training that could help on this task?' If you combine this with generalization of the models actually being inadequate, that has the potential to explain a lot of what we are seeing, this disconnect between eval performance and actual real-world performance"

Dwarkesh Patel

502,484 次观看 • 10 个月前

Fetterman: What I am saying is like as a Democrat you know I will absolutely call this out who they are you know like I would never pretend to say anything good about any of this new crop of people that are coming into congress so I refuse to do that. Why can’t you just say that? You know every time you tell voters to say oh it’s ok, no problem here, no problem here, people—people though they have eyes, they have ears, they’re appalled by it to across you know people then you lose your credibility. So, so, that’s, that’s the way every Democrat should. We should poli—we should poli—we should police our own ranks right now and say look, you know this is a not a big tent kind of situation. These are people that have intensely extreme Anti-American kinds of of of views. And now, you seen the new polls. You know, 45% of Democrats are only proud to be American. You know.

Fetterman: What I am saying is like as a Democrat you know I will absolutely call this out who they are you know like I would never pretend to say anything good about any of this new crop of people that are coming into congress so I refuse to do that. Why can’t you just say that? You know every time you tell voters to say oh it’s ok, no problem here, no problem here, people—people though they have eyes, they have ears, they’re appalled by it to across you know people then you lose your credibility. So, so, that’s, that’s the way every Democrat should. We should poli—we should poli—we should police our own ranks right now and say look, you know this is a not a big tent kind of situation. These are people that have intensely extreme Anti-American kinds of of of views. And now, you seen the new polls. You know, 45% of Democrats are only proud to be American. You know.

Acyn

175,560 次观看 • 2 个月前

This is always why I ask my girls "What do you want today" As players you should know what gives you problems and what you need to work on. Those answers can spark convos around why those things give you probelms, which can lead to productive training sessions that translate.

This is always why I ask my girls "What do you want today" As players you should know what gives you problems and what you need to work on. Those answers can spark convos around why those things give you probelms, which can lead to productive training sessions that translate.

Carlton Salters

11,498 次观看 • 28 天前

Mark Cuban says anyone can become a millionaire by following these 4 rules #1: “Find something you can be good at and then be great at it” #2: “You have to know how to sell. Whatever it is you’re good at, somebody’s gonna need that service, someone’s gonna need that product” “Whatever it is that you’re creating but you have to be able to sell because you don’t wanna be in a position where you’re dependent on other people” #3: “Be curious and always be learning because one thing that’s certain about business is that it changes every single day” #4: “When you have that knowledge and you have that ability to sell, when you know that when you walk into a room, you know your stuff better than anybody else in the room, that’s when it’s time to start a company” “Then you can start to control your own destiny”

Mark Cuban says anyone can become a millionaire by following these 4 rules #1: “Find something you can be good at and then be great at it” #2: “You have to know how to sell. Whatever it is you’re good at, somebody’s gonna need that service, someone’s gonna need that product” “Whatever it is that you’re creating but you have to be able to sell because you don’t wanna be in a position where you’re dependent on other people” #3: “Be curious and always be learning because one thing that’s certain about business is that it changes every single day” #4: “When you have that knowledge and you have that ability to sell, when you know that when you walk into a room, you know your stuff better than anybody else in the room, that’s when it’s time to start a company” “Then you can start to control your own destiny”

Jack

82,641 次观看 • 4 个月前

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Microsoft CEO Satya Nadella on why winning against ChatGPT, Gemini, and Claude was never the goal: The Hard Fork hosts ask him directly how Microsoft plans to overtake the competition in the AI model race. His answer reframes the entire question. "Our real goal is to get everyone across the ecosystem to the frontier." Satya explains the problem with how frontier models are currently built. You hill climb, you do reinforcement learning, and then you need data. But at this point, the world has essentially saturated publicly available data. So the only way to keep scaling is to pull data from everywhere. He asks: "What if you turn that around and said no, there's a base model that has reasoning, that has the agent loop, but you can bring it into your RL. Every company." This is where his thinking gets interesting. Satya Nadella argues that the future of the firm runs on human capital and token capital together: "If the future of the firm is human capital and token capital, I want every balance sheet, every income statement in every company to have both." AI becomes a financial asset sitting on a company's books the same way its people do. And Microsoft's role in this? To provide the best possible base model. One that companies build on top of with their own data, their own context, their own weights. One they can even replace. That last part is the striking bit. Satya is explicitly building a platform where customers are free to walk away. He frames it not as a risk, but as the whole point: "I always ask the question — why does Microsoft, or why does the world need Microsoft? And if we are successful, can the world around us be successful? This, I believe, is a more sustainable way to go at it."

Big Brain AI

11,770 次观看 • 2 个月前

MAJOR Developments In The Idaho Farmland Water Shutoff Story 🚨 Not only are they putting millions into cobalt mining in the area but Mark Zuckerberg META is opening huge data centers, some a million square feet, that need the Idaho groundwater for the facilities “That is where we can get cobalt out of the ground in the United States. So they put $15, 000, 000 out of the this cobalt mine in in Idaho hasn't been profitable. I mean, it was purchased from a Canadian company in 2019. They reopened it in 2022, then they shut it down because it just wasn't profitable. Um, but now that they've got the DOD coming in and funding them through the Ukraine bill, you know, they're they're back on track. They're they're exploring, they're finding new cobalt, they're finding ways to extract that cobalt, and the the entire mine is moving forward. So that is a major water user. The other major user in this area, I don't know if you guys have been paying attention to this, but META is opening these massive data centers, close to a million square feet in some locations. And they're opening 1 right there in Idaho. And they're going to be another competitor for groundwater because they use a ton of groundwater to cool their system. Some people said as much as an Olympic swimming Olympic swimming pools are 660,000 gallons. They're using as much as 2,500,000 gallons of water a day to cool their systems. And these databases are also important to our government because our government is able to access information on its constituents, citizens without their knowledge. I mean, think about it. Every time you post something on Facebook or you're you're searching on Facebook, do you, you don't think about the fact that everything you do, every move you make, that that system is tracking you, learning you, learning what your face looks like, learning what your kids look like. Facebook data centers are one of the most important things to our government, and they're putting these data centers in everywhere, and they're constantly taking water rights away from US citizens, US farms, just to put in a data center so they could have more information on you. So that's 2 big things that I think the farmers in Idaho are facing right now. 2 major water users are coming in.”

MAJOR Developments In The Idaho Farmland Water Shutoff Story 🚨 Not only are they putting millions into cobalt mining in the area but Mark Zuckerberg META is opening huge data centers, some a million square feet, that need the Idaho groundwater for the facilities “That is where we can get cobalt out of the ground in the United States. So they put $15, 000, 000 out of the this cobalt mine in in Idaho hasn't been profitable. I mean, it was purchased from a Canadian company in 2019. They reopened it in 2022, then they shut it down because it just wasn't profitable. Um, but now that they've got the DOD coming in and funding them through the Ukraine bill, you know, they're they're back on track. They're they're exploring, they're finding new cobalt, they're finding ways to extract that cobalt, and the the entire mine is moving forward. So that is a major water user. The other major user in this area, I don't know if you guys have been paying attention to this, but META is opening these massive data centers, close to a million square feet in some locations. And they're opening 1 right there in Idaho. And they're going to be another competitor for groundwater because they use a ton of groundwater to cool their system. Some people said as much as an Olympic swimming Olympic swimming pools are 660,000 gallons. They're using as much as 2,500,000 gallons of water a day to cool their systems. And these databases are also important to our government because our government is able to access information on its constituents, citizens without their knowledge. I mean, think about it. Every time you post something on Facebook or you're you're searching on Facebook, do you, you don't think about the fact that everything you do, every move you make, that that system is tracking you, learning you, learning what your face looks like, learning what your kids look like. Facebook data centers are one of the most important things to our government, and they're putting these data centers in everywhere, and they're constantly taking water rights away from US citizens, US farms, just to put in a data center so they could have more information on you. So that's 2 big things that I think the farmers in Idaho are facing right now. 2 major water users are coming in.”

Wall Street Apes

471,924 次观看 • 2 年前

Jaylen Brown on teaming up with LeBron: "I love learning, so to have the opportunity to learn from one if, if not the greatest player of all time ... On the court, off the court, learn and gather experiences that can accumulate to my own knowledge base."

Jaylen Brown on teaming up with LeBron: "I love learning, so to have the opportunity to learn from one if, if not the greatest player of all time ... On the court, off the court, learn and gather experiences that can accumulate to my own knowledge base."

Underdog NBA

337,548 次观看 • 1 个月前

Mark Cuban says there are four rules anyone must follow if they want to become a millionaire "If you want to be a millionaire, you can do it. But there's a couple of things you have to be able to accomplish. Rule number one, find something you can be good at and then be great at it." "Second, you have to know how to sell. Whatever it is you're good at, somebody's going to need that service. Someone's going to need that, whatever it is that you're creating. But you have to be able to sell because you don't want to be in a position where you're dependent on other people." "Number three, be curious and always be learning. Because the one thing that's certain about business is that it changes every single day." "And then the fourth thing, when you have that knowledge, when you have that ability to sell,"

Mark Cuban says there are four rules anyone must follow if they want to become a millionaire "If you want to be a millionaire, you can do it. But there's a couple of things you have to be able to accomplish. Rule number one, find something you can be good at and then be great at it." "Second, you have to know how to sell. Whatever it is you're good at, somebody's going to need that service. Someone's going to need that, whatever it is that you're creating. But you have to be able to sell because you don't want to be in a position where you're dependent on other people." "Number three, be curious and always be learning. Because the one thing that's certain about business is that it changes every single day." "And then the fourth thing, when you have that knowledge, when you have that ability to sell,"

Rad

176,514 次观看 • 3 个月前

So here’s the deal, THE TREASURY DEPARTMENT DATA IS COMPROMISED. Actors without security clearance have been given access to scrape your data. Elon Musk now controls your data and it represents the same level of control exerted by the Chinese government’s social credit system. Musk is now able to profile every name on the treasury data systems. There is no way to sugar coat the fact that your data is no longer secure. It can now be used to target you and not for your benefit, be sure of that. Democrats and advocacy groups can take all the legal action they like, THE DATA IS COMPROMISED and you can’t put the genie back in the bottle. This data can now be used to literally social engineer services to target any perceived enemy of ‘the state’. Call me a conspiracy theorist if you like, but then sit back have a think if any of what I am suggesting is not possible. I analyse big data for a living and I have no solution to this problem and neither does any Democrat telling you it can be fixed.

𝔗𝔯𝔲𝔱𝔥 𝔐𝔞𝔱𝔱𝔢𝔯𝔰

16,366 次观看 • 1 年前

🚨NEW: Joe Rogan: "How crazy is it that Biden deported 1.49 million while letting in 20?" "Like, what do you think they were doing!?" "I know there was also efforts to move people to specific states so that you can get a larger number for the census so you get more congressional seats." "This is a dark thing that people don't want to admit because they're a dyed-in-the-wool Democrat." "I had a conversation with my parents about it and they're like, 'No!' I'm like, 'Yes! It's about congressional seats. It's like, the census counts illegals!'" "And not only that, they're trying to make it so that those people can vote!" "And if you can make it so that those people can vote, then all of the sudden you ship these people to a place, you give them EBT cards, you get them on food stamps, you give them Medicaid money — they have money." "And now, who are you going to vote for? You're going to vote for the people who got you money!" Daily Caller

🚨NEW: Joe Rogan: "How crazy is it that Biden deported 1.49 million while letting in 20?" "Like, what do you think they were doing!?" "I know there was also efforts to move people to specific states so that you can get a larger number for the census so you get more congressional seats." "This is a dark thing that people don't want to admit because they're a dyed-in-the-wool Democrat." "I had a conversation with my parents about it and they're like, 'No!' I'm like, 'Yes! It's about congressional seats. It's like, the census counts illegals!'" "And not only that, they're trying to make it so that those people can vote!" "And if you can make it so that those people can vote, then all of the sudden you ship these people to a place, you give them EBT cards, you get them on food stamps, you give them Medicaid money — they have money." "And now, who are you going to vote for? You're going to vote for the people who got you money!" Daily Caller

Jason Cohen 🇺🇸

259,092 次观看 • 1 年前

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Why aren't more people talking about how difficult it is to turn documents into structured data? This is literally a problem that every single company I talk to is trying to solve. They have a buttload of documents with forms and tables, and they want to turn them into usable data. Let me show you how you can do this using Tensorlake. Probably the best parser I've found:

Santiago

46,413 次观看 • 1 年前