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We are announcing our newest initiative: an interactive math engine by isaacmason. Math is core to everything interactive from geometry to collision to color, yet JavaScript lacks a high-performance kernel. Instead libraries reinvent math structures and transformations, with varying success, and now LLMs generate bespoke functions on a case...

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Poimandres

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56,745 次观看 • 1 个月前 •via X (Twitter)

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Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.
0:39

Sam Altman in a new interview says Orbital Data Centers are not going to matter at scale this decade: “I honestly think the idea, with the current landscape of putting data centers in space is ridiculous. If you do the very rough math of launch costs relative to the cost of power on earth and to say nothing how you are going to fix a broken GPU in space— they do break a lot unfortunately— we are not there yet. There will be a time, space is great for a lot of things, Orbital Data Centers are not something that is going to matter at scale this decade.” This clip isn’t going to age well for Sam.

Nic Cruz Patane

72,344 次观看 • 6 个月前

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

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

ANI

163,581 次观看 • 1 年前

Dario Amodei just revealed that the AI training bottleneck everyone is worried about doesn’t exist anymore. The industry spent years obsessed with scraping the open web. More data. More text. More human output to feed the models. Amodei: “I don’t think data is quite the most central thing anymore.” The shift is fundamental. Amodei: “Static data is becoming less important. A lot of the data we use today is RL environments that we train on. Dynamic data that the model creates itself.” Not scraped. Not licensed. Not written by humans. Generated by the model through pure trial and error. When you train on complex math or agentic coding, you don’t feed it a textbook. You give it an environment. The model experiments. Fails. Adjusts. Tries again. Amodei: “You’re getting some math problems and the model experiments with trying the math problems.” It generates its own experience. Millions of iterations. Each one building on the last. No human required. This destroys the entire narrative around AI hitting a data wall. You cannot throttle a competitor by locking down copyright. Cannot slow the race by putting up a paywall. When a model learns through its own synthetic experience, the open web becomes irrelevant. The only true bottleneck left is compute. And this is where the geopolitical stakes become impossible to overstate. The nation that wins the compute race doesn’t just build smarter models. It builds models that generate their own intelligence, compounding on themselves, iterating past every limit human knowledge ever imposed. We are no longer training AI on the past. We are letting it simulate the future. The machine has stopped reading the dictionary. It’s doing the math itself now.
1:07

Dario Amodei just revealed that the AI training bottleneck everyone is worried about doesn’t exist anymore. The industry spent years obsessed with scraping the open web. More data. More text. More human output to feed the models. Amodei: “I don’t think data is quite the most central thing anymore.” The shift is fundamental. Amodei: “Static data is becoming less important. A lot of the data we use today is RL environments that we train on. Dynamic data that the model creates itself.” Not scraped. Not licensed. Not written by humans. Generated by the model through pure trial and error. When you train on complex math or agentic coding, you don’t feed it a textbook. You give it an environment. The model experiments. Fails. Adjusts. Tries again. Amodei: “You’re getting some math problems and the model experiments with trying the math problems.” It generates its own experience. Millions of iterations. Each one building on the last. No human required. This destroys the entire narrative around AI hitting a data wall. You cannot throttle a competitor by locking down copyright. Cannot slow the race by putting up a paywall. When a model learns through its own synthetic experience, the open web becomes irrelevant. The only true bottleneck left is compute. And this is where the geopolitical stakes become impossible to overstate. The nation that wins the compute race doesn’t just build smarter models. It builds models that generate their own intelligence, compounding on themselves, iterating past every limit human knowledge ever imposed. We are no longer training AI on the past. We are letting it simulate the future. The machine has stopped reading the dictionary. It’s doing the math itself now.

Dustin

149,633 次观看 • 6 个月前

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓
43:30

IN 1986 MIT FILMED THE LECTURE WHERE CODE STOPPED BEING CODE AND BECAME DATA 43 minutes from Gerald Sussman, in the most legendary intro programming course ever recorded. -> The idea that lands: to a program, your code is just data it can read and rewrite. He builds a program that does real calculus -- not by crunching numbers, but by reading the math as a list and transforming it piece by piece. Then he shows the trick hiding under all of it: in this language, code and data are the same material. Forty years later that is exactly what an AI coding agent is -- a program that reads your code as data, rewrites it, and hands it back. Sussman drew the whole idea on a blackboard in 1986. Writing code was never the deepest skill -- understanding that code itself is data a program can manipulate is. This is where you learn it. Most people think AI writing code is brand new. The ones who watch this saw the blueprint 40 years ago. Bookmark & Watch it. This one's a legend ↓

slash1s

22,107 次观看 • 2 个月前

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”
0:56

Axiom Math founder Carina Hong on why math is the path to general superintelligence: “Math is the sandbox for reality. You very quickly see verifiable rewards because in math, there’s an absolute right or wrong.” “And especially when you have Lean, you can check the proof or solution step by step. You’ll be able to apply reinforcement learning in a much more efficient way.” “We have currently scaled from winning a Putnam perfect score to solving a batch of research problems that professional mathematicians find really challenging. And we also see this transfer to code verification.”

TBPN

39,589 次观看 • 5 个月前

Mark Cuban: A lot of these data centers are going to end up as pickleball courts. “Everybody thinks there’s going to be so much more utilization and there will be… but there’s going to be technological breakthroughs as well. Just like we saw with fiber. It went from 1GB to 10 to 100GB and then there wasn’t a fiber problem anymore. How is it not going to be the case that we don't get the same price performance improvements on the AI side and on the data center side?”
1:38

Mark Cuban: A lot of these data centers are going to end up as pickleball courts. “Everybody thinks there’s going to be so much more utilization and there will be… but there’s going to be technological breakthroughs as well. Just like we saw with fiber. It went from 1GB to 10 to 100GB and then there wasn’t a fiber problem anymore. How is it not going to be the case that we don't get the same price performance improvements on the AI side and on the data center side?”

jay plemons

307,170 次观看 • 1 个月前

There is no such thing as a safe or trustworthy ALPR. Companies like Flock are driven by profit and in order to compete for contracts they have to create more and more powerful surveillance tools so they can sell more of our data. As long as we live in a system driven by profit where private companies own and control our data there is no way to remove the dangers of ALPRs. That is why we are here today to call for a total ban on ALPRs in Chandler.
1:04

There is no such thing as a safe or trustworthy ALPR. Companies like Flock are driven by profit and in order to compete for contracts they have to create more and more powerful surveillance tools so they can sell more of our data. As long as we live in a system driven by profit where private companies own and control our data there is no way to remove the dangers of ALPRs. That is why we are here today to call for a total ban on ALPRs in Chandler.

Orwell Day

11,169 次观看 • 22 天前

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.
4:06

Here it is: The algorithm that toppled the United States government on November 3, 2020. The 2020 election Edison data confirms fluctuations in vote counts—meaning a straight-up algorithm. “Scytl is receiving the election data directly from election officials in at least nine states, meaning there is no projection—this is raw data. When you look at raw data in an election, we would all agree that elections are supposed to be additive processes. So, with each interval report, you would expect to see more votes—not deletions. More votes, not deletions. Now look at this real-time video of the Edison reports. You have in-person votes, then mail-in votes, and look how it pauses—there is oscillation and deletion. This is the algorithm trying to figure out the math problem that is you. That is you. And if I let this video play to completion, you’ll find out it wasn’t just Pennsylvania. We have three counties in North Carolina. We have three counties in Georgia. And guess what— in Georgia, we know for a fact that Scytl contracts directly with the state to get that data. So you should see no oscillation. Instead, what you see is absolute manipulation, and this is all possible because of how wide open the system is.” Ban all voting machines and Pass the Save Act. And arrest Chris Krebs.

🇺🇸RealRobert🇺🇸

18,982 次观看 • 6 个月前

Rep. Roy on FISA reform: "I would just note that all four witnesses, including the Democrat witness, just said there is no legitimate basis for us not to include a warrant protection requirement in FISA reform. I want that to ring through the halls of Congress between now and April, so that when we are forced with the vote that is inevitably going to occur on this issue, that we not have what happened almost two years ago, where we left the American people without the protection the Constitution affords them from a tool that is being used by on a bipartisan basis to target the American people and collect their data, which is offensive and it is wrong,"
1:58

Rep. Roy on FISA reform: "I would just note that all four witnesses, including the Democrat witness, just said there is no legitimate basis for us not to include a warrant protection requirement in FISA reform. I want that to ring through the halls of Congress between now and April, so that when we are forced with the vote that is inevitably going to occur on this issue, that we not have what happened almost two years ago, where we left the American people without the protection the Constitution affords them from a tool that is being used by on a bipartisan basis to target the American people and collect their data, which is offensive and it is wrong,"

Rep. Chip Roy Press Office

54,266 次观看 • 8 个月前

Which one is our priority for securing a digital future for Zimbabwe? The Honourable Speaker's position while providing a clear, logical, and data-informed counter-argument we appreciate his efforts and submission. Let us have a conversation on our priorities in the current Zimbabwean context which we presented yesterday: This is a critical national conversation on our digital future. The proposition of a Google data centre is undoubtedly appealing. Building a centre of Technological Excellence that houses multiple Tech companies and a Start-up Ecosystem is also prudent, though we are calling it a Technology Park. However, I was of the following view which I am willing to be criticised and guided on. The strategic prioritisation of a national ICT Park ecosystem currently is the foundational path to sustainable, sovereign growth and capacity for Zimbabwe. We are reaching out to every province through the Digital Centres, Innovation Hubs at Universities and Colleges. Infrastructure which we need to capacitate better. This is a decentralised system utilising and expanding the infrastructure we have. The central question is not if we want major players like Google; that is not questionable, but when, how and on what terms. Why are we building our own foundation, house, techno park and mini ICT Parks in the provinces and districts? We absolutely need at least a one hyperscale data centre, but centralisation in the current and future environment can be restrictive. This was our plan: 1.The Foundation Before the Skyscraper: Capacity and Traffic We need to increase our national data volumes and internet traffic . Our urgent priority should be upgrading our national backbone, specifically the Optical Fibre on Power Transmission Lines (e.g., Powertel's network from Insukamini to Johannesburg), to peer efficiently with global giants. This builds the foundational "digital highway" we manage. 2.Generating Traffic Through Digitalization A data centre is a response to demand, not a creator of it. We must first drive digitalization aggressively through digital payments, process automation, and e-governance to generate the significant local traffic that would make a data centre viable. Furthermore, we must incorporate Edge Computing strategies to process data closer to the source, a more efficient model for our current needs and in compliance to our Cyber and Data Protection Act which we can amend if need be. Being a regional internet and cybersecurity hub is crucial as the Speaker alluded to me mentioning it to SADC Parliamentary Forum, this is the ultimate goal. 3.PPPs are key to lessen the burden to the fiscus and we are ready to accommodate organisations willing to assist government.(Cont)
3:05

Which one is our priority for securing a digital future for Zimbabwe? The Honourable Speaker's position while providing a clear, logical, and data-informed counter-argument we appreciate his efforts and submission. Let us have a conversation on our priorities in the current Zimbabwean context which we presented yesterday: This is a critical national conversation on our digital future. The proposition of a Google data centre is undoubtedly appealing. Building a centre of Technological Excellence that houses multiple Tech companies and a Start-up Ecosystem is also prudent, though we are calling it a Technology Park. However, I was of the following view which I am willing to be criticised and guided on. The strategic prioritisation of a national ICT Park ecosystem currently is the foundational path to sustainable, sovereign growth and capacity for Zimbabwe. We are reaching out to every province through the Digital Centres, Innovation Hubs at Universities and Colleges. Infrastructure which we need to capacitate better. This is a decentralised system utilising and expanding the infrastructure we have. The central question is not if we want major players like Google; that is not questionable, but when, how and on what terms. Why are we building our own foundation, house, techno park and mini ICT Parks in the provinces and districts? We absolutely need at least a one hyperscale data centre, but centralisation in the current and future environment can be restrictive. This was our plan: 1.The Foundation Before the Skyscraper: Capacity and Traffic We need to increase our national data volumes and internet traffic . Our urgent priority should be upgrading our national backbone, specifically the Optical Fibre on Power Transmission Lines (e.g., Powertel's network from Insukamini to Johannesburg), to peer efficiently with global giants. This builds the foundational "digital highway" we manage. 2.Generating Traffic Through Digitalization A data centre is a response to demand, not a creator of it. We must first drive digitalization aggressively through digital payments, process automation, and e-governance to generate the significant local traffic that would make a data centre viable. Furthermore, we must incorporate Edge Computing strategies to process data closer to the source, a more efficient model for our current needs and in compliance to our Cyber and Data Protection Act which we can amend if need be. Being a regional internet and cybersecurity hub is crucial as the Speaker alluded to me mentioning it to SADC Parliamentary Forum, this is the ultimate goal. 3.PPPs are key to lessen the burden to the fiscus and we are ready to accommodate organisations willing to assist government.(Cont)

Hon Tatenda.A. Mavetera

490,465 次观看 • 10 个月前

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!
1:33

Enrollment is now open for the Data Engineering Professional Certificate! Data engineers are the architects of modern organizations, ensuring data is reliable, accessible, and ready for analytics and machine learning. This professional certificate is tailored to equip you with the critical skills, through frameworks and hands-on practice, to excel in this role. Taught by industry expert Joe Reis, co-author of the best-selling book "Fundamentals of Data Engineering," along with 17 guest instructors from the data field, you will gain expertise to start and further your career in the high-demand field of data engineering. Key focus areas: 🗂️ Data Engineering Lifecycle: Learn the important stages of building an efficient data pipeline that creates business value. 📥 Data Ingestion: Learn how to efficiently gather data from various sources. 💾 Data Storage: Master the techniques for storing data securely and cost-effectively. 🔄 Data Transformation: Understand how to clean, organize, and prepare data for analysis and machine learning. 🏗️ Data Architecture Design: Build robust architectures that support scalable, efficient data workflows. 📊 Serving Data: Ensure that data is available to stakeholders when and where they need it to drive business decisions. Enroll now!

DeepLearning.AI

20,833 次观看 • 1 年前

The conversation is complicated. Things are not as simple as we hope or want them to be. We are all saddened, frightened and furious about everything. SO much is on the line; people’s lives, everywhere, are on the line. There are people that want to condemn reality, rather than working from within any opposition to change it. To make it safer. The system(s) we have in this world are far beyond flawed. And yet, through horrid strife, unimaginable violence and suffering, and terrible, terrible mistakes, America still, somehow, remains a country that people can hope to be free in. That is worth something. That is worth voting for.
3:21

The conversation is complicated. Things are not as simple as we hope or want them to be. We are all saddened, frightened and furious about everything. SO much is on the line; people’s lives, everywhere, are on the line. There are people that want to condemn reality, rather than working from within any opposition to change it. To make it safer. The system(s) we have in this world are far beyond flawed. And yet, through horrid strife, unimaginable violence and suffering, and terrible, terrible mistakes, America still, somehow, remains a country that people can hope to be free in. That is worth something. That is worth voting for.

Bon Iver

91,501 次观看 • 1 年前

🚨 Naval going absolutely off on people trying to overregulate AI🔥: “Almost all innovation has come from unregulated industries. Regulated industries are a nightmare because there’s too many bureaucrats there saying no. This is why all the innovation is coming in bits, not atoms.” “People who are trying to regulate AI out of a doomsday fear from having watched Terminator too many times are regulated the free exercise of mathematics. They are innumerate people, the modern day illiterate. They don’t know how anything works. They don’t actually understand math. They’re scared of it. These are the same kids that were scared of math in high school now writing regulation.” “These guys are idiots. The only exception they make is for the military. So we end up in the worse of all worlds. They want to ban math yet somehow they expect our military to keep up with the Chinese and whoever else we consider to be our enemies.” 🔥 🔥 🔥
3:27

🚨 Naval going absolutely off on people trying to overregulate AI🔥: “Almost all innovation has come from unregulated industries. Regulated industries are a nightmare because there’s too many bureaucrats there saying no. This is why all the innovation is coming in bits, not atoms.” “People who are trying to regulate AI out of a doomsday fear from having watched Terminator too many times are regulated the free exercise of mathematics. They are innumerate people, the modern day illiterate. They don’t know how anything works. They don’t actually understand math. They’re scared of it. These are the same kids that were scared of math in high school now writing regulation.” “These guys are idiots. The only exception they make is for the military. So we end up in the worse of all worlds. They want to ban math yet somehow they expect our military to keep up with the Chinese and whoever else we consider to be our enemies.” 🔥 🔥 🔥

Autism Capital 🧩

39,080 次观看 • 1 年前

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics
1:14

Jason Ma reveals the first true scaling law in robotics: robots trained on 1 million hours of first-person human video predictably get better without ever seeing robot data "Yesterday we announced our new flagship robot foundation model called Dyna-2. It's very significant for the entire field for many different reasons. First of all, it's the first robot foundation model trained on at least one million hours of data. That in itself is a very challenging infrastructure challenge." "What we demonstrated is that even if the one million hours of data is entirely first-person video of humans doing manipulation tasks, we actually see scaling law for transferring to robot embodiments that the model has never seen before." "By training on more human data, we actually see predictable performance improvement on robots. That's a big deal because the biggest challenge facing robot foundation models is that we don't have enough data." "Unlike language models, there are just not enough robotics data out there, and it's very difficult to collect these robot data. Only by passively observing humans doing things can we scale this kind of data, and this is the first time we showed there is a lot of improvement that can transfer." Jason Ma Dyna Robotics

MTS

17,561 次观看 • 24 天前

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.
0:51

Why do we do math in school? When I asked this question as a kid, my teachers always told me, "You'll use it one day." That's almost always false. Most adults are never going to be in a sticky situation with two binomials, thinking, thank goodness I can FOIL my way out of this mess. Most of us get by just fine without ever solving for x again. And especially in an Al age, fewer and fewer people will "use math" in the narrow, practical sense. But the origins of math have never been primarily about utility. Math is formative. It trains the mind to love what is true, to recognize what is orderly, and to be drawn toward what is beautiful. It teaches us that the universe is not chaos, but something intelligible, something structured, something that can be known. And you can see that truth made visible in the world's most breathtaking churches and cathedrals: arches, vaults, domes, proportions, symmetry, harmony, light-geometry turned into glory. The mathematics that shapes a cathedral is not cold or sterile. It's the language of wonder, carved into stone. We don't teach math because everyone will use it. We teach math because it forms the kind of person who can see that reality has meaning.

Jeremy Wayne Tate

95,200 次观看 • 7 个月前

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.
1:57

The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

elvis

140,692 次观看 • 1 年前

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."
2:39

#WATCH | Delhi: On National Statistics Day, Saurabh Garg, Secretary, Ministry of Statistics and Programme Implementation, says, "The theme for National Statistics Day celebration is ' Unlocking the potential of administrative data'. Over the past decade, we have a lot of administrative data, especially because of the growth of Digital India. How we can leverage this administrative data for decision-making is the theme for today's discussions. The PM has been focusing on data-driven decision-making. This is in the context that if we are talking of a Viksit Bharat by 2047, the policies that we take, the decisions that we make, have to be so much more focused and more data-driven. Ministry of Statistics is helping to ensure that the data is more harmonised to make it interoperable."

ANI

30,758 次观看 • 2 个月前

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.
29:44

Alan Oppenheim, MIT professor: "Jump Trading pays quants $650K to pull one real signal out of market noise. I wrote the textbook on that exact math and taught it for free." this free MIT lecture is the math the whole "quant edge" is built on, and almost nobody selling you a course will name it. the man teaching it wrote the signal-processing textbook every engineer on earth learned from, and he put the lectures online for nothing. here is the idea. a price chart is a signal buried in noise. most of what you see is random static, and hidden inside is a faint, repeating pattern with real mathematics for pulling it out. that is not a metaphor. jim simons was a codebreaker who did exactly this to enemy transmissions, then pointed the same math at the market and made a hundred billion dollars. none of it is new. oppenheim taught this in 1987, the math is older, and every lecture is free. same story i told in the article above. the edge was never a secret formula. it was knowing a market is a noisy signal, and having the tools to read it. here is what the gurus skip. the math to separate signal from noise is free and everyone has it. the edge is cleaner data than the next desk, and the discipline to trust a faint signal across thousands of trades instead of chasing the static. the filter is free. the patience to use it is not.

Rossst.03

281,905 次观看 • 2 个月前

In Move, resource accounts are used to ensure certain functions and/or data can be only accessed programmatically. Whether you're building a DEX, NFT marketplace, game, etc., one of the most important things as a builder is being able to ensure to your users that what they see is what they get. To learn more, watch the video below! (It may be election season but this is not stopping the Movement team from shipping.)
0:44

In Move, resource accounts are used to ensure certain functions and/or data can be only accessed programmatically. Whether you're building a DEX, NFT marketplace, game, etc., one of the most important things as a builder is being able to ensure to your users that what they see is what they get. To learn more, watch the video below! (It may be election season but this is not stopping the Movement team from shipping.)

Shayan

12,334 次观看 • 1 年前

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:
1:21

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:

Jim Fan

364,565 次观看 • 2 年前