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Genetic data is often on the order of 1-million-Dimensional. How on earth does one visualize this? PCA! In my sixth popgen / ancient DNA video, I give a (very) high level intuition for what PCAs are doing and how we can use them to reduce them dimensions of genetic data

Daniel Tabin

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17,156 views • 6 months ago •via X (Twitter)

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Can we visualize higher dimensions? After 40+ years working on the math, the best I can do is imagine lower-dimensional analogs and projections, like this hypercube. Some claim to truly see higher dimensions. I’m not one of them.

Can we visualize higher dimensions? After 40+ years working on the math, the best I can do is imagine lower-dimensional analogs and projections, like this hypercube. Some claim to truly see higher dimensions. I’m not one of them.

Brian Greene

71,806 views • 1 year ago

Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.

Data often lie on a low-dimensional manifold embedded in a high-dimensional space. But these manifolds are often highly non-linear, making linear dimensionality reduction methods like PCA insufficient. This has motivated the development of non-linear dimensionality reduction.

Alec Helbling

212,725 views • 10 months ago

A tutorial on our player cards explaining what we included on them and why, along with how to interpret all of the data!

A tutorial on our player cards explaining what we included on them and why, along with how to interpret all of the data!

Pitcher List Stats

365,033 views • 6 months ago

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

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

17,697 views • 1 month ago

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.

Culture is genetic because behavior is genetic. This beaver never saw a dam in its life. No beavers or anything else ever taught it to build a dam. It wants to build a dam because it is a beaver. Many beavers together build a big dam. That is beaver culture. Humans are not different. Nothing is different. This is what life is. This is how life works. Your body is your mind. A caterpillar wants to build a chrysalis. A bee wants to build a hive. A lion wants to build a pride. You are not special. You are not above your nature. you are INSIDE of it. The thoughts that we think are genetic thoughts. The crimes we commit are genetic crimes. The art we create is genetic art. Just like this beaver, you can give the animal different sticks and it will build a different dam, but it will always build a dam. And you can give humans different "education," but the human will always use it to do what its genes tell it to do. This is the first big answer that you need. This is the biggest piece of the puzzle. This is how to understand people 90% of the way. You just... notice what they do, and get out of the way, and watch them do it. And if they need sticks, you give them sticks. And if you don't like what they do, you have to get away from them. You cannot train dam-building into them or out of them any more than you can with a beaver. A beaver wants to build a dam because it is a beaver. Whatever you see people build, that's what they wanted to build from the sticks they got in the river they were in. Stop pretending you can change it.

hoe_math = PsychoMath

1,190,914 views • 1 year ago

David George on SpaceX: “It’s getting harder & harder to get data centers online on Earth. I think it’s only a matter of time before we have them in space. Orbital data centers will provide incremental capacity in space, on top of what we have on Earth.”

David George on SpaceX: “It’s getting harder & harder to get data centers online on Earth. I think it’s only a matter of time before we have them in space. Orbital data centers will provide incremental capacity in space, on top of what we have on Earth.”

DogeDesigner

60,516 views • 2 months ago

How much money is being pumped into data centers in the Permian? Are they building them on top of old “plugged” wells? How much does it cost to relocate servers when one of those wells starts leaking? Ask the First Baptist Church in Grandfalls how that’s going for them. Follow me on zombiewells if you want updates.

How much money is being pumped into data centers in the Permian? Are they building them on top of old “plugged” wells? How much does it cost to relocate servers when one of those wells starts leaking? Ask the First Baptist Church in Grandfalls how that’s going for them. Follow me on zombiewells if you want updates.

Sarah Stogner

41,649 views • 4 months ago

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.

This is Anagha Rajesh. And she is crazy. She wants to store data in bacterial DNA. Her startup is called BioCompute. And last year, they actually did it. They stored data in DNA and retrieved it in their tiny lab in Bengaluru. This is a huge moment. But why is she even doing it? Because DNA is the ultimate storage tool available to us. Just 1 gm of DNA can store about 215 petabytes of data - that's like storing over 2 million movies in 4K. On top of that - this data can last for literally 1000s of years. Right now, they still need to figure out a way to make the reading and writing process faster and cheaper. But if BioCompute solves this problem - we could theoretically store all the data created in the world every year in the palm of our hands. And that would be insane. P.S. Check out this video from vyom going to Anagha's lab and actually doing it.

Varun Guru

255,644 views • 2 months ago

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

With enough data, robots and AI can learn “world models” that let them predict the results of their actions. These models are a way to learn how embodied AI agents can perform a wide variety of useful tasks — but they require a huge amount of data. The team at General Intuition General Intuition has a solution: use data from video games! Games teach movement, problem solving, and complex spatial reasoning, and they come in a staggering diversity of forms, covering a wide variety of problems. What’s more, the captured data is high-quality, without the noise or annotation error that can come from We sat down with Pim de Witte and Adam Jelley from the General Intuition team to learn more about their history, their plans, and their philosophy.

RoboPapers

85,927 views • 10 months ago

(1) I have put here some more movies of our models of the Western Mediterranean, which help to visualize what is going on. Feel free to use them for any purpose. I often used them for teaching and it helps students understand the dynamics of rolling back subduction zones.

(1) I have put here some more movies of our models of the Western Mediterranean, which help to visualize what is going on. Feel free to use them for any purpose. I often used them for teaching and it helps students understand the dynamics of rolling back subduction zones.

João C. Duarte

26,717 views • 2 years ago

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

Santiago

40,627 views • 1 year ago

How does one tell if some population A is closer to population B or C? The f4-statistic has you covered! In the 9th part of my popgen and aDNA methods series, we cover the basic intuition behind an f4-statistic and how it can be used to learn about relatedness and history.

How does one tell if some population A is closer to population B or C? The f4-statistic has you covered! In the 9th part of my popgen and aDNA methods series, we cover the basic intuition behind an f4-statistic and how it can be used to learn about relatedness and history.

Daniel Tabin

16,802 views • 5 months ago

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

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

Will Chamberlain

21,345 views • 11 days ago

Why are we forced to pay for data centres? Ireland to be the dumping group of these big tech companies data centres & their algorithms. We should be able to know what's going on in them. Instead, this government is allowing them to use up massive amounts of water & electricity.

Why are we forced to pay for data centres? Ireland to be the dumping group of these big tech companies data centres & their algorithms. We should be able to know what's going on in them. Instead, this government is allowing them to use up massive amounts of water & electricity.

Paul Murphy 🇵🇸

14,333 views • 9 months ago

#WATCH | Delhi | On National Statistics Day, Minister of State for Statistics and Programme Implementation and Planning, Rao Inderjit Singh says, "The idea is that when we give credible data to our government and not only our government but also to people who are dependent on us like in our neighboring countries, third world countries, we should be able to guide them in giving credible data so that their respective countries can progress based on this data. The Prime Minister and the PMO are both very concerned that data should not be half-hearted..."

#WATCH | Delhi | On National Statistics Day, Minister of State for Statistics and Programme Implementation and Planning, Rao Inderjit Singh says, "The idea is that when we give credible data to our government and not only our government but also to people who are dependent on us like in our neighboring countries, third world countries, we should be able to guide them in giving credible data so that their respective countries can progress based on this data. The Prime Minister and the PMO are both very concerned that data should not be half-hearted..."

ANI

25,823 views • 2 months ago

WOW 🚨 Illinois resident says everyone showed up to their City Council meeting to oppose 14 data centers being built She said so many people showed up to oppose it, it took 6 hours with public comments At the end Democrats didn’t care and UNANIMOUSLY VOTED TO APPROVE THEM “I just spent six hours at a city council meeting — where they were discussing the 14 data centers they're gonna build in my city. And public comment went on for hours and hours and hours of people telling them how badly our community does not need this, and how detrimental it will be to the people, the wildlife, the health, the everything, all of the negative impacts of the data centers. We told them, and we told them over and over and over again, and then at the end, after sitting here for six hours, telling them this is not what we want — they unanimously voted yes for the data centers in our face”

WOW 🚨 Illinois resident says everyone showed up to their City Council meeting to oppose 14 data centers being built She said so many people showed up to oppose it, it took 6 hours with public comments At the end Democrats didn’t care and UNANIMOUSLY VOTED TO APPROVE THEM “I just spent six hours at a city council meeting — where they were discussing the 14 data centers they're gonna build in my city. And public comment went on for hours and hours and hours of people telling them how badly our community does not need this, and how detrimental it will be to the people, the wildlife, the health, the everything, all of the negative impacts of the data centers. We told them, and we told them over and over and over again, and then at the end, after sitting here for six hours, telling them this is not what we want — they unanimously voted yes for the data centers in our face”

Wall Street Apes

1,468,253 views • 6 months ago

Who else is glad they never took a ‘Covid Test’? China’s BGI offered free ‘Covid Testing’ to Countries to gather genetic data on entire populations. “They are openly saying we are sequencing as much as we can of the world population looking for genes for intelligence…”

Who else is glad they never took a ‘Covid Test’? China’s BGI offered free ‘Covid Testing’ to Countries to gather genetic data on entire populations. “They are openly saying we are sequencing as much as we can of the world population looking for genes for intelligence…”

Liz Churchill

30,042 views • 1 year ago

How is data calculated before it’s counted? If one person can create an app that tells him specifically what the results are before the rest of us, how can we be sure that he can’t control them?

How is data calculated before it’s counted? If one person can create an app that tells him specifically what the results are before the rest of us, how can we be sure that he can’t control them?

Meidas_Charise Lee

525,910 views • 1 year ago

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

#WATCH | India AI Impact Summit 2026 | Delhi: Founder Chairman and CEO of Sampark Foundation & former CEO of HCL Technologies, Vineet Nayar says, "...From an employment point of view I think it is very important for us to understand that Indian companies, including Indian IT companies, are going to be profit-driven and therefore if you believe that they are going to create employment you must be dreaming. Therefore, the question is how do we create employment in this environment, and that employment comes from mass scale startups, which is what this government has already doing. So, how do we create new sets of people who are trying to solve new sets of problems not new sets of technology and if we do that we will get it right. I think we as Indians have to be very careful on who does data belong to and that is the debate we have a problem with. The LLM models which exist worldwide are far superior than the Indian models. Unfortunately, in India, we never develop products, so therefore we do not have SLMs and LLMs which are world-class. On one side, we have global LLM products which are coming to India and trading on our Indian data. Should we allowed that or should we not allowed that? But on the other side if we don't allow that then we have the data but we don't have the LLM models. So, how do we encourage technology completely to develop the LLM models. This needs radicals strategic thinking and a very important aspect otherwise we will either give up a data. So, I think it's a very critical aspect for us to think about - who does this data belong, what is the kind of incentives we are going to give to develop LLM technologies or SLM technologies fast so that we train on our data otherwise an LLM will come in with our data and we'll immediately see return and we'll celebrate and we will do all these kind of press releases but the India will lose a competitive advantage on something which is very critical for the next decade."

ANI

18,753 views • 7 months ago