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This is why we need to gate-keep science. Two pseudo-intellectuals thinking they discovered something deep, conflating >social attention >transformers attention >quantum physics observer (attention) These have nothing in common, other than the ambiguity of English language. Naked ladies on Instagram have nothing to do with a weighted average followed...

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Julia Turc

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119,322 görüntüleme • 1 ay önce •via X (Twitter)

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Creators, marketers, entrepreneurs, executives .. please pay attention … to the actual attention … social media feeds have so much a wariness it’s profound and the best storytellers and strategists on these feeds will prevail .. it’s time you get serious - > or DM me the word “attention” #socialmedia #attention #garyvee
1:01

Creators, marketers, entrepreneurs, executives .. please pay attention … to the actual attention … social media feeds have so much a wariness it’s profound and the best storytellers and strategists on these feeds will prevail .. it’s time you get serious - > or DM me the word “attention” #socialmedia #attention #garyvee

Gary Vaynerchuk

18,805 görüntüleme • 11 ay önce

Woman says ICE agents are rebranding themselves. “We need to pay attention to these acronyms on their vests. We need to know, are they deploying agents from other departments of Homeland Security that have nothing to do with immigrations or customs enforcement?”
0:46

Woman says ICE agents are rebranding themselves. “We need to pay attention to these acronyms on their vests. We need to know, are they deploying agents from other departments of Homeland Security that have nothing to do with immigrations or customs enforcement?”

ICE of TikTok

13,827 görüntüleme • 6 ay önce

"We're not in business so my employee is happy. I'm in business so my customer is happy." JPMorgan CEO Jamie Dimon just ended the remote work debate in 45 seconds. "People on Zoom — they're texting each other. That's not full attention." "They learn by going on a sales call with you. They learn by seeing you make a mistake. They learn by how you deal with the mistake.” “If you go to a meeting with me, you've got my full friggin attention the whole time." The Hill & Valley Forum 2026 The Hill & Valley Forum J.P. Morgan
0:49

"We're not in business so my employee is happy. I'm in business so my customer is happy." JPMorgan CEO Jamie Dimon just ended the remote work debate in 45 seconds. "People on Zoom — they're texting each other. That's not full attention." "They learn by going on a sales call with you. They learn by seeing you make a mistake. They learn by how you deal with the mistake.” “If you go to a meeting with me, you've got my full friggin attention the whole time." The Hill & Valley Forum 2026 The Hill & Valley Forum J.P. Morgan

The Hill & Valley Forum

429,206 görüntüleme • 3 ay önce

You have average looking women getting tons of attention on social media and it's inflating their egos into thinking they're 10s. Guys, you have only one move here 👇
0:25

You have average looking women getting tons of attention on social media and it's inflating their egos into thinking they're 10s. Guys, you have only one move here 👇

Jedediah Bila

12,464 görüntüleme • 2 ay önce

The city doesn't seem to care what's going on so I'm gonna have to see who else cares besides Me And 15 neighbors maybe I gotta call the News see if I can get them to pay attention but someone's going to pay attention to this matter. Fox News
3:22

The city doesn't seem to care what's going on so I'm gonna have to see who else cares besides Me And 15 neighbors maybe I gotta call the News see if I can get them to pay attention but someone's going to pay attention to this matter. Fox News

Horny Mike

83,433 görüntüleme • 3 ay önce

Ben McCollum on not buying into external narratives (such as, “the world’s falling when you’re losing” and “the world can’t be any better when you win a game”): “For me, I don’t pay attention. I just try not to pay attention. If my bosses tell me something, then I pay attention. If my wife tells me something, I pay attention. If my mom tells me something, I pay attention. If an outside Twitter source says something, I don’t really pay attention because it’s not something that is going to benefit me. It’s not going to help us improve. And so, you just have to stay grounded in that."
2:29

Ben McCollum on not buying into external narratives (such as, “the world’s falling when you’re losing” and “the world can’t be any better when you win a game”): “For me, I don’t pay attention. I just try not to pay attention. If my bosses tell me something, then I pay attention. If my wife tells me something, I pay attention. If my mom tells me something, I pay attention. If an outside Twitter source says something, I don’t really pay attention because it’s not something that is going to benefit me. It’s not going to help us improve. And so, you just have to stay grounded in that."

Tyler Tachman

111,149 görüntüleme • 5 ay önce

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:
2:41

New short course: Attention in Transformers: Concepts and Code in PyTorch. Last week we released a course on how LLM transformers work. This week, go deeper and learn about the technical ideas behind the attention mechanism, and see how to code it in PyTorch. This course is built with Joshua Starmer, Founder and CEO of StatQuest. The attention mechanism was a breakthrough that led to transformers, the architecture powering large language models like ChatGPT. Transformers, introduced in the 2017 paper: "Attention is All You Need" by Viswani and others, took off because of its highly scalable design. In this course, you’ll learn how the attention mechanism, a key element of transformer-based LLMs, works and implement it in PyTorch. You'll develop deep intuition about building reliable, functional, and scalable AI applications. What you will do: - Understand the evolution of the attention mechanism, a key breakthrough that led to transformers. - Learn the relationships between word embeddings, positional embeddings, and attention. - Learn about the Query, Key, and Value matrices, and how to produce and use them in attention. - Walk through the math required to calculate self-attention and masked self-attention to learn why and how they work. - Understand the difference between self-attention and masked self-attention and how one is used in the encoder to build context-aware embeddings and the other is used in the decoder for generative outputs. - Learn the details of the encoder-decoder architecture, cross-attention, and multi-head attention and how they are all incorporated into a transformer. - Use PyTorch to code a class that implements self-attention, masked self-attention, and multi-head attention. There're lots of exciting technical details in this course. Please sign up here:

Andrew Ng

132,204 görüntüleme • 1 yıl önce

Noam Shazeer is the co-author of "Attention is All You Need", by 2030 we have: - Personal AI assistants everywhere + Robots - World GDP to go way up to orders of magnitude higher than it is today. - We will have solved unlimited energy.
1:16

Noam Shazeer is the co-author of "Attention is All You Need", by 2030 we have: - Personal AI assistants everywhere + Robots - World GDP to go way up to orders of magnitude higher than it is today. - We will have solved unlimited energy.

Chubby♨️

46,704 görüntüleme • 1 yıl önce

DEEP STATE MIND CONTROLLED ASSASSINS Mind controlled assassins are patsies used by the deep state to kill a target under no control of their own. Usually have been in a program similar to MKUltra. Pay attention. They use the same playbook. Our government is 100% involved.
16:54

DEEP STATE MIND CONTROLLED ASSASSINS Mind controlled assassins are patsies used by the deep state to kill a target under no control of their own. Usually have been in a program similar to MKUltra. Pay attention. They use the same playbook. Our government is 100% involved.

Redpill Drifter

101,404 görüntüleme • 1 yıl önce

The American Dream is dead because our politicians have been compromised by a foreign nation! If you think we are going to vote our way out of this than you should pay attention to men like Jose Vega.
2:50

The American Dream is dead because our politicians have been compromised by a foreign nation! If you think we are going to vote our way out of this than you should pay attention to men like Jose Vega.

RyanMatta 🇺🇸 🦅

146,339 görüntüleme • 9 ay önce

This is one of my favorite speeches of all time… It always hits me hard: “You get up every day—you’re entitled to nothing. Nobody owes you nothing. You could have talent, but if you don’t have discipline, and you don’t execute, you don’t focus… What do you get? Nothing. If you’re complacent and not paying attention to detail… What does that get you? Nothing. So nothing is acceptable but your best.” This is a harsh reality of life: Talent is meaningless without discipline. Ambition is worthless without execution. Focus is the difference between potential and reality. You’re not entitled to success—it’s earned in the quiet, thankless moments when no one is watching. The antidote to entitlement is relentless ownership. Show up fully. Pay attention to the details. Do your best—every single day. Because in a world where everyone wants the reward, it’s those who refuse to accept anything less than their best who build something worth being proud of.
1:28

This is one of my favorite speeches of all time… It always hits me hard: “You get up every day—you’re entitled to nothing. Nobody owes you nothing. You could have talent, but if you don’t have discipline, and you don’t execute, you don’t focus… What do you get? Nothing. If you’re complacent and not paying attention to detail… What does that get you? Nothing. So nothing is acceptable but your best.” This is a harsh reality of life: Talent is meaningless without discipline. Ambition is worthless without execution. Focus is the difference between potential and reality. You’re not entitled to success—it’s earned in the quiet, thankless moments when no one is watching. The antidote to entitlement is relentless ownership. Show up fully. Pay attention to the details. Do your best—every single day. Because in a world where everyone wants the reward, it’s those who refuse to accept anything less than their best who build something worth being proud of.

Sahil Bloom

1,074,462 görüntüleme • 11 ay önce

This morning, I was almost attacked by a black man who didn’t speak English who followed me in to my building and tried to follow me into my apartment. The only reason he didn’t was that started screaming at him to draw attention to it. We shouldn’t have to live like this.
8:16

This morning, I was almost attacked by a black man who didn’t speak English who followed me in to my building and tried to follow me into my apartment. The only reason he didn’t was that started screaming at him to draw attention to it. We shouldn’t have to live like this.

Karlyn Borysenko, anti-communist cult leader

11,114 görüntüleme • 8 ay önce

Both the Trump & Harris campaign will have all the money they need, but Trump has the advantage because he gets so much earned media and Kamala does nothing to get media attention. NEWSMAX
0:56

Both the Trump & Harris campaign will have all the money they need, but Trump has the advantage because he gets so much earned media and Kamala does nothing to get media attention. NEWSMAX

Matt Schlapp

410,438 görüntüleme • 1 yıl önce

[Self-Attention] by Hand ✍️ Self-attention is what enables LLMs to understand context. How does it work? This exercise demonstrates how to calculate a 6-3 attention head by hand. Note that if we have two instances of this, we get 6-6 attention (i.e., multi-head attention, n=2). -- 𝗚𝗼𝗮𝗹 -- Transform [6D Features 🟧] to [3D Attention Weighted Features 🟦] -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- [1] Given ↳ A set of 4 feature vectors (6-D): x1,x2,x3,x4 [2] Query, Key, Value ↳ Multiply features x's with linear transformation matrices WQ, WK, and WV, to obtain query vectors (q1,q2,q3,q4), key vectors (k1,k2,k3,k4), and value vectors (v1,v2,v3,v4). ↳ "Self" refers to the fact that both queries and keys are derived from the same set of features. [3] 🟪 Prepare for MatMul ↳ Copy query vectors ↳ Copy the transpose of key vectors [4] 🟪 MatMul ↳ Multiply K^T and Q ↳ This is equivalent to taking dot product between every pair of query and key vectors. ↳ The purpose is to use dot product as an estimate of the "matching score" between every key-value pair. ↳ This estimate makes sense because dot product is the numerator of Cosine Similarity between two vectors. [5] 🟨 Scale ↳ Scale each element by the square root of dk, which is the dimension of key vectors (dk=3). ↳ The purpose is to normalize the impact of the dk on matching scores, even if we scale dk to 32, 64, or 128. ↳ To simplify hand calculation, we approximate [ □/sqrt(3) ] with [ floor(□/2) ]. [6] 🟩 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [7] 🟩 Softmax: ∑ ↳ Sum across each column [8] 🟩 Softmax: 1 / sum ↳ For each column, divide each element by the column sum ↳ The purpose is normalize each column so that the numbers sum to 1. In other words, each column is a probability distribution of attention, and we have four of them. ↳ The result is the Attention Weight Matrix (A) (yellow) [9] 🟦 MatMul ↳ Multiply the value vectors (Vs) with the Attention Weight Matrix (A) ↳ The results are the attention weighted features Zs. ↳ They are fed to the position-wise feed forward network in the next layer.
0:32

[Self-Attention] by Hand ✍️ Self-attention is what enables LLMs to understand context. How does it work? This exercise demonstrates how to calculate a 6-3 attention head by hand. Note that if we have two instances of this, we get 6-6 attention (i.e., multi-head attention, n=2). -- 𝗚𝗼𝗮𝗹 -- Transform [6D Features 🟧] to [3D Attention Weighted Features 🟦] -- 𝗪𝗮𝗹𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 -- [1] Given ↳ A set of 4 feature vectors (6-D): x1,x2,x3,x4 [2] Query, Key, Value ↳ Multiply features x's with linear transformation matrices WQ, WK, and WV, to obtain query vectors (q1,q2,q3,q4), key vectors (k1,k2,k3,k4), and value vectors (v1,v2,v3,v4). ↳ "Self" refers to the fact that both queries and keys are derived from the same set of features. [3] 🟪 Prepare for MatMul ↳ Copy query vectors ↳ Copy the transpose of key vectors [4] 🟪 MatMul ↳ Multiply K^T and Q ↳ This is equivalent to taking dot product between every pair of query and key vectors. ↳ The purpose is to use dot product as an estimate of the "matching score" between every key-value pair. ↳ This estimate makes sense because dot product is the numerator of Cosine Similarity between two vectors. [5] 🟨 Scale ↳ Scale each element by the square root of dk, which is the dimension of key vectors (dk=3). ↳ The purpose is to normalize the impact of the dk on matching scores, even if we scale dk to 32, 64, or 128. ↳ To simplify hand calculation, we approximate [ □/sqrt(3) ] with [ floor(□/2) ]. [6] 🟩 Softmax: e^x ↳ Raise e to the power of the number in each cell ↳ To simplify hand calculation, we approximate e^□ with 3^□. [7] 🟩 Softmax: ∑ ↳ Sum across each column [8] 🟩 Softmax: 1 / sum ↳ For each column, divide each element by the column sum ↳ The purpose is normalize each column so that the numbers sum to 1. In other words, each column is a probability distribution of attention, and we have four of them. ↳ The result is the Attention Weight Matrix (A) (yellow) [9] 🟦 MatMul ↳ Multiply the value vectors (Vs) with the Attention Weight Matrix (A) ↳ The results are the attention weighted features Zs. ↳ They are fed to the position-wise feed forward network in the next layer.

Tom Yeh

101,010 görüntüleme • 2 yıl önce

I need to know why I don't see anyone talking about THIS kdrama!! So far we only have 2 episodes and I'm already completely moved by them. These two will make me cry a lot, but I will love having my heart broken by them #OurMovie
0:56

I need to know why I don't see anyone talking about THIS kdrama!! So far we only have 2 episodes and I'm already completely moved by them. These two will make me cry a lot, but I will love having my heart broken by them #OurMovie

𝑩𝒆𝒍𝒍𝒆.

15,899 görüntüleme • 1 yıl önce

I've had a lot of potential Kittens reach out to me (due to my alpha-ness) wondering what they have to do to get my attention, so here you go!
1:25

I've had a lot of potential Kittens reach out to me (due to my alpha-ness) wondering what they have to do to get my attention, so here you go!

Reveredd 👹👴🏼

12,912 görüntüleme • 1 yıl önce

🚨*ATTENTION* My Posts on the WEATHER are being throttled (Censored) by X! PLEASE COMMENT If You are seeing my posts! (This is important to break through the algorithm!) MANY of my subscribers are reaching out telling me they do NOT get my notifications! ***IF THIS HAPPENS to you please go to my page and Unsubscribe, than refresh your browser and Re-subscribe again and click the bell icon to ALL! That SHOULD help reset how my notifications reach you! IF you regularly see my posts than you do not have to do anything at all! :)
3:36

🚨*ATTENTION* My Posts on the WEATHER are being throttled (Censored) by X! PLEASE COMMENT If You are seeing my posts! (This is important to break through the algorithm!) MANY of my subscribers are reaching out telling me they do NOT get my notifications! ***IF THIS HAPPENS to you please go to my page and Unsubscribe, than refresh your browser and Re-subscribe again and click the bell icon to ALL! That SHOULD help reset how my notifications reach you! IF you regularly see my posts than you do not have to do anything at all! :)

In2ThinAir

136,751 görüntüleme • 1 yıl önce

🚨Attention: You have problem with me and my post, Let’s get something straight. Here is my message to you.
5:44

🚨Attention: You have problem with me and my post, Let’s get something straight. Here is my message to you.

Terrence K. Williams

248,724 görüntüleme • 1 yıl önce

Nothing will top MatPat trying to get a MASSIVE read on my character, followed by CaseOh telling me to GET OUT 😭
0:30

Nothing will top MatPat trying to get a MASSIVE read on my character, followed by CaseOh telling me to GET OUT 😭

Zach 🎙️

799,825 görüntüleme • 1 yıl önce