The Bitcoin white paper, at 9 pages, defines the future of money, a supranational currency. Yuki's video discusses "Attention Is All You Need," a 15-page paper that defines the development of the entire AI industry. 1/ Previously, computers read sentences like a game of telephone; now it's like a roundtable discussion. Telephone is passed from one person to another, and by the time it reaches the last person, the beginning is long forgotten. Now, the entire sentence is laid out on the table, and each word is heard simultaneously by everyone. 2/ Meaning can be added and subtracted like coordinates. "Kitten" minus "cat" plus "dog" roughly equals "puppy." The direction from cat to kitten is almost the same as the direction from dog to puppy, demonstrating that computers truly translate meaning into location. 3/ Grammar isn't taught by humans; it's something the computer figures out on its own. Translating "The animal didn't cross the road because it was too tired," drawing its attention path shows that "it" points to "animal," not "road." No one told it this rule. 4/ The meaning of a word can be seen by what's standing next to it. "Apple" is fruit at a fruit stand, but at a press conference, it represents a company. The words haven't changed, but the surrounding text has, and the meaning has changed. 5/ Each word can choose its own path, prioritizing which one to listen to. "The kitten didn't eat the fish because it wasn't hungry." Upon seeing "it," the sentence will look back and find "kitten" to be the best match, then it will take the kitten's information. 6/ The first word and the hundredth word can be directly matched. No need for 98 intermediaries; one step is enough. Therefore, no matter how long the sentence, it won't forget the beginning as it reads the end. 7/ Its true strength lies in its ability to calculate simultaneously. It doesn't process words one by one in a queue; it calculates all words together, with thousands of GPU cores working at full capacity. This is why it can continuously add personnel and grow larger. 8/ Adding personnel is truly effective; this principle has been verified for nine years. Ten times larger models and ten times more data elevate capabilities to a new level. This is why companies dare to continuously buy GPUs and build server rooms. 9/ It analyzes from eight angles simultaneously, a division of labor it creates. One focuses on syntax, one on what refers to whom, and one on who performs the action. The division of labor isn't predetermined; it develops itself during training. 10/ It reads the entire text at once, so it can't distinguish the order. "Dog bites man" and "man bites dog" were once identical in its eyes, so each word needs to be numbered to know the order. 11/ Memory has a price. Doubling the sentence length quadruples the computation. The longer the data you provide, the more money you burn, which is why AI memory has always been expensive. 12/ It doesn't look up information; it just responds. Like you say half a sentence, it guesses your next sentence. So it will seriously fabricate, because it never looks up information; it only responds. 13/ During training, it's deliberately kept from becoming too confident. Forcing it to leave some leeway in its answers actually results in higher accuracy. Overly confident models are more prone to glaring errors. 14/ Each layer is modified only slightly, without starting from scratch. The new layer equals the old layer plus the modifications made in that layer, so it can stack dozens or hundreds of layers without becoming chaotic.