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μ΄λ»κ² μ΄ν΄ν΄μΌ ν κΉμ? | How do I make sense out of it? | IWSLT2017 | null | null | null |
μ¬λ¬λΆμ΄ μ€μ¨λ΄μ° κ°κ΅¬λ₯Ό μΌλ§λ μ 쑰립νλμ§μ μκ΄μμ΄ μ΄κ²μ μΌμμ λ°μ³λ ν μ μμ κ²λλ€. | Well, for however good you can be at assembling Swedish furniture, this instruction manual is nothing you can crack in your life. | IWSLT2017 | null | null | null |
κ·Έλμ 2014λ
μ λͺ
ν TED κ°μ°μμ΄μ νΌν° λ€μ΄μ맨λμ€μ ν¬λ μ΄κ·Έ λ²€ν°λ νμ¬λ₯Ό μ€λ¦½νκΈ°λ‘ νμ΅λλ€. | And so, in 2014, two famous TEDsters, Peter Diamandis and Craig Venter himself, decided to assemble a new company. | IWSLT2017 | null | null | null |
βHuman Longevityβλ ν λͺ©μ λ§μ μν΄ μκ²Όμ΅λλ€. νμν λͺ¨λ μλ¨μ μ΄μ©νμ¬ μ΄ μ±
μμ κ°λ₯ν ν λͺ¨λ κ²μ λ°°μ°λ κ²μ
λλ€. λ§μΆ€ν μμ½μ νμ€νλ ν λͺ©μ μ μν΄μμ. μ΄λ₯Ό μν΄ μΈλ₯μ 건κ°μ μν κ³Όμ λ₯Ό μ°Ύκ³ μ±
μ μ¨κ²¨μ§ λΉλ°μ μ°Ύλ κ²μ
λλ€. | Human Longevity was born, with one mission: trying everything we can try and learning everything we can learn from these books, with one target -- making real the dream of personalized medicine, understanding what things should be done to have better health and what are the secrets in these books. | IWSLT2017 | null | null | null |
μ ν¬ νμ 40λͺ
μ λ°μ΄ν° κ³Όνμμ λ λ§μ μ¬λμΌλ‘ μ΄λ£¨μ΄μ‘μ΅λλ€. λͺ¨λ μ‘΄κ²½μ€λ¬μ΄ λΆλ€μ΄μ£ . | An amazing team, 40 data scientists and many, many more people, a pleasure to work with. | IWSLT2017 | null | null | null |
μ ν¬μ μ κ·Όλ²μ μ¬μ€ κ΅μ₯ν κ°λ¨ν©λλ€. | The concept is actually very simple. | IWSLT2017 | null | null | null |
μ ν¬λ κΈ°κ³ νμ΅μ΄λΌλ κΈ°μ μ μ¬μ©ν©λλ€. | We're going to use a technology called machine learning. | IWSLT2017 | null | null | null |
λ¨Όμ μ μ μλ₯Ό μμ² κ° μ±μ·¨νκ³ | On one side, we have genomes -- thousands of them. | IWSLT2017 | null | null | null |
λμμ μΈκ°μ κ΄ν λͺ¨λ μ 보λ₯Ό μ‘°μ¬ν©λλ€. ννν, 3D μ€μΊ, NMRμ ν¬ν¨ν λͺ¨λ κ²μμ. | On the other side, we collected the biggest database of human beings: phenotypes, 3D scan, NMR -- everything you can think of. | IWSLT2017 | null | null | null |
μ΄ λ κ° μ¬μ΄μ μ μ μλ₯Ό μ½κΈ° μν λΉλ°μ΄ μκ² μ£ . | Inside there, on these two opposite sides, there is the secret of translation. | IWSLT2017 | null | null | null |
κ·Έλ¦¬κ³ μ΄ λ¨κ³μμ κΈ°κ³κ° μ¬μ©λ©λλ€. | And in the middle, we build a machine. | IWSLT2017 | null | null | null |
κΈ°κ³λ₯Ό λ§λ€κ³ , νλ ¨ν©λλ€. ν κ°κ° μλ μμ²λ μμ κΈ°κ³λ€μ μ μ μμ λ΄μ©μΌλ‘λΆν° νννμ μ°Ύλλ‘ νλ ¨ν©λλ€. | We build a machine and we train a machine -- well, not exactly one machine, many, many machines -- to try to understand and translate the genome in a phenotype. | IWSLT2017 | null | null | null |
κ° DNA μνλ²³μ 무μμ΄κ³ μ΄λ€ μν μ νλμ§ μ‘°μ¬νλλ‘ λ§μ΄μ£ . | What are those letters, and what do they do? | IWSLT2017 | null | null | null |
κΈ°κ³ νμ΅μ λͺ¨λ λΆμΌμμ μ¬μ©λμ§λ§, μ μ 체νμμ μ¬μ©νλ κ²μ νΉν μ΄λ ΅μ΅λλ€. | It's an approach that can be used for everything, but using it in genomics is particularly complicated. | IWSLT2017 | null | null | null |
μ‘°κΈμ© μ±κ³Όλ₯Ό λ΄λ©΄μ μ ν¬λ κ³Όμ λ€μ νμ₯ν΄κ°μ΅λλ€. | Little by little we grew and we wanted to build different challenges. | IWSLT2017 | null | null | null |
λ¨Όμ μΈκ°μ μΌλ°μ νΉμ§λΆν° ν΄λ
νμ΅λλ€. | We started from the beginning, from common traits. | IWSLT2017 | null | null | null |
μΌλ°μ νΉμ§μ λͺ¨λκ° κ°μ§ νΉμ§μ΄μ΄μ λ€λ£¨κΈ° νΈν΄μμ΄μ£ . | Common traits are comfortable because they are common, everyone has them. | IWSLT2017 | null | null | null |
κ³Όμ λ€μ λ€μκ³Ό κ°μμ΅λλ€. ν€λ₯Ό μμΈ‘ν μ μμκΉ? | So we started to ask our questions: Can we predict height? | IWSLT2017 | null | null | null |
μ΄ μ±
μμ μ¬λμ ν€λ₯Ό μ μ μμκΉ? | Can we read the books and predict your height? | IWSLT2017 | null | null | null |
μ λ§ κ°λ₯ν μΌμ΄λκ΅°μ. 5cm μ€μ°¨λ‘μ. | Well, we actually can, with five centimeters of precision. | IWSLT2017 | null | null | null |
체μ§λμ§μλ μνμ΅κ΄μ μ’μ°λ©λλ€λ§ μ¬μ ν 8kg μ€μ°¨λ‘ μΌμΆ λ§λκ΅°μ. | BMI is fairly connected to your lifestyle, but we still can, we get in the ballpark, eight kilograms of precision. | IWSLT2017 | null | null | null |
λ μκΉλ μκΉμ? | Can we predict eye color? | IWSLT2017 | null | null | null |
κ°λ₯ν©λλ€. 80%λ‘μ. | Eighty percent accuracy. | IWSLT2017 | null | null | null |
νΌλΆ μκΉμμ? | Can we predict skin color? | IWSLT2017 | null | null | null |
μμ 80%λ‘ κ°λ₯ν©λλ€. | Yeah we can, 80 percent accuracy. | IWSLT2017 | null | null | null |
λμ΄λ λ κΉμ? | Can we predict age? | IWSLT2017 | null | null | null |
κ·ΈλΌμ. μΈμμ΄ μ§λλ©΄μ μνΈκ° λ°λκ±°λ μ. | We can, because apparently, the code changes during your life. | IWSLT2017 | null | null | null |
짧μμ§κ³ , λ΄μ©μ΄ λΉ μ§κ³ , λ€μ΄κ°κΈ°λ νμ§μ. | It gets shorter, you lose pieces, it gets insertions. | IWSLT2017 | null | null | null |
μ΄λ° μ§νλ₯Ό μ°Ύμμ λͺ¨λΈννλ©΄ κ°λ₯ν©λλ€. | We read the signals, and we make a model. | IWSLT2017 | null | null | null |
μ΄μ μ¬λ°λ λ΄μ©μ΄ λμ΅λλ€. μ¬λμ μΌκ΅΄μ μ μ μμκΉμ? | Now, an interesting challenge: Can we predict a human face? | IWSLT2017 | null | null | null |
μ΄ κ³Όμ κ° μ΄λ €μ΄ μ΄μ λ μΌκ΅΄μ μ΄λ£¨λ λΆλΆμ΄ μ±
κ³³κ³³μ νΌμ ΈμκΈ° λλ¬Έμ
λλ€. | It's a little complicated, because a human face is scattered among millions of these letters. | IWSLT2017 | null | null | null |
μΌκ΅΄μ΄λ κ°λ
μμ²΄κ° λͺ
ννμ§ μκΈ°λ νκ³ μ. | And a human face is not a very well-defined object. | IWSLT2017 | null | null | null |
κ·Έλμ λ¨Όμ μΌκ΅΄μ μ μν΄μ κΈ°κ³μ κ°λ₯΄μΉκ³ μ½λ©, μμΆνλ μΌμ λͺ¨λ ν΄μΌ νμ΅λλ€. | So, we had to build an entire tier of it to learn and teach a machine what a face is, and embed and compress it. | IWSLT2017 | null | null | null |
κΈ°κ³ νμ΅μ μ μμλ λΆμ΄μλ©΄ μ΄ κ³Όμ μ΄ μΌλ§λ νλ€μ§ μμ€ κ²λλ€. | And if you're comfortable with machine learning, you understand what the challenge is here. | IWSLT2017 | null | null | null |
κ·Έλ¦¬κ³ μΈλ₯κ° DNA λ°°μ΄μ μμλΈ μ§ 15λ
μ΄ μ§λμ μ¬ν΄ 10μλΆν° μ€λ§λ¦¬κ° 보μ΄κΈ° μμνμ΅λλ€. | Now, after 15 years -- 15 years after we read the first sequence -- this October, we started to see some signals. | IWSLT2017 | null | null | null |
μμ£Ό κ°λμ μΈ μκ°μ΄μμ΅λλ€. | And it was a very emotional moment. | IWSLT2017 | null | null | null |
μ΄ μΌκ΅΄μ μ°λ¦¬ μ°κ΅¬μ ν λͺ
μ μΌκ΅΄μ
λλ€. | What you see here is a subject coming in our lab. | IWSLT2017 | null | null | null |
κΈ°κ³λ‘ μμΈ‘ν΄μΌ ν μΌκ΅΄μ΄μ£ . | This is a face for us. | IWSLT2017 | null | null | null |
μ€μ μ¬μ§μ μ°κ³ λ¨μν κ³Όμ μ μ‘°κΈ κ±°μ³€μ΅λλ€. μΌκ΅΄μ μλ λ§μ νΉμ§, ν , λΉλμΉ κ΅¬μ‘°κ° μνμ μκΈ΄ κ²μ΄κΈ° λλ¬Έμ΄μ£ . | So we take the real face of a subject, we reduce the complexity, because not everything is in your face -- lots of features and defects and asymmetries come from your life. | IWSLT2017 | null | null | null |
μΌκ΅΄μ λμΉ κ΅¬μ‘°λ‘ νΈμ§ν ν μκ³ λ¦¬μ¦μ μ€νν©λλ€. | We symmetrize the face, and we run our algorithm. | IWSLT2017 | null | null | null |
μ§κΈ 보μ¬λ리λ μ΄λ―Έμ§λ νμ‘μμ μΌκ΅΄μ μμν κ²°κ³Όμ
λλ€. | The results that I show you right now, this is the prediction we have from the blood. | IWSLT2017 | null | null | null |
μ μλ§μ. | Wait a second. | IWSLT2017 | null | null | null |
μ¬λ¬λΆλ€μ μ§κΈ λ μ΄λ―Έμ§λ₯Ό μ’μ°λ‘ λ²κ°μ 보면μ | and your brain wants those pictures to be identical. | IWSLT2017 | null | null | null |
μμΌλ‘ λ μ¬μ§μ΄ λΉμ°ν κ°μ κ²μ΄λΌ μ¬κΈΈ μ μμ΅λλ€. | So I ask you to do another exercise, to be honest. | IWSLT2017 | null | null | null |
μ λ μ¬λ¬λΆμ΄ μ μ§νκ² λ³΄μκΈΈ λ°λλλ€. μ°¨μ΄μ λ€μ μ°Ύμ보μκΈ° λ°λλλ€. | Please search for the differences, which are many. | IWSLT2017 | null | null | null |
λΉμ·νμ§λ₯Ό νλ¨νλ κΈ°μ€μ μ±λ³, λμ΄, 체μ§λμ§μ, λ―Όμ‘±μ±μΌλ‘ ν¬κ² λλκ² μ£ . | The biggest amount of signal comes from gender, then there is age, BMI, the ethnicity component of a human. | IWSLT2017 | null | null | null |
κ·Έ μ¬μ΄μμ μ€μλλ₯Ό λ°μ§λ κ²μ λ 볡μ‘ν κ²μ
λλ€. νμ§λ§ μ°¨μ΄λ€μ μκ°ν΄λ κ²°κ³Όλ₯Ό 보μλ©΄ μ ν¬κ° λͺ©νλ‘ μ λλ‘ κ°κ³ μκ³ | And scaling up over that signal is much more complicated. But what you see here, even in the differences, lets you understand that we are in the right ballpark, that we are getting closer. | IWSLT2017 | null | null | null |
κ·Όμ ν΄κ°μ μμ€ κ²λλ€. | And it's already giving you some emotions. | IWSLT2017 | null | null | null |
κ°λμ νμ λΆλ κ³μ€ κ²μ
λλ€. λ€λ₯Έ μ€νλμμ μ¬μ§κ³Ό μμκ²°κ³Όμ
λλ€. | This is another subject that comes in place, and this is a prediction. | IWSLT2017 | null | null | null |
μΌκ΅΄μ΄ μ’ μκ² λμκ³ λμμ΄ μμ νμ§λ μμ§λ§ μ¬μ ν λμ²΄λ‘ κ°μ΅λλ€. | A little smaller face, we didn't get the complete cranial structure, but still, it's in the ballpark. | IWSLT2017 | null | null | null |
λ€λ₯Έ μ°κ΅¬μμ μ¬μ§κ³Ό μμκ²°κ³Όμ
λλ€. | and this is the prediction. | IWSLT2017 | null | null | null |
μ ν¬λ κΈ°κ³λ₯Ό νλ ¨νλ©΄μ μ΄ μΌκ΅΄λ€μ 보μ¬μ£Όμ§ μμμ΅λλ€. | So these people have never been seen in the training of the machine. | IWSLT2017 | null | null | null |
μ΄λ κ² ν
μ€νΈμ νλ ¨μ΄ λΆλ¦¬λ κ²μ βν¬λ μμβμ΄λΌ ν©λλ€. | These are the so-called "held-out" set. | IWSLT2017 | null | null | null |
νμ§λ§ λͺ¨λ₯΄λ μ¬λλ€μ μΌκ΅΄λ§ λ΄μλ λ―Ώμμ΄ μ κ°μκ² μ£ . | But these are people that you will probably never believe. | IWSLT2017 | null | null | null |
μ ν¬λ μ λμ κ΄λ ¨μ 보λ₯Ό λͺ¨λ κΈ°κ³ νκ³ μμΌλ μ½μ΄λ³΄μ€ μ μμ΅λλ€. | We're publishing everything in a scientific publication, you can read it. | IWSLT2017 | null | null | null |
κ·Έλμ ν¬λ¦¬μ€κ° μ κ² μ μμ νλκ΅°μ. | But since we are onstage, Chris challenged me. | IWSLT2017 | null | null | null |
κ°μ°μμ μ¬λ¬λΆμ΄ μλ μ¬λμ λΆμ κ²°κ³Όλ₯Ό λΉκ΅ν΄λ³΄λΌκ³ μ. | I probably exposed myself and tried to predict someone that you might recognize. | IWSLT2017 | null | null | null |
μ, μ¬κΈ° νμ‘ ν λ³μ΄ μκ³ μ§κΈ μ¬λ¬λΆμ μ΄κ² λꡬ κ²μΈμ§ μ ν λͺ¨λ₯΄μλλ€. μ΄ ν λ³μλ μ ν¬κ° μ μ μ λΆμμ μλ²½νκ² ν μ μλ μμ μλ¬Όνμ μ λ³΄κ° μμ΅λλ€. | So, in this vial of blood -- and believe me, you have no idea what we had to do to have this blood now, here -- in this vial of blood is the amount of biological information that we need to do a full genome sequence. | IWSLT2017 | null | null | null |
μ΄ μμ΄λ©΄ μΆ©λΆν©λλ€. | We just need this amount. | IWSLT2017 | null | null | null |
λΆμ κ²°κ³Όλ₯Ό μ¬λ¬λΆκ» 보μ¬λλ¦¬κ² μ΅λλ€. | We ran this sequence, and I'm going to do it with you. | IWSLT2017 | null | null | null |
κ²°κ³Όλ€μ νλμ© μ΄ν΄λ΄
μλ€. | And we start to layer up all the understanding we have. | IWSLT2017 | null | null | null |
νμ‘μμ λμμ΄ λ¨μ±μΌ κ²μ΄λΌ μμνμ΅λλ€. | In the vial of blood, we predicted he's a male. | IWSLT2017 | null | null | null |
λ§μμ. λ¨μ±μ΄μ£ . | And the subject is a male. | IWSLT2017 | null | null | null |
ν€λ₯Ό 1m 76cmλΌ μμνλ€μ. | We predict that he's a meter and 76 cm. | IWSLT2017 | null | null | null |
μ€μ λμμ 1m 77cmμμ. | The subject is a meter and 77 cm. | IWSLT2017 | null | null | null |
μμμ 76kgμ΄μκ³ μ€μ λ 82kgμμ. | So, we predicted that he's 76; the subject is 82. | IWSLT2017 | null | null | null |
λμ΄λ 38μΈλ‘ λμκ΅°μ. | We predict his age, 38. | IWSLT2017 | null | null | null |
μ¬μ€μ 35μΈμ£ . | The subject is 35. | IWSLT2017 | null | null | null |
λμ μκΉ μμ κ²°κ³Όμ
λλ€. | We predict his eye color. | IWSLT2017 | null | null | null |
μ’ μ΄λ‘λ€μ. | Too dark. | IWSLT2017 | null | null | null |
μμν νΌλΆμμ
λλ€. | We predict his skin color. | IWSLT2017 | null | null | null |
κ±°μ κ·Όμ νλ€μ. | We are almost there. | IWSLT2017 | null | null | null |
μμν μΌκ΅΄μ
λλ€. | That's his face. | IWSLT2017 | null | null | null |
μ΄μ μ λ΅μ 곡κ°ν©λλ€. λμμ μ΄ μ¬λμ΄μμ΅λλ€. | Now, the reveal moment: the subject is this person. | IWSLT2017 | null | null | null |
μ λ₯Ό νν 건 μλμ μ΄μμ΅λλ€. | And I did it intentionally. | IWSLT2017 | null | null | null |
λ¨Όμ μ λ λ§€μ° νΉλ³ν λ―Όμ‘±μ μν΄μμ΅λλ€. | I am a very particular and peculiar ethnicity. | IWSLT2017 | null | null | null |
λ¨μ λ½, μ΄ν리μμΈμ λͺ¨λΈμ μ λ§μ§ μμ΅λλ€. | Southern European, Italians -- they never fit in models. | IWSLT2017 | null | null | null |
λ¨μ λ½μΈμ μ ν¬ λͺ¨λΈμ λμ μ€ νλμ
λλ€. | And it's particular -- that ethnicity is a complex corner case for our model. | IWSLT2017 | null | null | null |
λ€λ₯Έ μ΄μ λ μμ΅λλ€. | But there is another point. | IWSLT2017 | null | null | null |
μ¬μ€ μ ν¬κ° μ¬λμ μμλ³Ό λλ μ μ μμ λ°°μ΄μ κ³ λ €νμ§ μμ£ . | So, one of the things that we use a lot to recognize people will never be written in the genome. | IWSLT2017 | null | null | null |
λ°λ‘ 보μ΄λ κ·Έλλ‘ νλ¨νμ£ . | It's our free will, it's how I look. | IWSLT2017 | null | null | null |
μ κ²½μ°μ μ νΉμ΄ν μμΌμ μ§μ€νκ² λμ£ . | Not my haircut in this case, but my beard cut. | IWSLT2017 | null | null | null |
κ·Έλμ μ‘°κΈ μ΄λ―Έμ§λ₯Ό νΈμ§ν΄μ 보μ¬λλ¦¬κ² μ΅λλ€. λ³κ±΄ μλκ³ ν¬ν μ΅μΌλ‘ μμ
ν΄μ μμΌμ ν©μ±ν κ²λλ€. | So I'm going to show you, I'm going to, in this case, transfer it -- and this is nothing more than Photoshop, no modeling -- the beard on the subject. | IWSLT2017 | null | null | null |
νμκ°μ ν¨μ¬ λ λΉμ·νκ² λ³νμ£ . | And immediately, we get much, much better in the feeling. | IWSLT2017 | null | null | null |
μ ν¬λ μ μ΄λ° μΌμ ν κΉμ? | So, why do we do this? | IWSLT2017 | null | null | null |
νμ‘μΌλ‘λΆν° ν€λ₯Ό μμΈ‘νκ±°λ λλΌμ΄ μ¬μ§μ λ§λ€κΈ° μν΄μ μλλλ€. | We certainly don't do it for predicting height or taking a beautiful picture out of your blood. | IWSLT2017 | null | null | null |
κ·Έ μ΄μ λ μ΄ κ³Όμ κ³Ό κ°μ κΈ°μ κ³Ό μ κ·Όλ²μ κ°μ§κ³ κ°μ κΈ°κ³ νμ΅ μ½λλ‘ μ΄λ»κ² μ°λ¦¬κ° μλνλμ§ μ΄λ»κ² λͺΈμ΄ μλνκ³ μ΄λ»κ² λμ΄κ° λ€κ³ μ΄λ»κ² λ³μ΄ λ€κ³ μ΄λ»κ² μμ΄ νΌμ§κ³ μ½μ΄ μ΄λ»κ² λͺΈμ μμ©νλμ§ μ μ μκΈ° λλ¬Έμ
λλ€. | We do it because the same technology and the same approach, the machine learning of this code, is helping us to understand how we work, how your body works, how your body ages, how disease generates in your body, how your cancer grows and develops, how drugs work and if they work on your body. | IWSLT2017 | null | null | null |
μ΄κ±΄ λͺΉμ μ΄λ €μ΄ κ³Όμ μ
λλ€. | This is a huge challenge. | IWSLT2017 | null | null | null |
μ΄ κ³Όμ λ μΈκ³ μ μμμ μμ² λͺ
μ΄ ν¨κ» λ΅μ μ°Ύκ³ μμ΅λλ€. | This is a challenge that we share with thousands of other researchers around the world. | IWSLT2017 | null | null | null |
λ§μΆ€ν μμ½μ΄λΌλ κ³Όμ μ
λλ€. | It's called personalized medicine. | IWSLT2017 | null | null | null |
μ΄κ²μ μμ½μ ν΅κ³μ μΈ μ κ·Όμμ, λ§νμλ©΄ μ¬λ¬λΆ κ°κ°μ μμ μλ―ΈλΏμΈ λ°©λ²μμ κ°κ°μΈμ λ§μΆ μ κ·ΌμΌλ‘ μ΄ μ±
μ μ°μΈ λ΄μ©μ ν λλ‘ μ°λ¦¬κ° μ νν μ¬λ¬λΆμ μνλ₯Ό μ΄ν΄νλ λ₯λ ₯μΈ κ²μ
λλ€. | It's the ability to move from a statistical approach where you're a dot in the ocean, to a personalized approach, where we read all these books and we get an understanding of exactly how you are. | IWSLT2017 | null | null | null |
μ΄ κ³Όμ μ λ§€μ° λ³΅μ‘ν©λλ€. μ€μ λ‘ λͺ¨λ μ±
μμ μ€λκΉμ§ μ°λ¦¬κ° μ΄ν΄νλ λΆλΆμ 2%μ λΆκ³Όν©λλ€. 175κΆ μ€ 4κΆ λΆλμ΄μ£ . | But it is a particularly complicated challenge, because of all these books, as of today, we just know probably two percent: four books of more than 175. | IWSLT2017 | null | null | null |
μ΄λ μ κ° νκ³ μΆμ μ΄μΌκΈ°λ μλμ§λ§ μμΌλ‘ μ°κ΅¬νλ©΄μ λ μκ² λ κ²μ
λλ€. | And this is not the topic of my talk, because we will learn more. | IWSLT2017 | null | null | null |
μΈκ³ μ΅κ³ μ μνλ€μ΄ μ°κ΅¬νκ³ μμ΅λλ€. | There are the best minds in the world on this topic. | IWSLT2017 | null | null | null |
μμμ λ μ λ§κ³ λͺ¨λΈμ λ μ νν΄μ§ κ²μ
λλ€. | The prediction will get better, the model will get more precise. | IWSLT2017 | null | null | null |
λ λ€μν μ§μμ μμμλ‘ μΈλ₯λ μ΄μ κΉμ§λ μ νν μ μμλ μΆ, μ£½μ, μ‘μμ κ΄ν μ νμ ν μ μκ² λ κ²μ
λλ€. | And the more we learn, the more we will be confronted with decisions that we never had to face before about life, about death, about parenting. | IWSLT2017 | null | null | null |
μ ν¬λ μΆμ΄ μλνλ μ리μ ν΅μ¬μ λ€κ°κ°κ³ μμ΅λλ€. | So, we are touching the very inner detail on how life works. | IWSLT2017 | null | null | null |
μ΄λ κ² ν° νλͺ
μ μΌμΌν¬ λ°κ²¬μ κ³ΌνκΈ°μ μ μμμλ§ κ°λμ΄μ μ λ©λλ€. | And it's a revolution that cannot be confined in the domain of science or technology. | IWSLT2017 | null | null | null |
μ μμμ μν΅μ΄ νμν©λλ€. | This must be a global conversation. | IWSLT2017 | null | null | null |
μ ν¬λ ν μΈλ₯λ‘μ ν¨κ» λ§λ€μ΄κ° λ―Έλλ₯Ό μκ°ν΄μΌ ν©λλ€. | We must start to think of the future we're building as a humanity. | IWSLT2017 | null | null | null |
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