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搜狗推廣王小川·•◕◕◕:人機大戰的啟示,人工智慧的機

他認為╃•₪▩☁,人工智慧的前提是要理解深度學習╃•₪▩☁,機器是模仿人腦去學習•◕│✘·。在 1985 年╃•₪▩☁,人類就提出了人工智慧╃•₪▩☁,但那時做不到╃•₪▩☁,計算量太大╃•₪▩☁,而現在技術已經成熟╃•₪▩☁,主要表現在計算力的提升•◕│✘·。在這樣的背景下╃•₪▩☁,要定位好自己•◕│✘·。
He thinks, the premise is to understand the deep learning of artificial intelligence, machine is to imitate the human brain to learn. In 1985, humans have put forward the artificial intelligence, but can't do that, then calculate the amount is too big, and now the technology is mature, mainly displays in the ascension of computing power. In this context, to locate yourself. 
 
人機大戰的啟示,人工智慧的機遇與挑戰
人工智慧但凡與實踐結合╃•₪▩☁,是否就會降低人工智慧的水平•◕│✘·。人工智慧有三個層次·•◕◕◕:
In artificial intelligence and the practice union, whether can reduce the level of artificial intelligence. Artificial intelligence has three levels: 
 
 
1.將傳統規則教給機器
1. The traditional rules to teach machines 
2.將答案教給機器記憶學習
2. Teach your answer to machine learning memory 
3.將目標給機器自我學習
3. The target for self learning machine 
 
 
這是一個不斷進化的過程
This is a process of constant evolution 
基於這樣背景╃•₪▩☁,什麼樣的人容易被取代╃·╃✘?如果人類的工作環境相對封閉╃•₪▩☁,工作結果的更加標準化╃•₪▩☁,資訊需求較少╃•₪▩☁,那麼這樣的工作更加被機器取代•◕│✘·。相反╃•₪▩☁,認知邊界越寬廣╃•₪▩☁,需求資訊越多╃•₪▩☁,這樣的工作╃•₪▩☁,就不容易被取代•◕│✘·。
Based on this background, what kind of person easy to be replaced? If human relatively closed environment, the work is the result of more standardization, information demand is less, then the more replaced by machines. In contrast, the broader cognitive boundary, demand information, the more of this work, it is not easy to be replaced. 
 
 
人最終是否會被取代╃·╃✘?
People will eventually be replaced? 
想象力是目前機器取代不了的•◕│✘·。但如果科學家╃•₪▩☁,以創造生命的態度去做╃•₪▩☁,那就不一樣了╃•₪▩☁,要理解這人工智慧和創造生命這是不一樣的概念•◕│✘·。未來╃•₪▩☁,人類是和機器一起的進步的╃•₪▩☁,如果我們目標清晰•╃、環境封閉╃•₪▩☁,那麼機器會代替我們一些繁重的活╃•₪▩☁,把我們解放出來╃•₪▩☁,技術會帶來與人的融合╃•₪▩☁,讓我們生活水平提高•◕│✘·。
Imagination is the machine can't replace. But if scientists to create life attitude is to do it, it won't be the same, to understand the artificial intelligence and create life this is not the same concept. In the future, the progress of human is together with the machine, if we clear goal, the environment is closed, the machine will be instead of some of our hard work, liberating us, technology will bring and the fusion of people, let us raise living standards. 
 
 
以下是王小川分享要點記錄·•◕◕◕:
The following is wang xiaochuan share points record: 
今天我抬頭第一眼看到咱們講的是“新硬體生態”•◕│✘·。我們在提一個詞叫智慧硬體╃•₪▩☁,其實每次提到這個詞我反而焦慮╃•₪▩☁,我們有時候弄了沒有想明白的詞做這個事情╃•₪▩☁,可能帶來很多風險•◕│✘·。
Today, I looked up first saw is we speak of "ecological" new hardware. We in a word, called intelligent hardware, in fact, every time I mentioned the word I anxiety instead, we want to understand the word sometimes got no do this thing, can bring a lot of risk. 
 
 
就像我們很多公司在做一個技術╃•₪▩☁,這個技術能做什麼•╃、不能做什麼╃•₪▩☁,沒有判斷╃•₪▩☁,要麼產生恐懼感╃•₪▩☁,要麼產生盲目膜拜╃•₪▩☁,不知道有什麼意義╃•₪▩☁,可能投資或者做事就偏了•◕│✘·。當我們回到“新硬體”反而給我留下空間詮釋什麼是智慧•◕│✘·。
As we did a lot of companies in a technology, this technology can do, can't do anything, no judgement, or fear, or produce blind worship, don't know what's the meaning, may the Angle of investment or doing things. When we went back to the "new hardware" instead, give me space to interpret what is intelligence. 
 
 
今年3月8號開始的一週時間有一個Alpha Go李世石的人機大戰╃•₪▩☁,人跟最頂尖的科技公司進行了一場較量•◕│✘·。這場比賽的賽前很有意思╃•₪▩☁,我們回顧一下╃•₪▩☁,但是Google一發布我就興奮了╃•₪▩☁,因為兩年前我看到了他們的生物科學的發展╃•₪▩☁,很可惜自己沒有把氣場攢起來╃•₪▩☁,我跟清華的同事和實驗室都提到這個想法╃•₪▩☁,但是沒有提做下圍棋這件事情╃•₪▩☁,說這件事情太難了•◕│✘·。
March 8, began a week of time have an Alpha Go lee se-dol man-machine war, along with the top technology companies fought a battle. Before the game is very interesting, we review, but Google released a I am excited, because two years ago, I saw the development of their biological science, it is a pity that you didn't have the aura saved up, my colleagues and with tsinghua lab would talk about the idea, but do not go the matter, said it was too difficult. 
 
 
沒有參與這件事情是挺可悲的事情╃•₪▩☁,有很多朋友說你怎麼積極的在這件事情╃•₪▩☁,包括2月份就在知乎上寫檔案說Google會有╃•₪▩☁,沒有參與╃•₪▩☁,圍觀總是可以的╃•₪▩☁,所以這個事情參與了比較多心思•◕│✘·。賽前的時候我發現大部分人看好╃•₪▩☁,有兩類人不看好╃•₪▩☁,第一類人是圍棋選手╃•₪▩☁,尤其是參加世界大賽拿到9段的選手╃•₪▩☁,包括聶衛平•◕│✘·。
Not involved in this matter is quite sad things, there are a lot of friends say you how positive in this matter, including in February on zhihu said Google would have write files, didn't participate, onlookers can always, so the things more involved in the state of mind. Before the game when I found that most people, there are two kinds of people, the first kind of people is a go player, especially in the world competition to paragraph 9 players, including wei-ping nie. 
 
 
我在五局棋裡面參加了兩局╃•₪▩☁,我跟大家講我對圍棋理解不同╃•₪▩☁,雖然知道規則╃•₪▩☁,但是根本沒有辦法判斷這個局面好不好╃•₪▩☁,所以下棋比賽當中我就看一件事情就知道這個比賽人是否會贏•◕│✘·。我就看教練的臉色╃•₪▩☁,臉色越難看機器勝算越大╃•₪▩☁,最後教練崩潰了╃•₪▩☁,最後機器贏了•◕│✘·。
I attended two innings in five innings chess, I tell you to go I understand different, although know the rules, but there is no way to judge the situation is good, so the chess game I saw one thing will know who will win the game. I will look at the coach's face, and his face more ugly machine the odds, the greater the last coach crash, finally won the machine. 
 
 
我講這個例子是╃•₪▩☁,我們似乎面臨一些威脅╃•₪▩☁,曾經擅長的思考能力開始由機器侵入進來•◕│✘·。一個圍棋選手的情況就是一個機器把你的成功和引以為豪的東西替代的時候╃•₪▩☁,這是一種什麼恐懼•◕│✘·。我想以後各位可能多多少少會經歷一點╃•₪▩☁,包括騰訊開始改BUG了╃•₪▩☁,我們可能有這種壓力•◕│✘·。
I speak this example is that we seem to be facing some threats, ever good at thinking began to invade by machines. A go player is a machine to your success and proud of things instead of, this is what a kind of fear. I think you more or less likely to experience a little later, including tencent started to change BUG, we may have this kind of pressure. 
 
 
在這個比賽前╃•₪▩☁,很多網際網路的代表人物╃•₪▩☁,甚至有技術代表的人都認為人會贏╃•₪▩☁,機器贏不了╃•₪▩☁,包括我們公司十幾個人╃•₪▩☁,我們一一去問╃•₪▩☁,百分之八九十的人給我的答覆是未來機器會贏╃•₪▩☁,但是這次人會贏•◕│✘·。他們認為下圍棋會有區別╃•₪▩☁,認為以後機器沒有問題╃•₪▩☁,但是現在時機不成熟╃•₪▩☁,很不幸╃•₪▩☁,最後機器確實戰勝了人•◕│✘·。
Before the game, a lot is the representative figure of the Internet, and even have a technical representative of people believe that people will win, the machine can't win, a dozen people, including our company we ask, what people give my answer will be the future machines will win, but the people will win. They think go there will be a difference, think later machine, there is no problem, but the timing was not mature now, unfortunately, the machine did win over the people. 
 
 
我們當時記小黑板說不夠情懷╃•₪▩☁,即便做科技的人也沒有想到這個這麼突然╃•₪▩☁,這是這個事情給大家的感受•◕│✘·。
When we work to remember that not enough feelings, even if to do science and technology of people did not think of this all of a sudden, so this is the thing to feel. 
 
 
但是我想我們不要有恐懼或者有浪漫性的關懷╃•₪▩☁,我們瞭解它到底能做什麼•╃、不能做什麼╃•₪▩☁,這對我們生活態度和工作有幫助•◕│✘·。不是說這個東西到底怎麼賺錢╃•₪▩☁,很多朋友說到底是不是商業機會╃•₪▩☁,我們究竟怎麼理解這件事情╃•₪▩☁,包括人自身的提升╃•₪▩☁,我們對機器的瞭解可能會更加長遠•◕│✘·。
But I think we don't have fear or a romantic caring, we know what it can do, can't do anything, it is helpful to our attitude towards life and work. Not to say that this thing how to make money, a lot of friends at the end of the day is business opportunities, how we understand the matter, including one's own ascension, our understanding of the machine could be more in the long run. 
 
 
不知道大家有沒有聽過“深度學習”這個詞•◕│✘·。大部分都聽過╃•₪▩☁,因為這個詞像Alpha Go一樣╃•₪▩☁,講到了一個特別神秘的概念╃•₪▩☁,就是機器的深度學習或者智慧•◕│✘·。
Don't know if you have heard the word "deep learning". Most have heard, because the word like Alpha Go, speak to a mysterious concept in particular, is the depth of the machine learning or intelligence. 
 
 
深度學習講的是兩個概念╃•₪▩☁,第一個概念是機器學裡面用神經語言模擬人腦的語言模型做訓練或者機器的識別╃•₪▩☁,就是把你的輸入變成向量╃•₪▩☁,中間經過迭代做到結果•◕│✘·。第二是迭代的網路結果很深╃•₪▩☁,不是一層可以做到╃•₪▩☁,需要多層•◕│✘·。後來研究人員不斷提升這樣的模型•◕│✘·。
Deep learning is about two concepts, the first concept is inside the machine learning neural language was used to simulate the brain language model for training or machine identification, is your input into vector, intermediate results through iteration do. The second is the network result of iterative deep, is not a layer can be done, need to multilayer. Later the researchers improve the model. 
 
 
這個概念很早就提出來了╃•₪▩☁,1985年這個理論就已經趨於成熟╃•₪▩☁,它是反向傳播╃•₪▩☁,機器怎麼進行訓練這個已經有了•◕│✘·。有了這個機器之後有一個問題╃•₪▩☁,計算量太大╃•₪▩☁,做不到•◕│✘·。當時十幾個結點的時候機器已經不夠用了╃•₪▩☁,但是現在最大的不是理論體系變化╃•₪▩☁,而是計算力的提升•◕│✘·。
This concept proposed early in 1985, this theory has been mature, it is a back propagation, the machine how to training the already have. Then there is a problem with this machine, amount of calculation is too big, can't do. Ten several nodes at the time when machine is not enough, but now the largest theoretical system is not change, but the calculations of ascension. 
 
 
這也告訴大家╃•₪▩☁,從人工智慧到理論深度學習的做法╃•₪▩☁,包括之前的這些機器的理解能力已經慢慢成熟╃•₪▩☁,今天我們用的方法沒有超過當年理論的框架和計算模式•◕│✘·。這不是一個新東西•◕│✘·。
This also tells us, from the theory of artificial intelligence to the deep learning approach, including the machines before understanding has been mature, today we use the method of no more than when the theoretical framework and computing model. This is not a new thing. 
 
 
發生了什麼變化呢╃·╃✘?其實變化在兩件事情╃•₪▩☁,一件事情是計算力的極大的提升•◕│✘·。Alpha Go的機器計算力是深藍的2.5萬倍•◕│✘·。第二個是我們採集了大量的資料╃•₪▩☁,資料採集比較困難╃•₪▩☁,現在有大資料之後╃•₪▩☁,資料有多大╃·╃✘?其實下圍棋沒有多大╃•₪▩☁,基本上資料基本上用了30萬臺曾經下過圍棋的用來做訓練•◕│✘·。
What has changed? Actually change the two things, one thing is a tremendous improvements in the computing power. Alpha Go machine force is calculated by deep blue 25000 times. The second is that we collected a large amount of data, data acquisition is difficult, there are big data, data have how old? Actually go there is not much, basically data basically with 300000 units have been under go for training. 
 
 
沒有網際網路不敢想╃•₪▩☁,有了網際網路之後╃•₪▩☁,國外下圍棋的網站上已經有對應的資料╃•₪▩☁,30萬臺╃•₪▩☁,每一臺大概100步的樣子╃•₪▩☁,所以一共3000萬步棋做了訓練•◕│✘·。我們怎麼把這個理論用來下圍棋╃•₪▩☁,這是Google的創新•◕│✘·。第一件事情cnn網路(音)╃•₪▩☁,用點看圖的方法來下棋╃•₪▩☁,以前說棋子是邏輯分析╃•₪▩☁,而不是網路•◕│✘·。
Without the Internet did not dare to think, after the Internet, go abroad website have corresponding data, 300000 units, each about 100 steps, so a total of 30 million positions do the training. How do we put this theory to go, this is Google's innovation. First thing CNN network (sound), use the method of point at the picture to play chess, said before pieces is the logical analysis, rather than the network. 
 
 
現在就像看照片一樣看棋盤╃•₪▩☁,因此機器有了棋感•◕│✘·。最近五年有一個最大的提升是人臉識別╃•₪▩☁,之前是完全不知道怎麼樣的事情╃•₪▩☁,識別眉毛嗎╃·╃✘?識別眼睛嗎╃·╃✘?在座可能有寫程式的╃•₪▩☁,你想一下用什麼規則去描述人的臉╃•₪▩☁,但是今天我們用CNN影象的感覺做到了•◕│✘·。
Just like looking at photographs board now, so the machine have move feeling. The last five years has one of the biggest improvement is face recognition, before is completely don't know how, to identify their eyebrows? Identify the eyes? Everyone here may have write programs, it with what rules do you want to go to describe the person's face, but today we use the feeling of CNN image did. 
 
 
所以Google的第一個創新是用CNN網路對機器進行描述╃•₪▩☁,使得機器有了體感•◕│✘·。第二個是把跟深藍相關的搜尋作為理性的方式╃•₪▩☁,跟CNN的感性進行結合╃•₪▩☁,這是第二件創新•◕│✘·。第三件事情是用的強大的學習╃•₪▩☁,讓機器跟自己下╃•₪▩☁,當機器變得聰明之後自己可以跟自己下╃•₪▩☁,因此在這裡面提升•◕│✘·。這裡面並沒有帶來理論界的突破╃•₪▩☁,但是在創新應用裡面做了很大的貢獻•◕│✘·。所以Alpha Go的勝利背後融合了工程師的重新能力在•◕│✘·。
So Google's first CNN network was carried out on the machine is used to describe the innovation, makes the machine has the feeling of the body. The second is associated with deep search as a rational way, combination with CNN's perceptual, this is the second innovation. The third thing is to use powerful learning, let the machine with himself, as machines become smarter after you can talk with myself, so in this promotion. It does not bring theoretical breakthrough, but made great contribution in innovative applications. So Alpha Go behind the victory is a blend of the engineer's ability in again. 
 
 
這件事情真正重要的在於什麼地方╃·╃✘?不是在於技術本身╃•₪▩☁,而是所有人在關心我們自己的定位•◕│✘·。我把這一週的活動比擬成原來幾十年文藝復興的結果•◕│✘·。這樣一個星期過去╃•₪▩☁,我恍如隔世•◕│✘·。大家知道一個星期機器是什麼理解和態度╃•₪▩☁,一個星期之後有了很大的變化•◕│✘·。
It's really important is what place? Is not the technology itself, but to all people in the care of our own positioning. I compare the activities of this week as the original decades the results of the Renaissance. A week past, I like a lifetime ago. You know what a week machine understanding and attitude, has changed a lot after a week. 
 
 
我們人和人的關係╃•₪▩☁,包括我們看《聖經》╃•₪▩☁,人和人平等了╃•₪▩☁,距離拉近了•◕│✘·。現在我們怎麼看這臺機器╃·╃✘?比賽之前大家認為機器比較笨╃•₪▩☁,什麼幹不了╃•₪▩☁,比賽之後有兩個重要變化╃•₪▩☁,第一個是我們對機器的能力有了更高的評價╃•₪▩☁,機器可以戰勝人了•◕│✘·。之前說我們看病的時候╃•₪▩☁,你拍一個片子╃•₪▩☁,機器告訴你做診斷沒有什麼病╃•₪▩☁,我們難以接受╃•₪▩☁,很不相信•◕│✘·。
Our relationship between person and person, including we read the bible and human equality, closer. Now how do we see the machine? Before the game we think machines are stupid and what to do, after the game there are two important changes, the first is the ability of our machine with higher evaluation, the machine can be overcome. Said when we see a doctor, before you make a film, machine diagnosis, there is nothing to tell you do we find it difficult to accept, very don't believe it. 
 
 
現在機器告訴你一個什麼結果╃•₪▩☁,我們可能覺得比人還要準•◕│✘·。但是能想到這個變化嗎╃·╃✘?我們機器因為這一個事件之後╃•₪▩☁,對能力有一個巨大的認可•◕│✘·。這使得我們更多的工程師•╃、更多的創業公司•╃、更多的資本會投向人工智慧•◕│✘·。
Machine now tell you what a result, we may feel better than people. But can think of this change? Our machine because after this event, the ability to have a great recognition. This makes it more engineers and more entrepreneurial company, more capital to artificial intelligence. 
 
 
我開玩笑講A股人工智慧概念可能延續了好幾個漲停板╃•₪▩☁,我們看到了人工智慧的信仰•◕│✘·。但是很巧的是Alpha Go不是五局都勝利的╃•₪▩☁,輸了一局╃•₪▩☁,但是大家還是轉不過勁兒來•◕│✘·。它代表了整個圍棋界的共同的勝利╃•₪▩☁,就是機器變成另外的門派╃•₪▩☁,我們還是有自己的尊嚴•◕│✘·。
I joke that a-share artificial intelligence concepts may continue for several harden board, we see the faith of artificial intelligence. But very skillful is Alpha Go not five innings are victory, lost the game, but everyone still turn to. It represents the whole excel common victory, is the machine into the other factions, we still have their own dignity. 
 
 
想到二十年前的電影《獨立日》╃•₪▩☁,當時人類飛行員面臨太空船的時候╃•₪▩☁,把飛船開進去把太空船破壞掉╃•₪▩☁,所以面對機器我們還有尊嚴•◕│✘·。更多人╃•₪▩☁,我們很多年輕人開始乘Alpha Go叫狗狗•◕│✘·。我們想到年輕人90後或者00後會覺得機器人成為朋友╃•₪▩☁,還有人叫阿老師•◕│✘·。
Think of the movie "independence day" twenty years ago, when human pilots are faced with the spacecraft, the spacecraft in the spacecraft eroded, so in the face of the machine we have dignity. More people, many of our young people begin to take Alpha Go call a dog. 90 00 or after we think young people will think robot will become friends, there are also called the teacher. 
 
 
其實機器並沒有到不可戰勝的時候╃•₪▩☁,第一是要相信它╃•₪▩☁,第二要接受它•◕│✘·。拒絕它很難╃•₪▩☁,我們要接受它•◕│✘·。這是我們整理人機大戰的關鍵╃•₪▩☁,到底我們怎麼用•╃、怎麼跟它交朋友╃•₪▩☁,後面會提到╃•₪▩☁,所以這場啟蒙運動很重要•◕│✘·。
Machine is not actually to invincible, the first is to believe in it, the second to accept it. To reject it is very difficult, we have to accept it. This is our key to finishing the man-machine war, how we use and how to make friends with it, later, so it's very important to the enlightenment. 
 
 
技術的進步有三個層次╃•₪▩☁,不管是從軟體硬體•◕│✘·。這三個層次是什麼呢╃·╃✘?傳統最早的是智慧╃•₪▩☁,其實是把規則交給機器•◕│✘·。舉個例子╃•₪▩☁,我們做一個電飯鍋•╃、智慧冰箱╃•₪▩☁,它來幹嘛呢╃·╃✘?我們程式設計師要寫程式╃•₪▩☁,當溫度到103度的時候我就跳閘•◕│✘·。實際上我們可以把足夠複雜的東西交給機器╃•₪▩☁,把人類的智慧交給機器•◕│✘·。很不幸的時候╃•₪▩☁,老師講學生如果把規則給他╃•₪▩☁,他的能力會下降╃•₪▩☁,所以這時候機器比人落後╃•₪▩☁,智力少於人•◕│✘·。
Technological progress has three levels, no matter from software to hardware. What is the three level? Traditional earliest is intelligent, is actually the rules to the machines. For example, we do an electric rice cooker, refrigerator, intelligent it doing here? Our programmers to write programs, when the temperature to 103 degrees when I tripped. In fact we can put things complicated enough to the machines, the wisdom of human to the machines. Unfortunately, teachers tell students if give his rules, his ability will decline, so this time machine behind people, intelligence less than one. 
 
 
還有一種情況╃•₪▩☁,我們自己都不知道規則是什麼╃•₪▩☁,我們是用感覺•◕│✘·。就像剛才講的人臉識別╃•₪▩☁,這是一個非常經典的問題•◕│✘·。每個人都覺得很簡單╃•₪▩☁,可能臉盲吃力一點╃•₪▩☁,但是大部分沒有問題╃•₪▩☁,不像大家學外語這麼難╃•₪▩☁,只是可能記憶力不好╃•₪▩☁,識別沒有問題•◕│✘·。當把這個問題給機器的時候我們遇到了障礙╃•₪▩☁,幾十年裡面在影象識別方面我們舉步維艱╃•₪▩☁,國外就有做過把這種資料分大類╃•₪▩☁,每年進展非常緩慢╃•₪▩☁,搞影象的人基本上找不到工作╃•₪▩☁,因為不實用•◕│✘·。
There is a kind of situation, we do not know what are the rules, we are feeling. Like just about face recognition, this is a classic problem. Everyone thinks is very simple, may face blindness struggling a bit, but there is no problem, most don't like everyone to learn a foreign language so hard, just may be memory, recognition: no problem. When the problem to the machine we encountered obstacles, decades in the aspect of image recognition we struggling, abroad have done the data points, progress is very slow, every year make images of people basically couldn't find a job, because is not practical. 
 
 
很長時間裡麵人工智慧跟理論是脫節的•◕│✘·。我們前幾年跟清華做人工智慧的院士聊╃•₪▩☁,說人工智慧但凡跟實踐結合就拉低水平╃•₪▩☁,因為連線不上╃•₪▩☁,但是現在不是╃•₪▩☁,現在連線到一塊了╃•₪▩☁,為什麼呢╃·╃✘?因為我們到了第二個階段╃•₪▩☁,我們開始不用跟機器講規則•◕│✘·。深度學習的美妙之處是我們把問題和答案對應的交給機器╃•₪▩☁,告訴他這是張三的臉╃•₪▩☁,那個臉是李四的╃•₪▩☁,不用告訴機器為什麼他是張三或者李四•◕│✘·。
For a long time with artificial intelligence theory is disjointed. A few years ago we chat with tsinghua do academician of artificial intelligence, said the artificial intelligence in combination with practice is low, because the connection is not on, but not now, now connected to a, why? Because we came to the second stage, we began to speak rules with the machine. Depth study of the beauty is corresponding to the machines, we put the question and answer told him it was zhang SAN's face, the face is li si, don't tell why he is zhang SAN and li si machine. 
 
 
機器透過大量的資料訓練就能夠學會•◕│✘·。就像我們教小孩一樣╃•₪▩☁,透過這些方法一步一步的展開╃•₪▩☁,而在這裡我們一併給到機器╃•₪▩☁,影象和聲音領域已經非常好了•◕│✘·。去年開始影象人臉識別機器超過了人╃•₪▩☁,準確率超過了人一倍•◕│✘·。我們可以告訴機器答案╃•₪▩☁,機器可以自己學習•◕│✘·。這還不夠╃•₪▩☁,甚至有一些問題我們連答案還不夠•◕│✘·。我們說學圍棋有3000萬步答案╃•₪▩☁,機器就學會了下棋的基本規則╃•₪▩☁,把6段到9段的方法告訴它╃•₪▩☁,它達到了6段水平•◕│✘·。
Through a large amount of data training can learn to machine. Just like we teach children, through these methods, step by step, and here we give to the machine, along with all the images and sound field has been very good. Last year began to face recognition machine over the images, the accuracy of more than one times. We can tell the machine, the machine can learn myself. It is not enough, there are even some problems we even the answer is not enough. We said 30 million steps on go to learn the answer, the basic rule of machine learns to play chess, take 6 to 9 paragraphs way to tell it, it reached the 6 segment level. 
 
 
後來Google讓兩個機器隨機下╃•₪▩☁,下完之後不告訴你贏了還是輸了╃•₪▩☁,機器透過自己最佳化演算法找到更好的答案•◕│✘·。第二件事情是把答案給機器╃•₪▩☁,第三個是告訴你機器給你一個答案╃•₪▩☁,我評價答案是好或者不好•◕│✘·。這是三個層次的進度•◕│✘·。特別是第三件事情╃•₪▩☁,像Google團隊或者微軟頂級的人裡面╃•₪▩☁,甚至可能有宗教色彩╃•₪▩☁,當我們給機器一個目標╃•₪▩☁,機器是否自己學會找規則和答案╃•₪▩☁,我只給他目標╃•₪▩☁,而不是告訴他怎麼做╃•₪▩☁,這是演進中很重要的一步•◕│✘·。
Google later for the two random machine, after not tell you win or lose, machine through optimization algorithm to find a better answer. The second thing is the answer to the machine, the third is to tell you machine to give you an answer, I evaluate the answer is good or bad. This is three levels of progress. Especially the third thing, people like Google or Microsoft's top team, and may even have a religious color, when we give the machine a goal, a machine if I learn to find rules and answers, I just give him a target, instead of telling him what to do, this is very important step in evolution. 
 
 
我看到一個文獻╃•₪▩☁,他們想重新訓練一臺Alpha Go的機器╃•₪▩☁,一開始不是讓它學習人怎麼下的╃•₪▩☁,一開始是兩張白紙的Alpha Go╃•₪▩☁,自己跟自己下╃•₪▩☁,只告訴它目標是贏棋•╃、輸棋╃•₪▩☁,看能不能訓練出新的棋手•◕│✘·。我覺得這件事情很有意義•◕│✘·。
I saw a literature, they want to training a Alpha Go machine, a began to not let it learn how, starting with two pieces of white paper of Alpha Go, with himself, only tell it goal is to win, check, can see the training of new players. I think it is very meaningful. 
 
 
一個人在中原學會了所有武功╃•₪▩☁,把它融匯貫通╃•₪▩☁,然後再進行提高•◕│✘·。另外一個人從來沒有來過地球和中原╃•₪▩☁,來學武功╃•₪▩☁,你說它的武功跟人一樣嗎╃·╃✘?這是人類好奇的╃•₪▩☁,看重新會長出什麼智慧來•◕│✘·。這是三個層次做的事情•◕│✘·。
A person learned all powers in central plains, it will converge, and then to improve. There was another man who had never been to the earth and the central plains, to learn martial arts, you said it's skill with people? This is the curious to see what wisdom will grow again. This is made of three layers. 
 
 
基於剛才三個層次╃•₪▩☁,我們來想╃•₪▩☁,什麼樣的職業•╃、什麼樣的人本身的工作更容易被取代╃·╃✘?我們可以看作是機會╃•₪▩☁,也可以看作對自身的挑戰•◕│✘·。
Based on three levels just now, let's think, what kind of person what kind of occupation, work itself is more likely to be replaced? We can be regarded as a chance, also can be seen as a challenge to itself. 
 
 
容易取代的有兩點╃•₪▩☁,第一是你的工作和環境封閉•◕│✘·。意味著做決策的時候╃•₪▩☁,決策來源的資訊是封閉的•╃、有限的甚至是結構化的•◕│✘·。比如說下圍棋這件事情決策很封閉╃•₪▩☁,只需要知道期盼上的規定就可以下決定•◕│✘·。
There are two easy to replace, the first is closed your work and environment. Means that when making decisions, decision making is closed and limited sources of information and even is structured. Such as chess this decision is very closed, just need to know the rules of the anticipation on can be decided. 
 
 
醫生會難很多╃•₪▩☁,醫生會知道病人的病史和病人當前的狀態;但是作為一個老師可能會面臨更復雜的環境╃•₪▩☁,做決策的時候資訊來源可能足夠開放╃•₪▩☁,你的答案越標準越容易被機器取代•◕│✘·。第二件事情定的是目標╃•₪▩☁,第一件事情是你處理的資訊的開放性或者封閉性•◕│✘·。從這一點我們知道╃•₪▩☁,有的決策資訊需要的少機器就容易做╃•₪▩☁,資訊需要的做機器也能做•◕│✘·。
The doctor will be a lot of hard, your doctor will know the patient's medical history and the current state of the patient; But as a teacher may face more complex environment and make decisions that source may be enough to open, your answer the standard the more likely it is to be replaced by machines. The second thing is the goal, the first thing is you deal with the information of the open or closed. From this we know, some decision-making information need machine is easy to do less, information need for the machine can do. 
 
 
我們做好一個翻譯或者一個作家╃•₪▩☁,需要很多生活閱歷╃•₪▩☁,作家就是讀萬卷書•╃、行萬里路的做法•◕│✘·。如此開放的環境對機器就是很大的挑戰•◕│✘·。反過來╃•₪▩☁,如果這個開放答案跟你有關╃•₪▩☁,機器就容易做到•◕│✘·。這就是機器是否能做好•╃、人是否會取代的一個標準•◕│✘·。
We do a translation or a writer, need a lot of life experience, the author is to read thousands of books, the view of practice. So open environment for machine is a big challenge. On the other hand, if the open the answer has to do with you, the machine is easy to do. That is whether the machine can do a good job, people will replace a standard. 
 
 
回到人是否會被取代的問題╃•₪▩☁,人是什麼概念╃·╃✘?人的目標是為了自己的生存或者繁衍╃•₪▩☁,機器更簡單╃•₪▩☁,我做診斷╃•₪▩☁,下一個棋╃•₪▩☁,或者做一個語言識別•◕│✘·。人已經到了很大的擴充套件空間╃•₪▩☁,我們的機器只是在侷限的空間去工作╃•₪▩☁,主要看機器的訓練空間多大╃•₪▩☁,如果演算法再好也不能脫離目標和機器適應的範圍╃•₪▩☁,所以今天的技術還遠不到這一步╃•₪▩☁,第二是我們不會去造一臺機器給它設定一個目標怎麼生存╃•₪▩☁,也不會說適應環境有特別大的空間•◕│✘·。
Back to the question of whether people will be replaced, what is the concept? Person's goal is to own survival or reproduction, the machine is more simple, I do the diagnosis, the next chess, or to do a speech recognition. Man has come to the expansion of the large space, our machines are only in a limited space to work, basically see the training of the machine space is how much, if the algorithm is again good also cannot from the target and the scope of the machine to adapt so today's technology is far less than this step, the second is we're not going to build a machine to set a goal how to survive, will not say to adapt to the environment has a particularly large space. 
 
 
我們即便有能力╃•₪▩☁,也沒有動力去製造一個能取代人的機器╃•₪▩☁,我們不認為機器自己會演化出一種生存能力來•◕│✘·。反過來╃•₪▩☁,如果有野心勃勃的科學家要做一件事情╃•₪▩☁,說要創造一個智慧機器╃•₪▩☁,這個機器有生存的概念╃•₪▩☁,可以面對整個地球環境•◕│✘·。
We even have the ability, also have no incentive to make a person to replace the machine, we don't think the machine itself will evolve to a survival ability. On the other hand, if have ambitious scientists to do one thing, to say to want to create a smart machine, this machine has the concept of survival, can in the face of the whole earth environment. 
 
 
其實我們不是在做人工智慧╃•₪▩☁,我們其實是在創造一種生命•◕│✘·。所以這個概念大家想清楚╃•₪▩☁,如果你朝著創造生命的態度去做╃•₪▩☁,機器可能有一種生命意識╃•₪▩☁,知道自己的存在•◕│✘·。反過來╃•₪▩☁,今天我們的做法大可以放心╃•₪▩☁,我們做這些事情目標足夠的簡單╃•₪▩☁,比如說Alpha Go機器╃•₪▩☁,把棋盤從19×19變成20×20╃•₪▩☁,人類可以理解和學習╃•₪▩☁,但是環境變了╃•₪▩☁,Alpha Go變得什麼都不會了•◕│✘·。
Artificial intelligence in fact we are not doing, we are creating a kind of life. So you want to clear the concept, if your attitude toward the creation of life to do, the machine may have a kind of life consciousness, know their presence. In turn, today we can rest assured, our goal to do these things simple enough, such as Alpha Go machine, the board from 19 lines into 20 x 20, human beings can understand and learn, but the environment changed, Alpha Go nothing. 
 
 
另外一個問題是想象力╃•₪▩☁,區別於人和動物的•◕│✘·。有一本書叫《人類簡史》╃•₪▩☁,這種歷史發展是相關的╃•₪▩☁,這也是一個路徑╃•₪▩☁,這是我認為人不會被取代的核心的兩個判斷標準•◕│✘·。
Another problem is the imagination, the difference in human and animal. There is a book called "a brief history of mankind, this is related to the historical development, it is also a path, this is I don't think people will be replaced at the core of the two judgment standard. 
 
 
人工智慧和人是怎樣的關係呢╃·╃✘?有技術我們可能變得更強大了╃•₪▩☁,但是也有可能技術讓我們變得更弱了•◕│✘·。在座很多人戴眼鏡╃•₪▩☁,眼鏡是一種技術╃•₪▩☁,當用了眼鏡之後我們視力變得更好•╃、更強大╃•₪▩☁,但是離開眼鏡我們更弱•◕│✘·。
Artificial intelligence and what is the relationship? Have the technology we may become more powerful, but it is also possible technology let us become more weak. Here a lot of people wear glasses, glasses is a technology, when using the glasses after our vision to become better, more powerful, but leave the glasses we are weaker. 
 
 
我們要拋離技術之後看自己孤立的行不行╃•₪▩☁,放下手機•╃、pad•╃、汽車交通工具是不是變強了╃·╃✘?我們發現是變弱了•◕│✘·。我們由於機器變得弱化╃•₪▩☁,我們掌握了能源之後體力被取代了╃•₪▩☁,今天的種植也被機器取代了•◕│✘·。
We're leaving technology after the isolated line not line, put down the phone, pad, car transport is better? We found to be weak. We become weakened due to machine, we mastered the energy strength was replaced, today's planting is replaced by a machine. 
 
 
以後目標清晰之後╃•₪▩☁,環境相對封閉╃•₪▩☁,機器能做的時候我們可以交給它╃•₪▩☁,我們可以利用Google做搜尋引擎╃•₪▩☁,透過手機變成千裡眼•╃、順風耳╃•₪▩☁,這是一個趨勢•◕│✘·。未來穿戴裝置可能會變成植入╃•₪▩☁,像Google眼鏡╃•₪▩☁,很多人眼睛不近視也會有很多人嘗試╃•₪▩☁,包括還有年輕的女孩子會整容╃•₪▩☁,這些東西會帶來新的植入•◕│✘·。
After goals clear, relatively closed environment, the machine can do we can to it, we can do it using Google search engine, by mobile phone into a clairvoyant clairaudient, it is a trend. Future apparel equipment may become implanted, like Google glasses, a lot of people eye myopia also can have a lot of people try, not including the young girl will cosmetic, these things will bring new implants. 
 
 
從這一點我認為人工智慧與人融合會帶來新的物種╃•₪▩☁,你不用害怕╃•₪▩☁,你問一個猴子你會變成人嗎╃·╃✘?透過我們對人工智慧的理解和對技術的理解╃•₪▩☁,技術可以帶來與人的融合╃•₪▩☁,可以把人的能力提升了╃•₪▩☁,也可能把人的能力降低了╃•₪▩☁,這是我們未來的進化•◕│✘·。
From the artificial intelligence fusion with people that I think will bring new species, you need not fear, you ask a monkey you will become a person? Through our understanding of the artificial intelligence and the understanding of technology, technology can bring with the fusion of people, can put the person's ability to ascend, may also reduce the person of ability, it is our future evolution. 

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點選次數·•◕◕◕:6110次    更新時間·•◕◕◕:2022-05-25 13:52
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