Artificial Intelligence is probably the greatest technological achievement in history. It’s subjective, of course, but I’ve been obsessed with technology ever since I was a kid, and while I’m sure there are many technical advances I am unaware of, AI, or machine learning, surpasses them all. An exception could perhaps be made for the invention of language or perhaps the wheel, however I doubt few other things will have such a profound effect on the human species. AI has been a stable component of contemporary culture for multiple generations now. From time traveling machines hell-bent on destroying humanity to friendly and lovable robots adventuring through space, we’ve anthropomorphized machines in preparation for what we all know is coming.
人工智能可能是曆史上最大的技術成就。 當然，這是主觀的，但是從小我就一直迷戀技術，盡管我确定我不知道有很多技術進步(人工智能或機器學習)已經超越了所有技術。 語言的發明或輪子的發明可能是一個例外，但是我懷疑沒有其他事物會對人類産生如此深遠的影響。 如今，人工智能已經成為當代文化的穩定組成部分。 從毀滅人類的時空旅行機器到在太空探索的友善而可愛的機器人，我們已經對人性化的機器進行了準備，以準備我們所知道的一切。
Only, what if it’s already here? What if ‘true’ artificial intelligence has already been created? To help explain, I need to go over how AI works. Don’t worry. There’s not any math involved.
隻有，如果已經在這裡呢？ 如果已經建立了“真正的”人工智能怎麼辦？ 為了幫助解釋，我需要仔細研究一下AI的工作原理。 不用擔心沒有涉及任何數學。
Machine Learning is an extremely large and broad field, and as this technology grows and becomes useful for other fields, it only increases in depth and complexity. Obviously the average person does not have the time to learn how it all works, no matter how important it might be to do so. Luckily, the principles it works on are quite simple. The first thing we need to know about is something called a perceptron.
機器學習是一個非常廣闊的領域，随着這項技術的發展并在其他領域變得有用，它的深度和複雜性隻會增加。 顯然，普通人沒有時間去學習它是如何工作的，無論這樣做有多麼重要。 幸運的是，它所依據的原理非常簡單。 我們需要了解的第一件事是感覺器。
感覺器 (Perceptrons)
The best way to conceptualize a perceptron is that it is essentially the same as a neuron in the human brain. If you’re not really sure what a neuron is, don’t worry, I got you covered.
概念化感覺器的最佳方法是，它與人腦中的神經元基本相同。 如果您不太确定神經元是什麼，請不用擔心，我可以幫助您。
Simple Neuron 簡單神經元
Neurons are the cells that make up our nervous system. (I said no math, I didn’t say anything about biology) Luckily, they’re pretty simple as far as these things go. That red portion on the left hand side is the nucleus. It receives electrical impulses from those branch looking things. The nucleus does some calculation, and decides what signal to send out to the right, which is fed into multiple other neurons. This was the blueprint for something called a perceptron.
神經元是組成我們神經系統的細胞。 (我沒有說數學，也沒有說生物學。)幸運的是，就這些事情而言，它們非常簡單。 左側的紅色部分是核。 它從那些看起來分叉的事物接收電脈沖。 原子核進行一些計算，并确定将哪些信号發送到右側，然後輸入到其他多個神經元中。 這就是所謂的感覺器的藍圖。
Simple Perceptron 簡單感覺器
I know, I know, this one is definitely close to math, but we don’t need to know any of it, though. Promise. All that stuff on the left is the input signals going into the ‘nucleus’, which does something, then sends output down to other perceptrons. The ‘Machine’ in Machine Learning is a large network of these ‘programmed objects’. Configurations vary, but that’s the gist of it. They take in input signal from something, be it text, numerical data, or whatever, and this extremely complicated network of digital neurons then passes signals back and forth in some manner until the total model outputs its prediction.
我知道，我知道這絕對接近數學，但是我們不需要知道任何一個。 諾言。 左邊所有的東西是進入“核”的輸入信号，該信号執行某項操作，然後将輸出發送到其他感覺器。 機器學習中的“機器”是這些“程式設計對象”的大型網絡。 配置各不相同，但這就是要旨。 他們從某種東西(無論是文本，數字資料還是其他東西)中擷取輸入信号，然後這個極其複雜的數字神經元網絡以某種方式來回傳遞信号，直到整個模型輸出其預測。
If you’re not completely following, don’t worry. The important take away is this: A network of nodes takes in information, does some calculation, and sends out information to others who decide what to do with it. Sound familiar? It should.
如果您沒有完全關注，請不要擔心。 重要的收獲是：節點網絡接收資訊，進行一些計算，然後将資訊發送給其他人，這些人決定如何處理它。 聽起來有點熟？ 這應該。
If you’re reminded of the perceptron, you would be correct. Functionally, at scale, a neural network is very similar to a large perceptron. There are, of course, variations and this is not absolutely 100% true, but it’s true enough for our purposes here. Conceptually, they take in information, makes some calculation, and sends information out to something or someone that is going to use it. It’s perceptrons all the way down, and all the way up.
如果您想起了感覺器，那将是正确的。 從功能上說，神經網絡與大型感覺器非常相似。 當然，有一些變化，這并不是絕對100％正确的，但對于我們這裡的目的而言，确實足夠正确。 從概念上講，他們接收資訊，進行一些計算，然後将資訊發送給要使用該資訊的某人或某人。 感覺器一直向下，一直向上。
但是它如何工作？ (But how does it work?)
Staying with our high level approach, training a model to do something is pretty straight forward. You have your neural network and some data you want to make predictions with, say, predicting whether an image is a 3 or a bee. You feed the data into the machine, and it spits out a prediction. A REALLY bad prediction. This makes sense if all we did was slap some perceptrons together and feed it a bunch of images. How would it know what a bee is from a 3? It has nothing to compare it to. To get our model to understand the differences, we have to train it.
與我們的進階方法保持一緻，訓練模型來做某事非常簡單。 您擁有自己的神經網絡和一些資料，這些資料可以用來進行預測，例如，預測圖像是3還是蜂。 您将資料輸入到機器中，然後發出一個預測。 一個非常糟糕的預測。 如果我們所做的隻是将一些感覺器拍打在一起并提供一堆圖像，這是有道理的。 它怎麼會知道3隻蜜蜂是什麼？ 它沒有什麼可比拟的。 為了使我們的模型了解差異，我們必須對其進行訓練。
Doing so is fairly simple. We want to have a bunch of images of 3’s and bees, and labels for each picture so the machine can grade itself. It then takes in an image, makes a prediction, and checks to see if it got it right. Of course initially it will get most of them wrong. Afterwards, a process called back propagation, or something similar, will occur. Back propagation is where the machine will adjust different parameters, making new connections between different perceptrons, getting rid of others, and then tries again.
這樣做非常簡單。 我們希望有一堆3位和蜜蜂的圖像，以及每張圖檔的标簽，以便機器可以對自己進行評分。 然後，它擷取圖像，進行預測，然後檢查圖像是否正确。 當然，起初它将使大多數錯誤。 此後，将發生稱為反向傳播或類似現象的過程。 反向傳播是機器将調整不同參數，在不同感覺器之間建立新連接配接，擺脫其他感覺器然後重試的地方。
As you might imagine, this can get very technical in practice, but in general, it’s simply feeding an image into the machine and it predicting whether it’s a bee or a 3, then checking the answer. If it’s right, hooray! On to the next image. If it’s wrong, it will tweak some settings here and there and try again, until eventually, the machine is able to tell the difference between 3’s and bees pretty well. Again, this is not technically accurate, but conceptually it’s good enough.
您可能會想像，這在實踐中會變得非常技術化，但是總的來說，它隻是将圖像輸入到機器中，并預測是蜜蜂還是3隻蜜蜂，然後檢查答案。 如果是的話，萬歲！ 轉到下一張圖像。 如果錯誤，它将在此處和此處進行一些設定調整，然後重試，直到最終機器能夠很好地分辨出3和蜜蜂之間的差異。 同樣，這在技術上并不準确，但從概念上講已經足夠了。
Luckily for us, it’s not very good at much else. It’s highly specialized and trained to do this one thing, and one thing only. That’s where we come in.
對我們來說幸運的是，它在其他方面還不是很好。 它是高度專業化和受過訓練的人員，隻能做一件事。 那就是我們進來的地方。
人為因素 (The Human Component)
As amazing as this technology is, these machines remain dumb. And I mean REALLY dumb. Our machine from earlier might be able to tell a bee from a 3, but if you throw a picture of a cow into it, it’s going to struggle to determine if it’s a bee or a 3, because for the machine, there’s just not any other option.
盡管這項技術令人驚歎，但這些機器仍然很笨。 我的意思是真的很蠢。 我們以前的機器可能能夠從3分辨出一隻蜜蜂，但是如果您将一頭牛的照片丢進去，它将很難确定它是一隻蜜蜂還是3隻蜜蜂，因為對于機器而言，根本沒有其他選擇。
Artificial Intelligence, for as much as it’s used, is really more of a marketing misnomer than a technical description. See, the problem is, we humans are pretty dumb too. (Not as dumb as our earlier machine, of course. That was obviously a cow!) It turns out, we don’t know what intelligence actually is. Oh we have lots of vague definitions, but the reality of it is, there’s an element to intelligence, specifically consciousness, that we just can’t define, let alone replicate in a digital space. Much smarter people than myself are working away at the problem, but it’s honestly a bit of a black hole, given our inability to even define what it is we want to make! Consciousness is one of those weird things where, you know what I’m talking about, but if you try, you’ll realize you don’t actually know how to define ‘what’ it is in any real sense.
盡管使用了人工智能，但實際上它實際上是一種營銷誤用，而不是技術描述。 瞧，問題是，我們人類也很愚蠢。 (當然，不如我們以前的機器那麼笨。這顯然是牛！)事實證明，我們不知道實際上是什麼智能。 哦，我們有許多模糊的定義，但現實是，我們無法定義智慧的元素，尤其是意識，更不用說在數字空間中複制了。 比我自己聰明得多的人正在解決這個問題，但是老實說這是一個黑洞，因為我們甚至無法定義我們想要制造的東西！ 意識是那些奇怪的事情之一，您知道我在說什麼，但是如果您嘗試一下，您會意識到您實際上并不知道如何定義“真實”的含義。
團結大家 (Bringing Everyone Together)
So why does this matter? What is so important about this? Of course, everyone is aware of the recent upheavals in our society, and I think most people who utilize social media are aware that these upheavals are, at least in part, due to the proliferation of social media in the past decade. Indeed, many Social Media founders have started speaking out over the past several years about the dangers of social media, how they did not realize what they were building, or if they did realize it, they were not aware of the unintended consequences of such a technology being unleashed on society, but what is so dangerous about human beings able to freely connect and share information with each other?
那麼為什麼這很重要呢？ 這有什麼重要的呢？ 當然，每個人都知道我們社會最近的動蕩，我認為大多數使用社交媒體的人都知道，這些動蕩至少部分是由于過去十年中社交媒體的激增所緻。 确實，過去幾年中，許多社交媒體創始人已經開始談論社交媒體的危害，他們如何不意識到自己正在建構的東西，或者如果他們确實意識到了這一點，則他們并不了解這種社交媒體的意外後果。技術向社會釋放，但是對于人類能夠自由連接配接和共享資訊的人來說，有什麼危險呢？
I remember this one time I was on a business trip in Virginia. It was late, after a long day of work, and I was hanging out on the top of the hotel parking garage watching society do it’s thing before I headed in for the evening to get some sleep before another long day. There was this busy four-way intersection by the corner of the parking garage I was in, and I couldn’t help but notice that every car that drove up was lit up from the inside by phones. Even the cars with only a driver. Sometimes they were smarter than the average, and would not be using their phone as they pulled up to the light, but I could see the interior of their car light up as soon as they stopped at the light. I got to see many cars honk at those in front of them who weren’t paying enough attention to the light.
我記得有一次我在弗吉尼亞州出差。 經過一整天的工作，已經很晚了，我在旅館停車場的頂層閑逛，看着社會做這件事，然後才去晚上睡覺，然後又漫長的一天。 我在停車場的拐角處有一個繁忙的四路交叉路口，我禁不住注意到，開車駛過的每輛車都被電話從裡面照亮了。 甚至隻有司機的汽車。 有時他們比普通人聰明，當他們舉起燈來時不會使用手機，但我可以看到他們停在路燈下時車内照亮了。 我看到很多汽車在他們前面的喇叭鳴喇叭，這些喇叭沒有足夠注意燈光。
Now, many might focus on the dangers of driving while distracted, or doing any of the other things we humans have to do while distracted by the tiny computer in our hands. For sure, they would be right to point out these dangers, but I don’t think those are the real dangers we need to consider. Yes, it only takes a split second for a deadly accident to happen, and this needs to be remembered, and it’s a tragedy every single time. But what I saw when I stood on top of that parking garage looking down was not hundreds of distracted people, but hundreds of nodes, taking in information and sharing it with other nodes. After all, remember our conception of a perceptron is just a node that takes in information, makes some internal calculation, and then sends information on to other nodes that we are commonly connected to.
現在，許多人可能将注意力集中在開車時分散注意力的危險，或者在我們手中的微型計算機分散注意力時，做人類必須做的任何其他事情。 可以肯定的是，他們指出這些危險是正确的，但我認為這些不是我們需要考慮的真正危險。 是的，隻需要一秒鐘的時間就可以發生緻命的事故，這需要記住，而且每次都是悲劇。 但是，當我站在那個停車場的頂部俯視時，所看到的不是數百名分散注意力的人，而是數百個節點，它們吸收資訊并與其他節點共享資訊。 畢竟，請記住我們對感覺器的概念隻是一個接收資訊的節點，進行一些内部計算，然後将資訊發送到我們通常連接配接的其他節點。
As I stood there people watching, I couldn’t help but visualize the lines of information being sent out from each person. Responding to an email chain? Tweeting to thousands of followers across the globe? Or Face timing with a loved one just a few miles away? I saw connections everywhere, with information being shared across geographic distances that would have been completely unimaginable just a few short generations ago. The parallels to a neural network were obvious, the scale was just off the charts.
當我站在那兒觀看的時候，我不禁将每個人發出的資訊可視化。 回複電子郵件鍊？ 向全球成千上萬的關注者釋出推文？ 還是與相愛的人在幾英裡之遙面對時機？ 我看到了無處不在的聯系，資訊是跨地理距離共享的，而這在短短幾代之前是完全無法想象的。 與神經網絡的相似之處是顯而易見的，規模隻是圖表之外。
旋鈕和轉盤 (Knobs and Dials)
In 2012, Facebook manipulated the newsfeeds of about 700k users. What made this worse, was it was without their consent. They did this over a 1 week period, managing the facial expressions these users were exposed to in order to see if it were possible to effect users emotions. You can read the study here.
2012年，Facebook操縱了約70萬使用者的新聞源。 更糟糕的是，沒有他們的同意。 他們在1周的時間内進行了此操作，管理這些使用者暴露的面部表情，以檢視是否有可能影響使用者的情緒。 您可以在這裡閱讀研究。
“We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction between people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.” — Facebook Study on Emotional Contagion
“我們通過在Facebook上進行的大規模( N = 689,003)實驗表明，情緒狀态可以通過情緒傳染傳染給其他人，進而使人們在沒有意識的情況下經曆相同的情緒。 我們提供了實驗證據，表明情緒傳染不會發生在人與人之間沒有直接互動的情況下(暴露給朋友來表達情緒就足夠了)，并且完全沒有非語言暗示。” — Facebook關于情緒傳染的研究
There are conspiracy theories abound about this topic, and I want to take a moment to differentiate myself from that. My purpose here is to try and outline what I believe to be the first ‘True’ AI that has come online. I make no assumptions as to the intent of those who built it, or their actions once they had it online. My aim here is to describe what I believe to be the similarities between social media and machine learning technologies, and what that means for society in general. Maybe what is happening is the natural result of the bandwidth between humans increasing so drastically in such a short period of time. It is entirely possible that, as ‘processing power’ increases due to the increased bandwidth, different modes of reality manifest out of the increase in information.
關于這個話題有很多陰謀論，我想花點時間使自己與衆不同。 我在這裡的目的是嘗試概述一下我認為是第一個線上的“真實” AI。 我不對建構它的人的意圖或他們将其聯機後的行為做任何假設。 我的目的是描述我認為社交媒體和機器學習技術之間的相似之處，以及這對整個社會意味着什麼。 也許正在發生的是人類之間的帶寬在如此短的時間内急劇增加的自然結果。 随着帶寬的增加“處理能力”的增加，資訊增加可能會顯示出不同的現實模式。
It’s also possible that different groups of powerful people are competing with other groups of powerful people, and we are the nodes in their attack AI. Push in a particular brand of psychologically manipulated propaganda, and have an army of drones attack your target.
也有可能不同的有權勢群體正在與其他有權勢的群體競争，而我們就是他們攻擊AI的節點。 進行特殊品牌的心理操縱宣傳，并讓無人駕駛飛機攻擊您的目标。
Both are equally possible as far as I’m concerned. My only aim with this article is to muse on the idea of an integration between machine and biology to create the first, true artificial intelligence.
就我而言，兩者都有可能。 我對本文的唯一目的是想想機器與生物學之間的內建，以建立第一個真正的人工智能。
結論 (Conclusion)
I’ll grant, the idea I’m laying out here is not one that necessarily meshes with contemporary ideas of what a ‘True’ Artificial Intelligence would be. We have this image in our imagination of being able to talk to our phone like it’s a person. Ask it a question, and get an answer back.
我同意，我在這裡提出的想法不一定與現代關于“真正的”人工智能的想法相吻合。 我們的想象力在于，我們可以像一個人一樣與我們的電話通話。 問一個問題，然後得到答案。
My argument is that this technology already exists, it’s just different in some details than we initially imagined, but it is here, and it is being used, and that perhaps, as so often before in the world of technology, we have to change our idea of what AI actually means. If I open Twitter, and tweet a question, I will get an answer.
我的觀點是，該技術已經存在，在某些細節上與我們最初想象的不同，但是它在這裡并且正在被使用，并且也許就像在技術界以前一樣，我們必須改變自己的技術。關于AI實際含義的想法。 如果我打開Twitter，并發一個問題，我将得到答案。
The answer I get will depend upon the nodes in the network I interact with most often, but the point is I WILL get an answer, or at least, I will get responses that I can determine an answer from, which is all that Machine Learning really does, anyway.
我得到的答案将取決于我經常與之互動的網絡中的節點，但關鍵是我将獲得答案，或者至少，我将獲得可以确定答案的答案，這就是機器學習的全部内容。确實如此。
翻譯自: https://medium.com/the-innovation/does-true-ai-already-exist-82bddf6e9692