大腦比機器智能

The race to Artificial Intelligence is a grueling, sweaty marathon that human beings have been running for over 80 years. We’ve faced steep climb after steep climb, the gradients cranking higher every time we run into a new funding gap or technological roadblock. Recent advances in deep learning and neural networks are leading some to wonder if we might, finally, be approaching the finishing line.

人工智能競賽是人類已經(jīng)運行了80多年的艱苦而汗流mar背的馬拉松比賽。 我們經(jīng)歷了一次又一次的陡峭攀爬，每次遇到新的資金缺口或技術(shù)障礙時，坡度都會不斷攀升。 深度學(xué)習(xí)和神經(jīng)網(wǎng)絡(luò)的最新進展使一些人想知道我們是否最終會接近終點線。

We were all impressed when Google’s DeepMind computer beat a human at the game of Go. This was the first breakthrough of artificial neural networks that captured popular imagination. Despite its simple rules, the popular Chinese board game is mind-bogglingly complex, and possesses more possibilities than the total number of atoms in the visible universe. Mastery of Go is supposed to be the ultimate expression of human intuition — so when world champion Lee Sedol was beaten by a computer program, Google’s groundbreaking AI made headlines worldwide.

當(dāng)Google的DeepMind計算機在Go游戲中擊敗人類時，我們都印象深刻。 這是人工神經(jīng)網(wǎng)絡(luò)的第一個突破，引起了人們的普遍想象。 盡管其規(guī)則簡單，但廣受歡迎的中國棋盤游戲卻令人難以置信地復(fù)雜，并且比可見宇宙中的原子總數(shù)具有更多的可能性。 精通Go被認為是人類直覺的最終體現(xiàn)-因此，當(dāng)世界冠軍Lee Sedol被計算機程序擊敗時，谷歌開創(chuàng)性的AI成為全球頭條新聞。

And from predicting the outcome of the US election to rushing an incapacitated driver with a blood clot to hospital, the achievements of artificial intelligence continue to excite.

從預(yù)測美國大選的結(jié)果到將無血量的血塊趕到醫(yī)院 ，人工智能的成就繼續(xù)令人興奮。

But really, if our finishing-line goal is a general artificial intelligence that can solve humankind’s biggest problems (climate change, inequality, resource depletion) then current techniques are running with their legs tied. Neural networks are inspired by the human brain but in no way think like a human brain. In truth these are largely deterministic systems, which means that the same sequence of inputs will result in the exact same output, each and every time. Their edge over regular, rules-based software is that they can be made to learn the rules using inputs with known outputs. This evolution of software is truly powerful, but is it enough?

但是，實際上，如果我們的終點線目標(biāo)是能夠解決人類最大問題(氣候變化，不平等，資源枯竭)的通用人工智能，那么當(dāng)前的技術(shù)將束手無策。 神經(jīng)網(wǎng)絡(luò)受到人腦的啟發(fā)，但絲毫不像人腦那樣思考。 實際上，這些在很大程度上是確定性系統(tǒng)，這意味著相同的輸入序列將每次產(chǎn)生完全相同的輸出。 與常規(guī)的基于規(guī)則的軟件相比，它們的優(yōu)勢在于，可以使它們使用具有已知輸出的輸入來學(xué)習(xí)規(guī)則。 軟件的這種發(fā)展確實強大，但這足夠了嗎？

We are right to begin dreaming about the possibilities such feats arouse and it is true we are living in exciting times. But if we are to make the first steps to a machine brain, we must be wary not to commit the first sin of AI research: the mistaking of complex behavior for intelligence.

我們開始夢想這種壯舉的可能性是正確的，我們生活在一個激動人心的時代。 但是，如果我們要邁向機器大腦的第一步，那么我們一定要警惕不要犯AI研究的第一個罪過：將復(fù)雜行為誤認為智能。

Humor, Creativity and Wisdom

幽默，創(chuàng)造力和智慧

Consider the female digger wasp. Before she brings food into her burrow, she drops it off at the entrance and goes inside to check for intruders. Only once satisfied her nest is safe will she bring in the food. If, while the wasp is busy securing her burrow, a meddling human moves the food a couple of inches, the wasp will move the food back to its original drop-off point and repeat her burrow-check all over again. Because the wasp is not capable of remembering she just checked the nest, she can be made to repeat this cycle of behavior more than 40 times. Complex and premeditated it may be — but her behavior is not intelligent.

考慮一下女性挖掘機的WaSP。 在將食物帶入洞穴之前，她將其放在入口處，然后走進去檢查入侵者。 只有滿足了她的巢是安全的，她才會帶進食物。 如果在胡蜂忙于挖洞的時候，一個有情調(diào)的人將食物移了幾英寸，那么WaSP會將食物移回原來的落點，并再次進行挖洞檢查。 由于WaSP無法記住她剛剛檢查過的巢穴，因此可以使她重復(fù)此行為循環(huán)40次以上。 可能是復(fù)雜而有預(yù)謀的-但她的行為并不聰明。

Learning from large-but-limited datasets, most commercial applications of artificial intelligence exhibit cognitive shortcomings similar to the wasp. You might ask why this is important: Is it not enough that we have the complex behavior? If a computer can automate a human task, does it really matter if that computer doesn’t exhibit real human intelligence?

從大型但有限的數(shù)據(jù)集中學(xué)習(xí)，大多數(shù)人工智能的商業(yè)應(yīng)用都表現(xiàn)出類似于WaSP的認知缺陷。 您可能會問為什么這很重要：我們擁有復(fù)雜的行為還不夠嗎？ 如果計算機可以自動執(zhí)行人工任務(wù)，那臺計算機是否沒有真正的人類智力真的很重要嗎？

Well, yes. Eminent AI thinker Jack Copeland contrasts the behavior of the digger wasp — and by extension most 21st century AI — with that produced by human intelligence. In the same situation, a human would probably wonder who has been messing about with their food — but they would not repeat the ritual of checking their home was free from invaders. We know we don’t need to do this again because our intelligence has naturally evolved to enable not only experiential learning but also the extrapolating of those experiences to new situations. It is obvious the burrow is still safe — without a moment’s thought, we would adapt our behavior.

嗯，是。 杰出的AI思想家杰克·科普蘭德(Jack Copeland)將挖掘者WaSP的行為(并延伸到大多數(shù)21世紀(jì)的AI)與人類智慧產(chǎn)生的行為進行了對比。 在相同的情況下，一個人可能會想知道誰一直在弄弄他們的食物-但他們不會重復(fù)檢查自己的房屋免受侵略者侵?jǐn)_的儀式。 我們知道我們不需要再做一次，因為我們的智力已經(jīng)自然發(fā)展，不僅可以進行體驗式學(xué)習(xí)，而且可以將這些經(jīng)驗推算到新的情況下。 顯然，洞穴仍然很安全-片刻之后，我們會適應(yīng)我們的行為。

Copeland literally defines intelligence as the ability to adapt one’s behavior to fit new circumstances, and this is what made AlphaGo so special; through deep learning, it could extrapolate its experience of playing Go to new in-game situations. Ask it to attend to any other task outside of the game, even one as simple as booking the flights for your next holiday, and it would not be able to do so. It must be equipped with tools, connected with the appropriate interfaces, trained again from scratch and possibly rewritten in large parts to do anything in the non-Go world. Google did announce another software to do some of these things, but that, too, is very specialized.

谷輪在字面上將智力定義為適應(yīng)個人行為以適應(yīng)新情況的能力，這就是AlphaGo如此特別的原因。 通過深度學(xué)習(xí)，它可以推斷出在新的游戲場景中玩Go的經(jīng)驗。 讓它參加游戲之外的任何其他任務(wù)，甚至只是為下一個假期預(yù)訂航班一樣簡單，而它將無法做到。 它必須配備有與適當(dāng)?shù)慕涌谶B接的工具，必須重新進行培訓(xùn)，并且可能需要大篇幅地重寫才能在非Go語言環(huán)境中執(zhí)行任何操作。 Google確實宣布了另一種軟件來執(zhí)行其中的某些功能，但是它也非常專業(yè)。

Robert Epstein, a research psychologist points out that the human brain does not store facts or data, like a computer does; it continuously edits itself and relives experiences, like an artist painting a landscape. Our unique combination of experiential learning, ability to abstract concepts and deft extrapolation leads to seemingly-incomprehensible phenomena like humor, creativity and wisdom.

研究心理學(xué)家羅伯特·愛潑斯坦(Robert Epstein)指出，人腦不像計算機那樣存儲事實或數(shù)據(jù) 。 它不斷地自我編輯并重現(xiàn)經(jīng)驗，就像畫家畫風(fēng)景一樣。 我們將體驗式學(xué)習(xí)，抽象概念的能力和靈巧外推的獨特結(jié)合，導(dǎo)致了諸如幽默感，創(chuàng)造力和智慧之類難以理解的現(xiàn)象。

If we are to build a truly intelligent artificial intelligence, we need to build something like a machine brain, and to build a machine brain we need to start being more ambitious.

如果我們要構(gòu)建真正的智能人工智能，就需要構(gòu)建類似于機器大腦的東西，而要構(gòu)建機器大腦，我們需要開始變得更加雄心勃勃。

The Incredible Wisdom of Human Thought Process

人類思維過程的不可思議的智慧

Trying to imitate human thought process is intoxicating.

試圖模仿人類的思維過程令人陶醉。

When we’re building artificially intelligence natural language systems, a key Coseer design principle is to encapsulate every point of data in the form of an idea — concepts, rather than keywords. This is still nothing close to the processing power of a human brain, but even a relatively simple emulation leads to incredible results: Accuracy shoots up. Latencies and training times collapse.

當(dāng)我們構(gòu)建人工智能自然語言系統(tǒng)時，關(guān)鍵的Coseer設(shè)計原則是以概念(而不是關(guān)鍵字)的形式封裝每個數(shù)據(jù)點。 這仍然遠不能與人腦的處理能力相提并論，但是即使是相對簡單的仿真也可以產(chǎn)生令人難以置信的結(jié)果：準(zhǔn)確性不斷提高。 延遲和培訓(xùn)時間崩潰了。

All this with a relatively simple emulation of human thought process. Word2Vec, ELMo, BERT, GPT — all popular natural language processing models — adopt the same philosophy to translate each word into an abstract representation of ideas.

所有這些都相對簡單地模擬了人類的思維過程。 所有流行的自然語言處理模型Word2Vec，ELMo，BERT，GPT都采用相同的哲學(xué)，將每個單詞轉(zhuǎn)換為思想的抽象表示。

Designing algorithms to mimic human thought processes makes business sense for real world problems even today: finding actionable stock market insights from three million documents everyday, assisting doctors through oncological pathways, seamless knowledge management are just some examples. AI is already helping largest and most innovative organizations across sectors. I know this first-hand.

設(shè)計模擬人類思維過程的算法甚至對于當(dāng)今的現(xiàn)實世界來說仍具有商業(yè)意義：每天從300萬份文檔中尋找可行的股市見解，通過腫瘤學(xué)途徑協(xié)助醫(yī)生，實現(xiàn)無縫知識管理只是其中的一些例子。 人工智能已經(jīng)在幫助跨部門的最大和最具創(chuàng)新性的組織。 我是第一手知道的。

First Steps to a Machine Brain

機器大腦的第一步

To continue in our journey towards an artificial general intelligence, let’s ask two simple questions:

為了繼續(xù)朝著人工智能邁進，請問兩個簡單的問題：

• First, what happens when all our applications can learn not only from the data they see, but from the experiences of other applications, and also from the data each application sees?
  首先，當(dāng)我們所有的應(yīng)用程序不僅可以從他們看到的數(shù)據(jù)中學(xué)習(xí)到其他應(yīng)用程序的經(jīng)驗時，還可以從每個應(yīng)用程序看到的數(shù)據(jù)中學(xué)習(xí)時，會發(fā)生什么？
• Second, what happens when each application (and all of them collectively) can learn from the repository of collective human knowledge, available in the form the Internet?
  其次，當(dāng)每個應(yīng)用程序(以及所有這些應(yīng)用程序)可以從以互聯(lián)網(wǎng)形式提供的集體人類知識庫中學(xué)習(xí)時，會發(fā)生什么？

To achieve any success on these two questions, a language must emerge that can translate insights from every context to a common set of binary code. Current approach to neural networks is woefully short on this. Each model must cater to a specific problem only, and with very limited data sets.

為了在這兩個問題上取得成功，必須出現(xiàn)一種語言，該語言可以將見解從每個上下文轉(zhuǎn)換為一組通用的二進制代碼。 目前神經(jīng)網(wǎng)絡(luò)的方法在這一方面簡直是可悲的。 每種模型都只能滿足特定問題，并且數(shù)據(jù)集非常有限。

Sure, humans also have specialists. Doctors and military generals are trained differently. However, the differences are at a level too abstract for a neural network to ever achieve. Both may have the same political views, sense of duty, familial commitment or investment acumen. Even at basic level, both may be excellent drivers, adept conversationalists, and masters at Go. One would need a different neural network to even scratch the surface on each of these.

當(dāng)然，人類也有專家。 醫(yī)生和軍事將領(lǐng)受到不同的訓(xùn)練。 但是，這些差異在某種程度上太抽象了，以至于神經(jīng)網(wǎng)絡(luò)都無法實現(xiàn)。 兩者可能具有相同的政治觀點，責(zé)任感，家庭承諾或投資敏銳度。 即使在基本的水平上，兩者都可能是Go的杰出司機，熟練的對話家和大師。 一個人需要一個不同的神經(jīng)網(wǎng)絡(luò)來均勻刮擦每個表面。

The long-term goal is bold: such a development will set technology free to pursue true intelligence. It will let AI apps benefit from the knowledge that already exists. Applications will ponder on their own time to bring more accurate decisions that the applications are more confident about. And each time that happens, every other application will become smarter.

長期目標(biāo)是大膽的：這樣的發(fā)展將使技術(shù)自由地追求真正的智慧。 它將使AI應(yīng)用程序受益于已經(jīng)存在的知識。 應(yīng)用程序?qū)⒖紤]自己的時間，以做出更準(zhǔn)確的決定，這些決定使應(yīng)用程序更加有信心。 每次發(fā)生這種情況時，其他所有應(yīng)用程序都會變得更加智能。

And that would just be a baby step towards a true machine brain.

這只是邁向真正的機器大腦的第一步。

翻譯自: https://medium.com/swlh/first-steps-to-a-machine-brain-90b38cfffeb

大腦比機器智能

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