Teaching AI Systems to Behave Themselves
教導人工智慧系統守規矩
By Cade Metz

At OpenAI, the artificial intelligence lab founded by Tesla’s chief executive, Elon Musk, machines are teaching themselves to behave like humans. But sometimes, this goes wrong.
在特斯拉公司執行長伊隆．馬斯克創建的OpenAI人工智慧實驗室內，機器正在教育自己行為要像人類一樣。但有時候會出差錯。

Sitting inside OpenAI’s San Francisco offices on a recent afternoon, the researcher Dario Amodei showed off an autonomous system that taught itself to play Coast Runners, an old boat-racing video game. The winner is the boat with the most points that also crosses the finish line.
某日一個下午，研究員達里歐．阿莫戴坐在舊金山OpenAI辦公室，得意展示一個能自學如何玩老式賽艇電玩遊戲「賽艇大亨」的自動系統。得分最高，且衝過終點線的船是贏家。

The result was surprising: The boat was far too interested in the little green widgets that popped up on the screen. Catching these widgets meant scoring points. Rather than trying to finish the race, the boat went point-crazy. It drove in endless circles, colliding with other vessels, skidding into stone walls and repeatedly catching fire.
結果令人驚訝：這艘船對出現在螢幕上的綠色小玩偶太有興趣。抓這些小玩偶可以得分。這艘船瘋狂得分，而不努力衝向終點。船一直不停地繞圈圈，與其他船相撞，滑到石牆上，並一再著火。

Amodei’s burning boat demonstrated the risks of the AI techniques that are rapidly remaking the tech world. Researchers are building machines that can learn tasks largely on their own. This is how Google’s DeepMind lab created a system that could beat the world’s best player at the ancient game of Go. But as these machines train themselves through hours of data analysis, they may also find their way to unexpected, unwanted and perhaps even harmful behavior.
阿莫戴著火的船示範了人工智慧技術的風險，而此技術正快速再造科技世界。研究人員正在建造大致可以自行學習如何執行任務的機器。Google的DeepMind實驗室就這麼創造出一個系統，能在歷史悠久的圍棋的比賽中打敗世界頂尖高手。然而，這些機器透過長時間的資料分析自我訓練之際，可能也學會了意料之外、不受歡迎，或許甚至有害的行為。

That’s a concern as these techniques move into online services, security devices and robotics. Now, a small community of AI researchers, including Amodei, is beginning to explore mathematical techniques that aim to keep the worst from happening.
隨著這些技術進入網路服務、安全設備和機器人，這也成為一個隱憂。現在，包括阿莫戴在內的一小群人工智慧研究人員已開始探究，如何以數學方法防止最壞情況發生。

At OpenAI, Amodei and his colleague Paul Christiano are developing algorithms that can not only learn tasks through hours of trial and error, but also receive regular guidance from human teachers along the way.
在OpenAI實驗室，阿莫戴和他的同事保羅．克里斯提亞諾正在研發演算法，不僅能透過長時間的嘗試錯誤學習執行任務，也能在過程中經常接受人類導師的指導。

With a few clicks here and there, the researchers now have a way of showing the autonomous system that it needs to win points in Coast Runners while also moving toward the finish line. They believe that these kinds of algorithms – a blend of human and machine instruction – can help keep automated systems safe.
研究人員在這裡那裡點幾下，就有辦法指示自動系統必須在「賽艇大亨」獲得分數，同時也要朝終點線前進。他們相信這類演算法－揉合人類和機器指示－有助於讓自動系統維持安全。

For years, Musk, along with other pundits, philosophers and technologists, have warned that machines could spin outside our control and somehow learn malicious behavior their designers didn’t anticipate. At times, these warnings have seemed overblown, given that today’s autonomous car systems can even get tripped up by the most basic tasks, like recognizing a bike lane or a red light.
多年來，馬斯克和其他學者專家、哲學家和科技人已警告，機器可能逸出我們的控制範圍，不知怎麼學到他們的設計者沒有料到的壞行為。有時，這些警告顯得言過其實，因為今天的自動駕駛車系統連最基本的任務都可能搞錯，像是辨識一條自行車道或紅燈。

But researchers like Amodei are trying to get ahead of the risks. In some ways, what these scientists are doing is a bit like a parent teaching a child right from wrong.
不過，像阿莫戴這樣的研究人員試圖防杜這些風險。在某方面，這些科學家正在做的事，有點像家長教小孩認清對錯。

原文參照：
https://www.nytimes.com/2017/08/13/technology/artificial-intelligence-safety-training.html

2017-09-10．聯合報．D4．紐約時報賞析 田思怡

說文解字看新聞 田思怡

科技界正掀起一場人工智慧（AI）革命，116位機器人和AI公司的創辦人8月連署一封公開信，敦促聯合國阻止有「殺人機器人」（Killer Robots）之稱的AI自動武器為人類帶來的危險。這封信的連署人包括特斯拉執行長馬斯克，以及開發出AlphaGo人工智慧圍棋程式的DeepMind創辦人蘇雷曼在內，

馬斯克早就警告AI發展可能失控，本文即探討專家如何防範AI做出超出人類預期的「惡意行為」（malicious behavior）。

標題用了behave themselves，behave oneself是守規矩的意思。例如，大人要小孩乖一點，會對小孩說Behave yourself!又例如，The child behaved himself all day.（這個小孩整天都很乖）

倒數第二段的get tripped up是被擾亂而犯錯的意思，例如，They tripped him up with that difficult question.（他們用困難的問題讓他犯錯）

AI Is Reshaping Art and Music
AI 重塑音樂與藝術創作
By Cade Metz

In the mid-1990s, Douglas Eck worked as a database programmer in Albuquerque, New Mexico, while moonlighting as a musician. After a day spent writing computer code inside a lab run by the Department of Energy, he would take the stage at a local juke joint, playing what he calls “punk-influenced bluegrass” – “Johnny Rotten crossed with Johnny Cash.” But what he really wanted to do was combine his days and nights, and build machines that could make their own songs. “My only goal in life was to mix AI and music,” Eck said.
1990年代中期，道格拉斯．艾克在新墨西哥州阿爾伯克基市擔任資料庫程式設計人員，並兼職當樂手。在能源部經管的實驗室內花上一整天撰寫電腦編碼後，他會在當地酒吧登台演奏他所謂的「受龐克風影響的藍草音樂」，即約翰·羅騰遇上強尼·凱許時的音樂風格。不過，他真正想做的是把白天和黑夜結合起來，打造出可製作歌曲的機器。艾克說：「我生活中唯一的目標就是把AI跟音樂融合。」

It was a naive ambition. Enrolling as a graduate student at Indiana University, in Bloomington, not far from where he grew up, he pitched the idea to Douglas Hofstadter, the cognitive scientist who wrote the Pulitzer Prize-winning book on minds and machines, “Godel, Escher, Bach: An Eternal Golden Braid.”Hofstadter turned him down, adamant that even the latest artificial intelligence techniques were much too primitive.
這是個太過天真的野心。在進入離他長大地方不遠的布魯明頓印第安納大學就讀研究所後，他把這項想法向認知科學家道格拉斯·郝夫斯臺特提出，郝夫斯臺特因為撰寫討論心智與機器的書籍《集異璧：一條永恆的金帶》而獲普立茲獎。郝夫斯臺特拒絕了他，堅信就連最新的人工智慧技術都還太幼嫩。

But during the next two decades, working on the fringe of academia, Eck kept chasing the idea, and eventually, the AI caught up with his ambition.
但在接下來的20年裡，艾克在學術界的邊緣不斷追逐這個理念，AI也終於趕上了他的野心。

Last spring, a few years after taking a research job at Google, Eck pitched the same idea he pitched Hofstadter all those years ago. The result is Project Magenta, a team of Google researchers who are teaching machines to create not only their own music but also to make so many other forms of art, including sketches, videos and jokes.
去年春天，在谷歌做了幾年研究工作後，艾克又提出多年前他被郝夫斯臺特拒絕的相同想法，結果誕生洋紅計畫，而該計畫是由一組谷歌研究人員教導機器不僅創作自己的音樂，並創作包括素描、影片及笑話在內的許多其他形式的藝術。

With its empire of smartphones, apps and internet services, Google is in the business of communication, and Eck sees Magenta as a natural extension of this work.
坐擁由智慧手機、應用程式與網路服務組成的帝國，谷歌正做著傳播（交流溝通）的生意，而艾克則把洋紅計畫視為這項工作的自然延伸。

“It’s about creating new ways for people to communicate,” he said during a recent interview inside the small two-story building here that serves as headquarters for Google AI research.
他不久前在此間（加州山景市）谷歌AI研究總部一棟兩層樓小建築內受訪時說：「這是為人們創造新的交流溝通方式。」

The project is part of a growing effort to generate art through a set of AI techniques that have only recently come of age. Called deep neural networks, these complex mathematical systems allow machines to learn specific behavior by analyzing vast amounts of data.
透過一套最近才成熟的AI技術來創作藝術的努力方興未艾，而前述計畫正是它的一環。這些複雜的數學系統稱為深度神經網路，讓機器能透過分析巨量資料來學習特定的行為。

By looking for common patterns in millions of bicycle photos, for instance, a neural network can learn to recognize a bike. This is how Facebook identifies faces in online photos, how Android phones recognize commands spoken into phones, and how Microsoft Skype translates one language into another. But these complex systems can also create art.
例如，透過在數百萬自行車照片中尋找共同模式的方式，一個神經網路就可學習識別自行車，而這就是臉書辨別線上照片中的人臉，安卓手機識別電話中語音指令，以及微軟Skype將一種語言翻譯成另種語言的方法，而這些複雜的系統也可創造藝術

原文參照：
https://www.nytimes.com/2017/08/14/arts/design/google-how-ai-creates-new-music-and-new-artists-project-magenta.html

2017-09-10．聯合報．D4．紐約時報賞析 田思怡