Will AI Replace Taylor Swift?人工智能會取代霉霉嗎？

The Beatles are back！ Strange how the perennial headline always signifies progress in the music business. So it was when Paul McCartney told the BBC about a Fab new song1 made with the help of artificial intelligence. “Kind of scary but exciting，” he said of reuniting his undead band once more. “Because it’s the future.”

“披頭士樂隊回歸！”說來奇怪，這個經久不衰的頭條總是標志著音樂產業的進步。這次也不例外：保羅·麥卡特尼向英國廣播公司透露，他借助AI制作了一首絕妙的披頭士新歌。談及讓這支不朽樂隊的成員重聚，他坦言：“整個過程既有些令人不安又讓人振奮，因為這就是未來。”

Each of the applications of AI is subtly different， involving varying degrees of creative and mechanical intervention. Which ones you consider “kind of scary” and which ones “exciting” for the future of music depends on what you mean by music.

每種AI工具都呈現出微妙的差異，有的偏重創意發揮，有的更依賴機械干預。對于音樂的未來，哪些AI工具會“有些令人不安”？哪些又會“讓人振奮”？答案取決于你對音樂本質的理解。

“People don’t listen to Taylor Swift because of her music，” says Stephen Phillips of Brisbane AI music company Splash. “They do in part， but it’s a lot to do with personality and celebrity and connection to the artist and all that stuff.”

“大家聽泰勒·斯威夫特不完全是因為她的音樂。”布里斯班AI音樂公司Splash的負責人斯蒂芬·菲利普斯指出，“音樂肯定是部分原因，但很大程度上還涉及了人格魅力、名人效應、與她的情感聯結，諸如此類。”

That’s the stuff AI can’t replace—yet. Splash already has the technology， roughly on par with Google’s MusicLM and Meta’s AudioCraft， to have a crack at2 anything. John Lennon and Taylor Swift together at last？ Let them at it， as soon as those artists’ respective IP holders agree to lease their data sets—i.e.， their back catalogues3—to train the machine.

這些正是AI無法替代人類的領域——至少目前還是如此。Splash公司已掌握的技術實力與谷歌的MusicLM和“元”公司的AudioCraft旗鼓相當，可以進行任何嘗試。想讓已故歌手約翰·列儂與泰勒·斯威夫特跨時空對唱？AI完全可以實現，但前提是兩位藝人的知識產權持有方同意將作品數據（即曲庫資源）出租用于機器學習。

This is the negotiation that industry players such as Phillips are waiting to have. Canadian singer Grimes is a pioneer in this space， having declared her voice open slather4 for creators—as long as they cut her in5 for 50 per cent. “I wanna be software， upload my mind，” she sings on her latest release. “Take all my data， what will you find？”

這正是菲利普斯等業內人士翹首期待的談判。加拿大歌手格蘭姆斯已先行試水，她宣布，若能分得50%的收益，愿意無限制向創作者開放自己聲音的使用權。她在新歌中唱道：“多想化作程序，上傳我心。讀取所有數據，可會窺見真心？”

“If they play this right，” Phillips says of artists and industry more broadly， AI will be “the biggest boon for the recorded music industry since the CD. Especially for that classic [breed of] artists that are not making new music any more. They’ve still got fans， but they’re not releasing anything new. This is a huge opportunity for them.”

菲利普斯談到藝人和整個音樂產業時表示，“如果使用得當”，那么AI將成為“自CD問世以來唱片業最大的福音，對（那群）不再創作新歌的老牌藝人尤為如此——他們仍有大批樂迷，但又不打算發表新作，AI對他們而言是巨大的機遇”。

While pop’s personalities prevaricate， there’s plenty of hold music6 on tap7. Want quirky， upbeat funk？ Want movie scene in a desert with percussion？ Want tropical jazz for breakfast？ Just type that into one of an exploding legion of AI music generators online and out it comes. Like ChatGPT for your ears， it’s flawed but getting smarter fast.

當流行巨星還在審慎觀望，大量由AI創作的通話等待音樂已觸手可得。想聽俏皮又具動感的放克節奏？渴望茫茫荒漠的電影場景中激蕩起鼓點？期待喚醒清晨味蕾的熱帶爵士？只需在層出不窮的AI音樂在線生成器中選擇一個輸入需求，旋律即刻流淌出來。如同聽覺版的ChatGPT，效果未臻完美，但進化速度驚人。

Most observers agree that AI will soon own this kind of faceless， generic music. That’s no small thing because it’s increasingly what consumers want. The vast majority of streamers aren’t typing artists’ names into apps， they’re searching “lo-fi hip-hop” or “mellow electronic” playlists， often neither knowing nor caring who made it.

多數行業觀察人士認同，AI即將主導這種去人格化的通用音樂領域。這絕非小事，因為消費者的需求越來越大。絕大多數流媒體用戶不再搜索特定歌手，而是直接搜索“低保真嘻哈”或“柔和電子”歌單，他們既不知道也不在意創作者身份。

Melbourne music producer and educator David Jacob sees this as a “race to the bottom”8 incentive for AI music developers to “flood the market with hundreds of thousands of really poor-quality tracks in order to monetise the technology”.

墨爾本音樂制作人兼教育工作者戴維·雅各布將這種趨勢視為一場“逐底競爭”，認為其促使AI音樂開發者通過“向市場傾瀉大量劣質歌曲以實現技術變現”。

Like most studio professionals， he’s loving the way AI is removing the “donkey work” from the mixing and mastering process， but he names Berlin-based American electronic artist Holly Herndon as one of few artists using AI in “genuinely creative， artistic ways”.

與大多數錄音棚里的從業者一樣，雅各布對AI技術消除了混音與母帶處理中的“枯燥工序”感到欣喜，又特別指出旅居柏林的美籍電子音樂人霍利·赫恩登是將AI用于“真正的創造性藝術實踐”的少數派之一。

Herndon’s 2019 album Proto is the latest culmination of her experiments in “voices， polyphony and artificial intelligence” using her bespoke “neural network”， Spawn. It’s essentially an expanding data set of her own voice recordings for AI to crunch9. The idea， she says， is to “expand past the limitations of my physical body”.

2019年，赫恩登發行專輯《普羅托》，這是她通過自行設計的“神經網絡”（名為“再生俠”）在“人聲、復調與人工智能”領域實驗的最新成果。再生俠本質上是一個不斷擴容的錄有她自己聲音的數據庫，供AI學習分析。她解釋說，借助再生俠創作意在“突破肉體凡胎的限制”。

From veteran music systems guy Brian Eno to Japanese anime pop star Hatsune Miku to Portland dance-pop outfit YACHT， the last few years have heard scores more AI-generated experiments that history may count as pioneering. So far， it’s fair to say experimental electronic systems have mostly yielded experimental electronic music.

從資深音樂系統專家布萊恩·伊諾到日本虛擬流行歌姬初音未來，再到波特蘭流行舞曲樂隊“游艇”，近年大量涌現的AI生成音樂實驗或可作為先驅載入史冊。不過目前可以公允地說，實驗性的電子系統大多只創作出了實驗電子音樂。

At the more traditional end of the spectrum， AI has more learning to do. On his tour， Beck performed a comically terrible song called Rebel Soul written by AI “in the style of Beck”. As Nick Cave has gruffly noted， artificial intelligence can offer only “a grotesque mockery” of a song without the human conditions required to conceive it.

在傳統音樂領域，AI仍需補課。貝克曾在巡演中演繹AI仿其“貝克風”創作的歌曲《反叛靈魂》，這是一首堪稱災難的滑稽之作。正如尼克·凱夫尖銳指出的，缺乏人類創作必備的情境體驗，AI作品不過是“荒誕的拙劣模仿”。

That said， if formulas can be taught， machines can learn them. Beautiful Life， recorded by Melbourne songwriting lecturer Greg Arnold， is an AI collaboration composed in the style of his band， Things of Stone and Wood. ChatGPT-generated lyrics， chords， genre and tempo cues in seconds. “For the bridge，” it thoughtfully suggested， “the melody could build up to higher pitch to signify the rise above adversity.”

但話說回來，若有公式可教，機器便可習得。墨爾本詞曲創作講師格雷格·阿諾德發表的《美麗人生》便是借助AI模仿其樂隊“石木物語”風格而創作的歌曲。ChatGPT在數秒內生成歌詞、和弦、曲風與節奏建議，甚至貼心地提示：“橋段可將旋律升調，象征戰勝逆境。”

Imagine an intelligence hungry enough for such structural wisdom to devour every tutorial ever committed to YouTube. Imagine it crunching 100 years of methodology and philosophy， beginning with Paul Zollo’s weighty interview compendium Songwriters on Songwriting， then David Byrne’s How Music Works for cultural context…

試想一種智能，它對音樂創作的結構性智慧如饑似渴，足以學習吸收發布在優兔平臺上的所有教程；試想它消化百年以來的創作方法與基本原理，從保羅·佐洛的鴻篇訪談錄《音樂創作人談創作》讀起，再讀大衛·拜恩解構文化語境的《制造音樂》……

There are thousands more academic resources out there， of course： centuries of human intelligence combined to codify countless esoteric tangents into an artform the layperson still quaintly perceives to be some kind of magic. How magic will it be when artificial intelligence learns those codes—in nanoseconds—and runs with them？

當然還有海量學術資源：數百年來不斷積累的人類智慧，將無數深奧支流編碼成一種藝術形式，而外行至今仍天真地視其為某種魔法。當人工智能以納秒級速度學會這些代碼并執行時，這種“魔法”又將呈現何種形態？

In her book This Is What It Sounds Like， Susan Rogers， American neuroscientist and former sound engineer to Prince10， set to demystifying the nebulous question of why people like the music they like. Spoiler alert： lots of reasons， but surprisingly quantifiable.

曾擔任“王子”錄音師的美國神經科學家蘇珊·羅杰斯在其著作《聽音解密》中，試圖破解“人們為何鐘情特定音樂”這個玄奧命題。劇透警告：原因眾多，卻可量化，這種量化令人吃驚。

Her take on whether AI will “downgrade the artform” is unsentimental. “My own attitude sides with the listeners who love it，” she writes. “Whenever music delights our [neurological] sweet spots， then who can say it is inferior to any other music？”

關于AI是否“貶損藝術形式”的問題，羅杰斯的見解理性冷峻。她在書中寫道：“我本人認同樂迷的判斷標準。只要音樂能觸發（神經系統的）愉悅開關，誰敢說它就遜色于其他作品呢？”

This goes to the heart of what we mean by music. Today， industry has successfully convinced us it’s all about an ingenious elite in a closed circuit of studios and stadiums and videos and magazines and charts and award shows. In fact， it’s a kinetic human activity that pushes air11 in the general direction of neural synapses.

這觸及我們所理解的音樂的核心。當下業界成功地讓我們相信，音樂出自才華橫溢的奇才，而他們身處由錄音室、體育場、視頻、雜志、榜單、頒獎禮構成的封閉圈子中。事實上，音樂創作不過是朝著神經突觸的大致方向傳遞旋律的動態人類行為。

“It’s not music that AI threatens， it’s a capitalist paradigm，” says Melbourne composer and audiovisual artist Robin Fox. “The music industry has done more to destroy music than any other entity under the sun. What AI threatens is authenticity; and authenticity is at the heart of commodification—so if we can’t pick between what’s authentic and what’s not， we don’t know what to buy.”

“AI威脅的并非音樂本身，而是資本主義范式。”墨爾本作曲家、視聽藝術家羅賓·福克斯指出，“音樂產業對音樂的摧毀遠超世間其他任何實體。AI真正動搖的是真實性，而真實性正是商品化運作的根基——當公眾無法分辨真偽，消費將難以抉擇。”

Fox’s current favourite AI music generator is Relentless Doppelganger， a YouTube portal that spews speed metal12 in real time， uninterrupted and forever. If speed metal musicians feel threatened by it， they need to transcend the genre that AI finds so easy to replicate， he says. That’s the role of human intelligence.

福克斯目前最看好的AI音樂生成平臺是“無盡分身”（又譯“永不消逝的幽靈”），這個優兔直播頻道能永不間斷地實時生成速度金屬樂。福克斯指出，如果速度金屬樂手感到威脅，就必須突破AI易于復制的風格，而這正是人類智能的使命所在。

So， where is AI leading music in our lifetime？ In one possible future lies the ongoing industrial myth of genius recycled by licensed gatekeepers in infinitely new but recognisably branded guises. The Beatles are back！ （feat. Taylor Swift）.

那么，在我們有生之年，AI將引領音樂走向何方？一種未來圖景是業界持續兜售所謂的“天才神話”，即經手握版權的守門人重新包裝，套上嶄新但有辨識度的品牌偽裝。正如“披頭士樂隊回歸（助演嘉賓：泰勒·斯威夫特）”式的商業噱頭。

In another future lies the utter destruction of everything we’ve been sold about scarcity value; about music as product， about genius creators versus lowly consumers. A world where music is everywhere， by everyone who cares to make it， with whatever tools and data they choose.

另一種未來圖景則可能完全顛覆人們慣于接受的觀念，比如稀缺價值的概念、音樂作為商品的屬性、天才創作者與平庸消費者的對立。在這樣的未來里，音樂無處不在，無論選擇何種工具與數據，有意創作的人皆可創作音樂。

Both futures are possible concurrently， of course. But even in a world already wallpapered from Netflix to Spotify with AI-generated mood soundtracks， there seems zero chance of machines storming into your local to put an end to music in its essential form.

當然，這兩種未來完全可能并行于世。但即使AI生成的氛圍音樂已無處不在，充斥奈飛、聲破天等平臺，機器的洪流也無法沖進離家不遠你常去的小酒館，終結音樂的本質形態。

“It’s an exchange of human frailties，” Fox says. “In that moment， when you’re sitting in a room with a musician， and they bare their soul to you， that’s a kind of magic. There’s no empathy exchange with an AI. You don’t see yourself in another when you’re interacting with an AI. That’s the kind of magic that an AI can’t replicate.”

“音樂關乎人性脆弱面的交流。”福克斯如此詮釋道，“當同處一室的音樂人向你袒露靈魂時，那是具有某種魔力的時刻。你與AI之間永遠不會產生情感共鳴。與AI互動時，你無法在對方身上看到自己的影子。那正是AI難以復制的魔力。”

（譯者單位：上海外國語大學）

1此處呼應披頭士樂隊自稱的Fab Four稱號，其中fab是fabulous（絕妙的）的縮寫。

2 have a crack at〈口〉嘗試。" 3 back catalogue過往作品。" 4 open slather〈澳新口〉完全開放；無限制許可。" 5 cut sb. in〈口〉把某人包括在內（分享利益）。

6 hold music在撥打客服熱線等情況下，用戶在等待的過程中聽到的背景音樂。" 7 on tap〈口〉隨時可得的；隨時可用的。" 8 race to the bottom逐底競爭，即不斷降低自身底線和標準的競爭方式。

9 crunch（用計算器或電腦）運算。

10即美國創作型歌手普林斯·羅杰斯·納爾遜（Prince Rogers Nelson），藝名“王子”（Prince）。" 11 air〈音〉曲調；旋律。

12 speed music速度金屬樂，一種以演奏速度快而準為特點的極端金屬子風格。