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Einleitung

Ausstellungsname

機器會夢見 NFT 嗎？ ─ 台灣首檔加密藝術收藏展 - Do Machines Dream of NFT? ─ A Crypto Art Collection on Tezos

Zeitraum

2022-03-26 ~ 2022-03-27

Veranstaltungsort

濕地 venue B1

Adresse

104 臺北市中山區林森北路 107 巷 10 號

藝術GAN什麼？ ──《機器會夢見NFT嗎？》展序 自伊恩‧古德菲婁（Ian Goodfellow）2014年提出「生成對抗網路」（Generative Adversarial Network，簡稱GAN）起，這項技術已經在遊戲設計、天文研究和廣告影像等領域獲得眾多目光，視覺藝術也不例外。 生成對抗網路屬於「機器學習」（Machine Learning）中的「深度學習」（Deep Learning）。機器學習是人工智慧（Artificial Intelligence）的根本課題，著重模擬神經網路，試圖辨識、產出和精進資訊。在「生成對抗網路」這個冗長的名詞中，「對抗」是最大特點：設立「生成器」（generator）和「辨識器」（discriminator）的雙重機制，讓它們相互抗拮，共同成長。 首先，生成器自資訊的「潛藏空間」（latent space）中獲取素材，編造幾可亂真的成品；其次，辨識器根據現實中的本真對象或資料庫標的，指認生成器編造資訊之真偽。若為真，則通過檢驗，資訊確立；若為假，則再次生成，直到「瞞天過海」。我們不妨將它們的關係比作偽鈔製作者和刑事局或贗畫家和檢驗者，就算不以真偽二分，兩者的互動亦可被想成藝術家和藝評家，資訊或作品的方方面面得以更加完善。 把上述邏輯和技術應用在視覺藝術，觀者不只見到寫實卻不存在的人事物，還能欣賞生產和校正的往返歷程。我們再次玩味「觀看的方式」：判斷真與假，捕捉變與常，想像萬物之間的連結及其外觀互換的可能。此外，若機器能夠繪畫，它們「想到」和「看到」的又是什麼？這麼問或許太誇張，畢竟這一切不仍是人類的創作嗎？諸多爭議或許正是重解人機關係的契機。 「生成對抗網路」剛起步時，「非同質化代幣」（Non-Fungible Token，即NFT）的技術尚未出現。如今，隨著區塊鏈和加密貨幣的普及，越來越多藝術家以「生成對抗網路」創作，並將作品鑄成NFT。本展選品多半出自泰卓鏈（Tezos）非同質化代幣平台「Hic et Nunc」（已更名為「TEIA」），如果機器在學習過程中逐步向人趨近，那麼「他／她們」是否也會發現作品正以NFT的形式販售，甚至在午夜夢迴時若有所思？ 機器會夢見NFT嗎？這個問題交給步入展間的您──和周邊越發聰明的所有手機、電腦和電子儀器──來回答。 Since Ian Goodfellow proposed the Generative Adversarial Network (GAN) in 2014, this technology has attracted a lot of attention in fields like game design, astronomical research and advertising image, and visual art is no exception. A GAN is a Deep Learning in Machine Learning, which is an essential topic of Artificial Intelligence that focuses on the neural network simulation to identify, produce, and improve information. Of the lengthy term “Generative Adversarial Network,” “Adversarial” is the core feature: setting up the duo models of “generator” and “discriminator” for both to fight and grow with each other. First of all, the generator acquires materials from the latent space and creates seemingly genuine products. Secondly, the discriminator identifies these products as true or false on the basis of objects in reality or targets in the database. If a product is identified as true, the product passes the test, and the information is verified. Otherwise, the generator will generate information again until they cheat the discriminator. The relation between the generator and discriminator is like the one between counterfeit money makers and criminal affairs bureau or that between art forgers and art forensic investigators. Even if we do not treat it as such, their interaction can be understood as that between artists and critics, which improves information or artworks in various aspects. Applying this logic and technology mentioned above to visual art, viewers can not only see something or someone that does not exist, but also appreciate the repeated process of production and correction. By distinguishing true from false, grasping the variable and the constant, and imaging the links among all and the possibilities in appearance change, we play with “ways of viewing” yet again. Besides, if machines can paint, what do they “think of” and “see”? This question may be exaggerated. After all, aren’t all these products creation of humans? These controversies may be chances to the reinterpretation of human-machine relationship. At the onset of GAN, the technology of Non-Fungible Token (NFT) was not born yet. Nowadays, along with the popularization of blockchain and cryptocurrency, there is an increasing number of artists creating and turning works into NFTs with GANs. Most of the exhibits in this exhibition are from Hic et Nunc (now TEIA), a Tezos NFT platform. If machines are approximating humans in the process of learning, will they also discover that their works are on sale in the form of NFTs, and have some thoughts to ponder over at midnight? Do machines dream of NFT? This question is left for everyone stepping into the exhibition, as well as the ever-smarter mobile phones, computers, and electronic devices around us, to answer.

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