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Media synthesis (AI)

From EverybodyWiki Bios & Wiki

Media synthesis (also known as synthetic media, personalized media, and AI-generated media, and often colloquially referred to as "deepfakes of a particular area of media") is the artificial production and manipulation of data and media by automated means, especially through the use of artificial intelligence.[1]. As a phrase, media synthesis is an umbrella term for an entire family of interrelated techniques, such as deepfakes, image synthesis, speech synthesis, natural-language generation, procedural generation, and more, and generally refers to the use of artificial neural networks for these purposes: individual methods such as deepfakes and text synthesis are not usually referred to as "media synthesis" but instead by their respective terminology (and often generally as the "deepfakes" of a particular area, e.g. "deepfakes for text" for natural-language generation; "deepfakes for voices" for neural voice cloning, etc.). Media synthesis as a field has grown rapidly since the creation of generative adversarial networks, primarily through the rise of music synthesis [2], text generation [3], and deepfakes [4]. Media synthesis is an applied form of computational creativity.

Outline[edit]

Media are the communication outlets or tools used to store and deliver information or data.[5][6] The term refers to components of the mass media communications industry, such as print media, publishing, the news media, photography, cinema, broadcasting (radio and television), and advertising.[7] Interactive media such as video games and simulations also qualify under the label.

The digitalization of data has allowed for creators and artists to develop media on a mass scale while also opening up avenues to alter media via altering the fundamental data with which it is made. Thus, media synthesis represents a form of automation of the media creation process.

History[edit]

Pre-1950s[edit]

Maillardet's automaton is drawing a picture

Media synthesis as a process of automated art dates back to the automata of ancient Greek civilization, where inventors such as Daedalus and Hero of Alexandria designed machines capable of writing text, generating sounds, and playing music.[8][9] The tradition of automaton-based entertainment flourished throughout history, with mechanical beings' seemingly magical ability to mimic human creativity often drawing crowds throughout Europe[10], China[11], India[12] , and so on. Other automated novelties such as Johann Philipp Kirnberger's "Musikalisches Würfelspiel" (Musical Dice Game) 1757 also amused audiences.[13]

Despite the technical capabilities of these machines, however, none were capable of generating original content and were entirely dependent upon their mechanical designs.

Rise of artificial intelligence[edit]

The field of AI research was born at a workshop at Dartmouth College in 1956,[14], begetting the rise of digital computing used as a medium of art as well as the rise of generative art. Initial experiments in AI-generated art included the Illiac Suite, a 1957 composition for string quartet which is generally agreed to be the first score composed by an electronic computer.[15] Lejaren Hiller, in collaboration with Leonard Issacson, programmed the ILLIAC I computer at the University of Illinois at Urbana–Champaign (where both composers were professors) to generate compositional material for his String Quartet No. 4.

In 1960, Russian researcher R.Kh.Zaripov published worldwide first paper on algorithmic music composing using the "Ural-1" computer.[16]

In 1965, inventor Ray Kurzweil premiered a piano piece created by a computer that was capable of pattern recognition in various compositions. The computer was then able to analyze and use these patterns to create novel melodies. The computer was debuted on Steve Allen's I've Got a Secret program, and stumped the hosts until film star Henry Morgan guessed Ray's secret.[17]

Before 1989, artificial neural networks have been used to model certain aspects of creativity. Peter Todd (1989) first trained a neural network to reproduce musical melodies from a training set of musical pieces. Then he used a change algorithm to modify the network's input parameters. The network was able to randomly generate new music in a highly uncontrolled manner.[18][19]

In 2014, Ian Goodfellow and his colleagues developed a new class of machine learning systems: generative adversarial networks (GAN).[20] Two neural networks contest with each other in a game (in the sense of game theory, often but not always in the form of a zero-sum game). Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proven useful for semi-supervised learning,[21] fully supervised learning,[22] and reinforcement learning.[23] In a 2016 seminar, Yann LeCun described GANs as "the coolest idea in machine learning in the last twenty years".[24]

Branches of media synthesis[edit]

Deepfakes[edit]

Main article: Deepfake

Deepfakes (a portmanteau of "deep learning" and "fake"[25]) are media that take a person in an existing image or video and replace them with someone else's likeness using artificial neural networks.[26] They often combine and superimpose existing media onto source media using machine learning techniques known as autoencoders and generative adversarial networks (GANs).[27][28] Deepfakes have garnered widespread attention for their uses in celebrity pornographic videos, revenge porn, fake news, hoaxes, and financial fraud.[29][30][31][32] This has elicited responses from both industry and government to detect and limit their use.[33][34]

The term deepfakes originated around the end of 2017 from a Reddit user named "deepfakes".[26] He, as well as others in the Reddit community r/deepfakes, shared deepfakes they created; many videos involved celebrities’ faces swapped onto the bodies of actresses in pornographic videos,[26] while non-pornographic content included many videos with actor Nicolas Cage’s face swapped into various movies.[35] In December 2017, Samantha Cole published an article about r/deepfakes in Vice that drew the first mainstream attention to deepfakes being shared in online communities.[36] Six weeks later, Cole wrote in a follow-up article about the large increase in AI-assisted fake pornography.[26] In February 2018, r/deepfakes was banned by Reddit for sharing involuntary pornography.[37] Other websites have also banned the use of deepfakes for involuntary pornography, including the social media platform Twitter and the pornography site Pornhub.[38] However, some websites have not yet banned Deepfake content, including 4chan and 8chan. [39] Other online communities remain, including Reddit communities that do not share pornography, such as r/SFWdeepfakes (short for "safe for work deepfakes"), in which community members share deepfakes depicting celebrities, politicians, and others in non-pornographic scenarios.[40] Other online communities continue to share pornography on platforms that have not banned deepfake pornography.[39]

Image synthesis[edit]

In terms of generative art, image synthesis is the artificial production of visual media, especially through algorithmic means. One subfield of this includes human image synthesis, which is the use of neural networks to make believable and even photorealistic renditions[41][42] of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work.The website This Person Does Not Exist showcases fully automated human image synthesis by endlessly generating images that look like facial portraits of human faces.[43] The website was published in February 2019 by Phillip Wang. The technology has drawn comparison with deep fakes[44] and the tells of poker, and its potential usage for sinister purposes has been bruited.[45]

Photo manipulation[edit]

Main articles: Photo manipulation and Image editing

Photo manipulation involves transforming or altering a photograph using various methods and techniques to achieve desired results. Some photo manipulations are considered skillful artwork while others are frowned upon as unethical practices, especially when used to deceive the public. Other examples include being used for political propaganda, or to make a product or person look better, or simply for entertainment purposes or harmless pranks. Artificial neural networks used for image synthesizing projects can engage in digital inpainting, where damaged, deteriorating, or missing parts of an artwork are reconstructed, can also be used for the opposite purpose: intelligently removing certain objects and subjects from an image without evidence of tampering.[46]

Music generation[edit]

Main articles: Computer music, Music and artificial intelligence, and Pop music automation

The capacity to generate music through autonomous, non-programmable means has long been sought after since the days of Antiquity, and with developments in artificial intelligence, two particular domains have arisen:

  1. The robotic creation of music, whether through machines playing instruments or sorting of virtual instrument notes (such as through MIDI files)
  2. Directly generating waveforms that perfectly recreate instrumentation and human voice without the need for instruments, MIDI, or organizing premade notes.

In 2016, Google DeepMind unveiled WaveNet, a deep generative model of raw audio waveforms that could learn to understand which waveforms best resembled human speech as well as musical instrumentation.[47] Other networks capable of generating music through waveform manipulation include TacoTron (by Google) and DeepVoice (by Baidu).

Speech synthesis[edit]

Main article: Speech synthesis

Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.[48]

Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.[49]

WaveNet, DeepMind's a deep generative model of raw audio waveforms, specialized on human speech.[50] TacoTron is another network capable of generating believably-human speech.

Audio synthesis[edit]

Beyond music and human speech, generative networks are also capable of generating any conceivable sound that can be achieved through audio waveform manipulation, which might conceivably be used to generate stock audio of sound effects or simulate audio of currently imaginary things.

Natural-language generation[edit]

Main articles: Computational creativity § Story generation, and Computational creativity § Poetry

Natural-language generation (NLG, sometimes synonymous with text synthesis) is a software process that transforms structured data into natural language. It can be used to produce long form content for organizations to automate custom reports, as well as produce custom content for a web or mobile application. It can also be used to generate short blurbs of text in interactive conversations (a chatbot) which might even be read out by a text-to-speech system. Interest in natural-language generation increased in 2019 after OpenAI unveiled GPT2, an AI system that generates text matching its input in subject and tone. GPT2 is a transformer, a deep machine learning model introduced in 2017 used primarily in the field of natural language processing (NLP).[51]

Natural-language generation alone theoretically can accomplish many other methods of media synthesis by way of generating binary code to create text, audio, and image files as a sort of applied undertaking of the Infinite monkey theorem.

Procedural graphics generation[edit]

Procedural generation is a method of creating data algorithmically as opposed to manually, typically through a combination of human-generated assets and algorithms coupled with computer-generated randomness and processing power. In computer graphics, it is commonly used to create textures and 3D models. In video games, it is used to automatically create large amounts of content in a game. Advantages of procedural generation include smaller file sizes, larger amounts of content, and randomness for less predictable gameplay.

Practical applications[edit]

The first auction sale of artificial intelligence art was at Christies Auction House in New York. The AI artwork sold for $432,500 which was almost 45 times higher than its estimate of $7,000-$10,000. The artwork was created by "Obvious", a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier.[52][53][54][55]

The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game. It also creates financial reports and real estate analyses.[56]

In 2018, GANs reached the video game modding community, as a method of up-scaling low-resolution 2D textures in old video games by recreating them in 4k or higher resolutions via image training, and then down-sampling them to fit the game's native resolution (with results resembling the supersampling method of anti-aliasing).[57] With proper training, GANs provide a clearer and sharper 2D texture image magnitudes higher in quality than the original, while fully retaining the original's level of details, colors, etc. Known examples of extensive GAN usage include Final Fantasy VIII, Final Fantasy IX, Resident Evil REmake HD Remaster, and Max Payne.

On 30 April, 2019, the world’s first collection of images produced using AI was published through Amazon. The collection was produced by a UK based team "AIArtMedia". [58][59]

In 2019, musical group Dadabots came to prominence due to streaming neural-network generated death metal endlessly[60]. As of January 2020, the stream is still ongoing.

Concerns and Controversies[edit]

Deepfakes have been used to misrepresent well-known politicians in videos. In separate videos, the face of the Argentine President Mauricio Macri has been replaced by the face of Adolf Hitler, and Angela Merkel's face has been replaced with Donald Trump's.[61][62]

In June 2019, a downloadable Windows and Linux application called DeepNude was released which used neural networks, specifically generative adversarial networks, to remove clothing from images of women. The app had both a paid and unpaid version, the paid version costing $50.[63][64] On June 27 the creators removed the application and refunded consumers.[65]

In 2019, voice deepfake technology was used to successfully impersonate a chief executive’s voice and demand a fraudulent transfer of €220,000.[66] The case raised concerns about the lack of encryption methods over telephones as well as the unconditional trust often given to voice and to media in general.[67]

Potential uses and impacts[edit]

Media synthesis techniques involve generating, manipulating, and altering data to emulate creative processes on a much faster and more accurate scale. As a result, the potential uses are as wide as human creativity itself, ranging from revolutionizing the entertainment industry to accelerating the research and production of academia. Potential future hazards include the use of a combination of different subfields to generate fake news[68], natural-language bot swarms generating trends and memes, false evidence being generated, and potentially addiction to personalized content and a retreat into AI-generated fantasy worlds.

In 2019, Elon Musk warned of the potential use of advanced text-generating bots to manipulate humans on social media platforms. [69] In the future, even more advanced bots may be employed for means of astroturfing apps, websites, and political movements, as well as supercharging memes and cultural trends— including those generated for the sole purpose of being promoted by bots until humans perpetuate them without further assistance.

Deep reinforcement learning-based natural-language generators have the potential to be the first AI systems to pass the Turing Test and potentially be used as advanced chatbots[70], which may then be used to forge artificial relationships in a manner similar to the 2013 film Her and spam believable comments on news articles.

One use case for natural-language generation is to generate or assist with writing novels and short stories[71], while other potential developments are that of stylistic editors to emulate professional writers[72]. The same technique could then be used for songwriting, poetry, and technical writing, as well as rewriting old books in other authors' styles and generating conclusions to incomplete series.[73] Furthermore, the ability to generate personalized content with no intent of being shared or with an extreme and usually unmarketable niche at any quality for virtually no price has the potential to lead to a breakdown in certain literary conventions, such as censorship, dramatic structure, conservation of detail (e.g. cutting out any detail in a story that does not immediately move a plot forward)[74], and writing to a particular demographic or for a specific patron in projects that, without media synthesis methods, would require very large budgets to create (which would otherwise greatly increase the risks of defying such conventions). Conceivably, this will also work for visual and audio media as well.

Image synthesis tools may be able to streamline or even completely automate the creation of certain aspects of visual illustrations, such as animated cartoons, comic books, and political cartoons.[75] Because the automation process takes away the need for teams of designers, artists, and others involved in the making of entertainment, costs could plunge to virtually nothing and allow for the creation of "bedroom multimedia franchises" where singular people can generate results indistinguishable from the highest budget productions for little more than the cost of running their computer. Character and scene creation tools will no longer be based on premade assets, thematic limitations, or personal skill but instead based on tweaking certain parameters and giving enough input.

An increase in cyberattacks has also been feared due to methods of phishing, catfishing, and social hacking being automated by new technological methods.[76]

Natural-language generation bots mixed with image synthesis networks may theoretically be used to clog search results, filling search engines with trillions of otherwise useless but legitimate-seeming blogs, websites, and marketing spam.[77]

There has been speculation about deepfakes being used for creating digital actors for future films. Digitally constructed/altered humans have already been used in films before, and deepfakes could contribute new developments in the near future.[78] Amateur deepfake technology has already been used to insert faces into existing films, such as the insertion of Harrison Ford's young face onto Han Solo's face in Solo: A Star Wars Story,[79] and techniques similar to those used by deepfakes were used for the acting of Princess Leia in Rogue One.[80]

In the future, video games may utilize media synthesis to perfect random asset creation and dialogue generation, allowing for 100% freedom in gameplay and design or even for automatic video game generation. Upscaling and asset synthesis may be used to mod older games, adding features and mechanics that did not previously exist or bringing them up to modern standards. For example, a 2D game may be redone in 3D or vice versa without the need for lengthy asset design.

GANs can be used to create photos of imaginary fashion models, with no need to hire a model, photographer, makeup artist, or pay for a studio and transportation.[81] GANs can be used to create fashion advertising campaigns including more diverse groups of models, which may increase intent to buy among people resembling the models.[82] GANs can also be used to create portraits, landscapes and album covers. The ability for GANs to generate photorealistic human bodies presents a challenge to industries such as fashion modeling, which may be at heightened risk of being automated.[83][84]

A combination of natural-language generation and image synthesis may allow future users to turn any literary passage into a full-motion movie or comic, as well as vice versa. In 2019, Dadabots unveiled an AI-generated stream of death metal which remains ongoing with no pauses.[85] In the future, this may evolve through the use of novel video generation, speech synthesis, and natural-language generation to allow for "24/7 media," such as never-ending AI-generated movies, concerts, and live-streams.

Algorithmic direction can democratize the skill set needed to master the use of programs like Adobe Photoshop and Audacity, allowing for vastly easier and more streamlined image and audio manipulation at a much higher quality.

Musical artists and their respective brands may also conceivably be generated from scratch, including AI-generated music, videos, interviews, and promotional material. Conversely, existing music can be completely altered at will, such as changing lyrics, singers, instrumentation, and composition.[86] Through the use of artificial intelligence, old bands and artists may be "revived" to release new material without pause, which may even include "live" concerts and promotional images.

Neural network-powered photo manipulation has the potential to reduce the ease by which totalitarian and absolutist regimes can engage in damnatio memoriae. A sufficiently paranoid totalitarian government or community may engage in a total wipe-out of history using all manner of media synthesis technologies, fabricating history and personalities as well as any evidence of their existence at all times. Even in otherwise rational and democratic societies, certain social and political groups may utilize media synthesis to craft cultural, political, and scientific cocoons that greatly reduce or even altogether destroy the ability of the public to agree on basic objective facts. Conversely, the existence of media synthesis will be used to discredit factual news sources and scientific facts as "potentially fabricated."

Theoretically, even Wikipedia may suffer from excesses of false evidence and facts being generated in an effort to write, rewrite, and censor articles.

See also[edit]

References[edit]

  1. ↑ "On Media Synthesis: An Essay on the Next 15 Years of Creative Automation - LessWrong 2.0".
  2. ↑ "How AI-generated music is changing the way hits are made". 2018-08-31.
  3. ↑ "Better Language Models and Their Implications". 2019-02-14.
  4. ↑ "A guide to 'deepfakes,' the internet's latest moral crisis".
  5. ↑ "What is media? definition and meaning". BusinessDictionary.com.
  6. ↑ Cory Janssen. "What is Communication Media? - Definition from Techopedia". Techopedia.com.
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    • McCorduck 2004, pp. 111–136
    • Crevier 1993, pp. 47–49, who writes "the conference is generally recognized as the official birthdate of the new science."
    • Russell & Norvig 2003, p. 17, who call the conference "the birth of artificial intelligence."
    • NRC 1999, pp. 200–201
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  66. ↑ "Fraudsters Used AI to Mimic CEO's Voice in Unusual Cybercrime Case".
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  68. ↑ https://www.cnbc.com/2019/10/15/deepfakes-could-be-problem-for-the-2020-election.html
  69. ↑ Hamilton, Isobel (2019-09-26). "Elon Musk has warned that 'advanced AI' could poison social media".
  70. ↑ https://dwjbosman.github.io/chatbot-using-open-ai-gpt-2-transformer-model
  71. ↑ https://www.theatlantic.com/technology/archive/2018/10/automated-on-the-road/571345/
  72. ↑ Merchant, Brian (1 October 2018). "When an AI Goes Full Jack Kerouac". The Atlantic. Unknown parameter |url-status= ignored (help)
  73. ↑ https://towardsdatascience.com/openai-gpt-2-writes-alternate-endings-for-game-of-thrones-c9be75cd2425
  74. ↑ https://thestorycoach.net/2017/07/21/the-law-of-conservation-of-detail-and-exceptions/
  75. ↑ https://venturebeat.com/2017/06/02/pixar-veteran-creates-a-i-tool-for-automating-2d-animations/
  76. ↑ Janofsky, Adam (2018-11-13). "AI Could Make Cyberattacks More Dangerous, Harder to Detect". Wall Street Journal.
  77. ↑ https://www.theverge.com/2019/7/2/19063562/ai-text-generation-spam-marketing-seo-fractl-grover-google
  78. ↑ Kemp, Luke (2019-07-08). "In the age of deepfakes, could virtual actors put humans out of business?". The Guardian. ISSN 0261-3077. Retrieved 2019-10-20.
  79. ↑ Radulovic, Petrana (2018-10-17). "Harrison Ford is the star of Solo: A Star Wars Story thanks to deepfake technology". Polygon. Retrieved 2019-10-20.
  80. ↑ Winick, Erin. "How acting as Carrie Fisher's puppet made a career for Rogue One's Princess Leia". MIT Technology Review. Retrieved 2019-10-20.
  81. ↑ Wong, Ceecee. "The Rise of AI Supermodels". CDO Trends.
  82. ↑ Dietmar, Julia. "GANs and Deepfakes Could Revolutionize The Fashion Industry". Forbes.
  83. ↑ https://research.zalando.com/welcome/mission/research-projects/generative-fashion-design/
  84. ↑ https://syncedreview.com/2019/08/29/ai-creates-fashion-models-with-custom-outfits-and-poses/
  85. ↑ https://newatlas.com/dadabots-death-metal-neural-network-livestream/59394/
  86. ↑ https://www.theverge.com/2019/4/26/18517803/openai-musenet-artificial-intelligence-ai-music-generation-lady-gaga-harry-potter-mozart


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