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Wycliffe Hill이 발명한 스토리저작 로봇의 이름이다. 이 기게의 정확한 작동 방식은 알려지지 않았다. 당시 인기있던 "Popular Machine" 잡지에 실린 기사는 다음과 같은 내용을 전한다.

기계적인 두뇌로 생각하는 로봇

과거 로봇은 단지 인간의 지시에 따라 다양한 묘기를 부리는 기계에 불과했지만, 새로 등장한 로봇은 생각하고, 영혼을 가지고 있으며, 창의적인 상상력을 발휘하는 등 현대의 정형화된 단편 소설을 쓰는 데 필요한 자질을 갖추었다.
로스앤젤레스의 시나리오 작가인 위클리프 힐이 발명한 이 로봇은 잡지나 영화 시나리오를 위한 수백만 개의 플롯을 만들 수 있는 것으로 알려져 있습니다.

힐은 로봇에 스토리 배경, 캐릭터, 장애물, 문제, 곤경, 합병증, 위기, 기후 등 8가지 요소를 각각 8개 섹션으로 나눈 인덱스 차트를 장착하고 다양한 변형을 통해 로봇이 기계 두뇌의 도움 없이도 필요에 따라 자료를 선택할 수 있도록 했습니다. 이 무궁무진한 소스는 기계적인 두뇌의 도움 없이는 작가가 상상할 수 없는 플롯을 구축합니다.

성공적인 작가가되고 싶다면 로봇을 구입하여 작업에 투입하기 만하면됩니다.

[사진 설명] 위클리프 힐이 기계 두뇌로 모든 종류의 플롯을 생각해낼 수 있는 새로운 스토리 작성 로봇을 시연하고 있습니다.

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MIT 교수 조셉 바이젠바움은 앨런 튜링의 연구에 관심을 갖고, 1966년 사람들이 실제 인간과 대화하고 있다고 믿게할만한 대화 프로그램 ELIZA를 개발했다. AI 캐릭터 엘리자는 여성 심리상담사로 설정되었고, 그녀는 개방형 질문을 하고 후속 질문을 이어가는 방식으로 대화했다. 

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위스콘신대학 테크 리포트에 따르면, Seldon Klein과 동료들이 개발한 ANW는 발전된 자연어 처리 능력을 토대로 2100단어 이내의 미스테리 추리 소설을 19초 이내에 작성할 수 있었다.

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예일대 박사과정 (논문은 캘리포니아 대학에서 발표) 이던 제임스 미한은 이야기의 problem-solving 구조에 기초하면, AI가 간단한 서사를 창작할 수 있다는 가설을 세웠다. 그의 Tale-Spin 동물을 의인화한 이솝 우화 동화 구조에 충실한 이야기를 개발할 수 있었다. Tale-Spin의 사용자는 Co-Creator로 캐릭터와 캐릭터 간의 관계, 플롯의 방향성을 결정하는 역할을 맡았다. 이 시스템의 세계관은 완전히 개방되지 않았고, 제한적인 서사 환경 내에서 작동했다.

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윌리엄 챔벌레인과 토마스 에터가 개발한 AI Racter는, AI를 이용해 저작된 상업적 목적을 지닌 최초의 픽션 "경찰 수염을 반만 만들어졌다" 를 생성했다. 챔벌레인에 따르면, Racter는 완전한 자동저술기계로, 일단 시작하고 나면 인간 창작자의 외적 개입이 이루어지지 않고, 오직 기계 스스로의 생성 능력으로만 창작을 했다.

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그림 5. 경찰수업은_반만_만들어졌다

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콜럼비아 대학의 레보위츠(Lebowitz)드라마와 소설 집필에서 플롯 확장 필요성에 주목했다. "UNIVERSE"는 플롯과 유사 단위인 '플롯 프래그먼트'를 사용하여 플롯의 윤곽을 생성하는 스토리텔링 프로그램이다. 플롯 조각 라이브러리와 잘 개발 된 캐릭터 세트를 사용해 다양한 이야기 구조와 아웃라인을 설계할 수 있다.

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나탈리 덴(Dehn N.)은 Tale-Spin이후의 AI 스토리제러네이터에 대해 고민하며, 작가의 창작에서 가장 중요한 '기억'활용에 집중했다. AUTHOR는 작가가 자신의 내러티브 목표에 따라 창의적으로 스토리를 생성하는 AI로 개발되었으며, 이 프로그램은 현실에서 일어나는 세계가 아니라 이야기를 구성하는 작가의 마음을 구현하는 것을 목표로 하였다.

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TALE-SPIN, the last major AI attempt at story generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR is a program (under development) which generates atoriea as a creative reasoner in pursuit of her own narrative goals. It is thus intended to simulate an author's mind as ahe makes up a story, rather than the world as things happen in it. The four major forces driving the story generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal.

Klein, Sheldon, et al. (1976). Automatic Novel Writing: A Status Report. Technical Report #186. Computer Sciences Department. University of Wisconsin-Madison.

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Story generation after TALE-SPIN | Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1

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TALE-SPIN, the last major AI attempt at story generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR is a program (under development) which generates atoriea as a creative reasoner in pursuit of her own narrative goals.

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민스트렐은 TRAM(변형-회상-적응 방법)과 사례기반 추론(Case based Reasoning)이라는 창의성 휴리스틱 이론을 기반으로 이야기에서 구현되는 작가 수준의 problem-solving 모델을 활용한다.  그밖에 주제, 드라마, 일관성, 프리젠테이션을 작가 수준의 품질 목표와 실행 계획에 근거해 작동 시킬 수 있다. 민스트럴은 이러한 목표와 계획을 사용해 아더왕 이야기 등의 여러 단편 스토리를 성공적으로 제작하였다.

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October 30, 2007

Last year I posted a series of thoughts about two story generation systems: Minstrel and Universe ( 1 2 3 4 5). I had some critical things to say about the Minstrel system, but they were based on my reading - I hadn't yet been in contact with the system's author, Scott Turner. This month I finally connected with Scott, and yesterday he sent me the following thoughtful response to the issues raised by our previous discussion on Grand Text Auto.

I'm particularly happy about this because Scott has graciously offered to try to respond to any further questions in the comments for this thread. Also, I'll be paying close attention to the conversation, given I'm writing about Minstrel in my forthcoming book. Below are Scott's thoughts.

I haven't worked in AI for many years, but I was delighted when Noah contacted me and I had a chance to read the discussion on this blog of my dissertation work. At the time I did this work there was no Internet as we know it today, and in some sense I worked virtually in isolation. No one else was working on computer storytelling, creativity or related subjects such as interactive fiction. The best that I could hope for in the way of a community of interest was occasionally meeting up with folks like Michael Lebowitz at a conference. I can't help but think that if I were doing my work today, the feedback I could get through the Internet would greatly improve my results. The Internet is truly wonderful in the way it can bridge space and economics to bring together similar interests in ways that could never happen in the physical world!

After Noah pointed me towards this blog I read through the discussion of Minstrel and found it very thought provoking. I thought I'd take a few minutes to share some insight into how Minstrel came to be and discuss some of the issues that Noah raised.

At the time I began my work on Minstrel the only previous work on storytelling was Meehan's Talespin. Talespin was intriguing because it was clearly an incomplete approach to storytelling and yet what it produced had many of the necessary elements of stories. Talespin's stories were, if nothing else, internally consistent and about the sorts of things we expect in stories. What was missing was purpose. An author doesn't just ramble a stream of character actions; he creates his story to try to achieve his own goals.

Now, Talespin was essentially a planning engine, so it seemed reasonable to build a better storytelling program by simply augmenting the Talespin model with a "meta" level of goals and plans representing what the author was trying to achieve with his storytelling. And, in fact, the first versions of Minstrel operated just this way.

One problem became immediately obvious with this approach: the stories weren't original. They just regurgitated what Minstrel already knew. In truth, Talespin had this problem as well, but because Talespin had a fairly large dictionary of character goals, plans and actions it wasn't as immediately obvious that it was only "shuffling the pieces around the board." And while re-ordering knowledge might provide a low level of creativity, it clearly wasn't sufficient for creating interesting stories.

Consequently my effort shifted from storytelling to creativity. Storytelling went from being an end in itself to being the domain in which Minstrel demonstrated creativity.

Now, there are many fascinating questions about creativity, but one that intrigued me was how people could be creative across many different problem domains. (I'm speaking here of normal day-to-day creativity, not Thomas Edison-level creativity.) It seemed unlikely to me that people developed wholly different creative processes for different problem domains. Instead, I looked for ways in which creativity could be embedded in the low-level cognitive processes that underly all intelligence.

The result was Minstrel's notion of "creative memory." In Minstrel, if you try to recall something and draw a blank, the creative process kicks in and tries to imagine something appropriate. This notion has a beautiful elegance to it, because (whether one believes specifically in case-based reasoning or not) it is clear that memory underlies intelligence. So a creative memory - imagination - immediately adds creativity to all the upper levels of cognition.

There's much more to be said (and argued) about creativity, but I will skip over that for the moment.

As I began to implement this model of creativity in Minstrel, I ran headlong into another difficult problem - knowledge and context.

Consider this (true) example of creativity:

A motorcyclist is motoring down a lonely road when his crankcase cracks. He pulls over in time to see all the oil drain from his engine. He has spare oil, but not welding equipment to fix his cracked crankcase. After some thought, he opens both ends of a can of beans, puts the cylinder on the road and builds a fire inside of it. He lets the fire burn for about a half-hour. Then he removes the can, uses a stick to scoop up the softened asphalt and patches the crack. He lets the patch dry, refills the oil, and limps into the next town for more permanent repairs.

Suppose we try to detail all the knowledge that the motorcyclist required to invent this solution to his problem: roads are made of asphalt, asphalt melts at a low temperature, asphalt is sticky when melted, asphalt adheres when dry, asphalt is impermeable to oil, food comes in cans that are cylindrical and made of metal, a can opener can remove the entire lid of a can, removing both ends of can forms a cylinder... A full listing of all the knowledge needed to understand/create this example would cover pages.

To put it another way, before we can be creative, we need all the knowledge and cognitive processes to be non-creative. Doug Lenat has had 40 people working twenty years on cataloging this sort of common-sense knowledge and hasn't yet finished. So clearly I, the lonely graduate student, faced a serious problem.

The solution I chose was to limit Minstrel to a "toy domain", and because of the depth of knowledge Minstrel required, it was a very tiny domain indeed. So when Noah asks why Minstrel was "starved for data" the primary answer is that I didn't have the manpower to represent and encode more data. I had to choose between coding up more example stories, or implementing the processes of storytelling and creativity. In addition, I was wary of the criticism I might receive if I gave Minstrel "interesting" data to start with. Certainly if I'd started Minstrel with a wide variety of unique and clever story scenes, it could have produced unique and clever inventions. But would the creativity lie in Minstrel or in me? So I chose instead to limit Minstrel to a small set of "uninteresting" data to make it clear that any creativity displayed was due to Minstrel.

Nonetheless, Minstrel was a brittle program. My contention is that if you give me a robust, non-creative program that demonstrates all the world knowledge, intelligence and problem-solving ability of the average 21 year old, I'll be able to implement a robust creative program atop that. But I didn't have that luxury. I had to build just exactly those parts of that robust intelligence I needed to demonstrate my thesis. Naturally, if you stray even slightly from those limits, things break.

Talking about Minstrel's brittleness, Noah says:

But it is important to remember that this problem arose with the completed system (and not an incomplete one, as with the mis-spun tales of Tale-Spin reprinted by Aarseth, Murray, Bolter, and others).

This is, I think, a false distinction. In this sort of research, the programs we write aren't products that are "complete" when the Ph.D. is awarded. They are vehicles for implementing, experimenting with, and understanding theories of artificial intelligence. Meehan fixed many of his mis-spun tales along the way to the "final" version of Talespin, and I did the same with Minstrel. At any rate, I'm dubious (as was Meehan) about drawing anything more than entertainment from these mis-spun tales. As one of my committee members said, "Don't these just prove that you're a bad programmer?"

Noah also repeats the comment from Rafael Pérez y Pérez and Mike Sharples:

[T]he reader can imagine a Knight who is sewing his socks and pricked himself by accident; in this case, because the action of sewing produced an injury to the Knight, Minstrel would treat sewing as a method to kill someone.

(p. 21, "Three Computer-Based Models of Story-Telling: BRUTUS,MINSTREL and MEXICA")

Like some of the commentors on the previous thread, I actually find this an (unintentionally) wonderful example of creativity, and exactly the sort of thing Minstrel ought to be capable of creating. There's an Irish folk song in which a woman imprisons her husband by sewing him into the bedsheets while he sleeps. Doesn't that show exactly the same creative process (magnifying a small effect to create a large one)? The problem with Minstrel is that it would do a bad job of the magnifying. It would write something like:

The Knight killed himself by sewing on himself.

because it doesn't have the knowledge and reasoning processes to create a more plausible "magnification" of the effect. But imagine a novel in which a terrorist in solitary confinement is given access to a sewing needle because "What could he do with a sewing needle?" He then manages to kill himself by plunging the needle into his carotid artery - seems like a fine plot device, yes? But all this really says about Minstrel is that it isn't a complete reasoner - which I'd not dispute.

As to the larger question of whether it is even possible to capture the knowledge necessary for human-level storytelling, or whether "scruffy" AI is a bankrupt idea, I'll not comment except to say that it takes the most highly-evolved learning machines on the planet at least 20 years to build that sort of knowledge base, and even then most of those do not become good authors. So expecting a complete solution from a program with a few grad-student-years of effort sets the bar unreasonably high.

Noah concludes by saying that whether Minstrel is an improvement over Talespin is "debatable." That seems like hyperbole to me. I can't imagine that anyone would seriously argue for a model of storytelling that didn't include explicit author goals, or creativity, or the notion of boredom - all of which Minstrel brought to the table. There's certainly plenty of room to criticize Minstrel, but I think it was a leap forward from Talespin and began to address some of the difficult (and fascinating) issues in both storytelling and creativity.

1. Indeed, the only way Talespin produced narratives that seemed at all like stories was to force the narrative into a supplied "storymold".
2. It's worth noting that Meehan understood this point very well.
3. My advisor once asked this question: "What are the fundamental differences between Talespin and blocks world planning?"
4. As we used to say in the UCLA AI Lab, "Anything you can do I can do meta."
5. However, Minstrel was also capable of doing device invention using the same creativity techniques it used in storytelling, and lately I've been applying the same creativity techniques to music composition.
6. Or find the answer you've recalled "boring."
7. Common-sense knowledge also serves as a back-end filter or "sanity check" on creativity, although Minstrel didn't model this.
8. A very common solution to that problem in those days.
9. Indeed, Minstrel was more a tool set than a monolithic program. It was constantly being changed, updated and used in new ways as I explored different issues.
10. If I recall correctly. Perhaps this comment was made to Meehan and I'm suffering from "creative memory".

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Last year I posted a series of thoughts about two story generation systems: Minstrel and Universe ( 1 2 3 4 5). I had some critical things to say about the Minstrel system, but they were based on my reading - I hadn't yet been in contact with the system's author, Scott Turner.

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이 시스템에 적용된 창의적 글쓰기 이론은 참여, 그리고 반영이다. 참여 모델은 사례기반추론과 유사하게 작동한다. CBR 모델은 연속성을 제공하나, 기존 스토리 요소와의 유기성을 보장하지 않으므로, 반영을 통해 유사한 것을 연결하며 새로운 이야기 조각을 수정, 연결하는 작업을 수행한다. 영국의교육 공학자이자, 크리에이티브 라이팅 전문가인 M. Sharples가 개발에 참여했다.

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마드리드 콤푸텐세 대학교 Pablo Gervás 등이 개발한 이 시스템은 CBR 모델을 보다 가벼운 형태로 변형 적용했다. ProtoPropp은 블라디미르 프롭의 러시아 민화 분석을 사용하여 사례 라이브러리를 구축한 것이 특징이다.

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조지아텍 교수 Riedl 이 개발했다. (당시 소속은 노스 캐롤라이나 대학) 이 시스템은 캐릭터 목표의 계층 구조와 캐릭터 의도를 생성하는 계획 구조를 구축하는 고유한 스토리 플래너를 적용했다. 계획 데이터 구조를 파뷸라로 생성하고, 수제의 일부인 액션을 하위 선택한 다음, 템플릿을 통해 액션을 자연어로 렌더링하는 삼자 프레임워크를 사용한다.

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Fabulist is an architecture for automated story generation and presentation. The Fabulist architecture splits the narrative generation process into three-tiers: fabula generation, discourse generation, and media representation. Originally developed at NC State University's Liquid Narrative Group as part of Mark Riedl's dissertation work, the most current information on Fabulist can be found now at Georgia Tech, where Mark now works.

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산타크루즈 대학, R. Swanson, 서던 캘리포니아 대학 A. Gordon 등이 개발한 텍스트 사례 기반 추론 시스템. 텍스트 사례 기반 추론은 대규모 텍스트 코퍼스를 비정형 사례 기반으로 취급한다. 대부분의 사례 기반은 검색 및 적용이 용이하도록 구조화되어 있으므로 구조화되지 않은 텍스트에서는 검색 및 적용이 훨씬 더 어려운데, Say Anything은 그 문제를 해결하고자 했다.

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I am currently an independent researcher working on problems involving narrative, discourse, dialogue and machine learning.

Previously I was a Research Scientist at the Institute for Creative Technologies. I worked on projects with Andrew Gordon involving computational narrative interpretation and generation and with David Traum on story adaptation for conversational agents.

Prior to rejoining ICT, I was a postdoc in Computer Science at the University of California Santa Cruz. While there I worked with Marilyn Walker in the Natural Language and Dialogue Systems lab, Michael Mateas in the Expressive Intelligence Studio and Arnav Jhala in the Computational Cinematics lab. I received my Ph.D. in 2010 from the University of Southern California while doing research at the Institute for Creative Technologies with my advisor Andrew S. Gordon. After graduating USC I spent a year at Disney Research & Development working on new technologies for enabling large-scale live action role-playing.

My research is focused around deep narrative understanding of personal stories. These types of stories contain causal and temporal knowledge about the world, people's expectations about events, their goals and motivations, and subjective evaluations the events. This knowledge is critical to developing rich virtual environments that not only model the physical world, but also react to people's social behaviors in a natural and believable way.

You can contact me at [my first name]@[this domain]

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I am currently an independent researcher working on problems involving narrative, discourse, dialogue and machine learning. Previously I was a Research Scientist at the Institute for Creative Technologies. I worked on projects with Andrew Gordon involving computational narrative interpretation and generation and with David Traum on story adaptation for conversational agents.

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이 시스템은 특정 주제에 대해 가장 높은 확률로 이어질 수 있는 이벤트를 연결하는 그래프를 통해 플롯을 이어나갈 수 있도로고 하였다. 예를 들어, 식당 방문에 대한 플롯 그래프에는 음식 주문, 식사, 계산, 퇴장 등과 같은 주요 이벤트가 제시되는 방식이다. 코퍼스 안에서 학습된 이야기를 분석해, 도메인 모델을 학습하고 학습된 도메인 모델에서 다시 이벤트를 생성하는 방식.

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2017년 Luka 사에서 공개한 유료 챗봇. 사용자의 구독료로 현재까지 개발 지속되고 있다. 리뷰어들은 레플리카를 통해 사용자들이 깊은 정서적 친밀감을 느낀다고 언급한 바 있다. 그러나 사용자들이 자신이 만든 레플리카 캐릭터와 성적인 대화와 관계 맺음을 시도하는 일이 빈번해 지자, 레플리카 개발자는 로맨틱 및 에로틱 기능을 비활성화했다. https://www.washingtonpost.com/technology/2023/03/30/replika-ai-chatbot-update/

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https://authors.ai/
작가가 소설 원고를 업로드하면 AI를 통해 상세한 비평과 분석을 받을 수 있는 무료 AI 소설 쓰기 지원 소프트웨어. 대화와 내러티브의 비율을 알려주고, 문장 구조를 분석하며, 글쓰기 스타일과 전반적인 형식을 베스트셀러와 비교. Marlowe – our name for the company’s fiction-savvy bot –을 이용해 15분 이내로 픽션 파인튜닝을 위한 분석 서비스를 제공.

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Save time and money - and write a better book. Marlowe, our artificial intelligence, will provide an instant high-level critique of your novel.

Marlowe Basic

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Two full A.I. reports per month

For serious authors who want to make a living with their writing

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Single Report

$45

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One full A.I. Pro analysis of your manuscript

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Includes practical tips for applying the findings to your novel​

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Not quite sure yet?

Write a book readers want to read!

Marlowe is a beta reader, developmental editor, and copy editor rolled into one smart, fiction-savvy bot. Created by Matthew Jockers, Ph.D., co-author of The Bestseller Code, Marlowe has just been updated with the latest developments in A.I. technology. Her 32-page full-color reports are available in less than 15 minutes and give an unvarnished critique of your novel to strengthen your book's pacing, story beats, writing and more. And now you'll receive comps, comparing your manuscript to four or more bestselling novels. Get started!

Instant access

Receive your in-depth report and start to implement the A.I.'s recommendations within minutes.

Structured tips

Each section of the report includes a helpful guide on how to use the findings to improve your novel.

Unbiased opinions

Our A.I. critiques your manuscript based on her past analysis of hundreds of bestselling novels.

Marlowe's report includes:

Comps for subject matter & writing style

Four bestseller comparisons

Subject analysis

Narrative arc and plot structure analysis

Story beats placement

Pacing analysis

Major characters' personality traits

Primary emotions color wheel

Cliché finder

Repetitive phrases

Explicit language & content flagged

Sentence stats and readability score

Dialogue vs. narrative breakdown

Repeated usage of adverbs & adjectives

Verb choice and use of passive voice

Potential misspellings

Punctuation data

View Marlowe Pro's analysis of 'The Da Vinci Code'
What writers are saying

"Authors A.I. created Marlowe to point out emotional beats, character strengths and weaknesses and so much more. She's helping me become a stronger writer! I was so happy that the way I always described my book - the comp I always used - was exactly what Marlowe gave me! I love her!"

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Get an analysis of your novel's manuscript in minutes!

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Save time and money - and write a better book. Marlowe, our artificial intelligence, will provide an instant high-level critique of your novel. Best for aspiring authors Unlimited Basic A.I. reports Run multiple drafts of your manuscript Great for first-time authors looking to strengthen their craft Get Started Best for established authors Two full A.I.

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https://aidungeon.io/
Latitude사가 개발한 AI Dungeon은 인공 지능을 사용하여 콘텐츠를 생성하는 무료 싱글 플레이어 및 멀티플레이어 텍스트 어드벤처 게임. AI던전 플레이를 통해 게이머는맞춤형 모험 스토리를 위한 설정을 만들고 공유할 수 있습니다.

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스토리 작성 과정을 간소화하는 AI 기반 글쓰기 프로그램. GPT 모델에 근거해 작동한다. 자연어 처리 및 머신 러닝 알고리즘을 활용하여 작가에게 유용한 키워드, 동의어 및 기타 글쓰기 팁이 제공되며, 작성자는 작성된 콘텐츠에 대한 독자의 반응에 대한 인사이트를 활용하여 독자의 요구에 맞게 맞춤 스토리를 창작할 수 있다.

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Write your novel or screenplay faster with best AI writing tool according to The New Yorker, NY Times, The Verge, and many more. Start for free today.

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스토리 아이디어와 개요를 작성한 다음 스토리의 캐릭터와 줄거리를 구체화할 수 있도록 도와주는 AI 콘텐츠 생성기.이 소프트웨어는 AI를 사용하여 사용자의 입력을 분석한다. 입력은 어떤 문장이나 단락이든 가능하며, AI를 통해 사용자가 지정한 장르 및 기타 선호도와 같은 특정 매개변수에 맞는 스토리 플롯과 캐릭터 프로필이 자동 생성된다.

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Scalenut | AI Powered Content Research & Copywriting Platform

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Scalenut is a content intelligence SaaS platform that helps you discover and create the most relevant content for your customers. It uses deep learning and AI to create the best content possible.

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2021년 설립된 AI 기업 JESPER가 개발한 스토리 생성  AI. Jasper AI는 인공지능을 사용하여 콘텐츠를 작성하는 도구입니다. 발전된 GPT 모델을 기반으로 100% 원본 콘텐츠를 생성할 수 있다고 주장한다. JESPER를 이용하면, 다양한 콘텐츠 생성이 가능하고, AI 이미지까지 생성할 수 있다. 후속 모델인 Jesper Chat은 AI와의 대화 난이도를 낮추고 자연스러어운 커뮤니케이션을 지원한다. 상용/유료 서비

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The State of AI in Marketing 2026. Download now

Orchestrate intelligent agents to run end-to-end marketing workflows-delivering speed, control, and measurable impact.

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Enterprise-grade security and an LLM-agnostic architecture prioritize your data protection & privacy while providing superior quality marketing outputs.

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Put AI agents to work for marketing | Jasper

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Orchestrate intelligent agents to run end-to-end marketing workflows-delivering speed, control, and measurable impact.

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노벨AI는 AI 지원 이야기 쓰기 및 텍스트 대 이미지 합성을 위한 온라인 클라우드 기반 SaaS 모델, 유료 구독 서비스이다. 2021년 6월 15일 베타 서비스로 처음 시작되었으며 이미지 생성 기능은 이후 2022년 10월 3일에 구현되었다.

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What even is NovelAI?

NovelAI is a monthly subscription service for AI-assisted authorship, storytelling, virtual companionship, or simply a LLM powered sandbox for your imagination.

Our Artificial Intelligence algorithms create human-like writing based on your own, enabling anyone, regardless of ability, to produce quality literature. We offer unprecedented levels of freedom with our Natural Language Processing playground by using our own AI models, trained on real literature. The AI seamlessly adapts to your input, maintaining your perspective and style.
Take me to Pricing Our Features

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GPT-powered AI Storyteller. Driven by AI, construct unique stories, thrilling tales, seductive romances, or just fool around. Anything goes!

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ChatGPT는 OpenAI에서 개발하여 2022년 11월에 출시한 AI 챗봇. 기서은 OpenAI의 대규모 언어 모델(LLM)인 GPT-3.5 및 GPT-4 제품군을 기반으로 구축.지도 학습 및 강화 학습 기법을 모두 사용하여 미세 조정(전이 학습 접근 방식)되었다. 픽션 생성을 비롯한 다양한 Writing 수행 능력이 뛰어나다.