mlsanityData to 7 Oct 2026

Pathway to Future Symbiotic Creativity

arXiv 2209.02388 ↗First posted 18 Aug 2022cs.AI cs.HCcited by 0

Not on the map. Why?

Abstract (arXiv)

This report presents a comprehensive view of our vision on the development path of the human-machine symbiotic art creation. We propose a classification of the creative system with a hierarchy of 5 classes, showing the pathway of creativity evolving from a mimic-human artist (Turing Artists) to a Machine artist in its own right. We begin with an overview of the limitations of the Turing Artists then focus on the top two-level systems, Machine Artists, emphasizing machine-human communication in art creation. In art creation, it is necessary for machines to understand humans' mental states, including desires, appreciation, and emotions, humans also need to understand machines' creative capabilities and limitations. The rapid development of immersive environment and further evolution into the new concept of metaverse enable symbiotic art creation through unprecedented flexibility of bi-directional communication between artists and art manifestation environments. By examining the latest sensor and XR technologies, we illustrate the novel way for art data collection to constitute the base of a new form of human-machine bidirectional communication and understanding in art creation. Based on such communication and understanding mechanisms, we propose a novel framework for building future Machine artists, which comes with the philosophy that a human-compatible AI system should be based on the "human-in-the-loop" principle rather than the traditional "end-to-end" dogma. By proposing a new form of inverse reinforcement learning model, we outline the platform design of machine artists, demonstrate its functions and showcase some examples of technologies we have developed. We also provide a systematic exposition of the ecosystem for AI-based symbiotic art form and community with an economic model built on NFT technology. Ethical issues for the development of machine artists are also discussed.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 97 of its 215 references that are in the map. First posted on arXiv 18 Aug 2022.

Builds on

97 of 215 references.

References · 97

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  6. 2022Training language models to follow instructions with human feedbackcited by 15,712
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  9. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  10. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  11. 2017Neural Discrete Representation Learningcited by 4,650
  12. 2013Auto-Encoding Variational Bayescited by 3,980
38 more
  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
  2. 2022Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedbackcited by 3,634
  3. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  4. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  5. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
  6. 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
  7. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  8. 2019SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systemscited by 1,718
  9. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  10. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  11. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  12. 2021Decision Transformer: Reinforcement Learning via Sequence Modelingcited by 1,528
  13. 2019Generating Long Sequences with Sparse Transformerscited by 1,523
  14. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  15. 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,376
  16. 2016Instance Normalization: The Missing Ingredient for Fast Stylizationcited by 1,187
  17. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,139
  18. 2014NICE: Non-linear Independent Components Estimationcited by 1,052
  19. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  20. 2016Conditional Image Generation with PixelCNN Decoderscited by 859
  21. 2016Pixel Recurrent Neural Networkscited by 814
  22. 2020Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenescited by 750
  23. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 698
  24. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  25. 2020Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humanscited by 634
  26. 2017Generative Adversarial Networks: An Overviewcited by 588
  27. 2019PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognitioncited by 563
  28. 2020Jukebox: A Generative Model for Musiccited by 535
  29. 2019ERASER: A Benchmark to Evaluate Rationalized NLP Modelscited by 523
  30. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
  31. 2017Learning Robust Rewards with Adversarial Inverse Reinforcement Learningcited by 485
  32. 2015A Neural Algorithm of Artistic Stylecited by 480
  33. 2021AI Choreographer: Music Conditioned 3D Dance Generation with AIST++cited by 479
  34. 2018Scalable agent alignment via reward modeling: a research directioncited by 438
  35. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  36. 2019MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesiscited by 351
  37. 2019Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogramcited by 337
  38. 2018WaveGlow: A Flow-based Generative Network for Speech Synthesiscited by 308

50 most cited of 97 All 97 (JSON)

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Under What Conditions Can a Machine Be Called Genuinely Creative?
  2. 2024On the stochastics of human and artificial creativity
  3. 2026On the Creativity of AI Agents
  4. 2026The Aura in the Machine: Genealogy and the Status of the Work of Art in the Generative Era
  5. 2026Creativity in AI as Emergence from Domain-Limited Generative Models
  6. 2024Building Artificial Intelligence with Creative Agency and Self-hood
  7. 2026An Embodied Companion for Visual Storytelling
  8. 2026Model Collapse: On Recursion, Noise, and Uncharted Machine Visions