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MIT 6.S192 - Lecture 8: "How Machine Learning Can Benefit Human Creators" by Rebecca Fiebrink
MIT 6.S192 - Lecture 8: "How Machine Learning Can Benefit Human Creators" by Rebecca Fiebrink
Rebecca Fiebrink, a researcher in the area of music and AI, emphasizes the importance of human interaction and keeping humans in the loop in the use and development of machine learning for creative purposes. She discusses her tool, Wekinator, which enables the use of machine learning in real-time music for human creation. She demonstrates building various gesture-controlled instruments such as a drum machine, a sound synthesis algorithm called Blotar, and a wind instrument called blowtar. She highlights how machine learning can be beneficial for creators allowing them to explore complex and nuanced sound palettes and make data analysis easier for sensors and real-time data. She also addresses the benefits of interactively manipulating training data and explains how machine learning enables us to communicate with computers in a more natural way, besides adding surprises and challenges to the creative work process.
MIT 6.S192 - Lecture 9: "Neural Abstractions" by Tom White
MIT 6.S192 - Lecture 9: "Neural Abstractions" by Tom White
In this video, artist and lecturer Tom White discusses his approach to incorporating machine perception and neural networks into his artistic practice. White shares his background in studying math and graphic design at MIT and his current work teaching creative coding at Victoria University. He also discusses his research on building tools to help others use the medium creatively and his own artwork that explores machine perception. White showcases his sketches and prints, created using AI algorithms, and talks about his collaborations with music groups and his recent art exhibitions. He also discusses the challenges of collaboration with neural networks and the unintended consequences of putting AI-generated art in the wild.
MIT 6.S192 - Lecture 10: "Magenta: Empowering creative agency with machine learning" by Jesse Engel
MIT 6.S192 - Lecture 10: "Magenta: Empowering creative agency with machine learning" by Jesse Engel
Jesse Engel, lead research scientist at Google Brain, discusses Magenta, a research group looking at the role of AI and machine learning in creativity and music. The group primarily focuses on machine learning models that generate media and makes them accessible through open-source code and a framework called magenta.js, which allows for the creation of interactive creative models in Javascript. Engel emphasizes the importance of viewing music as a social and evolutionary platform for cultural identity and connection rather than a commodity to be cheaply produced and consumed. They explore how machine learning can empower individuals with new forms of creative agency through expressivity, interactivity, and adaptivity. The lecture covers various topics, including designing machine learning models for music, using dilated convolution for predictive outputs, differentiable digital signal processing, and creating machine learning systems that produce beautiful failures. Additionally, he talks about collaborative challenges with artists and the grand challenge of coming out of distribution and compositionality in learning models.
MIT 6.S192 - Lecture 11: "Artificial Biodiversity", Sofia Crespo and Feileacan McCormick
MIT 6.S192 - Lecture 11: "Artificial Biodiversity", Sofia Crespo and Feileacan McCormick
In this lecture on "Artificial Biodiversity," Sofia Crespo and Feileacan McCormick explore the intersection of technology and nature to produce unique forms of art. The duo discusses their interest and use of machine learning and its connection to beauty and highlights the limitations of human perception. They also discuss their collaborative projects, including "Entangled Others," where they advocate for representing both individual species and their complex entanglements to create a better understanding of ecological systems. The speakers emphasize the importance of sustainability and collaboration in artistic practice and the relationship between tools and art, stating that algorithms cannot replace human artists.
MIT 6.S192 - Lecture 12: "AI+Creativity, an Art Nerd's Perspective" by Jason Bailey
MIT 6.S192 - Lecture 12: "AI+Creativity, an Art Nerd's Perspective" by Jason Bailey
Jason Bailey discusses how machine learning is impacting the field of art, from forgery detection to price prediction. He urges artists to be aware of the biases inherent in data-driven art, and urges the need for training data that is inclusive of all perspectives.
MIT 6.S192 - Lecture 13: "Surfaces, Objects, Procedures: Integrating Learning and Graphics for 3D Scene Understanding" by Jiajun Wu
MIT 6.S192 - Lecture 13: "Surfaces, Objects, Procedures: Integrating Learning and Graphics for 3D Scene Understanding" by Jiajun Wu
Jiajun Wu, an assistant professor at Stanford, discusses his research on scene understanding in machines through the integration of deep learning and domain knowledge from computer graphics. Wu proposes a two-step approach to recover a 3D object geometry from a single image by estimating the visible surface through the depth map and completing the shape based on prior knowledge from a large dataset of other similar shapes. Wu also proposes using spherical maps as a surrogate representation for surfaces in 3D to capture surface features better, allowing the system to complete shapes in a more detailed and smoother output. Additionally, Wu discusses how reconstructing shapes into shape programs can significantly improve modeling and reconstruction, especially for abstract and man-made objects. Finally, Wu discusses how domain knowledge from computer graphics can be integrated with machine learning to improve shape reconstruction, texture synthesis, and scene understanding.
MIT 6.S192 - Lecture 14: "Towards Creating Endlessly Creative Open-Ended Innovation Engines" by Jeff Clune
MIT 6.S192 - Lecture 14: "Towards Creating Endlessly Creative Open-Ended Innovation Engines" by Jeff Clune
Jeff Clune, a researcher at OpenAI, discusses his work on creating endlessly creative open-ended innovation engines in this MIT lecture. He seeks to create algorithms that can perform the natural evolution and human culture's recipe of starting with a set of things, generating new things, evaluating to keep what is interesting, and modifying it to keep the interesting novelty. Clune explores using neural networks to recognize new things, talk about the Map Elites algorithm, and introduce Compositional Pattern Producing Networks for encoding. He shows how these tools can be combined to generate complex and diverse images, solve hard problems, and create open-ended algorithms that can constantly innovate their solutions to challenges.
MIT 6.S192 - Lecture 15: "Creative-Networks" by Joel Simon
MIT 6.S192 - Lecture 15: "Creative-Networks" by Joel Simon
In this lecture, Joel Simon explores his inspirations and approaches towards creative networks that draw from natural ecosystems. He demonstrates the potential of computational abilities in the creative process, describing how techniques such as topology optimization, morphogens, and evolutionary algorithms can enable the emergence of incredible forms and textures. Simon also shares details about his GANBreeder project, an online tool for discovering and mutating images using a CPPN and a GAN, and discusses the potential of cross-recommendation systems in the creative process. Simon is optimistic about the future of technology and creativity, believing that humans can collaborate and optimize the functions of buildings and create something greater.
MIT 6.S192 - Lecture 16: "Human Visual Perception of Art as Computation" Aaron Hertzmann
MIT 6.S192 - Lec. 16: "Human Visual Perception of Art as Computation" Aaron Hertzmann
The lecture explores perceptual ambiguity and indeterminacy in art and the use of generative adversarial networks (GANs) in creating ambiguous images. It discusses the impact of viewing duration on perception and the relationship between image entropy and human preferences. The lecturer suggests an evolutionary theory of art, where art is created by agents capable of social relationships. The use of AI in art is also discussed, with the conclusion that while algorithms can be useful tools, they cannot replace human artists. The lecture concludes with a few remarks on concepts such as value.
MIT 6.S192 - Lecture 17: "Using A.I. in the service of graphic design" by Zoya Bylinskii
MIT 6.S192 - Lecture 17: "Using A.I. in the service of graphic design" by Zoya Bylinskii
Zoya Bylinskii, a research scientist at Adobe, explores the intersection of graphic design and artificial intelligence (AI) in this lecture. Bylinskii emphasizes that AI is meant to assist rather than replace designers by automating tedious tasks and generating design variations. Bylinskii gives examples of AI-assisted tools, including interactive design tools and AI-generated icon ideation. Bylinskii also discusses the challenges and potential in applying AI to graphic design, including the need for creative thinking, curation, and working with professionals from different fields. She advises candidates interested in AI and machine learning for graphic design to showcase project experience and pursue research opportunities.