Unmaking Sense

Episode 11.48: Image to Image as a metaphor for changing our sense by unmaking and remaking sense.

By unmaking sense we create and add noise and uncertainty in much the way an image to image AI diffusion model transforms one image into another. But in remaking sense according to some new principle we reconstruct our worldview in a way we at least hope will be better and intend to be better.

Episode 11.47: Creative acts as realisations of points in human & AI latent space; either can matter

Human creativity may be less deliberate than we like to think, and really just the expression of something that has evolved over tens of thousands, even millions of years. So AI creations can embody meaning too because they arise from neural nets and training that are reflections of human culture. It has little or nothing to do with intention.

Episode11.46: Authenticity, copyright, intellectual property and AI copies of identifiable artists.

Now that artificial intelligence can reproduce works of art, and indeed create new ones in the recognisable style of almost any artist, and to such an extent that they are almost indistinguishable from works that are original, raises the issue of whether an artist’s style is itself the principal legacy, rather than the works painted. We discuss some of the issues that this raises and the question of why being an original painting by the real artist matters so much to us and whether it should.

Episode 11.45: The bias implicit in some AI training means we are automatically fed skewed data.

Not only textual data but the kinds of images produced by AI are suffused with cultural biases and prejudices. We need to be aware of the distortions that can become ingrained if these implicit biases are not countered and held in check.

Episode 11.44: Motivational impacts of AI, especially those that may affect learning and creativity.

We have not even started to come to terms with the motivational implications of AI, whose dangers far outweigh the more dramatic science fiction scenarios so many love.

Episode 11.43: Uploading files to local AI models and creating deep fake videos based on tiny data.

We talk about ways to upload whole files, text files as prompts to locally running large language models, and after that we talk about creating false videos that look as though you’re saying things that you’re not saying, and have never said, on the basis of little more than about 30 seconds worth of data.

Episode 11.42: A three-part excursion on Diffusion Models, Recall and Human Attention and Creativity

We explore some of the intriguing parallels between diffusion models - noising and denoising - and human learning and recall with emphasis on innovation and creativity.

Episode 11.41: The RAG Trade: Retrieval-Augmented-Generation (RAG) and updating AI models.

A short introduction to RAG.

Episode 11.40: Creativity and the denoising of a diffusion model’s latent space.

Unmaking and remaking sense as a means of creating new worlds.

Episode 11.39: Collisions and Correlations; Quoting, misquoting and upscaling, improved originals.

A series of placeholders for what is yet to come in this extraordinarily rich new vein of thoughts, thinking and latent spaces.