A latent space, also known as a latent feature space or embedding space, is an embedding of a set of items within a manifold in which items resembling each other are positioned closer to one another in the latent space. Position within the latent space can be viewed as being defined by a set of latent variables that emerge from the resemblances from the objects.
The interpretation of the latent spaces of machine learning models is an active field of study, but latent space interpretation is difficult to achieve. Due to the black-box nature of machine learning models, the latent space may be completely unintuitive. (Wikipedia)
Generative AI is having its mainstream moment. Out of nowhere (not really) AI’s potential has captured the imaginations of millions. What makes it so different to prior innovations? It dramatically changes the way we interact with computers. And, whereas before, where we were in full control of IT technology this time we don’t have the same level of control anymore. This lack of control applies to all actors along the AI food chain. Despite all efforts on AI ethics and explainable AI this is an inherent issue of the technology and there seems to be no silver bullet to resolve it. This issue will have long lasting repercussions in economy and society.
„There is an ironic but highly valuable quality to AI in all its forms. The effort to simulate or surpass human intelligence is uncovering subtleties and paradoxes about the human mind we might never have imagined. By way of heroic failures, AI is teaching us how truly strange real intelligence is.“ (Theodore Roszak – The cult of information)