Forms II
Mount Audio

Forms is a collaborative film series devised by London based, creative sound studio Mount Audio. The ongoing project sees Mount team up with leading visual artists each month to create unique audiovisual works.Forms II showcases the vibrant motion work of LA based designer Bryant Nichols. The artists’s warped figures bend and contort, twisting around one another to form abstract human structures.Inspired by Bryant’s alternate reality, Mount have created an entirely synthesised soundtrack layering rich, modulating textures to create an unsettling atmosphere. The effect is hypnotic yet disorientating.

Useless Hands

FILE 2024 | Aesthetic Synthetics
International Electronic Language Festival
Infratonal – Useless Hands – France

When our hands become useless, what will we choose to do with them? We can use AI to visualize the unthinkable, the strangely familiar yet indescribable forms and structures. Generative AI could be used as an amplifier of our ability to explore abstraction and surrealism rather than a simple mirror of our usual perceptions.

Bio

Infratonal is an artistic project led by Louk Amidou, a Paris-based multidisciplinary artist who works at the intersection of digital arts, electronic music and interaction design. He uses algorithms to create hybrid visual and sound pieces which aim to be performed by the human gesture as intangible instruments. He questions the artwork’s nature at the age of AI and the relationship between the artist and the algorithm.

Abysmal
Abysmal means bottomless; resembling an abyss in depth; unfathomable. Perception is a procedure of acquiring, interpreting, selecting, and organizing sensory information. Perception presumes sensing. In people, perception is aided by sensory organs. In the area of AI, perception mechanism puts the data acquired by the sensors together in a meaningful manner. Machine perception is the capability of a computer system to interpret data in a manner that is similar to the way humans use their senses to relate to the world around them. Inspired by the brain, deep neural networks (DNN) are thought to learn abstract representations through their hierarchical architecture. The work mostly shows the ‘hidden’ transformations happening in a network: summing and multiplying things, adding some non-linearities, creating common basic structures, patterns inside data. It creates highly non-linear functions that map ‘un-knowledge’ to ‘knowledge’.