LoVid’s signature video work mixes handmade analog synthesizers with digital tools in immersive Red, Green, and Blue (RGB) compositions that celebrate luminosity, chaos, relationships between craft and engineering, and connections between body and tech.
 
Tide Predictor, LoVid’s first onchain generative work, builds a bridge from video synthesizer patches to algorithmic code-derived imagery. This collection is formally and technically inspired by hardware-based image generation, and places experimental moving-image, circuit boards, and electrical signal-based video art at the conceptual roots of generative art.

Tide Predictor is a modular video synthesizer patch model, designed to reflect the analog spirit by working with three channels: RGB. Tide Predictor reimagines NTSC-encoded, oscillator driven electrical current based video synthesis while adding digital randomness/unpredictability, and transformations based on code, along with each mint’s unique content.
 
In the late 19th century, mechanical analog computers used rhythm and repetition to anticipate the rise and fall of water levels. In addition to “tides”, which are low frequency oscillations of the ocean, higher frequency changes or “waves” are frequently discussed in reference to both water and electrical signals. By working with looping - and explicitly addressing the period (=1/frequency) for each RGB color - Tide Predictor extends analogies between the flow of water and the flow of electricity.

LoVid’s Tide Predictor brings these relationships from the blockchain, through your screen, straight to your retina!

Tide Predictor

The essence of all things digital is– the bit. One or Zero. On or Off. Where digital becomes analog. This piece pays tribute to our digital legacy of 8-bit color and 7-segment LED displays– technological limitations which are mirrored in the constraints of putting art on the blockchain. Although the image represents the art, the art of the token itself is the code– which renders everything bitwise, pixel by pixel, all contained within 27-bits of information.

27-Bit Digital

Genesis: Generative System. A block hash string is interpreted into a visual composition using a set of algorithmic rules. Compositions may contain grids, gradients, lines, shapes, and colors that harmonize with each other. Each mint is one of a kind and designed to stand-alone. Limited Edition.

Genesis

The Caretaker awakes. After a millennia of slumber it must again usher its minions into the expanse. It must sustain. It must replenish. So it guides its horde out to the stars, visiting countless worlds to restart the cycle. For an eternity the cycle has repeated, minding and tending this garden of worlds. Now it is time for the garden to give back. This event has come to be known as *The Harvest*.

*The Harvest* depicts worlds that exist in eternal symbiosis with the Caretaker. We witness the spectacle of monumental chromatic beams as they impact the surface of these beautiful, alien bodies. The Caretaker assigns different constellations to each planet, giving rise to unique combinations of beams as they extract the much needed nectar. This project has been inspired by a life-long fascination for science fiction and space exploration. Aiming to capture a moment of awe, the beams reach to their origin, emulating a cathedral-like atmosphere in the vein of Hugh Ferriss, while borrowing inspiration from Micheal Whelan’s landscapes. All in the effort of capturing a sense of how small we are in relation to the cosmos.

*The Harvest* is built on p5js, and takes advantage of its 3d api. With the orthogonal view enabled, the algorithm uses volumetric rectangular units to create the stylised illusion of alien landscapes. Each beam has a position, color, reach and yield that is calculated and procedurally drawn over time.

The Harvest

*Please Note: This is an Exponential Dutch Auction (no settlement). The price you purchase is the price you will pay.*

An ideal human being is one big optimization problem. We have a goal - we optimize our actions to maximize the outcome of this goal. 
In our initial days, we optimize to stay alive, during school, we optimize to study well, gain skills and obtain the best scores.

As we progress in our careers we optimize to get the most amount of money, material growth, and maybe even influential growth such as fame.

As we age we optimize to minimize diseases and maximize our lifespan - we workout, we eat healthily, we retire, and rest 

As ideal as it sounds, how do we know what exact actions would lead to maximizing my outcome? At this very moment - instead of reading this should you be doing something else that would make your life’s outcome better? Maybe, maybe not - We don't know... Although, we do find out through trial and error. 

In the grand scheme of things we’re just an agent exploring actions around us, learning which ones increase our reward and choosing the path that seems to lead to the maximum reward. Cerebellum is a window (pun intended) of the human mind - a continuously evolving art piece that is built on top of a reinforcement learning agent that is learning and optimizing and evolving as you see it. 

The project starts with an untrained model of a reinforcement learning agent that is deployed in an environment where it is faced with positive reward and negative reward. The agent is also equipped with a bunch of eyes that lets it sense the objects around it and judge if it wants to go ahead and consume the object or abstain and move in a different direction. The agent here is optimization to maximize its positive rewards while avoiding the negative ones. How does the agent know if a reward is positive or negative? - It learns that after experiencing a bunch of samples. And the magic is - all of this happens in your browser. The seemingly random motion of the agent at the beginning running into everything it sees subsides with time as it learns better about the environment and only hits on the positive rewards. How fast does this happen? Depends on your luck - every art has agents with varied number of eyes and range of these eyes. In addition with every refresh your agent would start from a new place, taking a new path - a path that might lead to it learning things at a different pace depending on what it comes across as it explores. To add some practicality - the environment also has some walls, elements that just block the agent from moving doesn’t add or decrease rewards. Hinders the agent’s movement, lets it find a different path - sometimes this is needed for the agent to strategize, sometimes it's just a hindrance… I’ll leave this to your imagination as you see the agent in action…
The seemingly random motion - the path towards true understanding - the process of optimisation is captured by the windows. Windows that show the beauty of this process expressed in a variety of ways. 

Cerebellum is a window into the black box of AI, an unapologetic exaltation of the unpolished, an ode to the process and its aesthetic vernacular.

+ Press 'p' to switch from 2d/3d views
+ Press 'e' to expose the reinforced learning layer
+ Press 't' to pause/unpause the training of the agent
+ Press 'w' to randomize the walls of the training environment

On mobile, tap to switch from 2d to 3d

Art Blocks Curated

Tide Predictor #197

LoVid

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