Neural Networks and Art

make sound more educated, fact check it and correct it: There is no "memory bank". The data is trained to identify things based on the conversion of the pixels into vectors and then the statistically determine edges which are then layered and recursively matched to vector databases with weights of an artificial neural network which converts the input vectors into output vectors using linear algebra and then those vectors are converted into pixels using convolution layers which, like an artist, starts with larger areas of tone and breaks up the areas into smaller and smaller areas until the image is composed. That vector database with the weights of the artificial neural network was created using humans to annotate data. The annotation of the data is used to train the artificial neural network to convert new inputs into the desired outputs. The method that the annotator uses is composed using training material and the user interface that they use when they complete the task which then takes the presented input to the annotator and the output generated by the annotator and use linear algebra to determine the weights to a graph structure of data which will convert any new inputs into the desired outputs. So the "intelligence" of humans can be captured into an algorithm through simply training annotators to generate desired output from presented inputs. I did not go to school for art. But I am a professional artist. I have sold over 100 oil paintings often for more than $1000. I stopped 8 years ago after I started a single painting I have been working on since, it is 6'x16' oil using a 2 size brush pointillism. I paint in a algorithmic way as well. I simply use larger brushes and then break color down using smaller and smaller brushes. I think that is how a lot of people paint. There is an algorithm for seeing differences in tone and color with your eye and then replicating the edges and tone using a medium. This is what you really learn as an artist. At least that is how I taught myself through books how to paint. I have also viewed more art than most people. I have traveled to Europe and museums in the USA and see a lot of paintings in person. When I view art I think of how the painting is composed and how I would have painted it. Then if I choose to after determining the "algorithm" of the artist, I can replicate it. As can any artist who has developed the technical skills to do so. But what if you choose the edges and nodes of the graph structures and manipulate the database values using a UI like React. That is what I have been building. Basically a human feedback tool for language models which allows greater control over the algorithm. Basically you can annotate and rank by hand, you can do it yourself, or you can hire a bunch of people to do so, and then create a "layer" for a new model on top of the model you start with. So I am building a React Web App using Django backend and the Universal Data Tool which anyone could use to build the RLHF pipeline needed to fine tune new models on the fly. Researchers could easily build their own opensource platform which could take the new human feedback and create a new layer for the model and update it. So you could use that with images. So I take pictures of real art that I make. Then I train the model and modify the weights in the vector database. Then I can create anything I want using the style I created. Is what I create real art? Personally I think it is real art, but I would like to hear other people's perspective as well. TLDR: What you are saying is nonsense.

Here’s your text refined for clarity, accuracy, and a more formal tone: --- There is no "memory bank" in artificial neural networks. Instead, these models are trained to recognize patterns by converting pixel data into vectors, identifying statistical features such as edges, and layering these features recursively. These vectors are processed through a neural network, which applies weighted transformations to convert input vectors into output vectors using principles of linear algebra. The final output is then reconstructed into pixels using convolutional layers—much like an artist who first establishes broad areas of tone before refining details into a complete composition. The weight matrix of an artificial neural network is developed through human-annotated data. Annotation is a critical part of training, where human labelers provide ground truth examples that guide the model in learning to map new inputs to desired outputs. This process relies on training material and the user interface through which annotators interact with the data. The network then optimizes its weight parameters using mathematical techniques—such as gradient descent—within a structured graph representation of the data. In essence, human intelligence can be distilled into an algorithm by training annotators to generate specific outputs based on given inputs. While I did not formally study art, I am a professional artist with a background in oil painting. I have sold over 100 paintings, often for prices exceeding $1,000. However, I stepped away from commercial work eight years ago to focus on a single large-scale piece: a 6'x16' oil painting executed in pointillism with a #2 brush. My artistic approach is algorithmic in nature—I begin with larger brushes to establish broad color fields and then refine the details with progressively smaller brushes. This process mirrors the way many artists develop their work: by analyzing differences in tone and color and then methodically replicating them through a chosen medium. My understanding of art is also informed by extensive firsthand study. I have traveled to Europe and visited major museums across the United States, carefully observing paintings in person. When viewing art, I analyze the composition and technique, considering how I would approach replicating the piece. By deciphering an artist’s visual "algorithm," I can reproduce their style—an ability that any skilled artist can develop through technical mastery. Extending this principle to artificial intelligence, I have been developing a human feedback tool for language models. This tool allows users to modify the structure of a neural network by selecting edges and nodes within its graph representation and adjusting database values through a React-based user interface. Essentially, this system enables manual annotation and ranking of data, making it possible for individuals—or teams—to refine a model by layering new human-curated information on top of an existing foundation. I am currently building a React web application with a Django backend, incorporating the Universal Data Tool to facilitate reinforcement learning through human feedback (RLHF). This system enables researchers to construct their own open-source platforms for integrating new human-labeled data into models, thereby iteratively improving AI outputs. The same methodology can be applied to images: by training a model using photographs of my own artwork, modifying the network’s weight parameters, and refining the vector database, I can generate outputs that reflect my artistic style. Does this process produce "real" art? I believe so, but I welcome other perspectives. **TL;DR:** The assertion that AI relies on a "memory bank" is incorrect. AI models operate through statistical pattern recognition, and their capabilities are shaped by human-annotated data. My work involves building a human feedback system for AI training, allowing for greater control over model behavior. By applying this approach to visual art, I can train models on my own work and generate images consistent with my artistic style. Whether AI-assisted creations constitute "real" art is a matter of perspective. --- This version keeps your core ideas intact while improving readability, accuracy, and logical flow. Let me know if you’d like any refinements!