AI in subjective domains

I am exploring how AI can scale expert feedback in subjective domains with a hobby project.

After visiting art museums around the world, I kept coming back to a familiar challenge: how do you consistently evaluate something inherently subjective? After having created medical devices in pain, the idea was worth exploring. It’s a question that extends well beyond art—into areas like clinical assessment, training, and decision-making in healthcare.

Article content

To explore this, I built an AI-driven application that scores line drawings on a 1–10 scale across five criteria, paired with real-time, constructive feedback. I created a custom GPU server to keep it all secure, private and have unlimited tokens. I then queried that server from Miami, FL with each drawing and art, as it was being created live by hundreds of participants at a local gathering of makers.

Article content

At Maker Faire Miami (April 25–26), several hundred participants created drawings at the table, and received immediate evaluation and guidance.

Article content

What stood out:

  • High engagement with structured, actionable feedback
  • Rapid iteration and improvement within minutes (some kept modifying their art for higher scores)
  • Interest from teachers and parents in using this to accelerate skill development in schools
Article content

It reinforced a broader idea: AI can help standardize and scale feedback in areas traditionally dependent on individual expertise.

In biotech and healthcare, the implications are significant—supporting training, improving consistency, and augmenting expert judgment in subjective evaluations.

Curious how others are thinking about applying AI to standardize decision-making without losing nuance?

Similar Posts

  • Text from Image

    Starting with a base image, it is possible to generate a description. An AI (Qwen-3-VL) simple description : This is a simple, hand-drawn black-and-white sketch of a person’s upper body. Here’s a breakdown of the key elements: Hair: Long, flowing hair depicted with two smooth, curved lines extending from the top of the head down…

  • |

    Virtual cell

    Hani Goodrazi – Arc institute has been working on virtual cell. Drugs fail due to overfit experimental models, You need screen drugs with better models of human biology. Geneva is platfrom that brings tumor models into perturbation model – that is a transcriptomics assay that deconvolves into effect. Take multiple cell line and then treat…

  • LoRa adaptors

    LoRa in the IOT community are the low power long range wireless standards devices that are utilized to send a signal over large distances. However, in the AI field LoRa implies Low rank adaptation – which is relatively more efficient compute way to fine tune pretrained models (LLM, vision transformers, diffusion models). With a pretrained…

  • LoRa in AI image generators

    This stands for Low Rank adaptation. In AI terms, this is a small add-on to the model that makes it do specific things. It is usually used in the context of stable diffusion. A large model takes very long to train with significant resources. However, sometimes, you need specific details – for example you could…

  • |

    Open source protein models

    A company called Profluent (profluent.bio) has been developing protein models that can be used for designing new proteins (https://www.nature.com/articles/s41587-022-01618-2), modeling of new CRISPR-Cas sequences (https://www.nature.com/articles/s41586-025-09298-z) and developing LLM for protein generation (https://www.biorxiv.org/content/10.1101/2025.11.12.688125v1.article-info). What is amazing is that they have open sourced all their models and Profluent-E1 is available in GitHub to download and use. (https://github.com/Profluent-AI/E1)…