Nono.MA

DECEMBER 16, 2022

According to OpenAI, "embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts."

They introduced a new text and code embeddings API endpoint in January 25, 20221 capable of measuring the relatedness of text strings.

Here's a list of common uses of text embeddings, as listed in OpenAI's documentation.

  • Search (where results are ranked by relevance to a query string)
  • Clustering (where text strings are grouped by similarity)
  • Recommendations (where items with related text strings are recommended)
  • Anomaly detection (where outliers with little relatedness are identified)
  • Diversity measurement (where similarity distributions are analyzed)
  • Classification (where text strings are classified by their most similar label)

I look forward to testing this API on my writing to see how well it recommends, classifies, and clusters my mini-essays.

SEPTEMBER 22, 2022

OpenAI has open-sourced Whisper, a real-time speech transcription system.

"We’ve trained and are open-sourcing a neural net called Whisper that approaches human level robustness and accuracy on English speech recognition."

JULY 23, 2022

Two months ago, HuggingFace open-source "state-of-the-art diffusion models for image and audio generation in PyTorch" at github.com/huggingface/diffusers.

"Diffusers provides pretrained diffusion models across multiple modalities, such as vision and audio, and serves as a modular toolbox for inference and training of diffusion models."

Here's a text-to-image example from the repository's README.

# !pip install diffusers transformers
from diffusers import DiffusionPipeline

model_id = "CompVis/ldm-text2im-large-256"

# load model and scheduler
ldm = DiffusionPipeline.from_pretrained(model_id)

# run pipeline in inference (sample random noise and denoise)
prompt = "A painting of a squirrel eating a burger"
images = ldm([prompt], num_inference_steps=50, eta=0.3, guidance_scale=6)["sample"]

# save images
for idx, image in enumerate(images):
    image.save(f"squirrel-{idx}.png")

Latent diffusion is the type of model architecture used in Google's Imagen or OpenAI's DALL·E to generate images from text and increase the resolution of output images.

JULY 11, 2022


Here's a video in which I test if OpenAI's DALL-E can generate usable texture maps from an uploaded image.

This texture comes with one of Apple's project examples and the idea of generating textures with DALL-E came from Adam Watters on Discord.

JULY 4, 2022


OpenAI's DALL-E 2 creates variations of my hand sketches.


See transcript ›

JULY 3, 2022

I continue to play with DALL-E 2 from time to time. I've posted a few videos and live streams on the topic and plan to share more clips with tiny bits from my experiments and some of my favorite results so far. Tomorrow, a video sharing how DALL-E can copy my hand drawings will come out on YouTube.

JUNE 25, 2022


Here are my impressions of OpenAI's latest iteration of DALL·E, an AI system that generates images from text. I've generated images in different styles and variations of my drawings, experimented with public pages, mask edits, uploads, and more.


See transcript ›

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