AI Nutrition Label

6/9/2025

Transparent AI

Not all food provides the same nutritional benefits. Similarly, not all AI solutions produce the same results. As modern professionals embrace emerging AI technology, a standard "nutrition label" for assessing the sound and reasonableness of AI capabilities is essential.

HTI-1

Attention is All You Need

tensor2tensor

Given the nature of how AI works (Attention is All You Need) one lens to view Artificial Intelligence is:

Tensor Library: Data Provenance (source and age of information)
Proxy Variables: Known Bias (weighting)
Data Drift Detection: Tests for counter factual

Public Private Partnerships

AI Safety is critical for achieving the exponential potential of dynamic web interfaces.

CHAI

AI annotated diagram of AI

X - Post

Hey Nano Banana Pro, please annotate the original Transformer architecture diagram.

Just look at how precisely it added little insights to the main operations.

Great for infographics and for improving technical visual communication.

Notebook: Transformer Architecture

Human Prompted Notebook

Guide a discussion between two AI researchers. Researcher A explains the limitations of previous sequence models (Recurrent Neural Networks or RNNs and LSTMs): they suffered from slow, inherently sequential processing (O(n) sequential operations) which precluded parallelization, and had difficulty modeling long-range dependencies. Researcher B introduces the Transformer, explaining its core innovation: relying entirely on attention mechanisms and positional encoding, dispensing with recurrence and convolutions. Highlight how this architecture achieves constant sequential complexity (O(1)) and massive parallelizability, directly leading to its superior performance, citing the achievement of state-of-the-art machine translation results on the WMT 2014 English-to-German task after only 12 hours of training on 8 GPUs.