Intuitive AI | Revenue & Trends

The hottest Substack posts of Intuitive AI

And their main takeaways

Tech companies are paying a lot for training data because it helps them improve their AI models. As AI use grows, high-quality data has become very valuable.
Having diverse and rich training data is crucial for AI to learn well. Just like a student needs various books to understand different subjects, AI needs various data to perform better.
Quality of the data matters even more than quantity. Rich, informative data leads to better AI outcomes, which is why companies are willing to spend big bucks on it.

Predicting the next token well requires understanding the underlying reality.
Training large language models on next token prediction tasks leads to general learning abilities.
A deeper understanding of reality boosts performance in predicting the next token in various tasks.

Training Large Language Models requires significant compute and financial investment.
Improvements in Large Language Models come from scaling up models, data, and compute in tandem.
Understanding scaling laws can help forecast the future performance of Large Language Models.

LLMs require a massive amount of compute to be trained due to billions of parameters.
Compute is measured in FLOPs (floating point operations per second) to quantify the work computers do.
GPUs, born out of video games, play a crucial role in handling the immense compute demands of training large language models.

The open-source large language model Vicuna-13B challenged ChatGPT in performance
Model IQ measures general large language model performance
Specific capability metrics measure skills like logical reasoning or medical knowledge

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Large language models (LLMs) are neural networks with billions of parameters trained to predict the next word using large amounts of text data.
LLMs use parameters learned during training to make predictions based on input data during the inference stage.
Training an LLM involves optimizing the model to predict the next token in a sentence by feeding it billions of sentences to adjust its parameters.

General Large Language Model performance can be predicted based on compute, dataset size, and parameter count.
Task-specific abilities in models show abrupt jumps in proficiency as the parameter count increases.
Abrupt skill emergence is observed in models for tasks like adding numbers or unscrambling words as they reach certain parameter thresholds.