An in-depth look into the current state of the art of Generative Pre-trained Transformer (GPT) language models, with a specific focus on the advancements and examples provided by OpenAI in their GPT4 Technical Report (https://arxiv.org/abs/2303.08774) as well as the Microsoft "Sparks of AGI" Paper (https://arxiv.org/abs/2303.12712).

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Contents:
00:00 - Introduction
01:31 - Multi-Modal/imagery input
05:44 - Predictable scaling
08:15 - Performance on exams
15:07 - Rule-Based Reward Models (RBRMs)
17:53 - Spatial Awareness of non-vision GPT-4
20:38 - Non-multimodel vision ability
21:27 - Programming
25:07 - Theory of Mind
29:34 - Music and Math
30:44 - Challenges w/ Planning
33:25 - Hallucinations
35:04 - Risks
38:01 - Biases
44:55 - Privacy
48:23 - Generative Models used in Training/Evals
51:36 - Acceleration
57:07 - AGI