AlexNet

After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs

Nov 25, 2025 · 1:00:39

World Labs co-founders Fei-Fei Li and Justin Johnson argue that spatial intelligence is the next frontier beyond LLMs, with their product Marble generating editable 3D worlds from text or images using Gaussian splats. They claim language is a lossy channel for describing the rich 3D/4D world, while spatial intelligence handles everything from picking up a mug to inferring DNA's double helix. Marble already serves gaming, VFX, and film, with precise camera control and real-time rendering on phones and VR headsets. They discuss scaling compute from AlexNet to today's million-fold increase, why world models can 'soak up' modern GPU clusters better than language alone, and the gap between pattern-fitting and causal understanding in physics. Fei-Fei worries more about under-resourced academia than open vs. closed models, advocating for national AI compute clouds and open benchmarks like the BEHAVIOR dataset. Justin notes transformers are natively set models, not sequence models, opening architectural possibilities for new spatial data.

Breaking down the OG GPT Paper by Alec Radford

Apr 23, 2024 · 1:05:03

This episode of the Latent Space Paper Club breaks down Alec Radford's seminal 2018 GPT-1 paper, which introduced generative pre-training of a Transformer decoder on BookCorpus (5 GB, 7,000 books) followed by supervised fine-tuning. Amget explains how the 117M-parameter model achieved state-of-the-art on 9 of 12 NLU tasks, with absolute gains of 8.9% on commonsense reasoning (Stories Cloze) and 5.7% on question answering (RACE). Key innovations include task-agnostic input transformations (e.g., concatenating premise and hypothesis with a delimiter for entailment, using a Siamese architecture for semantic similarity, and scoring answer choices for QA) and an auxiliary language modeling objective during fine-tuning (lambda=0.5). The episode also covers zero-shot heuristics (appending 'very' for sentiment, averaging token log probabilities for QA), ablation studies showing pre-training adds 15% average improvement, and the paper's prescient call to scale up—which became the blueprint for GPT-2, GPT-3, and beyond.