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Perspectives on the future of AI
13 minute read
Published:
How big are the models going to get and how much longer is the scaling hypothesis going to hold? It’s unclear, but according to current performance trends, which haven’t shown signs of plateauing (GPT-4o, Claude 3.5 Sonnet, Gemini-1.5-Pro, Llama-3.1-405B, Grok-2), and the power budget of announced data centres (5GW OpenAI/Microsoft Stargate campus), it is likely that there is an order of magnitude left (OOM) to climb in model size. This Epoch AI research covers these scenarios in depth and estimates training runs of the order of ~2e29 FLOPs being possible by 2030, which would be 4 OOMs larger than GPT-4 (2e25 FLOPs). These training runs will primarily be power constrained, followed by chips, data, and latency.
Understanding Hansen’s global warming in the pipeline
less than 1 minute read
Published:
In progress
The need for a critical mineral demand model incorporating technical change
17 minute read
Published:
Introduction
Studying the effects of technical change on critical mineral demand and supply in the context of the low-carbon energy transition is an important and open area of research. Despite the crucial role played by these minerals in low-carbon technologies, long-term demand projections remain uncertain due to intricate interactions between drivers of technical change. In this writeup, I lay out what a framework that studies the effects of technical change on critical mineral demand would look like, how it can be developed, and what are its potential use cases.
Developments in machine learning for antibody design
23 minute read
Published:
Protein structure and sequence modeling has seen a fresh wave of resurgence in the last couple of years owing to some interesting developments in machine learning (ML) and deep learning (DL) based techniques. These techniques appear in a variety of flavours including using Equivariant neural network modules to respect the structural properties of 3D macromolecules, deeper networks that can benefit from the increased available experimental structures, powerful node-to-node relationship learners like transformers, and masked language modeling on the protein sequence space to learn evolutionary information. While structure prediction methods like AlphaFold (AF) [1] and RosettaFold (RF) [2] have become ubiquitious in computational structural biology, there remain challenges to be tackled on multiple fronts, where ML will play an important role.