The Limits of Deep Learning

Author(s): Frederik Bussler

Originally published on Towards AI.

Deep Learning: Diminishing Returns? – Semiwiki

Photo by Luca Ambrosi on Unsplash

GPT-3, the latest state-of-the-art in Deep Learning, achieved incredible results in a range of language tasks without additional training. The main difference between this model and its predecessor was in terms of size.

GPT-3 was trained on hundreds of billions of words — nearly the whole Internet — yielding a wildly compute-heavy, 175 billion parameter model.

OpenAI’s authors note that we can’t scale models forever:

“A more fundamental limitation of the general approach described in this paper — scaling up any LM-like model, whether autoregressive or bidirectional — is that it may eventually run into (or could already… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI