---
title: "Meaning & Key Takeaway of: The question is, can we make neural…"
description: "\"The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?\" — Geoffrey Hinton"
image: "https://sms4smile.com/quotes/i/43c/geoffrey-hinton-12991_bigger_the-question-is-can-we-make-neural-netwo_2693882.jpg"
---

#  The question is, can we make neural networks that are…

![“The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?” quote by Geoffrey Hinton](https://sms4smile.com/quotes/i/43c/geoffrey-hinton-12991_bigger_the-question-is-can-we-make-neural-netwo_2693882.jpg)   [Download](https://sms4smile.com/quotes/i/43c/geoffrey-hinton-12991_bigger_the-question-is-can-we-make-neural-netwo_2693882.jpg) [Open image](https://sms4smile.com/quotes/i/43c/geoffrey-hinton-12991_bigger_the-question-is-can-we-make-neural-netwo_2693882.jpg)

> “The question is, can we make neural networks that are 1,000 times bigger? And how can we do that with existing computation?”

[Geoffrey Hinton](https://sms4smile.com/quotes/author/geoffrey-hinton/)

## About This Quote

This interpretation was drafted with AI assistance. It is one reading of the quote, not the author's own explanation.

Scaling neural networks a thousandfold challenges current hardware limits, prompting the need for new architectures and efficiency methods.

**In simple terms:** We need bigger networks but lack compute.

Key Takeaway

Innovate hardware and algorithms.

Themes

AI scaling hardware efficiency

Mood

curious optimistic

Type

technical forward‑looking

When to use this quote

- research labs
- industry AI development

Key Concepts

computer science deep learning

Questions to Reflect On

- What architectural changes could reduce compute?
- How can we make models more efficient?

A Different Perspective

Current tech may not keep pace without breakthroughs.

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