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Visualizing the deep learning revolution | by Richard Ngo | Medium
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Mixed(2)Mixed quality. Some useful content but inconsistent editorial standards. Claims should be verified.
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Written by Richard Ngo (OpenAI/DeepMind researcher), this piece is frequently cited as an accessible entry point for understanding why AI progress motivates safety concerns; suitable for onboarding newcomers to the AI safety field.
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Importance: 62/100blog posteducational
Summary
Richard Ngo surveys rapid deep learning progress across vision, games, language, and science over the past decade, arguing that capability gains have come primarily from scaling compute and data rather than algorithmic breakthroughs. The piece contextualizes why this unexpectedly fast progress has prompted serious concern among researchers about existential risks from advanced AI systems.
Key Points
- •Deep learning has achieved dramatic capability gains across four domains—vision, games, language, and science—often surpassing human-level performance.
- •Most progress stems from scaling compute and data rather than fundamental algorithmic innovations, suggesting continued gains may follow similar patterns.
- •The pace of progress has outpaced many expert predictions, lending credibility to concerns about near-term transformative or dangerous AI systems.
- •The article serves as an accessible, visual introduction to AI progress trends, making it useful for audiences new to AI safety concerns.
- •Rapid capability scaling without corresponding safety advances motivates growing warnings about existential risks from advanced AI in coming decades.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Deep Learning Revolution Era | Historical | 44.0 |
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Visualizing the deep learning revolution
Richard Ngo
14 min read
·
Jan 5, 2023
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The field of AI has undergone a revolution over the last decade, driven by the success of deep learning techniques. This post aims to convey three ideas using a series of illustrative examples:
There have been huge jumps in the capabilities of AIs over the last decade, to the point where it’s becoming hard to specify tasks that AIs can’t do.
This progress has been primarily driven by scaling up a handful of relatively simple algorithms (rather than by developing a more principled or scientific understanding of deep learning).
Very few people predicted that progress would be anywhere near this fast; but many of those who did also predict that we might face existential risk from AGI in the coming decades.
I’ll focus on four domains: vision, games, language-based tasks, and science. The first two have more limited real-world applications, but provide particularly graphic and intuitive examples of the pace of progress.
Vision
Image recognition
Image recognition has been a focus of AI for many decades. Early research focused on simple domains like handwriting; performance has now improved significantly, beating human performance on many datasets. However, it’s hard to interpret scores on benchmarks in an intuitive sense, so we’ll focus on domains where progress can be visualized more easily.
Image generation
In 2014, AI image generation advanced significantly with the introduction of Generative Adversarial Networks (GANs). However, the first GANs could only generate very simple or blurry images, like the ones below.
Press enter or click to view image in full size
Images with yellow borders are real, all others are GAN-generated.
Over the next 8 years, image generation progressed at a very rapid rate; the figure below shows images generated by state-of-the-art systems in each year. Over the last two years in particular, these systems made a lot of progress in generating complex creative scenes in response to language prompts.
Press enter or click to view image in full size
This is an astounding rate of progress. What drove it? In part, it was the development of new algorithms — most notably GANs, transformers and diffusion models. However, the key underlying factor was scaling up the amount of compute and data used during training. One demonst
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