Introducing Surge AI: High-Quality Data Labeling for NLP

In 2016, DeepMind began building an AI to beat StarCraft II — and by the end of 2019, its AlphaStar AI reached GrandMaster level. https://www.youtube.com/watch?v=5iZlrBqDYPM

👀 What’s happened since then?

Faster GPUs have dropped the cost of training neural networks and allowed for larger and larger models to be trained. New tools make the infrastructure work much easier.

A GPT-3-written blog post on productivity that reached the top of Hacker News. https://liamp.substack.com/p/my-gpt-3-blog-got-26-thousand-visitors

Then where’s the revolution?

So why hasn’t AI taken over the world?

https://www.theverge.com/2018/1/12/16882408/google-racist-gorillas-photo-recognition-algorithm-ai

What’s wrong with today’s data? Garbage in, garbage out 🦝

In some cases, models are trained on proxies like clicks and user engagement.

https://twitter.com/DeepStateExpose/status/1281503301540995072
https://twitter.com/sryxhaunting/status/1273413264769257473

🧗 What advancements do we need?

Dataset issues cause a host of problems.

https://twitter.com/alexismadrigal/status/1278186137115217920

🤖 A data-driven AI future

At its core, machine learning is about teaching computers to perform the job we want — and we do that by showing them the right examples.

So in order to build high-quality models, shouldn’t building high-quality datasets, and making sure they match the problem at hand, be the most important skill of an ML engineer?

Ultimately, we care whether AI solves human needs, not whether it beats artificial benchmarks.

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Surge AI

Surge AI

The world’s most powerful data labeling platform, designed from the ground up for stunning AI. https://www.surgehq.ai