Sweatshop Data Is Over (mechanize.work)

> This meant that while Google was playing games, OpenAI was able to seize the opportunity of a lifetime. What you train on matters.

Very weird reasoning. Without AlphaGo, AlphaZero, there's probably no GPT ? Each were a stepping stone weren't they?

> Despite being trained on more compute than GPT-3, AlphaGo Zero could only play Go, while GPT-3 could write essays, code, translate languages, and assist with countless other tasks. The main difference was training data.

This is kind of weird and reductive, comparing specialist to generalist models? How good is GPT3’s game of Go?

The post reads as kind of… obvious, old news padding a recruiting post? We know OpenAI started hiring the kind of specialist workers this post mentions, years ago at this point.

It's kind of reassuring that the old adage "garbage in, garbage out" still applies in the age of LLMs...

I am quite happy that this post argues in favor of subject-matter expertise. Until recently I worked at a national lab. I had many people (both leadership and colleagues) tell me that they need fewer if any subject-matter experts like myself because ML/AI can handle a lot of those tasks now. To that effect, lab leadership was directing most of the hiring (both internal and external) towards ML/AI positions.

I obviously think that we still need subject-matter experts. This article argues correctly that the "data generation process" (or as I call it, experimentation and sampling) requires "deep expertise" to guide it properly past current "bottlenecks".

I have often phrased this to colleagues this way. We are reaching a point where you cannot just throw more data at a problem (especially arbitrary data). We have to think about what data we intentionally use to make models. With the right sampling of information, we may be able to make better models more cheaply and faster. But again, that requires knowledge about what data to include and how to come up with a representative sample with enough "resolution" to resolve all of the nuances that the problem calls for. Again, that means that subject-matter expertise does matter.

It's still too early but at some point we are going to start to see infra and frameworks designed to be easier for LLMs to use. Like a version of terraform intended for AI. Or an edition of the AWS api for LLMs.

(Article is an employment ad.)

Is that actually true. Is the mini-industry of people looking at pictures and classifying them dead? Does Mechanical Turk still get much use?

It's interesting to compare this to the new third generation benchmarks from ARC-AGI, which are essentially a big collection of seemingly original puzzle video games. Both Mechanize (OP) and ARC want AI to start solving more real-world, long-horizon tasks. Mechanize wants to get AI working directly on real software development, while ARC suggests a focus on much simpler IQ test-style tasks.

> For example, to train an AI to fully assume the role of an infrastructure engineer, we need RL environments that comprehensively test what’s required to build and maintain robust systems.

Or we could just, you know, not do that at all.