The Humans Behind AI: How Invisible Technologies Trains 80% of the World's Top Models
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What is the invisible army behind AI responses?
So behind every AI response, there's an invisible army of humans who trained it, labeling images, rating answers, and teaching these models right from wrong. Today, we're going to talk to Casper Elliott from the company that's trained 80% of the world's top AI models. Welcome, humans, to the latest episode of the Neuron Podcast. I'm Corey Knowles, and we're joined, as always, by Grant Harvey, writer of the Neuron Daily AI newsletter. And today, we're diving deep into the human side of AI with Casper Elliott from Invisible Technologies. Casper, thanks so much for joining us.
Pleasure to be here. Thank you for having me. So, Caspar, Invisible Technologies just raised $100 million. Invisible also says it has trained 80% of the world's top AI models.
How does Invisible Technologies train AI models?
Both of those are incredible stats. Can you just tell us a little bit more about what that process actually looks like?
The way I think of it is, so large language models, they're not like traditional machine learning, right? They're non-deterministic. They're based on neural nets. They do some funky things. I think of a large language model like an enthusiastic teenager. Yeah, he really wants to answer questions, and he wants to get smart. But if you want a teenager to learn something, like there are some ways you can teach them, right? You could, for example, you could take them to a library, or you could give them a load of homework, or you could set them a test. We kind of do all those three things. When you hear supervised fine-tuning or reinforcement learning about human feedback or evaluations, that's actually one of those three things.
So supervised fine-tuning is giving a model loads of real high-quality examples of data sets to look like. That's taking your model to the library and saying, here's some textbooks to read. It's going to read the textbooks. They'll tell you what's true. Reinforcement learning is, okay, you're going to give the model some questions. It'll give some answers, and you're going to say if those answers are good or not. Like you might ask the model to write me a poem about... Russia and then you'll you'll check that poem and you'll have something about what if that poem is good or bad and you'll give it so great yeah it'll learn from that and so that's reinforcement learning or you can call it reward modeling you're basically allowing you to change the way you reward your model for different types of answers and then evaluation is building the like the test the model has to take to understand if it's good because companies will release loads of different versions of models and they've got to understand if it's better or worse like I mean you've seen the news about track dbt5 dbt5 they released it
Why is data quality more important than quantity in AI?
It was obviously better in some metrics, but the audience wasn't happy. And that's why human evaluation is so necessary because people are not deterministic too. People like to have opinions on things and you can't just be like, well, this was better on all our benchmarks. If it feels different to someone and the user doesn't like it, it doesn't matter if it's better. And that's evaluation. So we're kind of, we're the teachers behind the models.
I think a lot of people believe that all that's happening is AI is scooping up the Internet and just training itself on all of that, and then it just magically works. Can you elaborate a little bit on how that happens as far as, like, a little more technically? Have you met the Internet?
Like if everybody's running off the internet, like we're in trouble. Ultimately, a model is going to look at its huge data set and then you're going to have a large, an impossibly large set of hyperparameters that you're going to configure to try and understand what the best next token to predict is based upon the data it's looked at. But ultimately, you've either got to improve the underlying data set, which is hard. Like the data sets are huge. They'd set to petabytes of information. Or you've got to do what's called post-training where you use sort of, smaller sets of data to improve the weightings.
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Chapters
8 chapters
1
What is the invisible army behind AI responses?
0:00–0:53
2
How does Invisible Technologies train AI models?
0:53–2:26
3
Why is data quality more important than quantity in AI?
2:26–4:45
4
What common mistakes do enterprises make when deploying AI?
4:45–7:05
5
How do humans enhance AI model training?
7:05–9:24
6
What surprising skills contribute to effective AI training?
9:24–11:05
7
How does AI impact job automation and the workforce?
11:05–13:06
8
What does the future hold for AI and human collaboration?
13:06–1:01:53
Speakers
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