Episode Details

As much as image generation is fun, the power of GenAI is prediction.

The technology operates very similarly to people you might meet:

* Some people have studied and are experts in a single topic for a decade. They’re experts in that topic and can easily infer, correct, and complete tasks. They’re unreliable for everything else.

* Some people are generally knowledgeable and have a good understanding of many topics. They aren’t experts but can reliably assist you in many ways. But they’ll also be wrong sometimes.

OpenAI, Anthropic, etc.— are highly knowledgeable in almost every topic. That’s the result of being trained on all accessible information online, data they’ve licensed, plus data they’ve allegedly stolen.

AI products built on these frontier models are immediately powerful for completing any task. But if you build a point solution on proprietary data explicitly trained on a narrow topic, it can achieve an expert level.

That was the focus of our conversation with Tyler Hochman, the Founder and CEO of FORE Enterprise. We discussed unlocking AI’s predictive power by focusing on expensive and repeating problems. How any business or founder can leverage and/or specialized data sets to train AI models to deliver powerful prediction capabilities.

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He’s built AI-powered software to predict when employees may leave their jobs, offer fashion advice, and help professional sports teams improve performance.

This video explains how to train your model using Figma files.

This conversation highlights how important your first party will become. This data includes more than just your customer data; it should include documenting workflows, quantifying initiatives, and developing a matrix of your offerings/capabilities. Anything repeatable must be quantified as a learning tool.

Example of a data collection strategy for AI training

When OpenAI launched a new image generation feature in ChatGPT, everyone jumped on it. AI-generated images infested our feeds in the Studio Ghibli style.

These images sparked a lot of worthy debate about copyright infringement, which added to the ethical concerns about how OpenAI trains its model. A recent study highlighted evidence that ChatGPT is trained on copyrighted works.

Given that AI models are running out of data to consume, they need to find clever ways to access a new data set.

Enter ChatGPT’s image generation tool and Ghibli craze. Millions of people have been feeding their photos into the model, giving it access to an entire universe of new training data to improve the quality of its image generation capabilities.

Lesson: Collecting user-generated content can provide your custom model with access to training data that was never possible before. This holds true whether your product is a document scanner, video generator, accounting software, run tracking app, or