AI

We’re pleased to share that Rotational Labs has joined the NVIDIA Inception program. This milestone reflects the progress of our product and engineering teams in building Endeavor, our AI accountability and governance platform for …

Writing prompts has often made me feel like I’m learning a new language. Though I’m not, the responses I get aren’t always what I expect.

This is a post about how to use AI to make yourself smarter - not dumber.

There’s no “best AI model for all use cases.” You have to do the science each time. If you’re new to experimental design for AI/ML, here’s a quick and dirty intro to the Model Selection Triple methodology.

While the internet is an endless source of free data, everyone is training on the same tired datasets, and they’ve likely got more years of practice. Building a novel dataset tailored to your problem can be a game-changer (if done right).

AI is rapidly transforming the workplace, but don’t be fooled into thinking it’s as simple as replacing workers. If you want to stay ahead of the curve, try a human-centered approach.

Data scientists often struggle to deliver impact in production because they work in notebooks, outside the application stack. Could GenAI tools for UI creation help solve the low ROI problem in data science?

Workplace AI adoption can be hindered by unexpected human factors, including employee shame. In this post, an AI expert recounts real-world examples of overcoming these barriers to unlock a virtuous cycle of human-centered innovation.

AI hype often outpaces real results, especially for mid-market companies, because standalone models fall short. The key to success is a system-based approach. Read on to learn how to build custom AI solutions that return on investment.

Creating AI applications is easier than ever these days, especially in JavaScript. Discover how you can build your own in only a few minutes with Transformers.js!