Announcing the release of Spice.ai v0.5.1-alpha! 📈

This minor release builds upon v0.5-alpha adding the ability to start training from the dashboard plus support for monitoring training runs with TensorBoard.

There are two general ways to train an AI to match a given expectation: we can either give it the expected outputs (commonly named labels) for differents inputs; we call this supervised learning. Or we can provide a reward for each output as a score: this is reinforcement learning (RL).

Supervised learning works by tweaking all the parameters (weights in neural networks) to fit the desired outputs, expecting that given enough input/label pairs the AI will find common rules that generalize for any input.

Reinforcement learning's reward is often provided from a simple function that can score any output: we don't know what specific output would be best, but we can recognize how good the result is. In this latter statement there are two underlying concepts we will address in this post:

• Can we only tell if the output is good in a binary way, or do we have to quantify the output to train our AI?
• Do we have to give a reward for every AI's output? Can we give a reward only at specific times?

Those questions are already mostly answered, and many algorithms deal with those topics. Our journey here will be to understand how we tackle those questions and end up with a beautiful formula that is at the core of modern approaches of RL:

We are excited to announce the release of Spice.ai v0.5-alpha! 🥇

Highlights include a new learning algorithm called "Soft Actor-Critic" (SAC), fixes to the behavior of spice upgrade, and a more consistent authoring experience for reward functions.

If you are new to Spice.ai, check out the getting started guide and star spiceai/spiceai on GitHub.

A significant challenge when developing an app powered by AI is providing the machine learning (ML) engine with data in a format that it can use to learn. To do that, you need to normalize the numerical data, one-hot encode categorical data, and decide what to do with incomplete data - among other things.

This data handling is often challenging! For example, to learn from Bitcoin price data, the prices are better if normalized to a range between -1 and 1. Being close to 0 is also a problem because of the lack of precision in floating-point representations (usually under 1e-5).

As a developer, if you are new to AI and machine learning, a great talk that explains the basics is Machine Learning Zero to Hero. Spice.ai makes the process of getting the data into an AI-ready format easy by doing it for you!

In my previous post, Teaching Apps how to Learn with Spicepods, I introduced Spicepods as packages of configuration that describe an application's data-driven goals and how it should learn from data. To leverage Spice.ai in your application, you can author a Spicepod from scratch or build upon one fetched from the spicerack.org registry. In this post, we'll walk through the creation and authoring of a Spicepod step-by-step from scratch.