Redpoll Blog




@Baxter Eaves

Escaping the data science death spiral

How today's Machine Learning technology forces us into wasteful processes and how we can escape them

@Baxter Eaves

Faster and cheaper with stream-native artificial intelligence

@Bryan Dannowitz

Use Case: Gamma Ray Detection with the MAGIC Telescope

Hands-on with Reformer

@Baxter Eaves

The what, why, and how of synthetic data

Faster, more secure product development in four lines of code

@Baxter Eaves

Data drift

All data drift.

@Baxter Eaves

Components of safe Artificial Intelligence

What artificial intelligence needs — in addition to people — to be safe

@Baxter Eaves

On Humanistic Artificial Intelligence

Choosing cognition — not the brain — as the basis of Artificial Intelligence design

@Baxter Eaves

Why we are using plant breeding to test defense AI

Like the battlefield, nature is a complex and unpredictable system that crushes best-laid plans under constant and unprecedented change

@Redpoll

Redpoll partners with Rutgers and DARPA to build AI that adapts to dynamic worlds

The OTACON project aims to deliver introspective AI

@Baxter Eaves

Announcing rv 0.8.0

A rust crate for building probabilistic programming tools

@Baxter Eaves

Explainable AI is not enough.

Explaining black box AI won't prevent problems; it will make them worse.

@Baxter Eaves

How to design an efficient data collection plan for science and AI

To ensure that our AI predict accurately, we must provide it with the right data.

@Baxter Eaves

Three ways to get value out of machine learning in scientific research

Mainstream AI has neglected the needs of scientists, but that doesn't mean there is nothing for us to do.