Videos

Jan Ernst, Latent AI’s Director of AI shows how the LEIP SDK empowers developers to streamline machine learning model development using the ML Application Framework.

Jon Brookshire, Latent AI’s Director of Integrated Systems discusses how to enable deep learning processing on satellites with an edge machine learning operations (MLOps) pipeline that addresses challenges in the space domain.

Arizona State University intern Amogh Joshi shares his insights on developing a watermarking technique and integrating it into the Latent AI Application Framework (AF) training process.

Benefits, Challenges, and the Future of Edge AI with Lt. Gen. Jack Shanahan

Latent AI enables embedded adaptive AI by optimizing computing, power and memory without requiring changes to existing AI/ML infrastructure and frameworks

DSI AI for Defense Panel: “Tactical Edge AI for JADC2”

Reduce your time to market with LEIP Recipes

Demystifying Edge AI Development” Latent AI’s Fireside Panel with Steve Jurvetson

5G Open Innovation Lab Presents: Beyond What’s Next with Latent AI

Booz Allen Hamilton’s Executive Vice President and Chief Innovation Officer Susan Penfield and Jags Kandasamy on Partnering to Bring Emerging Tech to Government

LEIP Design: Find the Perfect Model for Your Edge Device (Python & ML)

LEIP: Faster Inference and Better Performance. Accelerated inference is the key to real-time vehicle detection, lower latency, and enhanced accuracy.

This demo shows how a tracking algorithm built with LEIP can increase the efficiency of models and help it detect and track individual persons/objects while operating on low SWaP devices like drones. The video shows individual persons being tracked across multiple data sources. The model is running on an Android phone.

LEIP optimized object detection demonstrates up to 30x speedup. Floating Point vs. Int8 on a Samsung smartphone.

LEIP 2.8 New Functionality Overview

Latent AI LEIP Recipes Demo

LEIP running on Android

Latent AI’s Adaptive AI