As AI jobs become more mainstream, there’s a big rush to define exactly what types of skills make a “AI Engineer.”

Here’s one take.

Apart from development experience, an AI engineer should have exposure to operations and support for AI applications. It is recommended that the person should be able to create CI/CD pipelines using Azure Boards, Azure Pipelines, Azure Repos, Azure Test Plans and Azure Artifacts on Microsoft Azure ore parallel DevOps platforms from other Cloud providers.

This is a fascinating development. We’re going to need real innovation in hardware (software, too), especially as Moore’s Law starts to run out of steam.

Computer scientists from Rice University, along with collaborators from Intel, have developed a more cost-efficient alternative to GPU.

The new algorithm is called “sub-linear deep learning engine” (SLIDE), and it uses general-purpose central processing units (CPUs) without specialized acceleration hardware.

One of the biggest challenges within artificial intelligence (AI) surrounds specialized acceleration hardware such as graphics processing units (GPUs). Before the new developments, it was believed that in order to speed up deep learning technology, the use of this specialized acceleration hardware was required.

QuickLogic Corporation and Antmicro jointly-announced QuickFeather, a small form factor development board designed to enable the next generation of low-power Machine Learning  capable IoT devices.

The QuickFeather board is powered by QuickLogic’s EOS™ S3, the first FPGA-enabled SoC to be fully supported in the Zephyr RTOS, with flexible eFPGA logic integrated with an Arm Cortex®-M4F MCU and functionality such as:

In this tutorial, learn how to visualize class activation maps for debugging deep neural networks using an algorithm called Grad-CAM.

Then you’ll learn how to implement Grad-CAM using Keras and TensorFlow.

While deep learning has facilitated unprecedented accuracy in image classification, object detection, and image segmentation, one of their biggest problems is model interpretability, a core component in model understanding and model debugging.

This tutorial on TensorFlow.org implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant.

Wow.

This tutorial implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant . This example demonstrates how to detect certain properties of a quantum data source, such as a quantum sensor or a complex simulation from […]