London Real interviews Dr Ben Goertzel, the Founder and CEO of SingularityNET and Chief Science Advisor for Hanson Robotics.

He is one of the world’s leading experts in Artificial General Intelligence (AGI), with decades of expertise in applying AI to practical problems like natural language processing, data mining, video gaming, robotics, national security and bioinformatics.

He was part of the Hanson team which developed the AI software for the humanoid Sophia robot, which can communicate with humans and display more than 50 facial expressions.Today he also serve as Chairman of the AGI Society, the Decentralized AI Alliance and the futurist nonprofit organisation Humanity+.

Ania Kubów walks through the Binary Search algorithm. 

In 5 minutes, we cover some cool facts about Binary Search, as well as two use cases, one even being a MAGIC TRICK that you might have seen before and wondered how it’s done!

I even show you one example from the book by Aditya Bhargava, Grokking Algorithms, to show you the power of numbers.

AlexNet is one of the popular variants of the convolutional neural network and used as a deep learning framework.

In this article, we will employ the AlexNet model provided by the PyTorch as a transfer learning framework with pre-trained ImageNet weights. The network will be trained on the CIFAR-10 dataset for a multi-class image classification problem and finally, we will analyze its classification accuracy when tested on the unseen test images. Our aim is to compare the performance of the AlexNet model when it is used as a transfer learning framework and when not used as a transfer learning framework.

Image segmentation is considered one of the most vital progressions of image processing.

It’s a technique of dividing an image into different parts, called segments.

The method is primarily beneficial for applications like object recognition or image compression because, for these types of applications, it is expensive to process the whole image.

Segmentation algorithms partition an image into sets of pixels or regions. The purpose of partitioning is to understand better what the image represents. The sets of pixels may represent objects in the image that are of interest for a specific application. How we partition distinguishes the different segmentation algorithms.