In 2008, I had the opportunity to tour SPAWAR, the Space and Naval Warfare Systems Command, now known as NAVWAR. SPA/NAVWAR is a research and development laboratory for the U.S. Navy. During my visit, I was fascinated by the various autonomous military robots that were being developed and tested there. I photographed the tour and wrote about it for WIRED News.
Fast-forward to 2023, and with the emergence of large language models like ChatGPT and Bing AI, it's possible to imagine how these robots could be controlled using AI in ways that are frankly somewhat terrifying. With great power comes great responsibility, and we must consider the potential risks of relying on AI-powered machines in warfare.
Machine learning is an exciting and rapidly evolving field that has the potential to transform virtually every industry. From natural language processing to computer vision, machine learning models are becoming an integral part of our daily lives, enabling new levels of automation and understanding. To explore the fascinating world of machine learning and share insights with a broader audience, I am launching a blog series on AI/ML.
In this post, I will discuss the topics I will be covering and what you can expect from the upcoming blog series.
NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.
Computer vision, the field of AI that enables computers to interpret and understand visual information from the world, has undergone significant advancements over the past decade. The ability to analyze images and videos, recognize objects, and understand visual scenes has opened up a multitude of applications in fields such as healthcare, autonomous vehicles, and security. In this blog post, we will explore the key milestones and breakthroughs that have shaped the evolution of computer vision over the last ten years.
NOTE: This post is part of my Machine Learning Series where I’m discussing how AI/ML works and how it has evolved over the last few decades.
Neural networks are the foundation of many artificial intelligence and machine learning applications. There are several types of neural networks, each designed to address specific types of problems. In this post, we'll explore the most common types of neural networks and their applications.
NOTE: This post is part of my Machine Learning Series where I discuss how AI/ML works and how it has evolved over the last few decades.
Autoencoders are a type of neural network architecture used for tasks such as dimensionality reduction, feature extraction, and data denoising. With their ability to learn efficient representations of data, autoencoders have found applications in various fields, from image processing to anomaly detection. In this post, we'll explore the structure and functionality of autoencoders and delve into their use cases.