In this post, we share predictions of the ChatGPT paradigm shift’s effects on software developers, its current benefits for development process, and introduce a custom ChatGPT GUI application developed with Go and Fyne.
Quickly Setup And Use CodeGPT in VS Code
Lately, the buzz about AI has been inescapable among my peers – especially around OpenAI’s GPT-4 and its implementations: Chat GPT, VideoGPT, and DALL·E. Tools like these are rapidly changing how we interact with and develop on the internet. They are defining our future. Web 5.0 is here, believe it or not, and AI is a big part of that.
Using Open AI’s GPT-4 doesn’t come without some controversy, but the implementation of CodeGPT within VS Code should not cause concern. Time-traveling, unstoppable intelligent robots are not coming after you… at least not just yet.
In this post, I’ll briefly cover why using CodeGPT is helpful, how to use it, and how to set it up. Let’s get started!
Predicting River Flow with a Supervised Learning Time Series Model
In this blog, we will build a flow rate prediction algorithm for the Norfork River in Arkansas. Both farmers and recreationists are heavily dependent on the flow rate of the river. As the river flows faster, the risk of flooding increases and flooding can destroy crops for farmers and ruin vacations for tourists.
My goal with this flow rate prediction algorithm was to give a better idea of what the next 4 days of river flow will look like. Below, I will discuss the data science process that I underwent to make these predictions.
Chatting with Rasa: An Intro to Chatbots
It seems like a lot of websites these days are equipped with a Chatbot. It’s used for customer service, simple Q/A, and more.
As chatbots have grown in popularity, so have the number of different frameworks that can be used to implement them.
In this blog, we will explore one of these frameworks, Rasa. First, I’ll cover some of the basics of chatbots. Then, I’ll explain how to write a simple Chatbot using Rasa, and we’ll highlight some of its additional functionality as well.
Machine Learning: The Time is Now!
Machine Learning enables a system to automatically learn and progress from experience without being explicitly programmed. It’s a subset of the artificial intelligence (AI) technology space being applied and used throughout your everyday life. Think Siri, Alexa, toll booth scanners, text transcription of voicemails – these types of tools are used by just about everyone.
Image recognition and computer vision are also widely being used in production; recently just heard that Los Angeles, CA has made it illegal for law enforcement to use face recognition technology in its numerous public video cameras. The current state of the art allows real-time identification.
Interestingly, the algorithms and know-how for Machine Learning have been around for a long time. Artificial Intelligence was coined and researched as far back as the late 1950s, the advent of the digital computer, and expert systems and neural networks, that theoretically mimics how our brain learns.
The increase in Machine Learning production-ready applications started around 2012, with increased processing, bandwidth, and internet throughput power. This is important as deep learning algorithms like Neural Networks require lots of data and FPUs/GPUs to train.
In this blog, we introduce a conceptual overview of Neural Networks with a simple Neural Net code example implementation using Go. We will interact with it by building a ReactJS interface and train the Neural Network to recognize hand-drawn images of the numbers 0-9. Let’s dive in….