AI

This article is an excerpt from the book Machine Learning with PyTorch and Scikit-Learn from the best-selling Python Machine Learning series, updated and expanded to cover PyTorch, transformers, and graph neural networks. Broadly speaking, graphs represent a certain way we describe and capture relationships in data. Graphs are a particular kind of data structure that is nonlinear and abstract. And since graphs are abstract objects, a concrete representation needs to be defined so the graphs can be operated on.…

This is the third piece in a series on developing XR applications and experiences using Oracle and focuses on XR applications of computer vision AI and ML and its related use in the metaverse.  Find the links to the first two articles below:  As with the previous posts, here I will again specifically show applications developed with Oracle database and cloud technologies HoloLens 2, Mixed Reality Toolkit, and Unity platform. Throughout the blog, I will reference this corresponding demo video below.…

Natural language processing (NLP) is continuing to grow in popularity, and necessity, as artificial intelligence and deep learning programs grow and thrive in the coming years. Natural language processing with PyTorch is the best bet to implement these programs. In this guide, we will address some of the obvious questions that may arise when starting to dive into natural language processing, but we will also engage with deeper questions and give you the right steps to get started working on your own NLP programs. Can…

Healthcare has been at the epicenter of everything we do for two years. While the pandemic has been a significant driver of the conversation, healthcare technology—artificial intelligence (AI) specifically—has been experiencing explosive growth. One only needs to look at the funding landscape: more than 40 startups have raised at least $20 million in funding specifically to build AI solutions for healthcare applications. But what’s driving this growth? The venture capital trail alone won’t help us understand the trends…

Introduction Attempts at hand-crafting algorithms for understanding human-generated content have generally been unsuccessful. For example, it is difficult for a computer to “grasp” the semantic content of an image - e.g., a car, cat, coat, etc....… - purely by analyzing its low-level pixels. Color histograms and feature detectors worked to a certain extent, but they were rarely accurate for most applications. In the past decade, the combination of big data and deep learning has fundamentally changed the way we approach…

Introduction We are in the golden age of AI (1). AI adoption makes businesses more creative, competitive, and responsive. The software-as-a-service (SaaS) model, coupled with the advancements of the cloud, has matured the software production and consumption process. Most organizations prefer to “buy” AI capabilities than “build” their own. Hence SaaS providers, such as Salesforce, SAP, Oracle, etc., have introduced AI platform capabilities, creating AI-as-a-Service (AIaaS) model. This evolution has made AI adoption easier…

The impact and the innovation of AI can be seen everywhere, and fintech is no exception. The disruptive power of financial industries to shape the traditional financial institution is growing because of the advances in artificial intelligence. AI-powered and machine learning technologies in fintech will help analyze large data sets in real-time and have the ability to make improvements. As the demand for such services increases, AI and ML become the key to sustainability and growth in the industry. Let's get into how this…

Machine learning models are not static pieces of code but, instead, dynamic predictors that depend on data, hyperparameters, evaluation metrics, and many other variables; it is vital to have insight into the training and deployment process to prevent model drift predictive stasis. That said, not all monitoring solutions are created equal. These are the three must-haves for a machine learning monitoring tool, whether you decide to build or buy a solution. Complete Process Visibility Many applications involve multiple…

We have received a fresh product owner for our AI-Analytics Product team.  We build analytics software for the insurance market. I have helped him with onboarding for the initial few days, as well as answered all his questions. First, what he did, Competitive Analysis to Deliver Better Value He was asking: How can we build & deliver stronger product Value by gaining inspiration from our competitors? According to him, we can gain a lot from our competitors. We also need to have a competitive advantage that competitors…

Choosing the right project management methodology can be crucial for your project development. It will help you avoid mistakes, speed up the whole process, and support in discovering the problems of your target groups. The last issue is fundamental. Only after a deep understanding of the needs of your target group will you be able to develop a solution that will solve their problems. There are many approaches to project management focusing on discovering problems, and design thinking is one of them. AI is becoming a more…