Generative AI is suddenly everywhere. Because of this, the future of AI looks very bright indeed. There are many opportunities for generative AI to impact life… Read More »Ten Productivity Hacks using ChaptGPT Generative AI Prompts
In the context of digital transformation and innovation, there is no lack of “hot topics” to discuss. Emerging technologies are truly emerging everywhere. What is… Read More »Innovation at the Convergence of Emerging Technologies: Business at the Edge
In a recent article on Autonomous Intelligent Systems (AIS) [1], Ajit Joakar described various features and characteristics of such systems, including associated technologies and research areas,… Read More »Five Principles of Safe Driving in AIS (Autonomous Intelligent Systems)
For this year, OpenAI is an easy choice for the top AI company. It’s ChatGPT platform – which was launched in late November – was… Read More »AI Companies to Watch for in 2023
As artificial intelligence (AI) becomes an increasingly important tool in health care, it offers unprecedented opportunities for improving patient outcomes, reducing costs, and impacting population… Read More »Training Data to Employ AI in Healthcare
It is well known that data science teams dedicate significant time and resources to developing and managing training data for AI and machine learning models… Read More »Annotation Strategies for Computer Vision Training Data
The possibility to render historical data into comprehensive patterns has added soundness to many areas, and when we consider trading, it has left a giant… Read More »How to Explore Historical Data Patterns with Machine Learning
This week saw the news that two major automotive companies, Ford and VW, were walking away from a multi-billion dollar investment into Argo AI, a venture intended to build self-driving vehicles. Instead, the companies hope to roll at least some of that effort back into augmenting drivers’ abilities to drive safely and efficiently.
Data science assists SEO experts in countless ways, like personalizing the customer experience, understanding client requirements, and many other things.
The aphorism acknowledges that models of our knowledge always fall short of the complexities of reality but can still be useful nonetheless. With this model background, let us delve into this article focusing on specific technical debt in Machine Learning System development.
