Intentional innovation and agile thinking…
AI tools are delivering real efficiency wins, but they’re also quietly reshaping how workers think, what skills atrophy, and where quality unexpectedly breaks down. Here’s what every business leader needs to understand before going all-in. The AI Productivity Paradox: a Continue reading The AI Productivity Paradox: Immediate Gains vs. Long-Term Risks
Spending on artificial intelligence is expected to cross the $2 trillion mark by 2026. This massive investment signals that AI is no longer a peripheral experiment but a central part of how global businesses function. Companies are quickly moving past Continue reading AI’s $2 Trillion Moment—and the Hidden Costs We’re Ignoring
Mapping the Living Mind: From Wiring Diagrams to Neural Forecasting Scientists have spent years trying to figure out how the biological brain works by looking at it from two different angles. One group has focused on connectomics, which is basically Continue reading From Connectomes to Digital Twins: Forecasting the Brain in Real Time
Introduction Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are terms that are commonly used in the technology industry. While these terms are often used interchangeably, they are not the same thing. Each technology has unique features, advantages, Continue reading Comparing Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL)
The concept of digital twins is not new, but rather built on ideas that have been explored for the last couple of decades. The technology (compute power, data management & analytics, etc..) and thinking (increasing regulatory and community acceptance of Continue reading Digital Twin – exploring the basics
MIT press has provided a free book on Algorithms for decision making. You can download it from MIT Press here, or alternatively it is available from this site if the original link fails. From the data science website: The book Continue reading Algorithms for decision making: Free book download from MIT
(The I in FAIR) Ontologies provide a common structure to bring disparate data together – for this post I will refer to the definition of Ontology from Tom Gruber below – emphasis added by me. Note the last highlighted statement as Continue reading Ontologies as applied to FAIR data
For many years, we have seen the proliferation of data as we increasingly instrument our scientific processes. We have developed a diverse landscape of tools and processes, making significant leaps from paper based documentation, but created a new nightmare of Continue reading Is your Scientific Data FAIR
As we have been taking our organization through an Agile Transformation as a part of an overall Digital Transformation, we have been leading extensive training across our business lines and technology teams. One of the most common questions as we Continue reading Scrum Roles vs Job Titles – Learning in a Digital / Agile Transformation
The impacts of the General Data Privacy Regulations are tremendous and cannot be overstated for any business that deals in personal or private data. In large enterprises, the subject of Master Data Management is often a 3rd rail, or considered Continue reading GDPR compliance steps, and impacts on MDM thinking
