Knowledge graphs are network graphs that link related concepts and properties together to create a form of inferencing engine, with knowledge engineering being the programming aspect of graph usage. Explore how knowledge graphs are created and queried, how they are used as part of a broader form of enterprise metadata management, and how they tie into ML and the IoT.
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)
Isn’t it fascinating how our brain processes the vast amounts of information that we receive throughout the day? Our sensory organs convert information into stimuli… Read More »A Practical Guide to Using Computer Vision for Business Growth
Social media is one of the most widely accessible online platforms people use to share their personal views and opinions and express their feelings with… Read More »How does social media content moderation work in the United States?
Irrational numbers such as π may have been the first ones used to create perfect randomness and strong cryptographic systems. They were also among the… Read More »New Military-grade Random Bit Sequences Based on Irrational Numbers and Fast Computations
For many people, synthetic data is synonymous with simulations, mock or fake data. The reality is very different. The purpose of this article is to… Read More »Little-known Secrets About Synthetic Data
Synthetic data is used more and more to augment real-life datasets. It enriches them and allow black-box systems to correctly classify observations or predict values… Read More »New Book: Synthetic Data – Generation and Applications
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
Enterprises increasingly deploy machine learning models to analyze corporate data that informs vital business issues.
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
Several hundreds of thousands of raw data files are uploaded by users every day to social media sites. Online user data provides access to an enormous amount of information regarding products, services, places, and events, which makes it suitable for sentiment analysis. Valuable information can be extracted by analyzing the sentiment of the data.
