This rubric covers the skills, opportunities and strategies to survive as a modern data scientist, knowledge engineer or digital professional, and how to avoid its pitfalls. This includes data optimization, finding business opportunities, advancing education, improving job hunting skills, and navigating the impacts of automation on individuals.
Interview with Kwaai’s Toby Morning and Karsten Wade In the latest episode of the AI Think Tank podcast, I had the privilege of hosting Toby Morning and Karsten Wade from Kwaai.… Read More »A Look at Kwaai’s Personal AI OS
Interview with Patrick Stingley During this very special 6th episode of the AI Think Tank Podcast, I had the honor to speak with Patrick Stingley, a… Read More »Zero Trust Architecture and AI
“Our goal is to redefine the interaction between technology and personal data. We envision an AI that is not just a tool used by the masses but an extension of the individual, respecting their privacy and enhancing their autonomy.” -Reza Rassool
It’s always wise to craft a killer data science portfolio if you want to get noticed in this increasingly competitive and in-demand niche. Of course,… Read More »How to build a robust data science portfolio from scratch
There are thousands of articles explaining the differences between data scientist and machine learning engineer. Data science gets broken down even further, with data analysts… Read More »The Rise of the Dual Data Scientist / Machine Learning Engineer
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
I’ve remarked more than once that the life cycle of social media platforms shares more than a passing resemblance to the evolution of stars.
There have been lots of buzz in companies around HR technology trends and the future of human resources. Covid has propelled digital transformation four years into the future, and the entire relationship between employer and employee has changed. Through this technology drives communication and collaboration. It allows us to share ideas and access extensive information effortlessly at once.
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.
In 1974, two distinct but interestingly similar milestones were achieved that would greatly affect the lives of data engineers: the Rubik’s Cube was invented, and IBM released the first relational database. Since its original rise in the 1980s, the Rubik’s Cube has become the world’s most popular puzzle toy.
