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.
The success and growth of AI is undeniable. Yet there are still basic tasks performing poorly, despite or because of automation. In some cases, you… Read More »6 Business Applications that Badly Need Better AI
Real-world production ML systems consist of two main components: data and code. Data is clearly the leader, and rapidly taking center stage. Data defines the… Read More »Data Discovery for ML Engineers
The Stanford HAI (Human cantered artificial intelligence) report is out. I track this report every year and it always has some good insights. The report… Read More »Biases in CLIP and the Stanford HAI report
While nowadays everyone talk about machine learning, data science and AI, few are mentioning statistical science. Its association, The American Statistical Association, founded in 1839,… Read More »New $1 Million Biennial Prize to Revitalize Statistics
Data Points Hybrid AI is already overcoming the limitations of single technique approaches Symbolic AI understands actual knowledge, not just data Hybrid solutions open the… Read More »The Hybrid to Give Your AI the Gift of Knowledge
In a week when privacy is very much on the agenda, we ask how can we protect AI models. This is already a mature field… Read More »Could an explainable model be inherently less secure?
We live in a complex world that is full of data, and it’s getting even more full every day. In 2020, the world collectively created,… Read More »Calling All Data Scientists: Data Observability Needs You
In this article, I explain how to easily turn data into videos. Data animations add value to your presentations. They also constitute a useful tool… Read More »Data Animation: Much Easier than you Think!
I have been exploring Bayesian strategies for the last year. Considering the limitations of neural network strategies (ex their need for large volumes of data)… Read More »Insights from workshop on Bayesian deep learning at neurips 21
The back-propagation algorithm is one of the foundations of neural networks and deep learning. It is also hard to understand for beginners. I found a… Read More »An Easy Way to Understand the Back-Propagation Algorithm
