Statistics and Machine Learning have undoubtedly proved to be useful in tackling problems in a variety of application areas including social science, public health, environmental science, media, or transportation. While these two scientific disciplines share a great number of tools, they have some fundamental differences.
Data is at the heart of decision-making today, and graphs are firmly embedded in the modern data stack. From fraud detection and drug discovery to market and supply modelling, graphs enable previously unachievable insights.
The use of artificial intelligence in creating synthetic media, either from scratch or by changing parts of an existing content, has become very popular. The drivers behind this popularity are in a wide spread access to often initially open source deepfake and Generative AI algorithms, but also in the low cost, their performance and their ease of use by nonprofessionals.
Cause and effect relationships play a central role in how we understand the world around us and how we act upon it. Causal associations intend to be more than descriptions, or summaries of the observed data, and instead relate to the underlying data-generating processes.
Cause and effect relationships play a central role in how we understand the world around us and how we act upon it. Causal associations intend to be more than descriptions, or summaries of the observed data, and instead relate to the underlying data-generating processes.
