We propose a notion of causal influence that describes the ‘intrinsic’ part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its…
There are more than 8,500 performance results in the MLCommons' latest benchmark, testing all manner of combinations and permutations of hardware, software and AI inference use cases.
The selection of the assumed effect size (AES) critically determines the duration of an experiment, and hence its accuracy and efficiency. Traditionally, experimenters determine AES based on domain knowledge. However, this method becomes impractical for online experimentation services managing…
Multi-Touch Attribution plays a crucial role in both marketing and advertising, offering insight into the complex series of interactions within customer journeys during transactions or impressions. This holistic approach empowers marketers to strategically allocate attribution credits for…
In this video presentation, our good friend Jon Krohn, Co-Founder and Chief Data Scientist at the machine learning company Nebula, sits down with industry luminary Sebastian Raschka to discuss his latest book, Machine Learning Q and AI, the open-source libraries developed by Lightning AI, how to exploit the greatest opportunities for LLM development, and what’s on the horizon for LLMs.
Accenture's $1 billion investment in LearnVantage, an AI-powered learning platform, aims to bridge the growing skills gap and help businesses upskill their workforces to capitalize on emerging technologies like generative AI, cloud computing, and cybersecurity.
Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers that, however, are not always grounded in the input image. We investigate this phenomenon, usually referred to as “hallucination” and show that it stems from an excessive reliance on the language…