“The Book of Why” Chapters 1&2 are part of the Read with Me series. For more information, visit Towards Data Science.

The text asks about the implementation and practical aspects of different partitioning/clustering methods in Delta.

Hypothesis-driven development can mitigate the drawbacks of the rapid emergence of new ML models, as new models are being developed hourly.

Language models excel with computationally optimized prompts, impacting prompt engineering. This topic is explored further in an article on Towards Data Science.

Meta has implemented new policies regarding political advertising. Advertisers must now disclose the use of third-party AI software in ads featuring synthetic depictions of people and events that could impact politics or social issues. Meta itself will not use AI for political or social issue-related ads. The move is part of Meta’s efforts to address…

An experiment showed that humans can accurately identify AI-generated human faces only 48.2% of the time. The study utilized StyleGAN2 to synthesize the faces. Interestingly, participants rated the synthetic faces as more trustworthy than real ones, potentially indicating a subconscious preference. The implications of this bias could be significant, leading to misuse and fraudulent activities.…

Brave has unveiled Leo, its native AI assistant, designed to enhance user privacy and improve AI interactions. Leo responds to user queries based on visited webpages and does not collect conversations or track users. Leo Premium, a paid service, offers more structured responses and additional features. Leo is currently available in Brave 1.6 for desktop,…

Researchers have created a virtual reality app that allows users to open and control 3D modeling tools simply by moving their hand.

In the context of voice assistant systems, steering is when users issue follow-up commands to direct or clarify previous turns. We propose STEER, a model that detects whether a follow-up turn is the user attempting to steer the previous command. To train the model, we created heuristic rules to sample usage data without annotations, overcoming…

EELBERT is an approach for compressing transformer-based models like BERT while preserving accuracy in downstream tasks. It replaces the input embedding layer with dynamic embedding computations, reducing model size. Evaluations on the GLUE benchmark demonstrate the effectiveness of this approach.