The article discusses the concept of Transformer distillation in large language models (LLMs) and focuses on the development of a compressed version of BERT called TinyBERT. The distillation process involves teaching the student model to imitate the output and inner behavior of the teacher model. Various components, such as the embedding layer, attention layer, and…

AI technology is facing challenges in monetization due to escalating costs. Companies like Microsoft, Google, and Adobe are experimenting with different approaches to create, promote, and price their AI offerings. These costs also affect enterprise users and can lead to high prices for AI workloads. Different strategies for AI monetization include enhancing productivity, hardware sales,…

Salesforce Research has developed CodeChain, a framework that bridges the gap between Large Language Models (LLMs) and human developers. CodeChain encourages LLMs to write modularized code by using a chain-of-thought approach and reusing pre-existing sub-modules. This improves the modularity and accuracy of the code generated by LLMs, leading to significant improvements in code generation performance.

Researchers have developed DeepMB, a deep-learning framework that enables real-time, high-quality optoacoustic imaging in medical applications. By training the system on synthesized optoacoustic signals, DeepMB achieves accurate image reconstruction in just 31 milliseconds per image, making it approximately 1000 times faster than current algorithms. This breakthrough could revolutionize medical imaging, allowing clinicians to access high-quality…

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory have developed a groundbreaking approach to materials research using neural implicit representations. Unlike previous methods, which relied on image-based data representations, this approach uses coordinates as inputs to predict attributes based on their spatial position. The model’s adaptability and real-time analysis capabilities have the potential…

Multimodal graph learning is a multidisciplinary field that combines machine learning, graph theory, and data fusion to address complex problems involving diverse data sources. It can generate descriptive captions for images, improve retrieval accuracy, and enhance perception in autonomous vehicles. Researchers at Carnegie Mellon University propose a framework for multimodal graph learning that captures information…

The author outlines five essential touchpoints for finding a balance between focus time and collaboration within a data science or data analytics team. These touchpoints include a morning standup meeting, a Friday “Work In Progress” presentation, a monthly Data Science team meeting, individual one-on-one meetings, and a department team meeting. These touchpoints foster focus, communication,…

This text introduces a new approach to combining conversational AI and graphical user interface (GUI) interaction in mobile apps. It describes the concept of a Natural Language Bar that allows users to interact with the app using their own language. The article provides examples and implementation details for developers. The Natural Language Bar can be…

Large language models (LLMs) have become widely used, but they also pose ethical and legal risks due to the potentially problematic data they have been trained on. Researchers are exploring ways to make LLMs forget specific information or data. One method involves fine-tuning the model with the text to be forgotten, penalizing the model when…

This text explores different perspectives on change in a data organization. Alex, the CDO, focuses on driving business value and staying ahead of market shifts, while Jamie, a data engineer, is more concerned with day-to-day challenges and keeping things running smoothly. The article emphasizes the importance of transparency, collaboration, and standardization in managing change effectively.…