Researchers investigated the complexity and scalability of Hierarchical Agglomerative Clustering (HAC) from 2021. They found that only a small fraction of the similarity matrix is relevant for computing high-quality clusters, and designed algorithms to exploit this sparsity, achieving proportional computation steps.

Source: https://blog.research.google/

Google uses machine learning to analyze build logs and version control data to aid in debugging code. This approach helps developers identify and resolve issues more efficiently, reducing the time spent on debugging.

Source: https://blog.research.google/

Google's keyboard app, Gboard, uses language models to enhance typing experience with features like next word prediction, autocorrection, smart compose, slide to type, and proofread. Google prioritizes responsible approaches and privacy standards while improving LM performance. Recent advancements include data usage disclosures and configuration controls for users.

Source: https://blog.research.google/

Developing deep learning solutions for audio signal processing requires access to large, high-quality datasets. Recording audio on the actual device has the benefit of capturing specific acoustics properties, but this process is time-consuming and difficult. Using simulated data can be an alternative.

Source: https://blog.research.google/

The minimum cut problem, also known as 'min-cut', is a fundamental question about the connectivity of a graph that asks for the least expensive way to disconnect a network. This problem is particularly relevant in undirected graphs, where edges have no orientation and are associated with positive weights.

Source: https://blog.research.google/

Audio spatial separation, isolating sounds from a mixture with various angles of arrival, is a fundamental topic in audio processing. The task is to leverage the spatial diversity of audio captured from multiple microphones to separate audio sources in designated angular regions from the remaining interference.

Source: https://blog.research.google/

The article discusses the advancements in large language models and the concerns associated with them, such as factuality and transparency. It also explores how understanding a model's hidden representations can help control its behavior and deepen scientific understanding.

Source: https://blog.research.google/

Google has developed new algorithms to improve the efficiency of vector similarity search, a crucial aspect of machine learning applications. These algorithms are used to compare and find similarities between objects, such as images or websites, which are represented as vector embeddings.

Source: https://blog.research.google/

Quantum computers can solve problems exponentially faster than classical computers, but their building blocks, qubits, are prone to errors due to interactions with the environment. These interactions introduce dissipation, which destroys quantum entanglement and drives a quantum processor toward classical states, introducing errors. Researchers are working on methods to stabilize quantum states and reduce errors for more efficient and accurate quantum computing.

Source: https://blog.research.google/

Source: https://blog.research.google/