Transformers, a deep learning architecture known for language tasks, have shown surprising ability in solving graph-based algorithmic problems like pathfinding and cycle detection. This is unexpected as transformers don't explicitly encode graph structure like message passing neural networks.

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

Google made significant progress in machine learning (ML) foundations, improving efficiency through new techniques and reducing inference times of large language models (LLMs). This led to faster generation of outputs without compromising quality, resulting in a better user experience and reduced energy consumption.

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

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

Articulate Medical Intelligence Explorer (AMIE) demonstrated potential in simulated specialist consultations, outperforming board-certified primary care physicians on diagnostic dialogue in 52 out of 58 aspects.

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

A study published in Nature has made a significant breakthrough in quantum computing. The research, titled 'Quantum error correction below the surface code threshold', has successfully combined quantum error correction with a superconducting quantum processor. This combination has resulted in a notable improvement in the performance of quantum computers, which is crucial for their potential applications in fields like chemistry, drug discovery, optimization, and cryptography. Quantum information is known for being delicate, and even the most advanced devices can experience errors. This new development aims to enhance the reliability of quantum computers by reducing the error rate.

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

In 2022, a technique called speculative decoding was introduced to reduce inference times for large language models, improving the speed of output generation without affecting quality. This method ensures the same output distribution and reduces the need for hardware, making it a significant development for user-facing AI products.

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

Researchers are working on extending masked autoencoders (MAEs) to process longer videos, currently limited to short clips due to computational constraints. This development could improve the learning of robust video representations for various applications, such as video search and robotics.

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

Machine learning models like Gemini Pro are being used to process time-series data, which represents changing values over time. This data can be used to understand complex real-world systems in fields like healthcare and climate.

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

AI can improve healthcare by increasing diagnostic accuracy, access to care, and reducing administrative burden, but its development is challenging due to data, expertise, and compute requirements.

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

A vulnerability gap exists in the privacy analysis of DP-SGD, a common method for differentially private models. This gap occurs when mini-batches have a fixed size, allowing examples to leak information about each other.

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