Robustness against adversarial attacks has recently been at the forefront of algorithmic design for machine learning tasks. In the adversarial streaming model, an adversary gives an algorithm a sequence of adaptively chosen updates $u_1,\ldots,u_n$ as a data stream. The goal of the algorithm is to compute or approximate some predetermined function for every prefix of the adversarial stream, but the adversary may generate future updates based on previous outputs of the algorithm. In particular, the adversary may gradually learn the random bits internally used by an algorithm to manipulate dependencies in the input. This is especially problematic as many important problems in the streaming model require randomized algorithms, as they are known to not admit any deterministic algorithms that use sublinear space.

Robustness against adversarial attacks has recently been at the forefront of algorithmic design for machine learning tasks. In the adversarial streaming model, an adversary gives an algorithm a sequence of adaptively chosen updates u_1,\ldots,u_n as a data stream. The goal of the algorithm is to compute or approximate some predetermined function for every prefix of the adversarial stream, but the adversary may generate future updates based on previous outputs of the algorithm. In particular, the adversary may gradually learn the random bits internally used by an algorithm to manipulate dependencies in the input. This is especially problematic as many important problems in the streaming model require randomized algorithms, as they are known to not admit any deterministic algorithms that use sublinear space.

Retrieval-augmented generation (RAG) has achieved significant success in information retrieval to assist large language models LLMs because it builds an external knowledge database. However, it also has many problems, it consumes a lot of memory because of the enormous database, and it cannot update the established index database in time when confronted with massive streaming data. To reduce the memory required for building the database and maintain accuracy simultaneously, we proposed a new approach integrating a streaming algorithm with k-means clustering into RAG. Our approach applied a streaming algorithm to update the index dynamically and reduce memory consumption. Additionally, the k-means algorithm clusters highly similar documents, and the query time would be shortened. We conducted comparative experiments on four methods, and the results indicated that RAG with streaming algorithm and k-means clusters outperforms traditional RAG in accuracy and memory, particularly when dealing with large-scale streaming data.

A'widely-used' algorithm on streaming services including Spotify is more likely to recommend songs by male musicians than female musicians, a new study finds. European researchers who analysed the listening habits of 330,000 people over nine years found only 25 per cent of the artists ever listened to were female. When they tested the algorithm they found, on average, the first recommended track was by a man, along with the next six, and users had to wait until song seven or eight to hear a song by a woman. Stats have already suggested female artists don't get as much exposure as male artists – the 2020 Spotify Wrapped statistics showed that the top five most streamed artists were all male, with similar trends across all categories. 'We showed a widely used recommendation algorithm is more likely to pick music by male than female artists,' said study author Dr Christine Bauer at Utrecht University in a piece for the Conversation.

NEW YORK--Amazon is dealing with striking workers in Europe, site disruptions during its Prime Day sale event and protestors inside and out of the Javits Convention Center, site of this week's AWS NYC Summit 2018. None of which appeared to bother Amazon Web Services executives at the Summit, who announced new capabilities for its artificial intelligence machine learning and compute services on the AWS cloud. With artificial intelligence and machine learning services in demand, AWS rolled out improvements to its SageMaker service, which enables users to build and deploy models in the cloud. Dr. Matt Wood, AWS's General Manager for Machine Learning, announced two updates to the help speed up the service: SageMaker Streaming Algorithms and SageMaker Batch Transform. Streaming Algorithms enables users to stream large amounts of training data from the S3 storage service into SageMaker.