Articles for category: AI Tools

DAY4 JAVA DATA TYPES & VARIABLES

Java Data TypesPrimitive Data Types Primitive data types are the most basic data types available in Java. There are eight primitive data types, each serving a specific purpose: byte: Size: 8-bit Range: -128 to 127 Usage: Memory-efficient storage in large arrays. Enter fullscreen mode Exit fullscreen mode byte b = 100;Powered By short: Size: 16-bit Range: -32,768 to 32,767 Usage: Suitable for saving memory in large arrays. Enter fullscreen mode Exit fullscreen mode short s = 10000;Powered By int: Size: 32-bit Range: -231 to 231-1 Usage: Default choice for integer values. Enter fullscreen mode Exit fullscreen mode int i =

The Tale of Bloom Embeddings and Unseen Entities · Explosion

Bloom embeddings provide a powerful way to represent a large number of tokens in a memory efficient way. To put them to the test we did an apples-to-apples comparison between them and traditional embeddings on a number of named entity recognition data sets in multiple languages. We wrote a technical report about our experiments and in this blog post we’ll highlight some of our results. We will have a special focus on the elephant in the NER room: entities not present in the training set, which we refer to as unseen entities. If you are mainly here in search of

Space secrets security update

Earlier this week our team detected unauthorized access to our Spaces platform, specifically related to Spaces secrets. As a consequence, we have suspicions that a subset of Spaces’ secrets could have been accessed without authorization. As a first step of remediation, we have revoked a number of HF tokens present in those secrets. Users whose tokens have been revoked already received an email notice. We recommend you refresh any key or token and consider switching your HF tokens to fine-grained access tokens which are the new default. We are working with outside cyber security forensic specialists, to investigate the issue

Monitoring system performance and stability with Deephaven and Prometheus

If you’ve ever used Prometheus, you know it’s pretty great. It’s free, open-source software that uses metric-based monitoring and allows users to set up real-time alerts. Prometheus generates tons of system data, and this data can be pulled from Prometheus through various methods. Using Prometheus’s REST API, it’s easy to look at historical data and see trends. Simply choose a time range and at what time intervals to pull the data, then analyze the data and generate metrics, such as maximum values and averages over that period. But what if you wanted to ingest real-time data from Prometheus, and analyze

Goodbye WordPress, Hello Jekyll!

Today I migrated all wordpress content to Jekyll where I can host it for free on Github Pages. There might still be some errors and incorrect links (pointing to the old WordPress site) or broken links. Please do comment if you come across them. Thank you! Update (2020-04-25): It allows for Latex! Here’s inline latex \(\nabla_\boldsymbol{x} J(\boldsymbol{x})\) and \(x^n + y^n = z^n\) Update (2020-06-07): And collapsables! Click here Update (2020-06-19): And syntax highlighting! def hello(): print('Hello World!') If you found this useful, please cite this write-up as: Yan, Ziyou. (Aug 2019). Goodbye WordPress, Hello Jekyll!. eugeneyan.com. https://eugeneyan.com/writing/goodbye-wordpress-hello-jekyll. or @article{yan2019jekyll,

Not all “open” AI is truly decentralized

DeepSeek’s code is open for anyone to view and contribute to, promoting openness and community participation. But the system operates on privately owned servers. This setup allows access to the code, but the processing power and decision-making stay with the server owners. Additionally, steering clear of sensitive subjects indicates rules are still in place, moving it away from a completely open approach. LLaMA also shares its code publicly, but it needs central systems to work. Whether hosted by a company or a cloud service, this reliance creates barriers. Users must have approval, funds, or entry to these systems to use

What’s new with Data Sharing & Collaboration

Databricks enables organizations to securely share data, AI models, and analytics across teams, partners, and platforms without duplication or vendor lock-in. With Delta Sharing, Databricks Marketplace, and Clean Rooms, businesses can collaborate in real-time while maintaining data privacy and governance. Databricks continues to push the boundaries of data sharing and collaboration. In this blog, we’ll dive into: The General Availability of Databricks Clean Rooms New Delta Sharing features and how they improve secure data exchange The role of Delta Sharing in our SAP partnership Why we integrated Databricks Marketplace with Partner Connect and how the Marketplace ecosystem is expanding Let’s

[2304.01331] Creating Custom Event Data Without Dictionaries: A Bag-of-Tricks

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv’s community? Learn more about arXivLabs. Source link

Faster assisted generation support for Intel Gaudi

As model sizes grow, Generative AI implementations require significant inference resources. This not only increases the cost per generation, but also increases the power consumption used to serve such requests. Inference optimizations for text generation are essential for reducing latency, infrastructure costs, and power consumption. This can lead to an improved user experience and increased efficiency in text generation tasks. Assisted decoding is a popular method for speeding up text generation. We adapted and optimized it for Intel Gaudi, which delivers similar performance as Nvidia H100 GPUs as shown in a previous post, while its price is in the same

Enable Amazon Bedrock cross-Region inference in multi-account environments

Amazon Bedrock cross-Region inference capability that provides organizations with flexibility to access foundation models (FMs) across AWS Regions while maintaining optimal performance and availability. However, some enterprises implement strict Regional access controls through service control policies (SCPs) or AWS Control Tower to adhere to compliance requirements, inadvertently blocking cross-Region inference functionality in Amazon Bedrock. This creates a challenging situation where organizations must balance security controls with using AI capabilities. In this post, we explore how to modify your Regional access controls to specifically allow Amazon Bedrock cross-Region inference while maintaining broader Regional restrictions for other AWS services. We provide practical