Executive Overview: The Rise of Open Foundational Models

Executive Overview: The Rise of Open Foundational Models

Moving generative AI applications from the proof of concept stage into production requires control, reliability and data governance. Organizations are turning to open source foundation models in search of that control and the ability to better influence outputs by more tightly managing both the models and the data they are trained on.Databricks has assisted thousands of customers in evaluating use cases for generative AI and determining the most appropriate architecture for their organization.Our customers have shared with us the challenge of building and deploying production-quality AI models, which is often difficult and expensive. As a result, most CIOs are not…
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Release notes for Deephaven version 0.29 | Deephaven

Release notes for Deephaven version 0.29 | Deephaven

Deephaven Community release 0.29 brought forth some new features, quality-of-life improvements, and better API documentation. This release also includes a number of bug fixes and performance improvements. For full release notes, see the GitHub release page.Function generated tables​Real-time table creation should be easy. Deephaven Community v0.29 introduces function-generated tables, which allow users to create ticking tables from pure Python functions. The function is then re-evaluated at regular time intervals, and updated data from the function flows seamlessly into your table. These intervals can be determined by a constant, or by one or more ticking tables that act as the trigger.For…
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To make AI safe, governments must regulate data collection

To make AI safe, governments must regulate data collection

Canadian Prime Minister Justin Trudeau recently announced a $2.4-billion investment in artificial intelligence. Part of the funding will create an AI Safety Institute. But what is AI safety? Many countries, including Canada, the United States and those in the European Union, have pushed to curtail AI’s harms. Most of them are focused on the deployment and effects of AI. Because AI systems are so pervasive and diverse, governments should approach safety by breaking down AI into its components — algorithms, data and computing resources known simply as “compute.” Innovation in compute and algorithms is lightning-quick; governance is not. Therefore, governments…
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NIST announces new initiative to create systems that can detect AI-generated content – SiliconANGLE

NIST announces new initiative to create systems that can detect AI-generated content – SiliconANGLE

The National Institute of Standards and Technology today announced it’s launching a new initiative called NIST GenAI aimed at assessing generative artificial intelligence models and create systems that can identify AI-created text, images and videos. The launch of the new program came as NIST revealed its first draft publications on AI risks and standards. NIST GenAI will work to create new AI benchmarks and attempt to build what it calls “content authenticity” detection systems that can detect AI-generated media such as text and “deepfake” videos. It’s an effort to counter the dangers of fake and misleading, AI-generated information. In a…
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Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics

Fast Inference Using Automatic Differentiation and Neural Transport in Astroparticle Physics

arXiv:2405.14932v1 Announce Type: new Abstract: Multi-dimensional parameter spaces are commonly encountered in astroparticle physics theories that attempt to capture novel phenomena. However, they often possess complicated posterior geometries that are expensive to traverse using techniques traditional to this community. Effectively sampling these spaces is crucial to bridge the gap between experiment and theory. Several recent innovations, which are only beginning to make their way into this field, have made navigating such complex posteriors possible. These include GPU acceleration, automatic differentiation, and neural-network-guided reparameterization. We apply these advancements to astroparticle physics experimental results in the context of novel neutrino physics and…
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Tired of SaaS clutter? UnifyApps uses AI to make disjointed apps talk to each other

Tired of SaaS clutter? UnifyApps uses AI to make disjointed apps talk to each other

Join us in returning to NYC on June 5th to collaborate with executive leaders in exploring comprehensive methods for auditing AI models regarding bias, performance, and ethical compliance across diverse organizations. Find out how you can attend here. The rise of software-as-a-service (SaaS) ecosystem has transformed how enterprises operate. No matter the problem, there’s always an app to deal with it. But, when these apps grow to the scale of hundreds across the enterprise stack, it can become difficult to make the most of them. Today, UnifyApps, a startup solving this problem by making apps talk to each other, raised…
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Redis acquires storage engine startup Speedb to enhance its open-source database – SiliconANGLE

Redis acquires storage engine startup Speedb to enhance its open-source database – SiliconANGLE

Redis Ltd. has acquired Speedb Ltd., the developer of a storage engine it uses to power its commercial database offerings. The company didn’t disclose the financial terms in its announcement of the deal today. Tel Aviv-based Speedb previously raised $4 million through a funding round that closed in 2021. It announced a partnership with Redis the following year.  Databases typically keep the information they hold in flash or disk storage. When an application requests a record, it’s fetched through a two-step process: The database loads the record from storage into memory and then makes it available to the workload that…
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LookUp3D: Data-Driven 3D Scanning

LookUp3D: Data-Driven 3D Scanning

arXiv:2405.14882v1 Announce Type: new Abstract: We introduce a novel calibration and reconstruction procedure for structured light scanning that foregoes explicit point triangulation in favor of a data-driven lookup procedure. The key idea is to sweep a calibration checkerboard over the entire scanning volume with a linear stage and acquire a dense stack of images to build a per-pixel lookup table from colors to depths. Imperfections in the setup, lens distortion, and sensor defects are baked into the calibration data, leading to a more reliable and accurate reconstruction. Existing structured light scanners can be reused without modifications while enjoying the superior…
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