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Category: Computing & data (Page 2 of 98)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

Adobe and NVIDIA announce strategic partnership

March 16, 2026 /

Adobe and NVIDIA today announced a strategic partnership that will bring together Adobe’s creative and marketing workflows, models and technology and NVIDIA’s open models, libraries, research and accelerated computing to deliver the next generation of foundational Adobe Firefly models and creative, marketing and agentic workflows.

Firefly models will be built on NVIDIA’s computing technology and tap into NVIDIA CUDA-X, NVIDIA NeMo libraries, NVIDIA Cosmos open models, and NVIDIA Agent Toolkit software to enable interactive, high-quality creation.

Adobe and NVIDIA will also work together on NVIDIA NemoClaw— an open source stack that simplifies running OpenClaw always-on assistants more safely.

With NVIDIA, Adobe is launching a cloud-native, brand identity-preserving 3D digital twin solution (public beta). The solution creates virtual replicas of physical products that act as permanent digital identities for marketing and commerce experiences. Integrating NVIDIA Omniverse libraries into Adobe technologies, the collaboration expands support for 3D digital twin workflows built on OpenUSD for marketing content automation.

Adobe will also harness NVIDIA AI infrastructure, AI libraries, services and models to optimize its AI-powered tools across creativity, productivity and customer experience orchestration.

Adobe and NVIDIA Announce Strategic Partnership to Deliver the Next Generation of Firefly Models and Creative, Marketing and Agentic Workflows

Databricks launches Genie Code

March 11, 2026 /

Databricks launched Genie Code, an autonomous AI agent that changes how data work gets done. Genie Code can carry out complex tasks such as building pipelines, debugging failures, shipping dashboards, and maintaining production systems. Just as agentic coding tools have transformed software engineering, moving developers from autocomplete-style assistance to agent-driven development, Genie Code brings the same paradigm shift to data engineering, data science, and analytics.

Genie Code is a new addition to Genie, which lets any knowledge worker chat with their data and get trusted answers instantly using the context and semantics captured by Unity Catalog. Genie Code extends this approach to data professionals, handling the complex engineering required to go from idea to production across all enterprise data.

Genie Code helps teams bridge the context gap to ensure the high levels of accuracy and governance required for production environments:

  • Handles full ML workflows end-to-end.
  • Accounts for differences between staging versus production environments, builds workflows for change data capture and applies data quality expectations.
  • Monitors Lakeflow pipelines and AI models to triage failures and investigate anomalies.
  • Integrated with Unity Catalog, enforces governance policies and access controls. It understands business semantics and audit requirements and federates enterprise data, including data from external platforms.

https://www.databricks.com/company/newsroom/press-releases/databricks-launches-genie-code-bringing-agentic-engineering-data

Flux voice AI platform now supports on-the-fly configurations

March 3, 2026 /

Deepgram announced Flux “on-the-fly configuration” for its voice AI platform, which lets developers dynamically update speech recognition settings — such as keyterms and end-of-turn detection — during a live voice conversation without disconnecting or restarting the audio stream.

A support call moves from identity verification to troubleshooting to scheduling a follow-up. A healthcare call shifts from intake questions to medication names to billing. Each phase has different intents, different critical phrases.

Today, teams configure their ASR (automatic speech recognition) once at connection time and live with it for the entire call. They load every keyterm they might need upfront, diluting biasing effectiveness across the board, or they keep the list minimal and accept lower accuracy on critical phrases. When the conversation shifts enough that the configuration truly doesn’t fit, the options are disconnecting and reconnecting mid-call or managing multiple concurrent streams and swapping between them.

Now your ASR configuration can shift with the conversation. No more choosing between loading every keyterm upfront or accepting lower accuracy. No more static configuration that’s “good enough” for the whole call. One connection that adapts as the call unfolds.

On-the-fly configuration is available now in the Flux v2 WebSocket API.

https://deepgram.com/learn/flux-on-the-fly-configuration

Research Solutions launches Scite MCP, connecting ChatGPT, Claude, & other AI tools to scientific literature

February 26, 2026 /

Research Solutions, provider of AI-powered scientific research tools, launched Scite MCP, which enables researchers and developers to search scientific literature and evaluate the trustworthiness of research findings without leaving the AI tools they already use.

Large language models can generate text on most topics, but coverage of scholarly material is limited, and they struggle to distinguish well-supported findings from contested ones.

Scite MCP solves this by giving AI tools direct access to over 250 million indexed articles, book chapters, preprints, and datasets, along with Scite’s proprietary Smart Citations, which classify each citation as supporting, mentioning, or contrasting findings it references.

  • Answers grounded in trustworthy research: AI tools connected to Scite can return responses backed by specific, verifiable papers rather than generating unsourced claims
  • Citation context: Users and AI agents can see not only that a paper was cited, but also whether subsequent research supported, mentioned, or contrasted its findings
  • Broad literature coverage: Access to over 250 million scientific articles, book chapters, preprints, and datasets
  • Works across tools: Compatible with ChatGPT, Claude, Microsoft Copilot, Cursor, Claude Code, and any MCP-enabled application

Scite MCP currently provides access to Open Access articles, with publisher discussions underway to expand coverage to paywalled content.

https://researchsolutions.investorroom.com/2026-02-26-Research-Solutions-Launches-Scite-MCP,-Connecting-ChatGPT,-Claude,-Other-AI-Tools-To-Scientific-Literature

Siteimprove expands its agentic content intelligence platform

February 26, 2026 /

Siteimprove released its latest AI agent capabilities. The updates include conversational analytics enabling non-technical users to get answers, generate reports, and dashboards using natural language. Customers also gain new content accessibility coverage for PDF and Images, and keyword intelligence for Search in the world of “Answer Engine Optimization (AEO)”.

These capabilities help customers meet digital accessibility regulations such as Americans with Disabilities Act (ADA) and European Accessibility Act (EAA) while helping brands improve discoverability across answer engines and generative engines. Capabilities include:

  • Conversational Analytics Agent: Ask questions in natural language and instantly get answers to understand what matters across analytics data – democratizing insights across teams. Teams can quickly task the agent to generate answers on campaign performance, funnel diagnostics, and recommended targets for course correction.
  • PDF and Image Accessibility Agent: PDF Validate and Contextual Image Analysis agent surfaces accessibility issues before content goes live, helping teams reduce risk earlier in the content lifecycle. This helps customers increase accessibility coverage across more content types.
  • Keyword Intelligence Agent: Expanded keyword and topic intelligence agent uncovers competitive and topical gaps, giving teams deeper insight into growth opportunities for both traditional and AI-driven search in the world of AEO.

https://www.siteimprove.com/press/siteimprove-expands-its-agentic-content-intelligence-platform

Krisp launches real-time Voice Translation SDK

February 18, 2026 /

Krisp announced the launch of its Voice Translation SDK, enabling CX platform developers to embed real-time multilingual voice-to-voice translation into live customer conversations. The technology has been live in production CX environments since 2025 as part of Krisp’s Call Center AI platform, operating in customer conversations globally before its SDK release.

Real-time voice translation must operate on continuous audio streams where latency, accuracy and conversational flow are tightly linked. Systems must recognize diverse accents, perform reliably in noisy environments and preserve natural turn-taking.

Krisp’s Voice Translation SDK is engineered to balance these competing constraints in live, two-way conversations. It supports any combination of over 60 languages and is optimized for synchronous interactions where clarity and conversational continuity are critical. This enables multilingual interactions within live conversations without requiring human interpreters.

The SDK is available for Windows, macOS and Web developers, allowing integration into both native and browser-based applications. To improve performance in real-world conditions, Krisp applies local Noise Cancellation before audio is processed in the cloud, isolating the primary speaker and improving recognition accuracy. The SDK also supports custom vocabulary and domain-specific dictionaries, enabling teams to enforce terminology and maintain consistency across professional environments.

https://krisp.ai/blog/real-time-voice-translation-sdk/

Dataiku launches 575 Lab, its new open source initiative for responsible AI

February 18, 2026 /

As AI moves from pilots to business-critical deployment, the issue is no longer access. It’s trust. Open source tools support that trust by keeping core components inspectable and standardizable, enabling stronger oversight across modern AI systems. Today, Dataiku announced the launch of the 575 Lab, Dataiku’s Open Source Office. The 575 Lab will release two new open-source toolkits designed to help enterprises make AI systems more transparent, governable, and fit for real-world use.

The 575 Lab will focus on delivering deployable tools that strengthen explainability, privacy, and governance across modern AI and agentic systems. The two initial open-source projects will be: 

  • Agent Explainability Tools that will help teams trace and understand decision-making across multi-step agent workflows, making agent decisions transparent for data scientists, compliance teams, and end users.
  • Privacy-Preserving Proxies that will enable safer use of closed-source models by protecting sensitive data end-to-end, and that teams will be able to run locally.

Both projects will be designed to support responsible enterprise AI, with a focus on reliability, security, transparency, and explainability.

The 575 Lab is now available to the community of AI specialists, data scientists, and developers responsible for creating, deploying, and scaling AI agents and applications.

https://www.dataiku.com/press-releases/dataiku-launches-575-lab/

Graphwise announced the immediate availability of GraphRAG

February 17, 2026 /

Graphwise announced the availability of Graphwise GraphRAG, a low-code AI-workflow engine designed to turn “Python prototypes” into production-grade systems instantly. It is based on a trusted semantic layer that reduces hallucinations and delivers precise and verifiable answers. GraphRAG unites LLMs, enterprise data, structured knowledge, and multiple search methods to deliver transparent, verifiable, enterprise-ready answers. Unlike standard RAG that “flattens” data into chunks leading to lost relationships and hallucinations, GraphRAG treats the knowledge graph as a trusted semantic backbone, ensuring AI responses are grounded in verifiable enterprise facts and complex relationships. Graphwise bridges the gap between complex enterprise data and functional AI agents. Features include:

  • Low-Code Visual Engine democratizes AI, enabling subject matter experts to adjust AI logic visually.
  • Out-of-the-Box Templates provide guardrails and support query expansion that deliver the fastest time-to-value.
  • Semantic Metadata Control Plane eliminates hallucinations and improves AI accuracy. AI responses are grounded in an organization’s “enterprise truth,” reducing risk.
  • Explainability and Provenance Panels support regulatory compliance. Built-in traceability affords transparency into how an AI response was produced.
  • Visual Debugging and Monitoring reduce maintenance costs by eliminating black box code.
  • SKOS-style Concept Enrichment harnesses domain-specific intelligence. This means AI understands company specific jargon, acronyms, and synonyms out-of-the-box.

https://graphwise.ai/news/new-graphrag-solution-moves-beyond-vector-only-rag-knowledge-graphs-provide-context-and-common-sense-to-ai

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