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Category: Computing & data (Page 15 of 80)

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.

Introducing ChatGPT Enterprise

From the OpenAI blog…

We’re launching ChatGPT Enterprise, which offers enterprise-grade security and privacy, unlimited higher-speed GPT-4 access, longer context windows for processing longer inputs, advanced data analysis capabilities, customization options, and much more. We believe AI can assist and elevate every aspect of our working lives and make teams more creative and productive. Today marks another step towards an AI assistant for work that helps with any task, is customized for your organization, and that protects your company data.

You own and control your business data in ChatGPT Enterprise. We do not train on your business data or conversations, and our models don’t learn from your usage. ChatGPT Enterprise is also SOC 2 compliant and all conversations are encrypted in transit and at rest. Our new admin console lets you manage team members easily and offers domain verification, SSO, and usage insights, allowing for large-scale deployment into enterprise…

ChatGPT Enterprise removes all usage caps, and performs up to two times faster. We include 32k context in Enterprise, allowing users to process four times longer inputs or files. ChatGPT Enterprise also provides unlimited access to advanced data analysis, previously known as Code Interpreter. This feature enables both technical and non-technical teams to analyze information in seconds, whether it’s for financial researchers crunching market data, marketers analyzing survey results, or data scientists debugging an ETL script. If you’re looking to tailor ChatGPT to your organization, you can use our new shared chat templates to collaborate and build common workflows. If you need to extend OpenAI into a fully custom solution for your org, our pricing includes free credits to use our API as well.

https://openai.com/blog/introducing-chatgpt-enterprise

Bloomreach announces Clarity

Bloomreach announced a conversational shopping product built for a new world of e-commerce. Using generative AI and large language models (LLMs), Bloomreach Clarity engages with shoppers to deliver personalized, product expertise straight from their favorite brands. Businesses connect these conversations directly to product catalogs, and can integrate individual conversations across channels, including website, chat, and SMS.

Bloomreach Clarity is built upon a real-time customer data engine and trained on more than a decade of commerce data. This gives it a understanding of how customers shop and how products perform globally, based on Bloomreach’s rich data and AI, and at the individual level, based on a business’s real-time data. While Clarity is showing customers relevant information and products, it’s also prioritizing what it knows they’ll actually buy. Clarity also offers control and optimizations for businesses as they introduce this conversational experience to customers, including:

  • The ability for merchandisers to combine their instinct and expertise with the speed and scale of AI
  • Real-time targeting that allows Clarity to prompt conversations based on a customer’s current journey or specific segment
  • Personalized conversations, even for unknown visitors based on browsing behavior
  • The ability to refine conversations using brand guidelines and tone of voice

https://www.bloomreach.com/en/products/clarity

Neo4j adds Vector Search within its Native Graph Database

Neo4j, a graph database and analytics company, announced that it has integrated native vector search as part of its core database capabilities. The result enables customers to achieve richer insights from semantic search and generative AI applications, and serve as long-term memory for LLMs, while reducing hallucinations.

Neo4j’s graph database can be used to create knowledge graphs, which capture and connect explicit relationships between entities, enabling AI systems to reason, infer, and retrieve relevant information effectively. The result ensures more accurate, explainable, and transparent outcomes for LLMs and other generative AI applications. By contrast, vector searches capture implicit patterns and relationships based on items with similar data characteristics, rather than exact matches, which are useful when searching for similar text or documents, making recommendations, and identifying other patterns.

This latest advancement follows from Neo4j’s recent product integration with Google Cloud’s generative AI features in Vertex AI in June, enabling users to transform unstructured data into knowledge graphs, which users can then query using natural language and ground their LLMs against factual set of patterns and criteria to prevent hallucinations.

https://neo4j.com/press-releases/neo4j-vector-search/

ElevenLabs releases Eleven Multilingual v2

ElevenLabs, a provider of voice AI software, launched a new multilingual voice generation model capable of accurately producing “emotionally rich” AI audio in nearly 30 languages. ElevenLabs also confirmed today that the platform is officially coming out of Beta.

The advancement, based on in-house research, will allow creators to produce localized audio content for international markets across Europe, Asia and the Middle East. ElevenLabs has spent the last 18 months analyzing the markers of human speech, building new mechanisms for understanding context and conveying emotions in speech generation, as well as synthesizing new, unique voices.

Regardless of whether a synthetic voice or cloned voice is being used, the speaker’s unique voice characteristics are maintained across all languages, including their original accent. This means the same voice can be used to bring content to life across 28 separate languages. Creators can use ElevenLabs’ tool to improve content accessibility for people with visual impairments or additional learning needs by supplementing visual content with speech available in multiple languages.

Supported languages include: English, Polish, German, Spanish, French, Italian, Hindi, Portuguese, Chinese, Korean, Dutch, Turkish, Swedish, Indonesian, Filipino, Japanese, Ukrainian, Greek, Czech, Finnish, Romanian, Danish, Bulgarian, Malay, Slovak, Croatian, Classic Arabic and Tamil. 

https://elevenlabs.io/blog/multilingualv2/

Adobe Firefly supports prompts in over 100 languages

Adobe announced the global expansion of Firefly, Adobe’s family of creative generative AI models, to support text prompts in over 100 languages, enabling users to generate images and text effects using their native languages in the standalone Firefly web service. The service will also be localized in 20 languages with versions in French, German, Japanese, Spanish and Brazilian Portuguese available now.

Firefly has been integrated into Photoshop, Express,  and Illustrator, helping customers build their creative confidence by removing the barriers between imagination and blank page, and bringing more precision, power, speed and ease directly into Creative Cloud applications and workflows.

Firefly for Enterprise is designed to be commercially safe and Adobe plans to enable businesses to custom train Firefly with their own branded assets, generating content in the brand’s unique style and brand language. The new company-wide offering enables every employee across an organization, at any creative skill level, to use Firefly to generate on-brand, ready-to-share content that can be edited in Express or Creative Cloud. Enterprises also have the opportunity to obtain an IP indemnity from Adobe for content generated by certain Firefly-powered workflows.

https://firefly.adobe.com

Brightspot CMS integrates OpenAI

Brightspot CMS, a content management system for global organizations to deliver digital experiences, announced a seamless integration with OpenAI, enabling organizations to elevate their content creation and personalization efforts. This integration helps content authors, editors, marketers and communications professionals to produce high-quality content that resonates with their target audiences. With the integration of OpenAI into Brightspot CMS, content creators can leverage AI-assisted suggestions for headlines, subheadlines and full body text, enabling them to expedite the content-creation process and meet tight deadlines.

OpenAI’s content-generation capabilities empower users to create variations of their content, ensuring that the right message reaches the right audience at the right time. With natural language queries and a search function that yields answers content creators can swiftly access and utilize specific database information, reducing time spent on manual searches. By automating the search and interpretation process, OpenAI’s tools free up content creators to focus on creativity, accelerating productivity and improving output quality.

https://www.brightspot.com/ai

Databricks announces LakehouseIQ

Databricks announced LakehouseIQ, a knowledge engine that learns what makes an organization’s data, culture and operations unique. LakehouseIQ uses generative AI to understand jargon, data usage patterns, organizational structure, and more to answer questions within the context of a business. Anyone in an organization can interact with LakehouseIQ using natural language to search, understand, and query data. LakehouseIQ is fully integrated with Databricks Unity Catalog to help ensure that democratizing access to data adheres to internal security and governance rules.

LakehouseIQ learns from signals within an organization using schemas, documents, queries, popularity, lineage, notebooks, and BI dashboards to gain intelligence as it answers more queries. LakehouseIQ helps employees get immediate answers to questions without requiring that they possess the technical skill required by traditional data analysis tools. The engine understands their unique business jargon and context to more accurately interpret the intent of the question, and can even generate additional insights that could spur new questions or lines of thinking. With LakehouseIQ, every employee, not just data scientists, can unlock the full potential of internal corporate data to make better, more informed decisions. The Databricks Assistant, powered by LakehouseIQ, is in preview.

https://www.databricks.com/blog/introducing-lakehouseiq-ai-powered-engine-uniquely-understands-your-business

Snowflake unveils large language model to extract data from documents

Snowflake announced new advancements to its single, unified platform that make it easier for organizations to get value from all of their data. With Document AI (private preview), Snowflake is launching a new large language model (LLM) built from Applica’s generative AI technology to help customers understand documents and put their unstructured data to work. Building on Snowflake’s support for unstructured data, Snowflake’s built-in Document AI will make it easier for organizations to understand and extract value from documents using natural language processing.

Document AI stems from Snowflake’s acquisition of Applica (Sept. 2022) and leverages its purpose-built, multimodal LLM. By integrating this model within Snowflake’s platform, organizations will be able to easily extract content like invoice amounts or contractual terms from documents and fine-tune results using a visual interface and natural language. Customers are using Document AI to help their teams be smarter about their businesses, and enhance user productivity in secure and scalable ways. Snowflake is starting with Document AI and plans to expand these capabilities to more types of unstructured data.

https://www.snowflake.com/news/snowflake-unveils-new-large-language-model-to-extract-deeper-insights-from-documents-while-continuing-to-advance-platform-speed-and-performance/

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