Category: Computing & data (Page 56 of 80)

Vectorspace AI and CERN, the European Organization for Nuclear Research and the largest particle physics laboratory in the world, are creating datasets used to detect hidden relationships between particles which have broad implications across multiple industries. These datasets can provide a significant increase in precision, accuracy, signal or alpha and for any company in any industry. Datasets are algorithmically generated based on formal Natural Language Processing/Understanding (NLP/NLU) models including OpenAI’s GPT-3, Google’s BERT along with word2vec and other models which were built on top of vector space applications at Lawrence Berkeley National Laboratory and the US Dept. of Energy (DOE). Over 100 billion different datasets are available based on customized data sources, rows, columns or language models.

For commercial use, datasets are $0.99c per minute/update and $0.99c per data source, row, column and context with additional configurations and options available on a case by case SaaS/DaaS based monthly subscription. Over 100 billion unique and powerful datasets are available based on customized data sources, rows, columns or language models.

While data can be viewed as unrefined crude oil, Vectorspace AI produces datasets which are the refined ‘gasoline’ powering all Artificial Intelligence (AI) and Machine Learning (ML) systems. Datasets are real-time and designed to augment or append to existing proprietary datasets such as gene expression datasets in life sciences or time-series datasets in the financial markets. Example customer and industry use cases include:

Particle Physics: Rows are particles. Columns are properties. Used to predict hidden relationships between particles.

Life Sciences: Rows are infectious diseases. Columns are approved drug compounds. Used to predict which approved drug compounds might be repurposed to fight an infectious disease such as COVID19. Applications include processing 1500 peer reviewed scientific papers every 24hrs for real-time dataset production.

Financial Markets: Rows are equities. Columns are themes or global events. Used to predict hidden relationships between equities and global events. Applications include thematic investing and smart basket generation and visualization.

Data provenance, governance and security are addressed via the Dataset Pipeline Processing (DPP) hash blockchain and VXV utility token integration. Datasets are accessed via the VXV wallet-enable API where VXV is acquired and used as a utility token credit which trades on a cryptocurrency exchange.

https://vectorspace.ai

An edited version of the announcement from the Microsoft Blog:

Microsoft is teaming up with OpenAI to exclusively license GPT-3 – an autoregressive language model that outputs human-like text. GPT-3 is the largest and most advanced language model in the world, clocking in at 175 billion parameters, and is trained on Azure’s AI supercomputer. This allows us to leverage its technical innovations to develop and deliver advanced AI solutions for our customers, as well as create new solutions that harness the power of advanced natural language generation.

We see this as an opportunity to expand our Azure-powered AI platform in a way that democratizes AI technology, enables new products, services and experiences, and increases the positive impact of AI at Scale. We want to make sure that this AI platform is available to everyone – researchers, entrepreneurs, hobbyists, businesses – to empower their ambitions to create something new and interesting. The scope of commercial and creative potential that can be unlocked through the GPT-3 model is profound, with genuinely novel capabilities – most of which we haven’t even imagined yet. Directly aiding human creativity and ingenuity in areas like writing and composition, describing and summarizing large blocks of long-form data (including code), converting natural language to another language.

Realizing these benefits at true scale – responsibly, affordably and equitably – is going to require more human input and effort than any one large technology company can bring to bear. OpenAI will continue to offer GPT-3 and other powerful models via its own Azure-hosted API, launched in June. While we’ll be hard at work utilizing the capabilities of GPT-3 in our own products, services and experiences to benefit our customers, we’ll also continue to work with OpenAI to keep looking forward: leveraging and democratizing the power of their cutting-edge AI research as they continue on their mission to build safe artificial general intelligence.

https://blogs.microsoft.com/blog/2020/09/22/microsoft-teams-up-with-openai-to-exclusively-license-gpt-3-language-model/

ThoughtSpot announced the release of ThoughtSpot Cloud, a fully-managed SaaS offering providing business users with the flexibility to glean instant insights from data in the cloud with search & AI-driven analytics. With ThoughtSpot Cloud, employees can access data across all of their cloud data in a matter of minutes, helping these organizations maximize their investments in cloud data warehouses like Amazon Redshift and Snowflake. Additional features include:

• Personalized onboarding: Specific onboarding flows by role tailor the experience for users, accelerating their time to value.
• Search assist: Digital assistant that provides a step by step guide for first time users to aid in their initial search.
• Prebuilt SpotApps: Reusable low-code templates to make getting insights from a particular application, like Salesforce, simple and scalable.
• In-database benefits: Run queries directly in both high-performance, zero-management, built-for-the-cloud data warehouses like Amazon Redshift and Snowflake.
• Pricing: Pay only for the data consumed and analyzed, not for the number of users.

https://www.thoughtspot.com/cloud

Cloudera announced enterprise data cloud services on Cloudera Data Platform (CDP): CDP Data Engineering; CDP Operational Database; and CDP Data Visualization. The new services are analytic experiences designed specifically for data specialists and include workflow automation, job prioritization, and performance tuning to help data engineers, data analysts, and data scientists. Data lifecycle integration is what enables data engineers, data analysts and data scientists to work on the same data securely and efficiently. CDP helps to improve individual data specialist productivity and data teams work better together through its hybrid data architecture that integrates analytic experiences across the data lifecycle and across public and private clouds.

CDP Data Engineering
CDP Data Engineering is an Apache Spark service on Kubernetes and includes productivity enhancing capabilities:

• Visual GUI-based monitoring, troubleshooting and performance tuning for faster debugging and problem resolution
• Native Apache Airflow and robust APIs for orchestrating and automating job scheduling and delivering complex data pipelines anywhere
• Resource isolation and centralized GUI-based job management
• CDP data lifecycle integration and SDX security and governance

CDP Operational Database
As businesses continue to generate large volumes of structured and unstructured data, developers are tasked with building applications that democratize data access, enable actions in real-time and are integral to business operations and revenue generation. CDP Operational Database is a high-performance NoSQL database service that provides scale and performance for business critical operational applications, offering:

• Evolutionary schema support to leverage data and allow changes to underlying data models without having to make changes to the application
• Auto-scaling based on the workload utilization of the cluster to optimize infrastructure utilization and cost
• Multi-modal client access with NoSQL key-value using HBase APIs and relational SQL with JDBC, making CDP Operational Database accessible to developers who are used to building applications that use MySQL, Postgres, etc.
• CDP data lifecycle integration and SDX security and governance

CDP Data Visualization
Business users need the ability to discover and curate their own visualizations from data and predictive models in a self-service manner. CDP Data Visualization simplifies the curation of rich, visual dashboards, reports and charts to provide agile analytical insight in the language of business:

• Technical teams can share analysis and machine learning models using drag and drop custom interactive applications.
• Business teams and decision makers can consume data insights to make more well-informed business decisions.
• All teams benefit from fast data exploration using AI-powered natural language search and visual recommendations.