ConsenSys, a blockchain software company, announced the acquisition of Quorum, an enterprise variant of the Ethereum blockchain developed by J.P. Morgan. With the addition of Quorum, ConsenSys now offers a full range of products, services, and support for Quorum, accelerating the availability of features and capabilities—such as digital asset functionality and document management. ConsenSys will merge its existing protocol engineering roadmap with Quorum, leveraging the best of both codebases. All Enterprise Ethereum protocol technology at ConsenSys will fall under the ConsenSys Quorum brand, and developers will have the option to choose their underlying technology stack. Quorum will remain open source and become interoperable with ConsenSys’ other leading blockchain products, such as Codefi’s finance and commerce application suite. J.P. Morgan will be a customer of ConsenSys’ advanced features and services deployed on Quorum.
J.P. Morgan and ConsenSys have a history of collaboration after leading the creation of the Enterprise Ethereum Alliance, helping bring a Mainnet Ethereum client, Hyperledger Besu, to The Linux Foundation, and working together on industry applications built on Quorum, such as komgo and Covantis. Since the launch of Quorum in 2016, ConsenSys and J.P. Morgan have collaborated to make Ethereum the platform for enterprises building secure and customizable business networks. In addition to ConsenSys’ acquisition of Quorum, J.P. Morgan made a strategic investment in ConsenSys.
https://consensys.net/blog/press-release/consensys-acquires-quorum-platform-from-jp-morgan/
ConsenSys is a Ethereum software company. We enable developers, enterprises, and people worldwide to build next-generation applications, launch modern financial infrastructure, and access the decentralized web. Our product suite, composed of Infura, Quorum, Codefi, MetaMask, and Diligence, serves millions of users, supports billions of blockchain-based queries for our clients, and has handled billions of dollars in digital assets. Ethereum is the largest programmable blockchain in the world, leading in business adoption, developer community, and DeFi activity. http://consensys.net/.
Xelex Digital announced the release of its new audio and text annotation platform, WebChartAi, designed to accelerate the adoption of machine learning applications by simplifying the creation of training data at scale. The initial MVP (minimum viable product) release of WebChartAi focuses on manual annotation of audio and text data objects. Upcoming releases include semi-automated annotation, and image and video annotation. In addition to its use by companies for in-house projects, WebChartAi is designed for use by AI and NLP companies acting as service providers. The company is now seeking partners for the platform’s expansion beyond its current MVP form.
https://www.webchartai.com
Intelligent Integration Platform provider SnapLogic announced new enterprise automation capabilities that help employees across the business easily connect applications and data, streamline workflows and processes. The new capabilities include prebuilt, end-to-end process automations; ELT (Extract, Load, Transform) features and a quick-start solution for faster data warehousing; with a visual interface powered by machine learning (ML) and natural language processing (NLP). With SnapLogic’s self-service, AI-powered integration platform, IT teams as well as business users across functions can use the low-code solution to connect apps and data and automate workflows and processes.
- New Prebuilt Automation Journeys: Prebuilt automation journeys unify all the applications and data that make up a complete business process, such as hire-to-retire, quote-to-cash, procure-to-pay, and customer 360. The SnapLogic platform learns, understands, and connects to all of an organization’s underlying systems, streamlining flows and processes, automated in their construction, with rich AI and machine learning (ML) capabilities layered on top, for faster, data-enriched outcomes.
- New NLP-powered Flow Interface: The visual flow interface guides non-technical business users through the integration and automation of business processes.
- New ELT Snaps: ELT Snaps accelerate the integration and movement of large volumes of complex data, whether in the cloud, on-premises, or in hybrid environments, into a cloud data warehouse and provide flexibility to leverage compute power for data transformations.
- New ‘Fast Loader’ Solution: The new quick-start solution helps enterprises load data from multiple cloud and on-premises applications and data sources into their cloud data warehouse faster with a new wizard-based interface and parallel loading.
https://snaplogic.com
Cloudera announced the general availability of Cloudera Data Platform Private Cloud (CDP Private Cloud). CDP Private Cloud extends cloud-native speed, simplicity, and economics for the connected data lifecycle to the data center. Operating CDP Private Cloud is simple for IT, with container-based management tools that reduce the time to deliver analytics and machine learning. Container-based analytics and machine learning help reduce data center costs by increasing server utilization and reducing storage and data center overhead. With CDP Private Cloud, IT can now meet the exponential demand for data analytics and machine learning services, with a petabyte-scale hybrid data architecture that can flex to use private and public clouds. CDP Private Cloud runs the same analytic experiences in the data center that are used in CDP Public Cloud on AWS and Azure.
CDP Private Cloud is available in Base and Plus editions. The Base edition includes SDX, storage management and traditional bare metal data lifecycle analytics. It is equivalent to CDP Data Center, which it replaces. It is the foundation of CDP Private Cloud. The Plus edition includes Base and adds container-based analytic experiences for Data Warehousing and Machine Learning, and container-based management and control plane services. CDP Private Cloud Plus pricing is based on compute and storage and is available as an annual subscription. An annual subscription of the Plus edition is $400 per compute unit (one physical core and 8 GB RAM) and $25 per TB of addressed storage.
https://www.cloudera.com
Google-affiliated researchers released the Language Interpretability Tool (LIT), an open source, framework-agnostic platform and API for visualizing, understanding, and auditing natural language processing models. It focuses on questions about AI model behavior, like why models made certain predictions and why they’re performing poorly with input corpora. LIT incorporates aggregate analysis into a browser-based interface that’s designed to enable explorations of text generation behavior. The tool set is architected so that users can hop between visualizations and analysis to test hypotheses and validate those hypotheses over a data set. New data points can be added on the fly and their effect on the model visualized immediately, while side-by-side comparison allows for two models or two data points to be visualized simultaneously. And LIT calculates and displays metrics for entire data sets to spotlight patterns in model performance, including the current selection, manually generated subsets, and automatically generated subsets.
LIT works with any model that can run from Python, the Google researchers say, including TensorFlow, PyTorch, and remote models on a server. And it has a low barrier to entry, with only a small amount of code needed to add models and data. The team cautions that LIT doesn’t scale well to large corpora and that it’s not “directly” useful for training-time model monitoring. But they say that in the near future, the tool set will gain features like counterfactual generation plugins, additional metrics and visualizations for sequence and structured output types, and a greater ability to customize the UI for different applications.
H/T VentureBeat: https://venturebeat.com/2020/08/14/google-open-sources-lit-a-toolset-for-evaluating-natural-language-models/
Savan Group announced that it has partnered with Amazon Web Services (AWS) to establish a cloud-based artificial intelligence (AI) and machine learning (ML) platform. Savan Group applies AI solutions to address the data and information challenges of the Federal Government. Much of this data is is unstructured data locked away in documents, videos, audio, images, and paper. Using ML and natural language processing (NLP), Savan Group is analyzing and extracting untapped potential, turning data into information and information into knowledge to help government agencies increase the value of data for mission, service, and public good. Savan Group is now developing ML models with near unlimited scale using distributed cloud storage and GPU compute within a FedRAMP-authorized environment.
https://www.savangroup.com/
Clarifai announced Clarifai Labeler, a new way of labeling unstructured image, video and text data in its AI platform. Clarifai has built one integrated tool for managing data annotation projects of any size. Labeler integrates within Clarifai’s platform so that users can manage the whole AI lifecycle in one place: labeling datasets, searching data using AI, training AI models and auto-scaling models in production.
Clarifai Labeler offers AI-assisted automation to prefill labels and speed up project completion. Using task management features designed for large human-in-the loop workforces, it’s easier to assign labeling tasks to a distributed group and gain transparency into annotators’ work.
For enterprises looking for additional help increasing productivity, Clarifai now offers a fully managed data labeling service. Expert annotators, assisted by AI-automated tools, help companies reduce the complexities of managing labeling workforces.
https://www.clarifai.com/label
