Doctor Evidence (DRE) has updated their newly launched DOC Analytics (“Digital Outcome Conversion”) platform with network meta-analysis (NMA) capabilities. DOC Analytics provides immediate quantitative insights into the universe of medical information using artificial intelligence/machine learning (AI/ML) and natural language processing (NLP). With the addition of indirect treatment comparison and landscape analysis using NMA, DOC Analytics is a critical, daily-use tool for strategic functions in life sciences companies. DOC Analytics allows users to conduct analyses comprised of real-time results from clinical trials, real-world evidence (RWE), published literature, and any custom imported data to yield insightful direct meta-analysis, network-meta analysis, cohort analysis, or bespoke statistical outputs. Analyses are informed by AI/ML and can be made fit-to-purpose with filters for demographics, comorbidities, sub-populations, inclusion/exclusion selections, and other relevant parameters.
Category: Semantic technologies (Page 19 of 72)
Our coverage of semantic technologies goes back to the early 90s when search engines focused on searching structured data in databases were looking to provide support for searching unstructured or semi-structured data. This early Gilbane Report, Document Query Languages – Why is it so Hard to Ask a Simple Question?, analyses the challenge back then.
Semantic technology is a broad topic that includes all natural language processing, as well as the semantic web, linked data processing, and knowledge graphs.
OpenAI API announce they were releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose “text in, text out” interface, allowing users to try it on virtually any English language task. You can now request access in order to integrate the API into your product, develop an entirely new application, or help us explore the strengths and limits of this technology. Given any text prompt, the API will return a text completion, attempting to match the pattern you gave it. You can “program” it by showing it just a few examples of what you’d like it to do; its success generally varies depending on how complex the task is. The API also allows you to hone performance on specific tasks by training on a dataset (small or large) of examples you provide, or by learning from human feedback provided by users or labelers. The API is designed to be both simple for anyone to use but also flexible enough to make machine learning teams more productive. In fact, many OpenAI teams are now using the API so that they can focus on machine learning research rather than distributed systems problems. Today the API runs models with weights from the GPT-3 family with many speed and throughput improvements.
The field’s pace of progress means that there are frequently surprising new applications of AI, both positive and negative. We will terminate API access for obviously harmful use-cases, such as harassment, spam, radicalization, or astroturfing. But we also know we can’t anticipate all of the possible consequences of this technology, so we are launching today in a private beta rather than general availability, building tools to help users better control the content our API returns, and researching safety-relevant aspects of language technology (such as analyzing, mitigating, and intervening on harmful bias). We’ll share what we learn so that our users and the broader community can build more human-positive AI systems.
Newgen Software, a global provider of low code digital automation platform for managing content, processes, and communication, announced it has launched an enhanced version of its document classification service for enabling the high-volume document-handling environment. Intelligent Document Classifier 1.0 allows users to gain hidden insights by classifying documents, based on structural features and/or textual features. It uses machine learning (ML) and artificial intelligence (AI), to enable layout- and content-based document classification. Organizations can leverage the solution to automatically classify various documents such as sales/purchase orders, enrollment and claim forms, legal documents, mailroom documents, contracts, correspondences, and others. This helps ensure important information is available thereby reducing risks and costs associated with manual document management.
Key features include:
- Image Classification – Allows users to automatically classify images using neural networks and deep learning algorithms based on structural features
- Content Classification – Enables document classification based on content, in the absence of structural features
- Trainable Machine Learning – Auto-learns definitions and features of a document class and creates a trained model
- Admin Dashboard – Generates analytics reports for a 360-degree view of the process
- Integration Capabilities – Facilitates easy integration with core business applications, content management platforms, and document capture applications
Natural language understanding is a subtopic of natural language processing in artificial intelligence that deals with machine reading comprehension.
Lucidworks announced the Advanced Linguistics Package for Lucidworks Fusion to power personalized search for users in Asian, European, and Middle Eastern markets. Lucidworks now embeds text analytics from Basis Technology, provider of AI for natural language processing. According to the companies, building, testing, and maintaining the many algorithms and models required to properly support each language is challenging and expensive. Asian, Middle Eastern, and certain European languages require additional processes to handle unique linguistic phenomena, such as lack of whitespace, compound words, and multiple forms of the same word. The combination of Basis with the AI-powered search platform of Lucidworks Fusion is expected to provide accuracy and performance enhancements in information retrieval for the digital experience. Lucidworks’ Advanced Linguistics Package provides language processing in more than 30 languages and advanced entity extraction in 21 languages. By accurately analyzing the text, in the language it was written, Rosette helps the Lucidworks Fusion platform deliver the right answers to every user, regardless of where they work or what language they use.
Franz Inc., developer of Artificial Intelligence (AI) and supplier of Semantic Graph Database technology for Knowledge Graph Solutions, announced AllegroGraph 7, a solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph 7 utilizes federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. Hidden connections in data are revealed to AllegroGraph 7 users through a new browser-based version of Gruff, an advanced visualization and graphical query builder.
To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). The rich functional and contextual integration of multi-modal, predictive modeling and artificial intelligence is what distinguishes AllegroGraph 7 as a modern, scalable, enterprise analytic platform. AllegroGraph 7 is a temporal knowledge graph technology that encapsulates a novel entity-event model natively integrated with domain ontologies and metadata, and dynamic ways of setting the analytics lens on all entities in the system (patient, person, devices, transactions, events, and operations) as prime objects that can be the focus of an analytic (AI, ML, DL) process.
Luminoso, who turn unstructured text data into business-critical insights, announced the newest features of ConceptNet, an open data semantic network whose development is led by Luminoso Chief Science Officer Robyn Speer. ConceptNet originated from MIT Media Lab’s Open Mind Common Sense project more than two decades ago, and the semantic network is now used in AI applications around the world. ConceptNet is cited in more than 700 AI papers in Google Scholar, and its API is queried over 500,000 times per day from more than 1,000 unique IPs. Luminoso has incorporated ConceptNet into its proprietary natural language understanding technology, QuickLearn 2.0. ConceptNet 5.8 features:
Continuous deployment: ConceptNet is now set up with continuous integration using Jenkins and deployment using AWS Terraform, which will make it faster to deploy new versions of the semantic network and easier for others to set up mirrors of the API.
Additional curation of crowd-sourced data: ConceptNet’s developers have filtered entries from Wiktionary that were introducing hateful terminology to ConceptNet without its context. This is part of their ongoing effort to prevent human biases and prejudices from being built into language models. ConceptNet 5.8 has also updated its Wiktionary parser so that it can handle updated versions of the French and German-language Wiktionary projects.
HTTPS support: Developers can now reach ConceptNet’s website and API over HTTPS, improving data transfer security for applications using ConceptNet.
http://blog.conceptnet.io/posts/2020/conceptnet-58/, https://luminoso.com/how-it-works
RDFa (or Resource Description Framework in Attributes) is a W3C Recommendation that adds a set of attribute-level extensions to HTML, XHTML and various XML-based document types for embedding rich metadata within Web documents. The RDF data-model mapping enables its use for embedding RDF subject-predicate-object expressions within XHTML documents. It also enables the extraction of RDF model triples by compliant user agents.
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