Curated for content, computing, and digital experience professionals

Day: March 1, 2023

Slang Labs launches CONVA

Slang Labs, a Google-backed startup from Bengaluru, announced the launch of CONVA, a full-stack solution that provides smart and highly accurate multilingual voice search capabilities inside e-commerce apps. CONVA is available as a simple SDK (Software Development Kit) that can be integrated into existing e-commerce apps in less than 30 minutes without developers needing any knowledge of Automatic Speech Recognition (ASR), Natural language processing (NLP), Text-to-Speech (TTS) and other advanced voice tech stack concepts.

CONVA-powered voice search comprehends mixed-code (multiple languages in one sentence) utterances, enabling consumers to speak naturally in their own language in order to search for products and information inside e-commerce mobile and web apps – while allowing the brand to maintain its app backend in only one language i.e. English. For instance, when people use English and another vernacular language within the same sentence for searching for something, CONVA will understand both languages and provide a seamless search experience to the consumer.

Customers can search for products inside the applications using their typical colloquial terms for well-known products using voice search that is enabled by CONVA, and the apps will still be able to recognise the correct product being searched.

TigerGraph expands cloud capabilities

TigerGraph, provider of an advanced analytics and ML platform for connected data, announced the latest version (3.9) of TigerGraph Cloud, a native parallel graph database-as-a-service, including new security, advanced AI, and machine learning capabilities to streamline the adoption, deployment, and management of the graph database platform. The underlying parallel native graph database engine is also available for on-prem or self-managed cloud installation.

Available as self-managed enterprise or on fully-managed cloud services including Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, TigerGraph Cloud equips users with a comprehensive, streamlined approach to deploy and maintain multiple graph database solutions with visual analytics and machine learning tools. ​​Users can get started in minutes, build a proof-of-concept model in hours, and deploy a solution to production in days. New capabilities include: Enhanced data ingestion, Parquet file format, multi-edge support, Enhanced graph data science package, improved DevOps support, expanded Kubernetes functionality, and expanded self-service graph visual analytics

TigerGraph Cloud users can choose from 20+ starter kits that cover industry use cases pre-built with sample graph data schema, dataset, and queries focused on specific use cases such as fraud detection, real-time recommendation, machine learning, and explainable AI.

Gilbane Advisor 3-1-23 — Emergent properties in ML, RDF modeling

This week we feature articles by Jacob Steinhardt, and Dean Allemang.

Additional reading from Rocío Txabarriaga, Eric Broda, and Tony Seale.

News comes from MadCap Software & IXIASOFT, BetterCommerce, Wondershare, and Contentstack.

Note that news items now link to the original source of the news rather than our 200 word summaries, which are always available here.

All previous issues are available at

Opinion / Analysis

Emergent deception and emergent optimization

Emergent properties are in common in nature, and are often surprising. They are also found in machine learning. Jacob Steinhardt has a series of posts on emergence in machine learning worth checking out, but you can start with his most recent, and timely, piece. (17 min).

I’ve previously argued that machine learning systems often exhibit emergent capabilities, and that these capabilities could lead to unintended negative consequences. But how can we reason concretely about these consequences? … I’ll describe two specific emergent capabilities that I’m particularly worried about: deception (fooling human supervisors rather than doing the intended task), and optimization (choosing from a diverse space of actions based on their long-term consequences).

Why I’m not excited about RDF-Star

Well, the title is a bit clickbaity. But Dean Allemang’s article illustrates an important point about RDF modeling in general. And if like me, you weren’t aware of RDF-Star, an added benefit is you’ll learn enough to consider how you might use it when the W3C standard becomes a recommendation. (10 min).

More Reading

All Gilbane Advisor issues

Content technology news

MadCap Software acquires IXIASOFT

Adds enterprise DITA CCMS to support content strategies for creating, translating, and delivering consistent, up-to-date content tailored to roles.

Contentstack announces Contentstack Launch

Extends Contentstack’s product suite, providing a composable, automated, digital experience stack from the front-end to the back-end.

BetterCommerce adds headless CMS functionality to its commerce stack

The headless, composable CMS functionality joins existing modules in the commerce stack including PIM, eCommerce, OMS, Analytics and Engage.

Wondershare releases EdrawMind 10.5

Features new collaborative mind mapping and brainstorming tools to design solutions collaboratively and respond to trends and changes.

All content technology news

The Gilbane Advisor is authored by Frank Gilbane and is ad-free, cost-free, and curated for content, computing, web, data, and digital experience technology and information professionals. We publish recommended articles and content technology news weekly. We do not sell or share personal data.

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