Authentic Meets Artificial - The New Intelligence - DM Radio Broadcast
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Authentic Meets Artificial – The New Intelligence – DM Radio Broadcast Understanding the world around us increasingly involves Artificial Intelligence. Once the stuff of elite corporations, the barrier has now dropped substantially, and practically anyone can get in on the game! Check out this episode of DMRadio to learn more, as Host @Eric_Kavanagh interviews Jans Aasman, Franz Inc. and Robert Nishihara, Anyscale. Listen to the recording. AllegroGraph Named 2022 “Trend Setter” AllegroGraph Named 2022 “Trend Setter” by Database Trends and Applications AllegroGraph has been named a 2022 Trend Setting Product by Database Trends and Applications. Additionally, AllegroGraph
was recently named “Best Knowledge Graph” by KMWorld Readers’
Choice award voting.
“The world is changing
rapidly, and so are enterprise
data requirements. Whether it
is anticipating supply chain
problems, addressing customer concerns with agility, or
identifying new opportunities and pouncing quickly, the
ability to achieve a comprehensive view of all available
information for real-time decision making has become a strong
requirement,” said Thomas Hogan, Group Publisher of Database
Trends and Applications. “That is why it is more important
than ever to identify products and services that help to
deliver results. This list focuses on products that represent
a commitment to innovation and provide organizations with
tools to address rapidly evolving market requirements.”
“Franz Inc. is continually innovating and we are honored to
receive this acknowledgement for our efforts in setting the
pace for Knowledge Graph Solutions,” said Dr. Jans Aasman,
CEO, Franz Inc. “We are seeing demand for Intelligent Data
Fabrics take off across industries along with recognition from
top technology analyst firms that Knowledge Graphs provide the
critical foundation for Data Fabric solutions. AllegroGraph
with FedShard uniquely provides companies with the
foundational environment for delivering Graph based AI
solutions with the ability to continually enrich and
contextualize the understanding of data.”
Read more about the award.
Franz’s AllegroGraph Named
“Best Knowledge Graph” by
KMWorld Readers’ Choice
AllegroGraph also wins Finalist position for “Best Cognitive
Computing and AI Platform”.
Franz Inc., is proud to announce it has been named the “Best
Knowledge Graph” in the 2021 KMWorld Readers’ Choice Award
voting. Additionally, AllegroGraph was considered a “Finalist”
in the category of Best Cognitive Computing and AI platforms
for the Readers’ Choice awards.
According to KMWorld, the world of knowledge management
continues to expand with the steady influx and evolution of
innovative products and technologies to help organizations
extract the right information for use by the right people at
the right time. The value of knowledge management solutions
and services is reflected in growth projections for the global
knowledge management market, which was valued at about $206.9
billion in 2016 and is expected to reach more than $1,232
billion by 2025, representing a compound annual growth rate of
more than 22%, according to Zion Market Research.
In this November issue,
KMWorld magazine
announces the winners of
the 2021 KMWorld
Readers’ Choice Awards.
The categories for
competition were wide-
ranging. In all, there
were 14 areas in which
products and technologies could be nominated and ultimately
voted upon. They include business process management,
cognitive computing and AI, customer service and support, e- discovery, knowledge graphs, text analytics and NLP. “As the stakes get higher for information-driven successes, businesses must make technology decisions from an increasingly diverse array of knowledge management offerings,” said Tom Hogan, Group Publisher at KMWorld. “The Readers’ Choice Awards put the spotlight on innovative and dependable solutions and services that can help companies solve pressing challenges and take advantage of new opportunities.” “Franz Inc. is continually innovating and we are honored to receive this acknowledgement for our efforts in setting the pace for Knowledge Graph Solutions,” said Dr. Jans Aasman, CEO, Franz Inc. “We are seeing demand for Intelligent Data Fabrics take off across industries along with recognition from top technology analyst firms that Knowledge Graphs provide the critical foundation for Data Fabric solutions. AllegroGraph with FedShard uniquely provides companies with the foundational environment for delivering Graph based AI solutions with the ability to continually enrich and contextualize the understanding of data.” AllegroGraph provides organizations with essential Knowledge Graph solutions, including Graph Neural Networks, Graph Virtualization, Apache Spark graph analytics, and streaming graph pipelines. These capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs. AllegroGraph v7.2 – Now
Available (GNN, Virtual Graphs, Spark, and Kafka) AllegroGraph 7.2, provides organizations with essential Data Fabric tools, including Graph Neural Networks, Graph Virtualization, Apache Spark graph analytics, and streaming graph pipelines. These new capabilities exemplify AllegroGraph’s leadership in empowering data analytics professionals to derive business value out of Knowledge Graphs. Graph Neural Networks With AllegroGraph 7.2, users can create Graph Neural Networks (GNNs) and take advantage of a mature AI approach for Knowledge Graph enrichment via text processing for news classification, question and answer, search result organization, event prediction, and more. GNNs created in AllegroGraph enhance neural network methods by processing the graph data through rounds of message passing, as such, the nodes know more about their own features as well as neighbor nodes. This creates an even more accurate representation of the entire graph network. AllegroGraph GNNs advance text classification and relationship extraction for enhancing enterprise-wide Data Fabrics. Graph Virtualization AllegroGraph 7.2 allows users to easily virtualize data as part of their AllegroGraph Knowledge Graph solution. When graphs are virtual, the data remains in the source system and is easily linked and queried with other data stored directly in AllegroGraph. Any data source with a supported JDBC driver can be integrated into an AllegroGraph Knowledge Graph, including Databases (i.e. Apache Cassandra, AWS Athena, Microsoft SQL Server,
MongoDB, MySQL, Oracle Database); BI Tools (i.e. IBM Cognos, Microsoft PowerBI, RapidMiner, Tableau); CRM Systems (i.e. Dynamics CRM, Netsuite, Salesforce, SugarCRM); Cloud Services (i.e. Active Directory, AWS Management, Facebook, Marketo, Microsoft Teams, SAP, ServiceNow) and Shared Data Files (i.e. Box, Gmail, Google Drive, Office365). Streaming Graph Pipelines using Kafka Enterprises that need real-time experiences are starting to adopt streaming pipelines to provide insights that adapt to new data in real-time rather than processing data in batches. AllegroGraph is often used as an Entity Event Knowledge Graph platform in diverse settings such as call centers, hospitals, insurance companies, aviation organizations and financial firms. AllegroGraph 7.2 can be used seamlessly with Apache Kafka, an open-source distributed event streaming platform for high- performance data pipelines, streaming analytics, data integration and mission-critical applications. By coupling AllegroGraph with Apache Kafka, users can create a real-time decision engine that produces real-time event streams based on computations that trigger specific actions. AllegroGraph accepts incoming events, executes instant queries and analytics on the new data and then stores events and results. Graph Analytics with Apache Spark AllegroGraph 7.2 enables users to export data out of the Knowledge Graph and then perform graph analytics with Apache Spark, one of the most popular platforms for large-scale data processing. Users immediately gain machine learning and SQL database solutions as well as GraphX and GraphFrames, two frameworks for running graph compute operations on data. A key benefit of using Apache Spark for graph analytics within AllegroGraph is that it is built on top of Hadoop MapReduce and extends the MapReduce model to efficiently use more types
of computations. Users can access interfaces (including interactive shells) for programming entire clusters with implicit data parallelism and fault-tolerance. Availability of AllegroGraph 7.2 AllegroGraph 7.2 is immediately available directly from Franz Inc. For more information, visit the AllegroGraph Quick Start page for cloud and download options. Examples Visit our Github AllegroGraph Examples page. Fuse Graph Neural Networks with Semantic Reasoning to Produce Complimentary Predictions Organizations can combine GNN reasoning capabilities with classic semantic inferencing in Knowledge Graphs to reach the next level AI and predict any business event based on context at scale. The ability for machines to reason — not just identify patterns in massive data amounts, but make rule or logic based inferences on domain specific knowledge — is foundational to Artificial Intelligence. The growing momentum around Neuro- Symbolic AI and the increasing reliance on Graph Analytics demonstrate how important these developments are for
the enterprise. Combining AI’s symbolic knowledge processing with its statistical branch (typified by machine learning) produces the best prescriptive outcomes, delivers total AI, and is swiftly becoming necessary to tackle enterprise scale applications of mission-critical processes like foretelling equipment failure, optimizing healthcare treatment, and maximizing customer relationships. Graph Neural Networks (GNN) exemplify the confluence of machine learning and AI reasoning. Their underlying graph capabilities are ideal for applying machine learning’s advanced pattern recognition to high-dimensional, non- Euclidian datasets that are too complex for other machine learning types. Organizations get two forms of reasoning in one framework by fusing GNN reasoning capabilities around relationship predictions, entity classifications, and graph clustering, with classic semantic inferencing available in Knowledge Graphs. Automatically mixing and matching these two types of reasoning is next level AI and is the basis for predicting any business event based on context at scale. Read the Full Article at Towards Data Science. Catalog and Cocktails – Fashion Week… but for data With the hype of graph databases and knowledge graphs, a common (mis)practice is to quickly migrate your existing
siloed data into a graph database. But be careful! You may just be bringing the complexity of your silos into the graph. Join Tim Gasper, Juan Sequeda and guest Jans Aasman from Franz Inc, the makers of AllegroGraph, for a conversation on why your graph-based machine learning and 360 projects should start with data modeling. This episode features – Data modeling approaches you should consider – Tips to avoid data modeling pitfalls – If you could be a top model for any product/brand, what would it be and why? Key takeaways – It’s “terrible” to start creating an ontology without knowing the application – Intelligent people make the schemas… this is not easy – Modeling is human problem solving! Listen to the Podcast or Read the Transcript. Here is the broadcast on YouTube:
Gartner – Hype Cycle for Artificial Intelligence Gartner released its 2021 Hype Cycle for Artificial Intelligence. Knowledge Graphs continue toward the peak. Read the press release – https://www.gartner.com/en/newsroom/press-releases/2021-09-07- gartner-identifies-four-trends-driving-near-term-artificial- intelligence-innovation Chaos Orchestra Join host, Boris Shalumov with special guest, Jans Aasman for this episode of Chaos Orchestra. Can Knowledge Graphs help to build better Cognitive Models? How will Knowledge Graphs look like in the future and how will we interact with them? Why didn’t Knowledge Graphs solve COVID-19-related data problems? How far away are Technocracy and Digital Immortality? Extrapolating from 40 years of Knowledge Graphs and cognitive models with Dr. Jans Aasman, CEO of Franz Inc.
About Chaos Orchestra In just a few years Knowledge Graphs have exploded in usage, as has their impact in the world of Artificial Intelligence. Semantic AI has become a significant part of text analytics, search engines, chat-bots and more. And yet, few people outside of niche tech communities are fully aware of how semantic knowledge graphs can be leveraged. In the Podcast “Chaos Orchestra” we will explore how Knowledge Graphs can be applied over the next decade to boost many areas of Artificial Intelligence and address the most pressing challenges of our times. No-Code Queries Can Accelerate AI and Data Analytics By Dr. Jans Aasman, CEO The low-code, no-code methodology is becoming highly sought- after throughout the modern IT ecosystem—and with good reason. Options that minimize manually writing code capitalize on the self-service, automation idiom that’s imperative in a world in which working remotely and doing more with less keeps organizations in business. Most codeless or low-code approaches avoid the need for writing language-specific code and replace it with a visual approach in which users simply manipulate on-screen objects via a drag-and-drop, point-and-click interface to automate code generation. The intuitive ease of this approach — which is responsible for new standards of efficiency and
democratization of no-code development — has now extended to
no-code query writing.
No-code querying provides two unassailable advantages to the
enterprise. First, it considerably expedites what is otherwise
a time-consuming ordeal, thereby accelerating data analytics
and AI-driven applications and second, it can help
organizations overcome the talent shortage of developers and
knowledge engineers. Moreover, it does so by furnishing all
the above benefits that make codeless and low-code options
mandatory for success.
Read the full article at DZone.
Why Young Developers Don’t
Get Knowledge Graphs
Dr. Aasman recently interviewed for this
Datanami article.
Business is booming these days for graph databases–maybe it
took COVID to show us how connected everything is–and that’s
good news for Franz, which develops a semantic graph database
called AllegroGraph. Just the same, you won’t find CEO Jans
Aasman spending much time convincing developers of a certain
age to use it.
“If you live in our world of semantic graph databases, I only
talked to people over 35, 40,” Aasman tells Datanami. “I never
talk to young developers.”The problem with younger developers, he explains, is that they’re usually interested in using the graph database to build point solutions to solve specific problems, as opposed to creating a wide base of knowledge that can not only solve a specific problem, but be used with future solutions too. Plus, building point solutions exacerbates the data silo problem, he says. “In our community of the semantic graph databases, literally everything is about integration and making sure that everything can interoperate,” the Franz CEO continues. “And there’s not a single young programmer that cares about that. Seriously. You’re young, you want to do a fun project, your managers are saying, in three months I need this thing done. You do whatever you want to do. Well, they get it done. And then you have new a data silo.” Read the full article at Datanami.
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