A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.

Explainable AI in real life could mean Einstein not just answering your questions, but also providing justification. Advancing the state of the art in natural language processing is done on the intersection of graphs and machine learning.

Gary Marcus, a prominent figure in AI, is on a mission to instill a breath of fresh air to a discipline he sees as in danger of stagnating. Knowledge graphs, the 20-year old hype, may have something to offer there.

In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.

AI chips, MLOps, and ethics. Knowledge, and Graphs. COVID-19 as a mixed bag for technological progress and adoption

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

Industry leaders discuss the "duct tape of data," if language models are a good way to approximate AI, and how to gain hybrid intelligence.

Co-authors: Sofus Macskássy, Carol Jin, Shiyong Lin, Xiaomin Wei, and Michael O’Neill

AI-chatbot software for complex requirements

This paper provides an extensive overview of the use of knowledge graphs in the context of Explainable Machine Learning. As of late, explainable AI ha…

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Should we all be making a Knowlege Graph part of our organisation's AI strategy? The ICLR is now recognised as one of the top conferences in deep learning… | 11 comments on LinkedIn

Contribute to dglai/Graph-Neural-Networks-in-Life-Sciences development by creating an account on GitHub.

Summarizing in figures this excellent systematic survey of 507 papers on the state of #KnowledgeGraphs in #NLP since the first Internet-age KG was announced 10… | 12 comments on LinkedIn

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In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of...

AWS developed the Deep Graph Knowledge Embedding Library (DGL-KE), a knowledge graph embedding library built on the Deep Graph Library (DGL). DGL is a scalable, high performance Python library for deep learning in graphs. This library is used by the advanced machine learning systems developed with Trumid to build a credit trading platform.

In a distributed world where data is federated in a Mesh internally and you value comes from how you collaborate externally the most…

Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national

Creates the first relational knowledge graph system; adds Bob Muglia to its board

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

Data on Native communities are not at the levels needed for accuracy in A.I.-driven tools. A group is trying to solve that problem.

Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships...

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"

Artificial Intelligence has a long history of oscillating between two somewhat contradictory poles. On one side, exemplified by Noam Chomsky, Marvin Minsky, Seymour Papert, and many others, is the idea that cognitive intelligence was algorithmic in nature – that there were a set of fundamental precepts that formed the foundation of language, and by extension,… Read More »Where Semantics and Machine Learning Converge