You don't have to be a fortune teller to identify AI as the key trend for the 2020s. But there is nuance regarding AI hardware and software that deserves to be highlighted.

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.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

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.

Research and industry breakthroughs, ethics, and predictions. This is what AI looks like today, and what it's likely to look like tomorrow.

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.

Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.

SambaNova just added another offering under its umbrella of AI-as-a-service portfolio for enterprises: GPT language models. As the company continues to execute on its vision, we caught up with CEO Rodrigo Liang to look both at the big picture and under the hood.

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

𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐆𝐫𝐚𝐩𝐡 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: 𝟐𝟓 𝐓𝐨𝐩 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬, 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬, 𝐓𝐲𝐩𝐞𝐬 𝐚𝐧𝐝… | 29 comments on LinkedIn

AI has revolutionized the way we model complex systems. From dynamic networks in biology to interacting particle systems in physics, AI for graphs has achieved… | 18 comments on LinkedIn

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…

Hot trends and major advancements

Our most recent edition of the G-Research Distinguished Speaker Series took place at the Royal College of Physicians in Central London. Our featured speaker, Amy Hodler, is a graph evangelist, network science expert, and co-author of 'Graph Algorithms'. In her talk 'Average is a Lie - Using Graph Analytics to Improve Predictions', Amy discusses a range of topics, including: Graph queries Graph algorithms Relationships and structures in data Predicting behavioural change Machine learning Graph embedding Link prediction Data lineage Financial contagion Use of graph analytics in security Learn more about G-Research (https://www.gresearch.co.uk/) and view talks from speakers such as Professor Sir Martin Hairer and Wes McKinney in our Distinguished Speaker Series playlist. Interested in attending a future Distinguished Speaker Series event? Register your interest here now: https://events.beamery.com/gresearch/all-dss-events-mntauiaxr

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Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.

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.

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa...

We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to...