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.

The butcher-on-the-bus is a rhetorical device that sheds light on human memory processes. Imagine recognising someone on a bus but struggling to place their… | 62 comments on LinkedIn

Graph databases are making a splash in the database market, with specialist, multimodal and cloud database suppliers jostling for a slice of the pie.

Irene Politkoff, Founder and Chief Product Evangelist at semantic modeling tools provider TopQuadrant, posted this description of the large language model (LLM) ChatGPT: “ChatGPT doesn’t access a database of facts to answer your questions. Instead, its responses are based on patterns that it saw in the training data. So ChatGPT is not always trustworthy.” Georgetown… Read More »Can we boost the confidence scores of LLM answers with the help of knowledge graphs?

Like many of you, I’ve been reading a lot lately about ChatGPT and LLMs, and how they will impact various aspects of our society. Some of…

Researchers develop a framework to capture the vast downstream impact and complex upstream dependencies that define the foundation model ecosystem.

Any database is pretty useless unless the user can ask, and get answers to, their questions.

In today's data-driven world, it is crucial to establish a clear boundary between your public and private data. Whilst banks, medical institutions, and spy… | 35 comments on LinkedIn

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

We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows training graph neural networks with forward passes only, without backpropagation. Our method is agnostic to the message-passing scheme, and provides a more biologically plausible learning scheme than backpropagation, while also carrying computational advantages. With GFF, graph neural networks are trained greedily layer by layer, using both positive and negative samples. We run experiments on 11 standard graph property prediction tasks, showing how GFF provides an effective alternative to backpropagation for training graph neural networks. This shows in particular that this procedure is remarkably efficient in spite of combining the per-layer training with the locality of the processing in a GNN.

AI-chatbot software for complex requirements

It can be hard to work out from Wikipedia if an Ontologist is a philosopher, a physicist or something completely different

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…

Digital Science has completed the acquisition of metaphacts, which has become the newest member of the Digital Science family.

SeeNews talks to Ontotext’s CEO Atanas Kiryakov about the latest developments in semantic technology and graph technology as well as GraphDB

Proper modeling of a graph database can be challenging. Let’s check how ChatGPT will perform with a semi-real case.

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

Using open data to make your own graph…

🔎🔬🔍 Improve your knowledge graph with GRAPE! 🦟🦠🪲 You may not be aware, but KGs can have topological oddities affecting your analysis and modelling…

Critical steps in improving the productivity of enterprise note-taking

Knowledge Graph Costs I just finished my primary research for a new paper on the costs and obstacles of adopting knowledge graph. The three themes that… | 10 comments on LinkedIn

#AI needs #KnowledgeGraphs to represent the physical world! See why AI needs @KnowledgeGraph for autonomous cars, for robotics, for smart homes and more...

And in the serendipitous discovery category today we have Google Cloud's "Enterprise Knowledge Graph", seemingly soft-launched a few days ago…

The most common way to find answers on the Internet is to enter keywords in a web search engine (Google, Yandex, etc) and browse the returned results. However, in some cases the user is looking for a…

500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

How to invert the data integration problem within your organisation

Success stories, shared at Ontotext’s KGF22, about building knowledge graphs in Industry, Healthcare, Life Sciences, Financials

Brands invest heavily in content. Knowledge graphs make this actionable, improving resources and providing deeper insights.