Graphs as a foundational technology stack: Analytics, AI, and hardware
How would you feel if you saw demand for your favorite topic — which also happens to be your line of business — grow 1,000% in just two years’ time? Vindicated, overjoyed, and a bit overstretched in trying to keep up with demand, probably.
Read More →Open-source software economics and community health analytics: Enter CHAOSS
Trying to capture the value open-source software generates can be a bit chaotic. The CHAOSS project may lend a helping hand.
Read More →Open source AI stack is ready for its moment
Open source stacks enabled software to eat the world. Now several innovative companies are working to build a similar open source software stack for AI development.
Read More →Weaviate is an open-source search engine powered by ML, vectors, graphs, and GraphQL
Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.
Read More →Databases, graphs, and GraphQL: The past, present, and future
GraphQL was never conceived as a query language for databases. Yet, it's increasingly being used for this purpose. Here's why, and how.
Read More →Data meets science: Open access, code, datasets, and knowledge graphs for machine learning research and beyond
A new interconnected ecosystem for research is shaping up, and machine learning is just the tip of the iceberg.
Read More →AI chips in the real world: Interoperability, constraints, cost, energy efficiency, and models
The answer to the question of how to make the best of AI hardware may not be solely, or even primarily, related to hardware
Read More →From data to knowledge and AI via graphs: Technology to support a knowledge-based economy
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.
Read More →Getting there: Structured data, semantics, robotics, and the future of AI
Leveraging structure in data is key to making progress in AI, says AI prodigy Gary Marcus. A forward-looking view on Software 2.0, AI chips, robotics, and the future of AI
Read More →Rebooting AI: Deep learning, meet knowledge graphs
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.
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