Alessandro Negro of GraphAware and author of Graph-Powered Machine Learning was our speaker this week. He delivered a talk titled Using Knowledge Graphs to predict customer needs, improve product quality and save costs and presented a demo, Fighting corona virus with Knowledge Graph and Hume. You can watch the recordings of the meetup below.
(more…)Crow Intelligence
AI is just a tool. To use it effectively, you must understand how humans think and communicate. We know the strengths of both natural and artificial intelligence and how to combine them for optimal results. By bridging cognitive science and AI, we create solutions that enhance human capabilities and ensure seamless interaction.
Our Approach
Just as a well-designed tool feels like an extension of your hand, AI should feel like an extension of human intelligence. The best AI systems are built on two key principles:
Human Cognition
Understanding human thought and language ensures AI integrates seamlessly with natural cognitive processes.
Advanced AI Engineering
Cutting-edge AI technology, designed with cognitive awareness, creates powerful and intuitive systems.
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Blog
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Graph Theory and Network Science for Natural Language Processing – Part 2, Databases and Analytics Engines
From keyword extraction to knowledge graphs, graph and network science offer a good framework to deal with natural language. We love using graph-based methods in our work so much, like generating more labeled data, visualizing language acquisition and shedding light on hidden biases in language, that we decided to start a series on the topic. The first part explored the theoretical background of network science and dealt with graphs using Python. This part focuses on graph processing frameworks and graph databases.
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Graph Theory and Network Science for Natural Language Processing – Part 1
From keyword extraction to knowledge graphs, graph and network science offer a good framework to deal with natural language. We love using graph-based methods in our work, like generating more labeled data, visualizing language acquisition and shedding light on hidden biases in language. This series gives you tips on how to get started with graph and network theory, which Python tools to use, where to look for graph databases and how to visualize networks, finally we offer a few resources on Graph Neural Networks.
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How to fuel your data-driven business with text data? – Part 2, Strategies and Tools
If data is the new oil, then getting and enriching data is like fracking and refining it, at least in the case of textual data. Our previous post introduced the basic idea of data gathering and annotation. Now we help you with the strategies and tools you can employ to fuel your algorithms.
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How to fuel your data-driven business with text data? – Part 1, Data gathering and annotation
If data is the new oil, then getting and enriching your own data is like fracking and refining it, at least in the case of textual data. This post gives you an overall picture on how to think about gathering and labeling data. You also get some tips on what kind of business questions should be considered.
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