We close our series on Graph Theory and Network Science for NLP with an overview of the recent developments in the field. It means deep learning enters the scene – finally.
(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.
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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:
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Blog
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The Nation in American Political Discourse
Our work on automatic metaphor identification has just been published in the Current Trends in Corpus Linguistics by Peter Lang.
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How Jina Saves Your Time on Building Cloud-Native Neural Search Systems
Today, with the ever more long documents and multimedia data, finding the right information is more important and challenging than ever. The rise of deep learning has ushered in a new era of “neural search”. However, building a neural search system is non-trivial work for many engineers. The main challenges are: (1) long dev cycle due to the complex tech stack (2) poor scalability due to the glued-architecture (3) strong requirements on the domain knowledge to fine-tune the results. With Jina (https://github.com/jina-ai/jina), engineers can quickly build up a search engine powered by state-of-the-art AI in just minutes. In this talk, I will introduce the design philosophy and the key features of Jina; and showcase how Jina bootstraps a QA semantic search system and a short-video search system in just lines of code.
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Graph Theory and Network Science for Natural Language Processing – Part 3, Visualization
If you believe that graph and network visualization is a kind of art, this post was written for you. If you believe that it isn’t, then you should also keep reading. Since 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, we started a series on graph theory and network science. The first part was devoted to the theoretical background of graphs and how to deal with them using Python, while the second part was about graph databases and analytics engines. Now we turn to graph and network visualization.
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