Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. This talk introduces the Spark NLP library – the most widely used NLP library in the enterprise, thanks to implementing production-grade, trainable, and scalable versions of state-of-the-art deep learning & transfer learning NLP research, as a permissive open-source library backed by a highly active community and team.
(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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Active Learning for Natural Language Processing
More than 90% of machine learning applications improve with human feedback. For example, a model that classifying news articles into pre-defined topics has been trained on 1000s of examples where humans have manually annotated the topics. However, if there are tens of millions of news articles, it might not be feasible to manually annotate even 1% of them. If we only sample randomly, we will mostly get popular topics like “politics” that the machine learning model can already identify accurately. So, we need to be smarter about how we sample. This talk is about “Active Learning”, the process of deciding what raw data is the most optimal for human review, covering: Uncertainty Sampling; Diversity Sampling; and some advanced methods like Active Transfer Learning.
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Growth Hacking with NLP and Sentiment Analysis
We developed a course, Growth Hacking with NLP and Sentiment Analysis during the past months. We loved working with Manning, and now we are excited to start mentoring our students. Join us if you’d like to learn about applied sentiment analysis using Python and libraries like simpletransformers and scikit-learn.
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Corpus Linguistics – the theoretical minimum
Corpus Linguistics is a neglected field of linguistics. Linguists tend to think that it cannot offer much, only some methodological tools to support their ideas. However, they often blame it, when it contradicts to their results. Corpus Linguistics was often considered the historic predecessor of Natural Language Processing in the pre-Big Data era. In this post, we claim that Corpus Linguistics offers a unique perspective on language, and it provides experts with theoretical and practical framework to analyze linguistic data. For the best resources of Corpus Linguistics, don’t stop reading!
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AI Ethics – a little book of big questions
Are you tired of talking about the trolley problem whenever you start a conversation on autonomous vehicles? Are you bored with the fear about robots? Do you want to be sure that while you are working on your deep reinforcement learning startup, autocracies can’t use your technology to strengthen their power? What if your technology deepens the gap between the rich and the poor further? Do you think that Ethics is inseparable from development and we have to care about moral questions? If your answer is “yes” to any of these questions, Mark Coeckelbergh’s AI Ethics is your book!
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