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Today's tutorial is about Conditional Random Fields (CRFs), which are a more general form of Hidden Markov Models (HMMs). CRFs are used to add extra features and complexity to models, transitioning from something specific like an HMM to something more general like a CRF. The tutorial discusses the importance of understanding why we are making models more complicated and how CRFs can be seen as an extension of HMMs. The narrator mentions using a new microphone and asks for feedback on sound quality.