Overcoming the Hurdles: Challenges in Natural Language Processing NLP by Niraj Kumar Nirala
Natural Language Processing: Top Key Challenges & Applications Such a collection needs to be versioned, to enable updates beyond the cycle of academic review and to enable replicability and comparison to prior approaches. In order to better understand the strengths and weaknesses of our models, we furthermore require more fine-grained evaluation across a single metric, highlighting on what types of examples models excel and fail at. ExplainaBoard (Liu et al., 2021) implements such a fine-grained breakdown of model performance across different tasks, which can be seen below. Another way...
Read MoreHow to Get the Most out of AI in 2023: 7 Applications of Artificial Intelligence in Business
8 ways to prepare your business for artificial intelligence Begin by researching use cases and white papers available in the public domain. These documents often mention the types of tools and platforms that have been used to deliver the end results. Once you build a shortlist, feel free to invite these vendors (via an RFI or another process) to propose solutions to meet your business challenges. Based on the feedback, you can begin evaluating and prioritizing your vendor list. Nearly 80% of the AI projects typically don’t scale beyond a PoC or lab environment. AI involves multiple tools and...
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