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ChatGPT and the Future of NLP Systems | DataHour by Dan Sullivan

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🔥 The release of ChatGPT is an inflection point in the history of Natural Language Processing. While there are many Large Language Models (LLM) that perform at or near human levels in language benchmarks, including PaLM, Chinchilla, and Megatron-Turing NLG, ChatGPT is the one that has caught the attention of the public. This success is accompanied by research into optimizations around performance, cost of training, and trade-offs between data set size and number of parameters in models. These are important topics for advancing the field but do not address the question of what other transformative concepts will enable the next round of radical advances in Natural Language Processing. Two sources should be mined for innovative ideas: the largely unsuccessful attempts of early Natural Language Processing and Cognitive Science. From the past, we can learn that a model that works well in some areas, like transformational grammar and modeling human language, is not as useful when it comes to engineering language understanding systems. 🔥 In this DataHour, Dan will explain how to efficiently use ChatGPT, and the future scopes of NLP while answering the following topics in detail: - Are there limits to current deep learning architectures that will prompt different approaches to advance AI? - Why are many models in cognitive science more top-down than deep learning’s bottom-up approach? - Can these approaches complement each other and provide paths forward with emerging challenges in NLP and AI? 🔥 Who is this DataHour for? - Students & Freshers who want to build a career in the Data-tech domain. - Working professionals who want to transition to the Data-tech domain. - Data science professionals who want to accelerate their career growth - Prerequisites: A strong interest in Data Science 🔥 About the speaker: Dan Sullivan, a lead solutions architect with Hydrolix, is a data and cloud architect with experience designing natural language processing systems for news classification, text mining, and analyzing biomedical literature. He is the author of over 20 online courses on machine learning, data science, data engineering, and cloud architecture. Dan is the author of the Google Cloud Certified Professional Cloud Architect Study Guide, the Google Cloud Certified Professional Cloud Data Engineer Study Guide, and the Google Cloud Certified Associate Cloud Engineer Study Guide. Connect with him on Linkedin

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