AI is developing at a rapid pace and is already changing how humans and machines work together.
The past 12 months have seen the global digital paradigm evolve tremendously, especially regarding how humans interact with machines. In fact, the space has undergone such a radical transformation that people of all ages are now fast becoming conversant with artificial intelligence models, most popularly OpenAI’s ChatGPT.
Moreover, NLP involves several key tasks and techniques, including part-of-speech tagging, named entity recognition, sentiment analysis, machine translation and topic extraction. These tasks help machines understand and generate human language-type responses. For example, part-of-speech tagging involves identifying the grammatical group of a given word, while named entity recognition involves identifying individuals, companies or locations in a text.
Language translation is another frontier where NLP and AI have made remarkable progress. Translation apps can now interpret text and speech in real time, dismantling language barriers and fostering cross-cultural communication.in The Lancet notes that these translation capabilities have the potential to redefine the health sector.
Newman sees sentiment analysis as a key driver, with a more sophisticated interpretation of data taking place through neural networks and deep learning systems. He also envisions the open-sourcing of data platforms to better cater to those languages that have traditionally been under-served by translation services.
Scott Dykstra, chief technical officer for AI-based data repository Space and Time, sees the future of NLP at the intersection of edge and cloud computing. He told Cointelegraph that in the near to mid-term, most smartphones would likely come with an embedded large-language model that will work in conjunction with a massive foundational model in the cloud. “This setup will allow for a lightweight AI assistant in your pocket and heavyweight AI in the data center,” he added.
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