From chatbots to sentiment evaluation, we’re seeing an explosion of real-world use instances for textual information. A few of the buzziest improvements in AI revolve round fashions skilled with ever-increasing portions of textual content; on the flip facet, we will hint most of the challenges the sector is dealing with to restricted, unrepresentative, or flat-out biased language datasets.
This week, we share six current posts that cowl information and language by a variety of matters and approaches—NLP followers can have a blast, however so will programmers, information engineers, and AI fans. Let’s dive in!
- The wall all massive language fashions run into (for now). GPT-3 and related generative fashions can produce textual content that sounds truthful even when it lacks factuality. Iulia Turc explores the difficulty of those fashions’ groundedness — “the power to floor their statements into actuality, or no less than attribute them to some exterior supply”—and why it’s been so tough to develop fashions that come near human efficiency.
- Pure language querying is making a splash. Up till not too long ago, people needed to invent (after which study) advanced languages as a way to talk with computer systems and manipulate digital information. Andreas Martinson discusses the rising world of NLQ—pure language querying—and the way it may remodel the work of information professionals for the higher, in addition to democratize entry to databases.
- Choosing the proper instruments to simplify advanced NLP duties. The distinction between clunky and streamlined workflows can generally come all the way down to seemingly trivial selections. Kat Li surveys 5 less-known Python libraries—from Pyspellchecker to Subsequent Phrase Prediction—and explains how they will save effort and time when utilized in the appropriate NLP context.
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