From chatbots to sentiment evaluation, we’re seeing an explosion of real-world use instances for textual knowledge. Among the buzziest improvements in AI revolve round fashions educated with ever-increasing portions of textual content; on the flip aspect, we will hint lots 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 knowledge and language via a variety of matters and approaches—NLP followers may have a blast, however so will programmers, knowledge engineers, and AI fans. Let’s dive in!
- The wall all giant 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 problem of those fashions’ groundedness — “the flexibility 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 lately, people needed to invent (after which study) complicated languages with a purpose to talk with computer systems and manipulate digital knowledge. Andreas Martinson discusses the rising world of NLQ—pure language querying—and the way it would possibly rework the work of knowledge professionals for the higher, in addition to democratize entry to databases.
- Selecting the best instruments to simplify complicated NLP duties. The distinction between clunky and streamlined workflows can generally come right 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 suitable NLP context.
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