NLP Progress So Fast, New Benchmarks Created
Air St Capital released a technical research overview of the state of AI for 2019. One of the NLP highlights we like is about GLUE, a benchmark created just last year to foster competition against researchers into diversity and quality of NLP algorithms.
However, progress towards human-level targets was so rapid that a new benchmark has already been created! SuperGLUE sets even more ambitious goals for the rest of 2019 and beyond.
How is this relevant for businesses? Excitingly, much of this increased performance is directly accessible to small teams without needing the processing power or data sources the big companies have.
Big companies are increasingly providing repositories that hold vast bodies of language in a secure and anonymised way called “pre-trained models” (e.g. BERT, GPT-2, and XLNet).
These pre-trained models are secure to the extent that they don’t store the actual words, and are turning out to be surprisingly versatile. Application-specific enhancements, called “fine-tuning”, are possible for a growing range of use cases (e.g. summarisation, translation, question & answering) involving relatively small changes with very manageable sources of data.
This “leg-up” is making new levels of text intelligence widely available at low cost. Certainly we see some opportunities for our own Knowledge Network that we’re working on fervently — but drop me a line if you would like some guidance for your business.
Read on to learn about Microsoft’s Cortana strategy, chatbots around the world, and more.
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