Cutting edge applications of natural language processing

2020 was a year of significant growth in terms of commercial applications of natural language processing (NLP). According to Gradient Flow, 53% of technical leaders say their NLP budget was up 10% last year against 2019, despite the Covid-19 pandemic putting a halt to some plans.

The power to automate tasks and support voice and mobile technology means leaders realize the benefits of NLP in augmenting critical functions. Models and frameworks like GPT-3 are facilitating the ability to create fully written documents, stories, articles, and PR in a particular writer’s style without human intervention.

Although NLP technology is far from reaching full maturity, some of the most cutting-edge applications of natural language processing show that a new stage of AI is upon us. Combining technology like Google Bert, GPT-3, and GPT-4 will help scale digital innovation as non-technical staff will be able to use language rather than programming to create customer-facing applications.

Cutting edge applications of natural language processing

Google BERT and the transformation of NLP models

In 2019, Google released BERT to improve the search engine’s language understanding capability. The major update can successfully comprehend a search’s intent, rather than just reading the words, generating more relevant results.

The Google natural language API includes five stages.

  • Syntax analysis – a query is broken down into individual words to get linguistic data from each of them. It will understand the pronoun, determiner, singular, and preposition words from a sentence.
  • Sentiment analysis – assigns a score to each query based on the emotional context. For example, words like good, great, and excellent would positively score.
  • Entity analysis – if you were to ask, “How old is Donald Trump?” Google will detect Donald Trump as an entity and return an answer related to him.
  • Entity Sentiment Analysis – Google will assign a sentiment to the overall document which contains entities. For example, if the algorithm crawls a webpage, sentiment scores are assigned to entities on that page depending on the context they are used in
  • Text Classification – The Google algorithm will look for the closest subcategory of webpages based on the user query.

You can read more about how BERT works here. While previous NLP models would focus on statistical techniques and hard-coded rules, algorithms like BERT rely on artificial neural networks that can learn from raw data, meaning less time spent labeling and the ability to find deep, contextual relationships between words and text.

How do cutting edge applications of natural language processing impact the way content is served?

As Google can now understand the context and intent of search queries, marketers need to ensure they deliver content that is highly relevant to target audiences. When it comes to natural language, online content now needs to be written for people’s benefit and not for search engines. With voice and mobile search growing, people want accurate and fast answers to their questions. The latest NLP updates from Google will make this happen by focusing on intent rather than keywords like traditional marketing.

Tapping into NLP with GPT-3 and GPT-4

Back in 2019, Open AI published the GPT-2 model, which was the first NLP framework to have over one billion parameters. The 2020 GPT-3 model now handles a staggering 175 billion parameters. In 2021, we are now looking towards the first model to handle over one trillion parameters, likely to be the iterative GPT-4.

For companies, GPT-3 can respond to any text that a person says or types and understand it in the appropriate context. Unlike other neural networks, GPT-3 is generative (generative pre-training) can it can create sequences of unique text as an output rather than numeric scores or yes/no answers.  In 2020, GPT-3 mislead readers with a fully AI written news article

Although much of the article is about word correlation rather than a genuine understanding of language and context, it was a big breakthrough in terms of applications of natural language processing.

Google Brain trying to beat GPT-4 to one trillion parameters

At the start of 2021, researchers from Google Brain unveiled the next cutting edge AI language model, a one trillion parameter transformer system. They say it is six times larger than GPT-3 and will start to be able to mix context with language (the same aim as GPT-4).

The larger the dataset, the better the chance of an AI-generated sentence being legible and in the same context as human writing. The Google Brain team uses a new concept called Switch Transformer that simplifies and improves previous approaches. In short, Switch Transformers aim to maximize parameter numbers in a computationally efficient way. Google Brain found they can scale and test out stable models up to 1.6 trillion parameters without any severe instability.

The Google Brain model is not open to researchers yet and has not been verified, but it is expected to revolutionize language processing in the coming year. The code for the Switch Transformer is available via GitHub.

Natural language generation

Applications like GPT-3, GPT-4, and Google Brain are taking NLP to a futuristic level known as natural language generation. While the likes of Alexa, OK Google, Siri, and Cortana are advanced NLP models, this new breed of technology is taking us to a new era of understanding language. The problem with Alexa or Siri is that you have to find apps to solve problems manually, and it returns you will get a cue card type response. GPT-3 uses real context clues to solve the problem of filling in the language gaps.

Additional parameters promised by GPT-4 and Google Brain will take language models from a reporting to a conversational level, pushing us closer to general AI. Through such developments, applications of natural language processing continue to advance, sky-rocketing it’s potential.

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