Chapter 4: Everyday with NLP

Business of AI May 12, 2021

In our NLP micro series we understood -

  1. What is NLP? - Natural Language Processing: Chapter 1
  2. NLP Chapter 2: With Google Cloud Platform
  3. NLP Chapter 3: With Spacy

In today's article let's take a look at the implications and utilities of NLP in our everyday life. There are numerous instances in our everyday routines where we tend to leverage highly advance NLP without our knowledge. It is very important to be vary of HOW AI is introduced into our routine as it becomes a part of our lives.

It was in the 2010s that our world finally started realising the importance and scope of AI. Which was again a stumbled upon discovery. Nvidia who had no intention of building GPUs for Neural computation is now the leading market brand for it. They built GPUs for heavy duty gaming and faster computation, it was the gradual research by the community that paved the road for them to imagine the scope of implementation and importance of GPUs in AI and how they could revolutionise the century.

Since then pretty much everything in our life has AI in it. Without wasting much time let's go ahead and open the PANDORA's Box and unravel the mystery.

NLP is that branch of AI that deals with our most fundamental human instinct - COMMUNICATION. It deals with understanding, interpreting and comprehending the linguistics of Human civilisation.

Let's first take an everyday examples to understand how it impacts our lives -

Search engines

Every search engine built within an application uses multiple layers of AI to enhance its performance. Some of the trivial necessities of such an engine are as follows —
Auto-correction
Language Model
TF-IDF Model (Term Frequency-Inverse Document Frequency)
Relational Mapping

It’s essential for the system to make corrections to the user feed if necessary, as if the user is searching for something. Mostly it implies that the individual is uncertain of it, hence correction is must.

TF-IDF is a must to remove the rule based convention of dictionary search, where the results are based on the assumption that the feed is alphabetically correct. It helps in understanding the context of the search not just from Left-to-Right but also while reading from Right-to-Left.

Relational Mapping is most crucial for such a task because as vast as the world has become today, there are hundreds of results catering to different topics with the same terminology. Hence it’s must to understand user content and relate it to the relevant information that the individual requires.

The most relevant example is Google Search Engine. All of us have extensively leveraged Google to resolve our everyday lives queries and concerns. Many a times we even perform spellchecks and similarity searches using the same platform and most of us know how phenomenal Google's recommendations and Auto-corrections are.

Another one of the trendiest upcoming trends of NLP is GPT-3 by OpenAI.

OpenAI's GPT-3

Imagine What If a machine had all the capabilities of a human brain instilled within. I know, horrors of Terminator come into mind.

What is GPT-3?

GPT-3 is an AI product launched by OpenAI to tackle problems related to NLP including but not limited to -

  1. Classification
  2. Semantic Search
  3. Data Processing
  4. Chat
  5. Question & Answer
  6. Interactive Games
  7. Summarisation
  8. Content Generation
  9. Code Generation
  10. Language Translation

It has the potential to cater to over 90% of the existing problems around linguistics. Now if you would have noticed there is a point mentioning it has the capacity of Content Generation. For most readers this raises and eyebrow. As a part of vision of OpenAI this is what they had to say -

"We think that we could eventually expand the value of a tool like GPT-3 far beyond the realm of just writers, but rather as a tool for businesspeople, scientists and engineers as well. A tool that generates not just coherent paragraphs, but also coherent slide decks, product ideas or designs would dramatically help a lot of people. In the world of science for example, we often generate hypotheses that then require us to run large-scale experiments to disprove them. We can’t run experiments on the origin of the universe for example, but with GPT-3 we could generate a possible hypothesis for why it works the way it does and then simply test that hypothesis."

Every word written above in Italics is generated by GPT-3 and wasn't actually spoken. I know it’s mind boggling, but this is the extent to which we are now vulnerable because of the same technology that we have built. As time passes by it becomes more and more difficult to differentiate reality from synthetic information.

Nevertheless there are PROS and CONS for everything on this planet. But it is essential for us to understand What we Use and Where we Use it. Because staying in the DARK doesn't help develop a society.

Conclusion

Based on our discussion by far we know the scope to which NLP can be implemented and the kind of revolution it can bring to our world. Therefore as AI Designers it is crucial for us to aware and make sound decisions in the process of product development.

In our future articles we will be exploring more about the implications and impact of AI and it's future scope for Human Civilisation. STAY TUNED for more mind boggling content. 😁

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Vaibhav Satpathy

AI Enthusiast and Explorer

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