Rest in Peace manual programming. No one ever liked you.

If you were living under a rock you wouldn’t know that Jensen Huang, CEO of Nvidia, proclaimed that coding, as we know it, is headed for extinction. He envisions a future where NLP supersedes traditional coding languages, making programming accessible to all, and coding unnecessary.

What is the purpose of coding? Coding acts as a translator between computers and humans, with NLP, humans and computers have learned how to communicate without a translator.

Taking a step back here, what is Natural language processing (NLP)?

Computers learn a new language using NLP which is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human languages.

It involves the development of algorithms and techniques to enable computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant.

Capabilities of NLP:

• Translate text from one language to another
• Respond to typed or spoken commands
• Recognize or authenticate users based on voice
• Summarize large volumes of text
• Assess the intent or sentiment of text or speech
• Part-of-Speech (POS) Tagging
• Generate text or graphics or other content on demand

Tasks of NLP:

• Understanding text
• Summarizing text
• Language Translation
• Sentiment/Emotion Analysis
• Answering Questions
• Language Generation
• Named Entity Recognition (NER)

We use NLP constantly in our day-to-day life, NLP is presented in virtual assistants, chatbots, search engines, information retrieval systems, language translation, speech recognition, email spam filtering, etc. 

NLP is a field that has two concepts under its umbrella, Natural Language Understanding (NLU) and Natural Language Generation (NLG).

Concepts in the field of NLP:

• NLU (Natural Language Understanding): a subfield of NLP that focuses specifically on the comprehension of human language by computers. 
  • Aims: to enable computers to understand the meaning and intent behind human language inputs. 
  • Tasks: part-of-speech tagging, named entity recognition, syntactic parsing, and semantic analysis. 
  • Used: to extract structured information from unstructured text data and interpret the meaning of user queries or statements.
• NLG (Natural Language Generation): a subfield of NLP that focuses on the generation of human-like language by computers. 
  • Aims: to produce coherent and contextually relevant text outputs based on structured data or input prompts. 
  • Tasks: text summarization, language translation, content generation, and dialogue generation. 
  • Used: to automatically generate reports, product descriptions, personalized messages, and other types of textual content.

Large Language Model (LLMs)

LLMs and NLP are related concepts, but they refer to different aspects of language understanding and artificial intelligence. LLMs are a specific type of model within the broader field of NLP, however, it is considered more advanced than most NLP models.

LLMs specifically refer to the type of artificial intelligence models that are trained on massive amounts of textual data. These models are designed to understand, generate, and manipulate human-like language. 

An example of NLP Application in the Egyptian tech industry:

At Valify, we harness NLP technology to power our Transliteration product. NLP enables the seamless transliteration of text from one language to another. With our Transliteration product, we focus on providing a literal translation of text. For instance, when transliterating Arabic names like ‘أمير’ (Amir), our product recognizes them as names rather than translating them to their semantic equivalents, such as ‘Prince’. As a result, it accurately transliterates them into their corresponding forms, such as ‘Amir’.

How will NLP shape the future of the tech industry?

Natural Language Processing (NLP) is transforming how we interact with computers. By enabling machines to understand and process human language, NLP is streamlining tasks and boosting productivity.

While NLP won’t replace the need for programmers entirely, it can automate repetitive coding tasks like generating basic code from natural language instructions. This frees up programmers to focus on complex problem-solving and core software development.

NLP’s impact extends beyond programmers.  For instance, chatbots powered by NLP can answer customer queries, reducing the burden on customer service teams. Additionally, businesses can leverage NLP tools for sentiment analysis of social media data, gaining valuable customer insights.

Overall, NLP advancements are fostering smoother human-computer communication, leading to faster data processing, analysis, and reaction times. This empowers businesses in the tech industry and beyond to adapt to new technologies and stay ahead of the curve.

This way, the race for technology will be fierce in the near future; you either run towards innovation or you fall back to the dumpster. Keep the Apple vs. Nokia race in mind; the minute Nokia thought they had conquered the world, was the same moment that they slipped off the top. We live in an era where you either constantly fight for more or you accept being a failure.

Do you think coding will actually become extinct? 

 Published on: May 13, 2024