In an era defined by rapid technological advancements, Artificial Intelligence (AI) has emerged as a transformative force revolutionizing industries worldwide.
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The term “Generative AI” popularly known as “Gen AI”, has been the talk of the town since the AI start-up OpenAI launched its flagship AI chatbot – ‘ChatGPT’ for public use. Since then, the spectrum of Gen AI has only expanded, with every global corporation investing in the technology.
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This has created a fertile zone for new Gen AI models to thrive, giving birth to new and advanced Gen AI models, with capabilities that range from text to image generation, and in a few corporate-level models, even advanced code generation. But what has made Gen AI the breakthrough technology of the 21st century? In this article, we will try and understand the same from the point of view of process enhancement.
What is AI?
Before we understand what Gen AI is, we must understand what AI is. Simply put, ‘AI is a computer program that enables the system to learn new things through trial runs, remember the results, and use critical analysis to provide expected results when it is executed in a new space.’ A simple example of an AI system is the Recommendations page of your social media platforms. It takes in data such as watch time for specific media content, the reaction it generated (Like or dislike, heart or report, etc.), and other complex data points like search history and preferences to curate a personalized experience on the application.
Unbeknownst to the general public, AI is a mainstay of today’s personalized app experience algorithms. Be it from job posts on LinkedIn to new music on Spotify, from suggested videos on YouTube to recommended purchases on Amazon, AI is a critical part of our technological ecosystem today, and the use cases of AI are growing every single day. However, AI is not a one-size-fits-all solution, and over the decades, it has branched into multiple specializations, and with time, these specializations are expected to niche out even further.
(Image source: AI Mind Map | by Samrat Kar | Machine Learning And Artificial Intelligence Study Group | Medium)
One such branch of AI is known as Natural Language Processing (NLP).
NLP models train themselves on expansive language-related data using a complex Machine Learning paradigm known as Neural Networking.
Neural Networking mimics the human brain’s neural networking complexities and is thus able to recognize complex patterns in inputs, memorize these patterns, and provide human-like responses when used in a novel environment.
Due to their complex nature, NLPs have been traditionally used in plagiarism and fraud detection software, but lately, their capabilities have been utilized to create Generative AI models, the backbone of any Generative AI tool.
What is Generative AI?
As introduced, Generative AI is an AI model developed over the complex NLP architecture. Initially developed as an enhancement to the classical Large Language model (LLM), Generative AI stands out due to its expansive training and generative capabilities.
Just like a child, an AI model is not created with all knowledge pre-loaded. It has to be trained, taught, tested, and re-trained. In this process, the Generative AI LLM models are trained data available on the Internet.
This gives it access to massive datasets, which creates innumerable complex patterns.
These patterns, along with the classic NLP architecture, made Generative AI what it is – a solution that can take conversational inputs, process them in real-time, and generate novel responses based on its stored memory and contextual references of the input.
Benefits of Generative AI
Generative AI is the future of multimodal content generation. Be it novel text, images, videos, or even complex code, Gen AI can create reliable content in seconds. The major benefits of Generative AI are as follows:
1. Low ideation times: Generative AI takes little to no time to ideate content. Once a prompt is fired, the expansive GPT model of a Generative AI retrieves data from its memory, analyses the context of the query, and accordingly, generates output.
2. No performance drop: Unlike humans, who drain out after a hard day of mental or physical labour, Gen AI never tires out. It may experience a response lag due to network fluctuations or lack of content in its memory, but once it starts generating, it can do the same work over and over again, without complaining, questioning, or dropping the output quality.
3. Ability to contextualize responses: Generative AI models can contextualize non-logical data points, such as the emotional state of the prompter, the age, and the requirements, to generate a personalized response. Thus, if the prompt is changed, but the subject remains the same, it is highly unlikely that the responses would match.
Limitations of Generative AI
Though Generative AI is the future of technology, it is not devoid of limitations. Generative AI, under its veil of accessibility and availability, is a highly sophisticated piece of technology, and it has its own set of drawbacks.
1. Hallucinations: Since Generative AI ‘generates’ new content, if the conditions are just right, it is likely to create entirely new, and generally bogus ideas to fill in places where factual data is limited. This can jeopardize the quality and dependability of one’s content.
2. Pre-trained biases: Since Generative AI has been trained on historical data available on the Internet, it is likely that biases available in those datasets have crept into the model. Thus, it is advisable to run multiple iterations and iron out biased responses.
3. Prompt quality affects response: Unlike the present search engines, Generative AI is not a searching tool. It is a conversational system, and thus the input provided is as important as the response. Hence, it is advisable that a basic understanding of the science of prompting is understood before Generative AI is used professionally
The future of process enhancements: Generative AI
With the foundation established, we find ourselves in the present, in a reality where Gen AI is everywhere.
But there is this fear, the uncertainty of AI replacing human labour.
However, as someone who is in constant touch with technology, I can positively claim that these fears are just rumours.
Gen AI is not a competitor, but rather it’s a tool that one can use to enhance their output, create novel ideas, and ensures that we spend less time conducting mundane tasks, and more time creating and ideating.
Ideation has never been more rewarding, and with Gen AI leading the pack of enhancements, there shall remain no use case that Gen AI cannot enhance.
Let us take an example of how Gen AI makes our lives easier.
Example 1: Summarization
In our hectic lives, it is imperative that we do not have adequate time to read lengthy documents and summarize them for easy use at our workplaces.
Generative AI proves to be a superb tool for summarizing lengthy documents into easy-to-understand summaries, which can later be used in our workplaces.
A fitting conclusion to this article would be using a Gen AI tool to summarize the same, giving a holistic view of the contents to the readers, and demonstrating its capabilities as an enhancement tool.
Prompt: Imagine you are the editor of a renowned newspaper in India. An article has been submitted by an aspiring technology columnist, but he has missed adding a conclusion. Summarise {content} in under 200 words, and provide a short, 50-word conclusion for the same, which can be added. Match the tone and flow of the content.
We will replace {content} with this article in the Gen AI tool. Let’s see the magic happen!
Reply: (Used ChatGPT 3.5)
In the bustling landscape of technology, the advent of Generative AI, or Gen AI, has sparked a revolution in process enhancement.
Stemming from OpenAI's ChatGPT, Gen AI has evolved into a versatile tool embraced by global corporations.
Its prowess spans text, image, and code generation, making it a cornerstone of 21st-century innovation.
At its core, Generative AI builds upon the foundation of Natural Language Processing (NLP) models, leveraging vast datasets to generate intricate patterns and responses akin to human cognition.
Its benefits are manifold: rapid ideation, unwavering performance, and personalized contextualization.
However, like any innovation, Generative AI is not without its limitations, including the risk of hallucinations and pre-trained biases.
Despite these challenges, the future of process enhancement lies with Generative AI.
It isn't a threat to human labour but a catalyst for creativity and efficiency. Embracing Gen AI empowers us to transcend mundane tasks, fostering a realm where ideation flourishes, and innovation thrives.
As we navigate this technological landscape, let us harness the potential of Gen AI to propel us towards a future where possibilities are limitless.
An apt end to the article, don’t you think?
(The author is a B.Tech graduate from Tezpur University and is currently working as a Systems Engineer at TCS, Delhi. All views and opinions expressed in the article are the author’s own.)
