Generative AI for Beginners

The emergence of generative AI systems, such as ChatGPT, DALL-E, and others in the league has led to a sudden surge in interest in AI from a wide audience base. What had so far been the playground of a limited group of scientists, technologists, and enthusiasts has suddenly opened the floodgates for curious commoners. But the questions that come to mind are:

What is generative AI?
Why has this transformative technology hit the mainstream headlines?
What is the potential impact that generative AI can have on industries and businesses?

Let’s take these questions one by one. But before answering the ‘what’ aspect, let’s first understand why there’s been so much hype around generative AI.

Well, we know that although research on AI has been ongoing for more than six decades, until now, no machine has ever been able to demonstrate behaviors or intelligence that is closer to human abilities. But the new breed of generative AI models has closed this gap significantly – not only are they capable of carrying out sophisticated conversations with users, but they are also able to generate original content. And the results seem startling – people with no coding experience or background in AI can harness this technology to get outcomes that were never thought of before. No wonder, mainstream media is so enamored of this newfound AI capability.

What is Generative AI?

Generative AI is a specific set of algorithms, capable of generating seemingly original and realistic content based on a given context—such as text, images, or audio. These algorithms have been built on foundation AI models that are trained on a massive quantity of unlabelled and unstructured data in a self-supervised way to identify patterns for a wide range of scenarios.

For instance, GPT-3.5, which is the underlying model for ChatGPT, is a foundation AI model trained on large volumes of text. It can be adapted for answering questions, summarizing text, or sentiment analysis. Similarly, DALL-E is a foundation model that can be adapted to create new images, expand existing images, or create variations of existing paintings based on text inputs.

What can Generative AI do?

While the hype phase is in full steam, one cannot discount the potential of generative AI becoming a general-purpose mainstream technology having an impact akin to that of the steam engine or telecommunications and the internet. Who knows, this might be the beginning of the next phase of the industrial revolution.

Based on the capabilities exhibited at this point, it may not be too far-fetched to predict that generative AI would accelerate AI adoption across organizations, even in those that lack deep AI or data-science capabilities.

Generative AI Use Cases

As research progresses rapidly, generative AI will evolve in terms of commercialization. The current use cases are mostly emerging in the following areas:

Written content generation/creation and improvisation: Producing a working ‘draft’ of a required text in a desired style and length, or improving an existing draft.
Question answering and discovery: Enabling users to locate answers based on data inputs and prompt information.
The tone of voice/language: Manipulating text inputs to soften language or make the same appear more professional.
Summarization: Generating summary versions of conversations, articles, e-mails, and webpages.
Simplification: Analyzing lengthy text documents and reorganizing them into titles, and outlines, and extracting sub-topics around key points.
Classification of content for specific use cases: Sorting by sentiment, topic, etc.
Improving the performance of chatbots: Enhancing ‘Sentity’ extraction, sentiment classification, and generation of journey flows from general descriptions.
Software coding: Code generation, translation, explanation, and verification.

Furthermore, the following evolving use cases are worth considering:

Generation of medical images that predict the future development of a disease.
Generation of synthetic data to help supplement scarce data, enable data privacy, and alleviate bias.
Development of applications that can simulate future events and proactively suggest actions to users and provide them with information.

If we look at the above use cases, it appears that the majority of the value that current generative AI models could deliver falls into four main buckets – customer operations, marketing and sales, software engineering, and R&D.

However, from an industry perspective, the technology is poised to have a significant impact across all sectors. Banking and Financial Services, High-tech, and Life Sciences are among the industries that could see the biggest impact in the short run.

But what is the outlook for work and the workforce? While still in the early stages, generative AI is likely to alter the anatomy of work in the next 10 to 20 years. It is not difficult to imagine or visualize a different level of automation – one that will further augment the capabilities of individual knowledge workers and their activities.

Conclusion

We’re only at the beginning of the era of generative AI. While the initial pilots are encouraging or maybe even compelling in some cases, the technology still needs to develop until it matures and reaches a point of trustworthiness. Meanwhile, several issues must be addressed, especially those around ethics and the management of risks inherent in generative AI. Most businesses are not even in the drawing board stage when it comes to reimagining core business processes and determining the new skills and capabilities that the workforce will need. Nonetheless, early starters and adopters will always stand a chance and competitive advantage.

Frequently Asked Questions on Generative AI

What is the definition of Generative AI?

Generative AI is a subset of artificial intelligence that focuses on creating and developing new content, including text, imagery, audio, and synthetic data.

What is the difference between Generative AI and Traditional AI?

It’s the objective and functionality that make Generative AI different from Traditional AI. Unlike Traditional AI performing specific tasks based on predefined rules and patterns, Generative AI creates new data resembling human-created content.

What business problems can Generative AI solve?

Generative AI automates repetitive tasks, allowing employees to focus on strategic work, leading to cost optimization and time and money savings for businesses.

HCLTech offers a comprehensive AI consultancy platform for organizations to identify Generative AI opportunities, develop strategies, and implement solutions, maximizing value and meeting industry-specific needs.