Generative AI: A Comprehensive Overview


Generative AI is a quickly advancing technology that is transforming how we tackle challenges in the modern world. This blog post will give an outline of generative AI, including how it functions, the current major players in the market, its current popularity, and potential uses for knowledge workers.

Recently, generative AI has been everywhere in the news—through the fame of ChatGPT, the growth of text-to-image in our social media posts. But beyond amusing smartphone apps and convenient ways for students to avoid writing essays, worldwide acceptance of AI will change the way businesses work, create new ideas, and expand in the near future.

What Is Generative AI?

What is Generative AI and how will most businesses and individuals use it in the near future?

Generative AI is a kind of artificial intelligence that makes new, original content or data using machine learning algorithms. It can be used to make text, pictures, music, or other kinds of media.

Generative AI can do many things. It can write and make pictures, like blog posts, programming code, poetry, and artwork (and even winning competitions, which caused surprise). The software uses complex machine learning models that predict the next word based on earlier words, or the next picture based on words that explain earlier pictures.

LLMs (large language models) started at Google Brain in 2017, and were used first for translating words while keeping the meaning. Since then, large language and text-to-image models have been developed by big tech firms including Google (BERT and LaMDA), Facebook (OPT-175B, BlenderBot), and OpenAI, a non-profit where Microsoft is the main investor (GPT-3 for text, DALL-E2 for images, and Whisper for speech). Online groups such as Midjourney (which helped win the art competition), and open-source providers like HuggingFace, have also made generative models.

How Generative AI Works

Generative AI is a kind of AI that uses deep learning algorithms to make new data, not just react to existing data. It works by making a generative model that can create new data similar to the original data. The model is taught by giving it a large set of existing data. As the model develops, it can be used to create data that is like the first data.

At this time, there are two common generative AI models that we'll look at:

- Generative Adversarial Networks (GANs) - technologies that can make visual and multimedia things from both images and text data.

- Transformer-based models - technologies such as Generative Pre-Trained (GPT) language models that can use information from the internet to make text content from web articles to press releases to whitepapers.

Generative AI Popularity

Generative AI is becoming more popular because it can make new information from existing data. It is used in many industries from finance to healthcare to create new knowledge and data that can help people make better decisions.

Gartner says generative AI is one of the most important and changing technologies that bring big benefits. Here are some predictions from Gartner about generative AI:

- By 2025, generative AI will create 10% of all data (now it's less than 1%) and 20% of all test data for consumer use cases.

- By 2025, 50% of drug discovery and development will use generative AI.

- By 2027, 30% of manufacturers will use generative AI to improve product development.

The rush to gold - Open AI and ChatGPT

OpenAI, backed by Microsoft, introduced a long-form question-answering AI called ChatGPT that answers complex questions conversationally. It’s the fastest growing software in history with over 1 million users in just 5 days (!).

It’s a revolutionary technology because it’s trained to learn what humans mean when they ask a question.

Many users are awed at its ability to provide human-quality responses, inspiring the feeling that it may eventually have the power to disrupt how humans interact with computers and change how information is retrieved.

Generative AI for Knowledge Workers

How generative AI will supercharge daily habits of knowledge workers:

Generative AI can be used to create new insights for knowledge workers. For example, it can be used to generate new ideas for marketing campaigns or to analyze customer sentiment. Additionally, it can be used to generate new insights into customer behavior or to develop new strategies for customer engagement. Also, it can be used to generate key insights and summarize any kind of content. And more.

Generative AI can already do a lot and are incredibly diverse. They can take in such content as images, longer text formats, emails, social media content, voice recordings, program code, and structured data. They can output new content, translations, answers to questions, sentiment analysis, summaries, and even videos. These universal content machines have many potential applications in business, and today marketing applications are among the most common uses of generative AI (see Jasper AI for example). In the future, there is potential for generative AI to make an impact in healthcare and life sciences—to make diagnoses, for example, or find new cures for disease.

Knowledge Management Applications

One emerging application of LLMs is to employ them as a means of managing text-based (or potentially image or video-based) knowledge within an organization. The labor intensiveness involved in creating structured knowledge bases has made large-scale knowledge management difficult for many large companies. However, some research has suggested that LLMs can be effective at managing an organization’s knowledge when model training is fine-tuned on a specific body of text-based knowledge within the organization. The knowledge within an LLM could be accessed by questions issued as prompts.

Some companies are exploring the idea of LLM-based knowledge management in conjunction with the leading providers of commercial LLMs. Morgan Stanley, for example, is working with OpenAI’s GPT-3 to fine-tune training on wealth management content, so that financial advisors can both search for existing knowledge within the firm and create tailored content for clients easily. It seems likely that users of such systems will need training or assistance in creating effective prompts, and that the knowledge outputs of the LLMs might still need editing or review before being applied. Assuming that such issues are addressed, however, LLMs could rekindle the field of knowledge management and allow it to scale much more effectively.


The main issues with Generative AI:

Generative AI raises questions about what is original and proprietary content and may have a significant impact on content ownership.

Since the created text and images are not exactly like any previous content, the providers of these systems argue that they belong to their prompt creators. But they are clearly derivative of the previous text and images used to train the models. Needless to say, these technologies will provide substantial work for intellectual property attorneys in the coming years.


Generative AI is a technology that is quickly growing and changing how we solve problems in the current world. It has the capability to produce new data and understanding which can be used to make better choices, as well as inventing new concepts and plans for people who work with knowledge. As more businesses start to put money into generative AI, we can expect to see a variety of uses for it in the coming years.