Why is it important to know about generative AI?
The world is still coming to terms with the full potential of generative artificial intelligence (AI). Over this past year and more the technology has grown in impact phenomenally.
In fact, in a recent interview to The Economic Times, Infosys’ founder, N.R. Narayana Murthy talked about how generative AI will enhance productivity, grow performance and help solve unsolvable problems. Of course, he did talk about the technology being used appropriately.
Now, when an industry veteran like Murthy talks about generative AI positively, it is indeed a pointer that this is an important, unignorable field of technology. It makes a strong case for everyone to understand what generative AI is and what it is capable of delivering.
What is generative AI?
Generative AI is a form of artificial intelligence technology. It describes machine learning systems that can generate images, texts, code, and any type of content. These responses are generated when a system user enters a prompt.
Generative AI is powering chatbots and online information-seeking and customer-facing tools around the world. Quite simply, when a user enters a question or command into a system, a near-human or a human-like response is generated by the chatbot or tool.
This response mechanism that mimics a human response is called generative AI.
How does this technology work?
Essentially, generative AI uses a computing process called machine learning. This is based on analyzing patterns of enormous sets of data. The analyzed result delivers fresh data that is very close to what a human may have generated. A machine learning process that uses neural networks makes this possible. This entire technology framework is based a deep understanding of how the human brain functions: How it thinks, how it learns and how it interprets information over time.
For instance, if you input large amounts of samples of artworks into a generative AI model, it will be able to create paintings that appear to have been done by a human artist. With a completely unique style. Which is, to the observer, this work generated by AI will come across as distinct and original. As if it is done by a human artist.
The way generative AI works is that the more data that its models receive, the better the models become at generating human-like outputs.
The generative AI universe
When OpenAI’s ChatGPT launched a few years ago, it opened the doors to the world of generative AI. Between ChatGPT and another model called DALL-E, both from OpenAI, generative AI now became available to anyone who wanted to use the technology. There are no more access barriers. Just about anyone has access to generative AI tools.
Among the most popular generative AI tools are the ones that generate text and images.
Here’s a quick listing of the tools that make up the generative AI universe:
ChatGPT: This is an AI language model that can receive a text prompt and respond to questions with answers like a human.
DALL-E 3: This is also from OpenAI. With this model, users can give the system prompts and it generates images and artworks.
Google Gemini: This is Google’s generative AI chatbot. It is a competitor to ChatGPT. It is trained on a large language model called PaLM. It answers texts and generates text responses to prompts. This chatbot was originally called Bard.
Apart from these leading models, there are other generative AI models too. These include Llama 2, Grok, Cohere AI’s Command R, GitHub Copilot, Midjourney and Claude 2.1.
The different types of generative AI models
There are a variety of generative AI models at play. Each model delivers a specific result because it is designed for such distinct outcomes. Here’s a quick listing of these models:
Generative adversarial networks: Two types of neural networks make up this model. The two neural networks are called generator and discriminator. They work at cross-purposes with each other. The outcome that is generated is very close to genuine or authentic data.
Transformer-based model: In this model, huge sets of data are used to train the system. This leads it to understand the relationship between all types of sequential information like, say, words and sentences. This, in turn, leads to the understanding of the context and structure of language. And the outcome is usually high-quality text-generation tasks are performed perfectly.
Multimodal model: This model can process different types of data simultaneously. Like text, images, video and audio. The outcomes are sharp and are of very high quality.
Frontier model: This is a futuristic model which will outperform today’s generative AI models. It is in the works.
Foundational model: All AI chatbots in vogue today are based on this model.
Variational autoencoders: This model uses two networks – an encoder and decoder. These networks interpret and generate data. The outcome is close to the original data but is not identical.
The importance of generative AI
The future of businesses globally depends on how we humans understand generative AI. Its importance can never be overemphasized. Simply because all business efficiencies depend on this technology. As more and more business processes use generative AI, human time and intelligence can be invested in more strategic issues. This will lead to more innovation and breakthrough ideas. This means a higher quality of products and services that impact human life will emerge in the next few years. Resultantly, businesses will be not just more profitable but more game changing.
Also, the impact of generative AI is sweeping. From generating new ideas to content creation to search engine optimization to planning and scheduling of tasks to marketing, audience analyses and to studying engagement metrics, AI will touch everyone all over the world.
Anyone who is not realizing the importance of generative AI is sure to be left behind.
