For a long time, there has been a buzz around generative AI. We have seen traditional AIs used in industries, but we have not seen widespread AI models that can be used in our daily lives. The real thing started after the launch of Chatgpt, which has given a boom in the field of generative AI and opened up many possibilities in the world of tech.
At this time, several generative AI models, including LLMS, image-generating models, and even code generation, are available for everyone to utilize to supplement their work, saving them time and allowing the creative juices to flow.
In this article, we will explore generative AI in more detail and learn how it is revolutionizing the tech industry.
What is Generative AI
Generative AI is a type of AI that can generate new data based on the input data it has been trained on. It can generate original text, images, code, or even videos.
What makes Generative AI unique is that it creates something that has never existed anywhere before. A traditional AI cannot do this. Generally, most traditional AI models are built for a specific function, such as making decisions, recognizing patterns, image classification, etc., depending on the requirement.
Generative AI does a lot more than traditional AI — for instance, it is not limited to simple tasks like figuring out whether a photo contains a cat or translating the text, as traditional AI has been good at this for years now! Instead, generative AI generates entirely new content, such as an image of a person who doesn’t exist.
The Technology Behind Generative AI
Foundation models serve as the base of generative AI, enabling it to create new and original content. These models are trained on very large datasets using unsupervised or semi-supervised learning techniques, which allows them to generate new data by understanding and predicting patterns.
They rely on neural networks inspired by the structure and function of the human brain, making their outputs more unique and original. Generative AI models are built on a neural network type called Transformer architectures to process multiple inputs and function seamlessly. This architecture, proposed by Google researchers in 2017, was a breakthrough that significantly advanced the field of AI.
Today, most renowned generative AI models are trained on vast amounts of data, often involving billions of parameters. Some well-known generative AI models include GPT-3, GPT-4, DALL-E, Google Gemini, GitHub Copilot, Midjourney, and others.
Is This a New Technology
AI has been around and doing things for quite a while, even though most of us haven’t been aware of it. Even though AI started to lend itself to practical use cases in the mid-to-late 90s, it became visible to everyone in the early 20s.
Renowned tools like Google Translate were introduced in 2006 and Siri later in 2010. By the 2010s, big tech giants like Google, Facebook, Twitter, and Netflix had already started integrating AI into their platforms for better experiences. So, in general, AI isn’t something new.
However, when it comes to generative AI in particular, its peak period started in 2017 when Google researchers introduced the Transformer model, which was revolutionary in neural networks. This led to the development of massive language models (LLMs) such as GPT-2, GPT-3, and now GPT-4, Gemini, which are made with billions of billions of parameters.
Not only this we have also entered a new era of image-generating models such as DALL-E and MidJourney, which takes text input and gives out amazing images. These developments have led this new wave of generative AI to become one of the hot topics in the mainstream and most active areas in AI development ever since.
How These Models are Being Used
With time, generative AI models are making our lives easier by solving many of our problems. Professionals across different industries are now using these models as their copilots, helping them complete tasks that otherwise might have taken much longer. Generative AI has made its way into many areas, from marketing to content creation and beyond.
Some writers and content creators use these large language models (LLMs) to help with their respective writing today. AI models like GPT can now help write scripts, dialogue, and even entire storylines for movies, TV shows, and video games.
And image generation tools such as DALL-E have become very usable for creators to create concept art, animations, and 3D models for films and games.
Nonetheless, generative AI cannot replace professionals entirely at this point. Rather, it acts as a “copilot,” assisting creators but not smartly enough to do the job by itself.
On the other hand, in the IT sector, generative AI is a great help for developers. GitHub Copilot is one of the models that can generate code automatically, which allows faster development. Coding work that used to take days to complete can now be done in a couple of hours.
Generative AI can perform tasks that previously took days in a matter of hours. In my opinion, this does not entirely eliminate the requirement of a human skill set but serves as a great help to developers in making their work easier.
Adoption of Generative AI
AI has already been a hot topic, with large enterprises adopting it to improve their services for quite some time. But the real boom came when OpenAI launched its first LLM model, Chatgpt. GPT reached 100 million users just two months after its release, which is one of the fastest adaptations of technology in human history.
It was a milestone in AI and indicated how those systems were seeping into everyday life. As of the time of writing, a whole range of generative AI models, like ChatGPT-4, DALL-E, MidJourney, and Google’s Gemini, are already reshaping the world of tech.
From large enterprises to even startups, businesses are harnessing the power of AI to create entirely new business models. Startups in sectors like media, gaming, and e-commerce use generative AI to develop innovative services, whether automating content production, generating personalized marketing campaigns, or building interactive user experiences.
According to a 2024 McKinsey report, around 72% of companies have incorporated some form of AI into their operations, with a significant portion being generative AI applications.
AI’s adoption is quickly expanding across all sectors, reshaping how businesses function and creating new opportunities for growth and efficiency.
What to Expect from this Technology
There is tremendous scope for generative AI, and many exciting developments are on the horizon. Today’s most generative AI models are trained on generic, widespread data available on the Internet, which is great for general tasks but not great for specific tasks like industry requirements.
But soon, we may see industry-specific generative AI. Models will be trained on data tailored towards sectors such as medical sciences or financial services and thus more focused and powerful to solve industry-specific challenges. This will allow businesses and professionals to outsource most tasks to AI, which enables highly specialized performance and increases productivity and innovation.
Today, AI models have the option of prompt engineering, which can tweak the AI’s response to make it more specific to tasks. However, AI is still not strongly aimed at delivering solutions that are finely tuned to industry-specific needs.
There are just so many exciting things to come. ChatGPT-5 is already generating a lot of buzz, and everybody’s high on hopes as they look forward to even greater promises this model may deliver.
OpenAI has also recently launched Sora, which soon be released. It takes text input and presents generation capabilities in the form of creating videos that are just incredible. That is a revolution as we haven’t seen any good video-generating model yet.
So these developments will change how we interact with technology and many capabilities for generative AI shortly.
Fears and Risks of Generative AI
While this is an exciting technology with plenty of potential applications opening up, the fear associated with its impact has some issues and concerns on the rise that we will touch upon.
The most critical and common fear around generative AI is, without a doubt, job loss. A 2023 report indicates that employees estimate that AI could replace up to 29% of their work tasks.
That’s a very sizable number, which automatically makes the fear of job loss very realistic, especially in the fields of content creation and writing and, importantly, customer support.
Where generative AI can do good, it can also cause a lot of harm in the wrong hands, be that through things like deep fakes, fake news, or audio. These problems are already being experienced in society and will only become more widespread in the future.
Its misuse to spread misinformation poses a great challenge, as it will make it more difficult to distinguish between original content and AI-generated content.
Another issue is that most AI-generated content is not guaranteed to be unbiased or contain negative sentiments or false information. Because the generative model is fueled by massive amounts of web data compiled from web sources, it may be full of biased information or just plain wrong and harmful. So transparency becomes an important issue here with these big AI models.
On the bright side, Generative AI has amazing power and potential. On the dark side, it can lead to job displacement, misinformation, bias towards unethical activities or entities, and many more. Such challenges need to be confronted in an era of increasing dominance of generative models, with technology and regulatory measures put into place to prevent misuse.
