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Generative AI: Scope, Risks, and Future Potential

  • Data & Analytics
  • Indium Software
  • April 17, 2023
  • Share ICon Share

From planning travel itineraries to writing poetry, and even getting a research thesis generated, ChatGPT and its ‘brethren’ generative AI tools such as Sydney and Bard have been much in the news. Even generating new images and audio has become possible using this form of AI. McKinsey seems excited about this technology and believes it can provide businesses with a competitive advantage by enabling the design and development of new products and business process optimizations.

ChatGPT and similar tools are powered by generative artificial intelligence (AI), which facilitates the virtual creation of new content in any format – images, textual content, audio, video, code, and simulations. While the adoption of AI has been on the rise, Generative AI is expected to bring in another level of transformation, changing how we approach many business processes.

ChatGPT (generative pretrained transformer), for instance, was launched only in November 2022 by Open AI. But, from then to now, it has become very popular because it generates decent responses to almost any question. In fact, in just 5 days, more than a million users signed up. Its effectiveness in creating content is, of course, raising questions about the future of content creators!

Some of the most popular examples of Generative AI are images and chatbots that have helped the market grow by leaps and bounds. The generative AI market is estimated at USD 10.3 billion in 2022, and will grow at a CAGR of 32.2% to touch $53.9 billion by 2028.

Despite the hype and excitement around it, there are several unknown factors that pose a risk when using generative AI. For example, governance and ethics are some of the areas that need to be worked on due to the potential misuse of technology.

Check out this informative blog on deep fakes: Your voice or face can be changed or altered.

Evolution of Machine Learning & Artificial Intelligence

From the classical statistical techniques of the 18th century for small data sets, to developing predictive models, machine learning has come a long way. Today, machine learning tools are used to classify large volumes of complex data and to identify patterns. These data patterns are then used to develop models to create artificial intelligence solutions.

Initially, the learning models are trained by humans. This process is called supervised learning. Soon after, they evolve towards self-supervised learning, wherein they learn by themselves using predictive models. In other words, they become capable of imitating human intelligence, thus contributing to process automation and performing repetitive tasks.

Generative AI is one step ahead in this process, wherein machine learning algorithms can generate the image or textual description of anything based on the key terms. This is done by training the algorithms using massive volumes of calibrated combinations of data. For example, 45 terabytes of text data were used to train GPT-3, to make the AI tool seem ‘creative’ when generating responses.

The models also use random elements, thereby producing different outputs from the same input request, making it even more realistic. Bing Chat, Microsoft’s AI chatbot, for instance, became philosophical when a journalist fed it a series of questions and expressed a desire to have thoughts and feelings like a human!

Microsoft later clarified that when asked 15 or more questions, Bing could become unpredictable and inaccurate.

ChatGPT, Dall-E, and Bard are the three most popular generative AI tools.


An AI-powered chatbot built on OpenAI’s GPT-3.5 implementation, it is interactive and provides nuanced responses with interactive feedback. It can simulate a real conversation by drawing context from past chats with the user.


Dall-E is a multimodal AI application that connects words with visual elements to create a multimodal result. Dall-E 2, released in 2022, facilitates the generation of multi-faceted imagery based on user prompts.


Google Bard was a hasty response to Microsoft’s Bing and paid a price because of incorrectly stating that a planet had been discovered in a foreign solar system for the first time by the Webb telescope.

Underlying Technology

There are three techniques used in generative AI.

Generative Adversarial Networks (GANs)

GANs are powerful algorithms that have enabled AI to be creative by making two algorithms compete to achieve equilibrium.

Variational Auto-Encoders (VAE)

To enable the generation of new data, the autoencoder regularizes the distribution of encodings during training to ensure good properties of latent space. The term “variational” is derived from the close relationship between regularization and variational inference methods in statistics.


A deep learning model, transformers use a self-attention mechanism to weigh the importance of each part of the input data differentially and are also used in natural language processing (NLP) and computer vision (CV).

Prior to ChatGPT, the world had already seen OpenAI’s GPT-3 and Google’s BERT, though they were not as much of a sensation as ChatGPT has been. Training models of this scale need deep pockets.

Generative AI Use Cases

Content writing has been one of the primary areas where ChatGPT has seen much use. It can write on any topic within minutes by pulling in inputs from a variety of online sources. Based on feedback, it can finetune the content. It is useful for technical writing, writing marketing content, and the like.

Generating images such as high-resolution medical images is another area where it can be used. Artwork can be created using AI for unique works, which are becoming popular. By extension, designing can also benefit from AI inputs.

Generative AI can also be used for creating training videos that can be generated without the need for permission from real people. This can accelerate content creation and lower the cost of production. This idea can also be extended to creating advertisements or other audio, video, or textual content.

Code generation is another area where generative AI tools have proved to be faster and more effective. Gamification for improving responsiveness and adaptive experiences is another potential area of use.

Governance and Ethics

The other side of the Generative AI coin is deep fake technology. If used maliciously, it can create quite a few legal and identity-related challenges. It can be used to implicate somebody wrongly or frame someone unless there are checks and balances that can help prevent such malicious misuse.

It is also not free of errors, as the media website CNET discovered. The financial articles written using generative AI had many factual mistakes.

OpenAI has already announced GPT4 but tech leaders such as Elon Musk and Steve Wozniak have asked for a pause in developing AI technology at such a fast pace without proper checks and balances. It also needs security to catch up and appropriate safety controls to prevent phishing, social engineering, and th generation of malicious code.

There is a counter-argument to this too which suggests that rather than pausing the development, the focus should be on developing a consensus on the parameters concerning AI development. Identifying risk controls and mitigation will be more meaningful.

Indeed, risk mitigation strategies will play a critical role in ensuring the safe and effective use of generative AI for genuine needs. Selecting the right kind of input data to train the models, free of toxicity and bias, will be important. Instead of providing off-the-shelf generative AI models, businesses can use an API approach to deliver containerized and specialized models. Customizing the data for specific purposes will also help improve control over the output. The involvement of human checks will continue to play an important role in ensuring the ethical use of generative AI models.

This is a promising technology that can simplify and improve several processes when used responsibly and with enough controls for risk management. It will be an interesting space to watch as new developments and use cases emerge.

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What is the role of neural networks in generative AI?

Neural networks are made up of interconnected nodes or neurons, organized in layers like the human brain. They form the backbone of Generative AI. They facilitate machine learning of complex structures, patterns, and dependencies in the input data to enable the creation of new content based on the input data.

Does Generative AI use unsupervised learning?

Yes. In generative AI, machine learning happens without explicit labels or targets. The models capture the essential features and patterns in the input data to represent them in a lower-dimensional space.

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