Artificial intelligence is the most important technological revolution in modern world. AI is spreading through almost every industry in a rapid speed.
There are several types of AI that are being developed during recent years. One of the major developments of AI is generative AI.
Generative AI or Gen-AI or general artificial intelligence is a type of AI development that is able to generate new content including images, videos, texts, or music by using user prompt as input. Gen-AI is simply a advanced AI-powered computer programs that can create a wide range of content by using user prompt as input. The most popular Gen-AI in the world currently is OpenAI’s Chat-GPT.
To build a Gen-AI, there are several subfields of Artificial intelligence are being used including natural language processing, deep learning, machine learning etc. They can take understand the human language and generate output results.
Generative AI models:
There are several types of Gen-AI models.
1. Gen-adversarial networks:
Generative adversarial networks consist of two neural networks – a generator that creates synthetic data and a discriminator that tries to distinguish between real and fake data. Simply put, a creator competes with a critic to make more realistic and creative outputs. This competition is like training, as the creator part keeps improving based on the critic’s feedback.
2. Gen-variational autencoder:
The variational autoencoder model works in two parts. The first one is the encoder – it analyses input data into very small parts, enabling the decoder to create absolutely new content from those tiny parts. Then, the decoder acts like a creative writer. It takes those key points from the encoder and uses them to produce new content, like writing a whole new story from a book summary.
3. Transformer:
This AI technology is based on large language models that enable it to understand and interpret human language. Thanks to the transformer architecture and self-attention mechanism, these generative models can pay attention to all words simultaneously, no matter their position and text length. As a result, it can assist you with various writing-related tasks, including translating, answering questions, or conducting research by analysing a large amount of raw data. Additionally, you can use generative AI to create various types of text from scratch, such as research papers, movie scripts, or even funny social media posts.
4. Autoregressive models:
Opposed to the transformed-based approach, autoregressive models can’t focus on all words in the text simultaneously. Instead, they analyse the text sequence-by-sequence. For this reason, this machine learning algorithm might give better results when working with shorter forms of text. As it relies solely on the preceding phrases, it might struggle to interpret the whole text at once. On the other hand, transformer-based models might not notice certain relations between sequential phrases.
5. Diffusion models:
Unlike transformer-based or autoregressive AI systems, diffusion models don’t predict the next token based on preceding information. Instead, generative AI in diffusion models focuses on how information gradually spreads through a sequence of data. A diffusion model often uses denoising score-matching techniques to understand the entire process step-by-step. As a result, this technology excels at creating high-quality work, particularly images or videos.
Generative AI use cases:
There are many real life use cases that uses the potentials of Gen-AI including:
- Creative arts
- Gaming industry
- Business and marketing
- research and development
- Education and training
commercial benefits of Gen AI:
Following are the commercial benefits for Gen AI.
Creativity implication:
Generative AI can be a main source for businesses to create more and more creative content with minimum resources. Imagine you want and introduction video for your new business/company. There are already several Gen AI applications that can be used to generate a content artificially based on user prompt.
Time and cost:
Gen AI applications are always simple and easy to use. you can use a simple application that can be very cost effective. Gen AI applications are normally built to minimize resources and as cost effective. Not only that, but by analyzing stakeholder feedback and market trends, it can help generate new product concepts and designs. The speed with which it can analyze massive amounts of data and make design recommendations is an immense source of its power.
Hyper Personalization:
When you search something on Amazon, automatically you feed will be filled wit h similar products that are related to your search even next time you use it. This is called customized product recommendation. This is a another marvel of Gen AI. Potential of Gen AI can be used recommend target product to customers based on their search history.
Enhanced Productivity and Efficiency:
With generative AI, businesses can automate complex and time-consuming processes, enabling them to optimize workflows, improve efficiency, and allocate resources effectively. For instance, in the manufacturing industry, AI algorithms can generate optimized production schedules, minimizing waste and maximizing efficiency. Not only that, in contact centers, generative AI can enable customer support agents with ticket summarizations, response and tone suggestions that best suit the context of the customer’s query, greatly increasing their productivity.
Data synthesis:
In the field of data synthesis, generative AI offers an attractive application. AI models can synthesise vast amounts of data and produce insightful results by utilising their capacity to analyse a variety of information. For example, in the financial industry, generative AI may create prediction models that help companies make informed investment decisions by analysing consumer behaviour, market trends, and economic indicators. Generative AI gives businesses in a variety of industries the ability to access actionable intelligence and obtain a competitive edge in the data-driven world of today by utilizing the power of data synthesis.
Adaptive learning:
In response to feedback and fresh data, generative AI systems are able to continuously learn and adapt. This feature makes it possible for the models to perform better over time and produce results that are more in line with the goals and preferences of the user. Generative AI models, for example, can dynamically modify instructional strategies and resources to fit the needs, preferences, and learning styles of each individual student in personalised education, increasing the efficacy of learning opportunities.
Enhanced knowledge organization and discovery:
By mining and drawing conclusions from a variety of sources, generative AI can aid in the discovery of new information. Moreover, it is capable of automatically classifying and organizing enormous volumes of knowledge assets. These systems can recognize and tag content according to topics, themes, and relationships by utilizing machine learning techniques, making access and retrieval simpler.
How Generative AI is different from Traditional AI:
In contrast to traditional AI, which is limited to analysing and interpreting preexisting data, generative AI is a subfield of artificial intelligence that focusses on producing new and original content, such as text, music, or images, using patterns and examples learnt from training data. Generative AI surpasses traditional AI’s predictive and analytical capabilities, opening up new avenues for creativity, content creation, and simulation.
Industries that can benefit from the potentials of Gen AI:
Numerous industries could undergo radical change as a result of generative AI. By producing original and motivational content, it enables musicians, designers, and artists to explore new creative horizons. It transforms personalised treatment planning, medical imaging, and drug discovery in the healthcare industry, improving patient outcomes. Furthermore, generative AI improves character development, virtual worlds, and interactive narrative in games and entertainment, giving viewers captivating and immersive experiences. The use of generative AI has the potential to greatly improve consumer experiences in these and other industries by offering highly customized, inventive, and user-friendly solutions.
Generative AI for decision making:
By enabling scenario simulations, enhancing data analysis, and offering insightful information, generative AI improves decision-making processes. Generative AI assists decision-makers in evaluating prospective outcomes, exploring alternative approaches, and making well-informed decisions by producing realistic and varied options. This technology makes it easier to make proactive decisions, lowers uncertainty, and gives businesses the ability to move more confidently and nimbly through complicated situations.
End Notes:
The most important thing we should understand is Generative AI is evolving in a rapid scale and it is going to change many fields and industries forever. It is becoming one of the major subfields/branches of Artificial Intelligence. Already it is being used in several industries, mainly in content generation. There are several major potentials of Gen AI that is being discussed here.