By: Hakimuddin Bawangaonwala and Soham Patankar

Introducing the future of AI – generative AI

We are in the golden period of AI (Artificial Intelligence). From automating tedious and strenuous activities to predictive analysis, AI has enabled organizations to break down barriers and achieve new heights of improved productivity and cost efficiency. AI has facilitated businesses to increase production and better utilize their resources. Grandview Research forecasts that the AI market will grow at over 38.1 % CAGR (Compounded Annual Growth Rate) between 2023 and 2030.

AI is just getting started in terms of applicability. Computer vision currently leads the market and natural language processing is considered the crown jewel of AI. But the future lies in generative AI. Gartner  predicts that the rising popularity of advanced AI-driven technologies such as generative AI, composite AI, and knowledge graphs will emerge as the driving force for further innovation, improvements, and growth in AI.

The generative AI is an innovative concept that is still in its early stages of application. Over the past decade, it has become one of the most successful deep learning frameworks. The unsupervised or semi-supervised machine learning method enables the creation of new materials, such as digital photos, video, audio, text, and code.

Advancements in neural networks and machine learning algorithms are driving newer generative AI applications. These technologies are specifically designed for data analysis and pattern recognition, which are expected to open up new avenues for bulk data evaluation and analysis.

Currently, the working generative AI models are confined to certain network models only. Out of these, GAN (Generative Adversarial Network) is the most well-understood and heavily researched model. It offers a plethora of use cases in the image and video processing domains.

In this article, we highlight why project leaders should start researching and investing in generative AI, despite it being in a nascent stage of development and in what areas can they develop use cases to gain market advantage.

Why should project managers explore generative AI?

Current generative AI models may appear experimental, but it is important to recognize its inevitability and continuous advancements. What the internet and Web 1.0 was in the 90s, Web 2.0 and AI/ML in the 2000s and 2010s, respectively, generative AI  will be the next big thing in the 2020s and beyond. Therefore, project managers and delivery executives must stay informed of its technicalities and fundamentals, as well as market use cases and solutions.

We will soon witness generative AI expanding beyond generative design and pervading into every industry. The industry will demand an unprecedented scale of generative AI capabilities, and we must be prepared to meet its demand when the need arises. Getting a first-mover advantage here will be key in developing tomorrow’s IT service market leaders.

Delivery managers must focus on the following actions to identify the relevant generative AI application areas and extract maximum benefits:

• Understand how a generative AI uses inputs to produce new material
• Identify how to optimize large generative models like Large Language models (LLM) using data-centric ML methodologies
• Evaluate hardware infrastructure capabilities to maintain high accuracy and performance of large and complex models
• Integrate generative AI with a distributed cloud solution to scale computing and memory capabilities
• Assess the existing architecture and algorithms of generative AI and improve the frameworks to optimize ML training and enhance generative AI application implementation

Generative AI – a game changer for your enterprise

While generative AI has yet to achieve commercial viability , the proliferating data, more powerful at decreasing costs, and advancing technologies are paving the way for an ideal environment, in which generative AI can thrive.

Enterprises should not just use it as a tool for enhancing productivity. They can in the following three ways:

Optimizing processes with data synthesis and augmentation

Generative AI can create data that is unavailable in the real world. The artificial data set can be supplemented with original data and used for testing new machine learning algorithms and deep learning architectures. It can also help tune the neurons in neural networks by automatically finding the best set of connections, thereby improving neural network performance. Data augmentation using generative AI can be used to improve data quality.

Accelerating discoveries using artificial general intelligence

AGI, or Artificial General Intelligence, has the ability to perform any intellectual task that a human can. Throughout history, humans have utilized tools to solve problems and innovate. Automating this process is necessary, and generative AI is a critical step in developing AI that can design superior machine learning algorithms and other AI applications.

Enhancing human labor, especially during software development

Generative AI has also influenced the software development sector by automating manual coding. Instead of coding the software completely, IT professionals now have the flexibility to quickly develop a solution by explaining the AI model about the specific requirements and desired outcomes.

For instance, GENIO, a model-based tool, can enhance a developer’s productivity multifold compared to a manual coder. This tool helps developers and non-coders build applications tailored to their specifications and reduces their dependency on the IT department.

However, to capitalize on this enormous potential, businesses across all industries will need to integrate generative AI models into their plans. It must include responsible AI systems aligned with society’s moral and ethical ideals for producing better outcomes for all people. Enterprises must also continue to invest in the innovation of new approaches and models to maximize the benefits of generative AI’s distinct qualities to rapidly scale, self-learn, and constantly improve over time.

The road ahead

Due to its nascent status, market trends and revenue potential of generative AI is difficult to assess. Much like any other new technology, generative AI will require a significant amount of training data to deliver commercially viable outputs. To devise generative AI-based industrial use cases, one must focus on understanding the fundamental generative AI models, especially GAN. This requires delving deeper into the current research and striving to develop a proof of concept.

A major challenge today is to perfectly narrow down the architecture backbone required to create viable generative AI solutions. Enterprises will need to answer questions related to:

• Choosing between on-prem and cloud
• The appropriate technology stack for optimizing the calculations
• The role of quantum in conducting matrix multiplications
• The right hardware for data storage
• The role of compression technology advances in data storage
• Ways to identify and train new technocrats in this field

Given the time available, a huge amount of work must also be put into safeguarding the data infrastructure and deployment to avoid any form of security, safety, and privacy issues. However, there will always be the persistent concern of fraudulent people abusing technology to make phony videos, spamming material, and misleading news.

Generative AI, like other AI streams, will continue to expand with an increasing number of generative AI applications emerging in the years to come. Our view is that it will become a major part of data and art creation and generation, which will shift human involvement toward servicing and exploring new avenues.