By Shumaila Handoo, Director Consulting Services - CGI, India
In the ever-evolving landscape of artificial intelligence, the partnership between traditional AI and generative AI mirrors the collaboration between a cookbook and an expert chef. Characterized by rule-based systems and explicit programming, traditional AI relies on huge volumes of data, predefined rules, pattern detections, and explicit programming to make decisions just like someone following a recipe in a cookbook. The recipe provides precise steps and if everything goes well, the dish will come out as expected. However, if something unexpected happens, such as running out of a key ingredient or a specific request from a guest, the cookbook recipe may fall short, and newer ways should be tried on the fly to meet the demand. In this case, a more experienced cook can make changes to the recipe accommodating any scenario like substituting and swapping ingredients, trying new flavors, adjusting the recipe, etc.
In a real work scenario, while the traditional AI leverages a predefined set of programs, business process automation, and patterns from bulk data; generative AI learns from the data and scenarios to adapt and evolve continuously from the knowledge it gains. It can adapt to scenarios and make changes dynamically. It also creates more realistic data and scenarios, further benefitting from its own experiences.
The self-supervised learning capability of the generative AI from the input data also forms the basis of the foundation models. This shift from the task-oriented models of traditional AI to these models that are self-trained on data sets has expanded the horizons in this modern wave of AI. This capability of generative AI allowing foundation models to adapt and learn makes the usability and applicability wider and not task-specific.
So, in this ever-evolving world of AI where it is influencing our lives directly and indirectly, a quite common question that comes up to everyone’s mind is if there is a shift from the traditional AI to generative AI. Is the generative AI replacing traditional AI? Which one of these is better and more powerful?
The answer to this question of whether we are witnessing a shift from traditional AI to generative AI is unambiguous. This shift is not a technical upgrade but a synergized eco system leveraging both. The key is to find the right solution to the right problem. Generative AI is opening avenues of creativity and reimagination compared to the traditional AI which focuses on bringing efficiencies. Traditional AI places a stronger emphasis on effectiveness, predictability, and consistency, whereas Generative AI thrives on creativity and diversity. The collaboration between these two forms of AI creates a powerful blend of efficiency and innovation. While the traditional AI strengthens the existing systems with a stable and reliable performance; the generative AI expands the boundaries of creativity leading to more personalized and insightful experiences.
Applications and platforms with synergized traditional AI and generative AI can help businesses navigate not only through the dynamic landscape but also be well prepared for the unknown nonlinear parameters.
A few avenues where traditional AI and generative AI complement each other to give businesses true value are –
Data Availability and Readiness
Preparing the data architecture with traditional AI considerations while automating more processes ensures clean data readiness that can be leveraged by generative AI for continuous learning and optimization.
To reap the benefits of generative AI, data management practices must be adaptable and reliant on robust design and integration. This calls for data architecture that can scale and adapt. Therefore, establishing an ecosystem where data is treated as a product and teams take ownership of the domain data making it available to the larger ecosystem becomes imperative.
Generative AI also creates bulk synthetic data that resembles real work data. It is also capable of processing unstructured data into structured data. This structured, synthetic data supplements limited labeled datasets, facilitating the training of more robust foundational models, especially in scenarios where extensive real-world data is limited.
Automation To Adaptative Automation
The creativity and adaptability of generative AI when added to the automation and predictability of traditional AI leads to applications that are more powerful and versatile. The versatility lies in handling complex, evolving patterns and nonlinear relationships that predefined rules, programs, and data cannot predict. With generative AI, multiple market and business scenarios can be simulated, further empowering the traditional AI to analyze them empowering traditional AI to analyze them.
Improved Decision Making and Insights
The convergence of traditional AI and generative AI is shaping the development of AI applications in various domains. Traditional AI focuses on completing tasks, making predictions, and informing decisions, while generative AI focuses on creativity, summation, and content generation.
This integration of traditional AI with generative AI empowers solutions, unlocking immense possibilities across domains to improvise and enhance the experience. Traditional AI uses algorithms to process data, whereas generative AI can provide valuable real-time insights into consumer behavior and market trends. Hyper-personalized content creation and capturing real-time insights through generative AI have transformed the marketing landscape.
For example, in healthcare, the integration of rule-based diagnostics with large language models trained on treatment-related medical data can generate more personalized treatment plans. Likewise, assistive technology and robotics are being harnessed with generative AI to have more tailored solutions helping individuals with special needs to have an improved experience. Similarly, the advertisement and marketing sector has traditional AI focuses on completing tasks, making predictions, and informing decisions using data and analytics while generative AI focuses on creativity, summation, and content generation.
In the current world, where the highest degree of certainty would help organizations to be future-ready; the integration of generative AI with automation that the traditional AI brings is very crucial. This is where the highest level of integration between traditional AI and generative AI to have digital twins such as product twins, data twins, or process twins is seen in action. This becomes helpful to predict scenarios, simulate behaviors, and have early warnings allowing organizations and businesses to take the right steps and be better prepared for scenarios.
More Adaptable with Faster Learning
The adaptability of generative AI and its ability to simulate scenarios facilitates faster learning. Generative AI augments traditional AI by injecting these scenarios into the systems and making them learn more. This reinforces learning and takes systems to a new realm that combines elements of both traditional AI and generative AI involving training models to making decisions by interacting with the environment and receiving feedback.
Democratized AI To Augment Human Creativity
Generative AI is de-centralizing and democratizing AI by making it easier for business solutions to be AI-enabled. Capabilities, where anyone can talk to the model in English, make it easier for the business solutions to be AI-enabled. With traditional AI, while the repetitive tasks are automated, generative AI is becoming a co-creator by inspiring, and ideas and creating amazing creative content.
In the synergized AI era, it is evident that the future lies in establishing a harmonious collaboration between traditional and generative AI. The discovery of newer ways to leverage its power is ongoing to have more powerful models in this ever-evolving landscape of AI technology. Even the latest models like CHATGPT 4.0 have a lot untapped and hence the trend is to make the current models more powerful, reliant, and efficient. With the pace at which the AI world is evolving, the future is paving the way toward artificial superintelligence (ASI). This would enable us to approach problems from diverse angles, identify complex relationships, and generate creative solutions that might escape human minds.
Ethical Considerations, Other Challenges, and The Need for Human Oversight
Being cognizant of the processing/memory usage to optimize the carbon footprints is going to be the key aspect driving the AI journey. Likewise, the responsible, trustworthy, and ethical usage of AI to harness the true benefits is equally important for society. Right guardrails and governance around AI usage should also not be ignored and well established.
While this is being done, AI-enabled platforms and applications are fallible and the assumption that they will operate with objectivity is flawed because the requirements as well as the information they feed on can contain inaccuracies, biases, or flaws, whether current or historical. This makes the need for human intervention in the training of the AI models more imperative and pivotal. The quality of data or information fed into the AI model defines the quality of the outcome, thereby training of models is the key. This implies that while the data volume strengthens the system, we cannot overrule the need for human oversight. Human oversight will ensure that the AI models are not learning and absorbing incorrect information, trends, or perpetuating flaws originally present in data. The right human intervention is critical to correct these biases and flaws that can be inherited by the AI models.
Conclusion
We therefore have an intelligent arsenal with the traditional and generative AI getting enhanced every minute that can manage both structured and unstructured, complex, and imaginative challenges, thereby paving the way for more advanced, intelligent systems in the future.
