Transformative AI – Part 2, A Basic Introduction

Transformative Artificial Intelligence

Predictive Equations is dedicated to developing groundbreaking technology that revolutionizes data processing from visual to audio formats. This cutting-edge advancement represents a paradigm shift in the field of artificial intelligence, bridging the gap between the present and the future, transforming the future with tomorrow’s AI, today. However, it is important to understand what transformative AI truly means. When researching this term online, one encounters numerous AI systems claiming to be transformative.

Unfortunately, many of them merely use the term as a buzzword to highlight their impact on a particular industry, which while may be profound in its own right also make it challenging to identify AI models that genuinely employ transformative deep learning techniques. To clarify the concept of transformative AI, it is essential to distinguish what it is not. Tasks like deblurring or super-resolution, which transformative AI can address, can also be accomplished using generative techniques. These generative techniques are similar, if not identical, to those found in existing AI software such as ChatGPT, ESRGAN, Midjourney, and others. Therefore, in this context, it becomes crucial to discern the characteristics of transformative AI and differentiate them from generative approaches. So in this setting what is transformative AI, what is generative?


Let us start with a similar task such as that mentioned above – Image super-resolution, or upscaling, where one takes an input image dimensions and increase them, with the AI techniques focused on either reproducing, enhancing the output larger image. Generative AI refers to techniques that aim to generate new data that resembles a specific distribution or training set. It involves modeling the underlying patterns and structures in the training data and using that model to generate new samples that have similar characteristics. Generative AI can be used for tasks like image generation, text generation, or even music composition. In the context of upscaling a generative method would analyze the surrounding pixel values and use them to create new pixel values that fit the desired upscale resolution. Transformative AI, on the other hand, focuses on transforming existing data or making changes to it while preserving the underlying distribution or characteristics of the original data. It aims to modify the input in a meaningful way without fundamentally altering its statistical properties. In the case of upscaling, a transformative approach would retain the existing pixel values and not introduce any new values. The main difference between generative AI and transformative AI lies in their objectives. Generative AI aims to generate new data that resembles the training set or a specific distribution, while transformative AI focuses on making meaningful modifications to existing data without fundamentally changing its distribution or characteristics. If we take another task such as deblur for faces, we find a common problem is that generative AI approaches for deblurring can produce visually sharp images, but introduce unintended distortions or alter facial features. Often, it is not even the same face from the input that ends up in the output. Transformative AI methods, on the other hand, are more likely to retain the original appearance and preserve facial details during the deblurring process.

Why is Generative Ai more known than Transformative?

Generative AI, especially deep learning models like generative adversarial networks (GANs) and variational autoencoders (VAEs), gained significant attention in recent years due to their ability to generate new and creative outputs. As a result, there has been a greater emphasis on generative models in research, leading to more publications and discussions around them. Popular creative based applications- Generative AI has gained popularity in areas such as image synthesis, text generation, and style transfer, which often produce visually striking or attention-grabbing results.

These applications tend to attract more attention from the general public and media, leading to increased visibility. There is also a general perception of transformative AI as “less exciting”: Transformative AI, by its nature, focuses on preserving and improving existing data or content. While it plays a crucial role in tasks like image enhancement that can save companies millions, such as denoising, and deblurring QA data, the outputs may not have the same novelty or eye-catching appeal as generative AI, which can create entirely new and imaginative content.

This perception may contribute to transformative AI receiving less attention in popular discussions, even while providing radically more useful applications for industrial, commercial and real world use cases. While transformative AI may not always receive as much public attention, it remains a critical aspect of AI research and development, and is employed in fields like medical imaging, surveillance, and forensic analysis- which may not be as publicly visible but are still significant in their respective domain. Contrast this with Generative AI which has found applications in areas like creative design, entertainment, and advertising, where the ability to generate new and unique content is highly valued. Transformative based AI applications contribute to improving existing data and enhancing various real-world tasks, even if they may not be as widely recognized outside specific domains or academic circles.

Limitations of Generative Technology vs Transformative:

Concerning generative Ai it is not only at the macro level that new details or pixels are generated, but that because of the hallucinatory method there is no true way to know how the AI reached those values in the specific instant. (in which case it isn’t just image manipulation too far from ground truth limiting practicality, but the irreproducibility in the methodology as well). Let us take two different potential applications, that of manufacturing and legal forensics: In manufacturing, precision and accuracy are crucial. Generative AI’s ability to hallucinate or create new details and values introduces uncertainty into the process. When generating new data or values, there is a risk of introducing errors or inconsistencies that could impact the quality or functionality of manufactured products. The lack of control over the exact methodology used by the AI to reach those values makes it difficult to understand or reproduce the process, which further hampers the reliability and reproducibility needed in manufacturing settings. In legal forensics, maintaining the integrity and authenticity of evidence is of utmost importance. Generative AI’s propensity to introduce new details can raise concerns about the reliability and admissibility of evidence. When using generative methods to enhance or modify images or videos for forensic analysis, it becomes challenging to establish the ground truth and differentiate between genuine information and artificially generated content. The inability to reproduce the exact methodology or understand how the AI arrived at specific values raises questions about the validity and trustworthiness of the evidence. Contrast that with transformative AI methods that aim to preserve the original content and characteristics of the input data while making improvements or modifications. In legal contexts, this preservation of the original evidence is crucial for maintaining the authenticity and integrity of the data. Similarly, in manufacturing, where accuracy and precision are essential, transformative AI techniques ensure that the original information is retained while enhancing or modifying it, enabling greater insight than the initial data or input could offer due to error, degradation, and artifacting. Transformative methods often rely on well-defined algorithms and techniques that can be reproduced and validated. Unlike generative, where the process (like its name) involves generating new content, transformative methods typically operate within known frameworks and algorithms.

This reproducibility makes it easier to understand, verify, and trace the steps taken to achieve the desired outcome. In legal settings and manufacturing, where accountability and traceability are important, transformative AI methods offer more transparency and reliability. With generative Ai often the same input with a given model will produce different outputs, often unpredictable. With transformative given the same parameters their output will be predictably the same. Whether it’s upscaling an image or deblurring an image, Transformative AI techniques strive to maintain consistency with the ground truth or original data. The goal is to retain the original details and minimize distortions or artifacts. By preserving the original information, transformative AI methods are more aligned with the factual and accurate representation of the data, which is essential for legal proceedings and manufacturing processes.


From a both philosophical and pragmatic perspective, the pursuit of truth holds immense value in industries such as legal forensics and manufacturing, where the accuracy and reliability of information are critical, transformative AI emerges as a favorable approach compared to generative AI. Transformative AI methods prioritize the preservation of original content and the faithfulness to the ground truth. They aim to uncover hidden details, restore obscured information, and refine imperfect data while adhering to established algorithms and reproducible methodologies.

Generative AI, on the other hand, despite its visually captivating outputs, presents challenges in terms of establishing the veracity of the generated information. The hallucinatory nature of generative AI, where it creates new content, can introduce uncertainties and make it difficult to trace the exact methodology behind the AI’s decisions. Given these considerations, transformative AI emerges as a more suitable approach, as it is grounded in preserving original content, reproducibility, consistency with ground truth, interpretability, and adherence to application-specific requirements make transformative AI methods more permissible and suitable for real-world applications.

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