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Introduction: At Predictive Equations our mission is to develop technology that can benefit others- we tend to describe it as ethical technology development and utilization, this ranges from how we configure a specific module in our system- facial recognition for example, is oriented to not scan large databases but rather match from submitted evidence, and only when a suspect is already identified, acting as a tool to enable the detective, instead of serving in the place of, and this would be a more ethical utilization compared to unleashing an AI to hunt across a database and make false detections. In development we focus on data handling and responsibility, and the data we use being given authorization of those producing it to do so. This is on top of goals including limiting mass incarceration, and enabling accessibility to AI. But while serving others is an incredibly valuable initiative in its own right, we believe the world itself is something to consider. From our perspective we hold that not only are we responsible for the well being of our shareholders, but also stakeholders; ranging from the community locally to globally. Limitations of Generative Technology vs Transformative: Many of our team came from other related technology industries, such as blockchain. In this project, as we continued to develop our AI system and advance to larger and more complex systems and hardware, we have found the power consumption increasing. I personally came out of the blockchain arena, and one of my biggest issues with the industry apart from it amounting to gambling on file structure and sharing, is also how much it harms the world in power consumption.   Costs of Excellence: Our technology makes use of a lot of that that enables mining and proof of work operations and smart contracts. Specifically in how one can create nodes and webs of gpus and cpus to enable math operations. In this regard, AI are incredibly similar to blockchain, at least when speaking of power consumption. Blockchain by 2022 was found to consume between 120 and 240 terawatts per hour (120 billion kilowatts) with AI following closely behind in 2023 at between 84 to 110 terawatts per hour. While it is true that the earth produces approximately 24,000 terawatts per hour. So while this may seem small, blockchain and AI respectively consume more power than 174 countries individually, and together more than Australia, the United Kingdom, or Italy. In our view this is not sustainable. We are committed to also developing technologies that can limit problems like the above, to help combat climate change and global warming, as well as the impact it will have on both human populations and animal. Data Processing, AI Inference & Solar: Our plan, and pledge, is in building the nodes of our system, powerful configurations of gpu and data racks that will power the engines that drive our AI, that each power consumption produced, we estimate in the range of 1 kwh per node, will be matched by an array of solar panels with the goal of being net energy neutral, and as green as possible to limit our own contribution to the above problem.

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? Tasks: 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

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? Tasks: 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 emergence of transformative-based artificial intelligence (AI) has revolutionized various fields, offering a powerful approach to directly modify and transform input data. In comparison to generative-based approaches, transformative-based AI demonstrates greater viability for real-world applications across domains such as medicine, law, manufacturing, communications, and defense. This article delves deeper into the advantages of transformative models, emphasizing their accuracy, speed, fidelity to input, and contrasts them with the limitations often associated with generative-based approaches. Transformative Accuracy: A Game-Changer Transformative-based AI holds a distinct advantage over generative-based approaches in terms of accuracy. By directly modifying input data, transformative models offer precise control over the output, enabling interpretable transformations and ensuring accurate results. In contrast, generative models might struggle to achieve the same level of accuracy due to their reliance on statistical patterns present in the training data. Medical Applications: Transformative models excel in medical imaging applications, where accuracy is paramount for diagnosis and treatment planning. Through precise transformations, these models facilitate tasks such as image segmentation, disease detection, and anomaly identification, ensuring reliable and accurate results. Generative models, on the other hand, may introduce artifacts or generate unrealistic features, potentially compromising accuracy and clinical interpretation. Legal Applications: Transformative-based AI has the potential to revolutionize legal processes by accurately transforming legal documents, expediting analysis and facilitating decision-making. This technology enables precise document classification, contract analysis, and extraction of relevant information. Generative models may struggle to provide the same level of accuracy and fidelity to input data, potentially leading to erroneous or unreliable results in legal applications. Manufacturing and Quality Control: Transformative models offer accurate and reliable transformations, playing a vital role in quality control processes within the manufacturing industry. They can precisely detect defects or anomalies in products or components, thereby improving efficiency, reducing waste, and enhancing overall product quality. In contrast, generative models may generate data that resembles the training set but lacks fidelity to the input, compromising their reliability in quality control tasks. Communications and Defense: Transformative-based AI ensures accurate and reliable signal processing, enhancing the reliability of communication systems, intelligence gathering, and defense operations. By leveraging precise transformations, these models can effectively suppress noise, improve signal quality, and extract valuable features. Generative approaches, though capable of generating signals resembling the training data, may not accurately capture the specific features required for reliable communications or defense applications. Transformative-based AI presents a more viable approach for real-world applications across diverse domains due to its advantages in accuracy, speed, and fidelity to input. By directly modifying data, transformative models provide precise control over transformations, ensuring accurate and reliable results. In contrast, generative-based approaches may fall short in terms of accuracy, fidelity to input, and interpretability, making them less suitable for critical real-world tasks. As transformative-based AI continues to advance, it holds tremendous potential for driving innovation and delivering tangible benefits across various industries and sectors. Input Image Our Technology

A GAN is a type of machine learning model that consists of two neural networks, the generator and the discriminator, which work in tandem to generate new data. The generator network takes random noise as input and generates synthetic data, such as images, based on patterns learned from a training dataset. The discriminator network, on the other hand, aims to distinguish between the generated data and real data from the training set. Through an adversarial training process, where the generator and discriminator compete against each other, the GAN learns to generate increasingly realistic outputs that can closely resemble the original data. While GANS can produce outputs that are visually or audio impressive, GANs can hallucinate their outputs by generating data that appears realistic but doesn’t actually exist in the training dataset. During training, the generator network learns to capture the statistical patterns and underlying structure of the training data. However, GANs are prone to hallucination. The term “hallucination” in the context of GANs refers to the phenomenon where the model generates data that appears to be realistic but introduces information or features that are not explicitly present in the original dataset. These hallucinated details can arise due to various factors, such as limitations in the training data, biases present in the data, or the model’s tendency to fill in missing information based on learned patterns. Even with access to GT data during testing, GANs can still generate outputs that exhibit hallucinatory elements. These elements could be a result of the model extrapolating or interpolating beyond the boundaries of the training data, resulting in the generation of data that has similarities to the GT but includes additional fictional or erroneous information. This means that while GANs can generate visually appealing images, they may introduce subtle or significant deviations from reality. These hallucinations can manifest as objects or details that were not present in the original data, resulting in generated images that may contain artifacts, unrealistic textures, or inaccurate features. The hallucination problem is an ongoing challenge in GAN research, as finding ways to mitigate and control these hallucinations is crucial for producing reliable and trustworthy outputs. 1. Time-consuming training and Overfitting: GNAS require extensive training on large datasets, which can take a significant amount of time and can be challenging to obtain in certain applications. This makes it difficult to use these methods in real-time applications that require immediate image enhancement and are often prone to overfitting, where they become too specialized in generating images or text from a specific dataset. This can lead to poor generalization, imbalanced biases and unreliable results in real-world scenarios. 2. Inconsistencies in generated images and Lack of control over the generated image properties: : GANs are known for their ability to generate visually impressive images, but they lack fine-grained control over the output.GAN-based image enhancement technology can produce inconsistent and unintended results when applied to different images or even the same image multiple times such as altering the color balance or removing specific objects in the image. This can limit its use in applications where fine-grained control over the image properties is essential. This makes it challenging to use these methods in applications that require consistency and reliability. 3. Difficulty in training on diverse datasets, and requiring large datasets: GANs require a diverse dataset to learn from, which can be challenging to obtain for certain applications. Without a diverse dataset, the generated images may not be representative of the real-world data. 4. Inability to handle complex features: GAN-based image enhancement technology may struggle with generating images that contain complex features or structures. This can limit their usefulness in applications that require the enhancement of complex images. 5. Limited interpretability: GANs are notoriously difficult to interpret, making it challenging to understand how they generate images. This can make it challenging to use these methods in applications where interpretability is critical, such as legal, medical or security image or audio analysis. 6. The risk of generating false data: GANs can generate images that appear realistic but contain false data or artifacts. This can be problematic in applications that require accurate image analysis, such as in security or forensics. 7. Legal and ethical concerns: The use of GAN-based image enhancement technology can raise legal and ethical issues, especially in cases where the generated images can be used to manipulate or deceive people such used to generate realistic fake images. This makes it essential to exercise caution when using these methods in real-world applications. Additionally data used for training the AI may have been obtained without consent, or without enough bias mitigation methodologies. 8.Inability to handle real-time inference and limited scalability: GAN-based image enhancement technology can be slow and computationally intensive, making it challenging to use in real-time applications. This can limit its use in applications where immediate image enhancement is essential, such as video processing or use in applications that require fast processing of large amounts of data. 9.Lack of robustness, Domain-specificity, and Difficulty in handling multi-modal data: GANs are designed to generate images with a single modality, such as color or texture. This can make it challenging to use these methods in applications where the input data contains multiple modalities, such as images with both color and depth information. They can be sensitive to perturbations in the input data, such as noise or variations in lighting conditions adding additional layers of impracticality utilizing these methods in real-world scenarios where the input data is noisy or contains artifacts. 10.It can be challenging to interpret the latent space because it does not have a direct mapping to human-understandable concepts or features. Instead, the latent space encodes complex and abstract representations of the training data. Due to the lack of interpretability, understanding how the model generates images becomes difficult. While we can manipulate the latent vectors to generate variations of images, it is not straightforward to determine which specific attributes or features in the latent space correspond to specific aspects of the generated image. This lack of interpretability hinders

Our groundbreaking proposed project is poised to revolutionize how we interact with visual information, the field of AI-based image and video enhancement systems, with unprecedented potential for impact across multiple industries. From faster and more accurate medical diagnoses to higher quality visual evidence for legal use, and from improving product quality in manufacturing to enhancing satellite and communications imagery, our technology has the power to revolutionize numerous fields. By enabling rapid scaling, the funding from this grant will allow us to accelerate our research and development efforts, bringing our game-changing technology to market faster and creating new job opportunities in the process. Our research will improve the accuracy and speed of our existing models, resulting in better equipment, faster testing, and higher quality output, and we are confident that our research will significantly enhance the accuracy and speed of existing models. This is not just a project for the advancement of AI and STEM fields – it is a project with far-reaching, tangible benefits for society as a whole. By enabling better equipment for faster testing and better results, we are poised to improve manufacturing efficiency and product quality across multiple industries. In the field of medical imaging, our technology could mean the difference between life and death for countless patients. And in law enforcement, our AI-based image and video enhancement systems have the potential to bring clarity to the most challenging visually based investigative cases. We are confident in our ability to successfully complete this project and deliver game-changing results. The funding from this grant will enable us to expand our team and increase our research efforts, which will accelerate the pace of our development. With our cutting-edge technology, we will create new job opportunities and advance knowledge in the field of AI image and video enhancement. With our cutting-edge technology, we will create new job opportunities and advance knowledge in the field of AI image and video enhancement.