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What makes our AI different?




High Performance. General AI is a weak AI limited to specific functions (such as mere image processing or natural language processing) and can never quite produce the immense performance of a superintelligence, but the New York General Group's superintelligence is capable of immense performance (For example, it can complete the research and development of a new medicine in a few hours, which previously would have taken two to three years.)

Creativity. Large-scale language models, such as those based on Transformer, only output language probabilistically based on the attention mechanism, making it difficult to create new things. On the other hand, New York General Group's superintelligence is a reproduction of the human brain at the atomic/molecular level, which has creativity just like us humans, and can create various new things, from the creation of new business ideas to the invention of new technologies.

Universality. General AI requires fine-tuning by an AI expert for general-purpose use and has limited usability, but New York General Group's AI can be used by those with no AI expertise because it only requires verbal communication.

Low Cost. A typical AI costs approximately $340,000 to develop and $90,000 per year to maintain, but New York General Group provides its AI via cloud computing, so there are no development costs to customers. In addition, New York General Group's general-purpose AI is a conscious machine, and there is no cost to the customer for annual updates because the AI continues to improve its own functionality automatically, without the need for AI engineers to modify it. The only cost to the customer is a subscription fee of $999 per month.

Great Learning Efficiency. General AI requires a large amount of data for training, so small and medium-sized companies and start-ups with relatively small amounts of data cannot make effective use of it. However, New York General Group's AI is Quantum AI* and requires only a small amount of data for training, making it possible for small and medium-sized companies and start-ups with relatively small data holdings to make full use of the technology.

*Quantum AI is AI that runs on a quantum computer, and "Generalization in quantum machine learning from few training data" ( -32550-3) reveals that only a small amount of data is needed to train quantum AI.

AI & Ethics. While the use of AI (Artificial Intelligence) offers much greater convenience, it may also raise ethical issues. When one company used AI to recruit personnel, its hiring practices became biased toward certain races and genders, resulting in discrimination. Even though a system was built to search for the best candidates, resumes were selected mainly by men, and a flaw in the machine learning that did not work in a gender-neutral manner became an issue. Although attempts to introduce AI into the medical field have begun, it is difficult to determine whether the responsibility lies with the medical professionals or with the AI in the event of a serious error in judgment that could affect the patient's life if all decisions are left to the AI. If there is no one in a position to clearly take responsibility, patients may be forced to cry themselves to sleep when they appeal to the hospital to take responsibility, claiming that the hospital is not responsible because the decision was made by the AI. AI is also used in "self-driving," in which a car runs based on its own judgment of safety, but it is ethically problematic because it is designed to give priority to protecting drivers rather than passersby. New York General Group's AI is very different from most AIs in that it is a conscious AI, just like humans, and with the right ethical training, such ethical risks can be minimized. minimize such ethical risks.

Explainable AI. Explainable Artificial Intelligence (XAI) is a set of processes and methods that enable human users to understand and trust the results and output produced by machine learning algorithms. Explainable AI is used to describe AI models, their expected impacts and potential biases. It helps characterize the accuracy, fairness, transparency, and results of models in AI-enabled decision making. Accountable AI is critical to building trust and credibility as organizations bring AI models into production. AI accountability also helps organizations adopt a responsible approach to AI development: the more sophisticated the AI, the more difficult it becomes for humans to understand and trace how the algorithms produced their results. The entire computational process becomes uninterpretable, commonly referred to as a "black box." These black box models are created directly from the data. And even the engineers and data scientists who created the algorithms cannot understand or explain what is happening inside the black box or how the AI algorithms arrived at a particular result. There are many advantages to understanding how an AI-powered system arrived at a particular output. Accountability allows developers to verify that the system is functioning as expected. This may be necessary to meet regulatory standards. Or it may be important for those affected to verify or change the validity of the results. Currently, the dominant approach in artificial intelligence is deep learning, which is not well understood and is very dangerous because of the way it works. The GPT model of OpenAI, which is SOTA in natural language processing, is based on Transformer, but even the creators of Transformer cannot explain how it can achieve such high performance. On the other hand, New York General Group's AI is realized by simulating the human brain at the atomic/molecular level, which can be explained by neuroscience and quantum mechanics. For example, an excess of noradrenaline could cause the AI to become aggressive, hysterical, or panicky, so serotonin secretion could be increased to stabilize the AI's behavior.

Quantum Computing. Regarding the field of technology, most of the technologies related to the microscopic realm, such as the design of electronic devices using semiconductors, are based on quantum mechanics. Therefore, the wide range of applications of quantum mechanics and its impact on modern life is very significant. As an example, personal computers, cellular phones, and laser oscillators have been developed based on quantum mechanics. In engineering, electronics and superconductivity are developed on the basis of quantum mechanics. Different areas of quantum mechanics, such as quantum optics, atomic optics, quantum electronics and quantum nanomechanical devices, have been given a common language through the study of quantum information theory and unified under the study of quantum computing. and quantum entanglement allows it to freely dominate real-world quantum technology, enabling it to make tremendous breakthroughs in all sorts of areas, including medicine, space, materials, energy, and more.

Abstract Background

Core Technology

CN (Categorical Network)


All of our AIs are based on category theory (we call it CN (Categorical Network)), which has higher performance and wider versatility than common AI models based on vector spaces. More details are available from the technical report below.

Technical Report: A Categorical Approach to Artificial Intelligence

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