It takes time before a new concept can be broken down into an interface specification from a perspective such as object-oriented analysis, and code that conforms to the interface specification can be implemented. The blind spot of "democratization of AI" occurs when a new concept is created throughout the society, including business. It is impossible to assume differentiating factors without taking commonality and identity between society and its history. Many models and algorithms are variants derived not only from research but also from the relationship with business domains. On the other hand, it must be remembered that any R&D builds on the history of the social structure and the semantics of the concepts envisioned by previous studies. In general terms, R&D must provide a differentiator to maximize the scarcity of its implementation artifacts. The R&D provides economic value when its R&D artifacts are distinguished from the models and algorithms realized by standard machine learning libraries and applications such as AutoML. If you fall in love with the concept of "Democratization of AI," you may forget the reality that the R&D is under the influence of not only democracy but also capitalism. If you just copy and paste the demo code from the library and use it, your R&D would fall into dogmatically authoritarian development, or so-called the Hype driven development. How the Research and Development(R&D) on the subject of machine learning including deep learning, after the era of "Democratization of Artificial Intelligence(AI)", can become possible? Simply implementing the models and algorithms provided by standard machine learning libraries and applications like AutoML would reinvent the wheel. Problem Setting: Deep Learning after the era of "Democratization of Artificial Intelligence(AI)". The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing. The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website: Accel Brain (Japanese) and Accel Brain Co., Ltd. and of the world.Accel Brain Code: From Proof of Concept to Prototype. We provide you with the latest breaking news today of the U.S. © Copyright 2022 by is your news, entertainment, political, financial and business, technology, lifestyle and health website.
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