Mediaboss Marketing

In the past few years, we have witnessed the gradual mainstreaming of large language models and the research of AI applications in the B2B industry. Despite the tremendous technological advancements made, we are still in the early stages of generative AI applications for B2B use cases. So far, the vast majority of generative AI applications have been creating new content based on a set of instructions.

Generative AI

To analyze generative AI, we first need to differentiate between B2C and B2B applications. When we, as consumers, apply generative AI, our goal is oriented towards playing, entertainment, and sharing. In terms of entertainment, quality and accuracy are not always the most important aspects, but rather the ability of the AI model to generate art or music, for example, that can be shared in a Discord channel, even if it is quickly forgotten. There is usually a psychological inclination to believe that more content equals more productivity equals better, so users are often drawn to generative, automatically-created AI tools.

When it comes to B2B applications, the business goals are different. The focus here is primarily on cost-benefit evaluations of time and quality. Either we want to produce higher quality in the same amount of time or the same level of quality in less time.

People mainly use B2B applications in the workplace, where quality is more important. However, the content generated by AI today is mainly for repetitive and low-risk work, which doesn't necessarily require high quality. It has been found that generative AI is unreliable in writing opinions or arguments. When it comes to innovation and collaboration in B2B production environments, large model-generated SEO information may be useful. But if we ask it to write a detailed blog post about a new product for developers, there will be a significant need for human input to ensure accuracy and resonance with the target audience.

Essentially, in brainstorming and early stages, the first wave of generative AI was successful in more substantive writing, but ultimately, the more creativity and domain-specific knowledge required, the more human input is needed.

Even in cases where generative AI is useful for longer blog posts, the prompt must be precise. That is, the author must already have a clear understanding of the substantive concepts that represent their blog post. Then, to get good results, the author must review the AI output, iterate the prompt, and, if necessary, rewrite the entire section.

An example here is using ChatGPT to generate legal documents, where someone familiar with the legal prompt provides all the necessary clauses and ChatGPT uses those clauses to generate a draft. However, these efforts still require a professional lawyer to review it, edit the output, and produce a sample that can be signed. This is also why the cost-benefit evaluation model is disrupted in the B2B context.

Trainable GPT-AI

Gathering information, training AI robots, and improving decision-making are crucial. When it comes to understanding the world and making decisions, humans must be involved. What AI can do is help humans apply more of their brainwaves to valuable and creative work, which means that we can not only spend more time doing important work in a day but also liberate ourselves to engage in the most valuable work. This vision is almost the opposite of ChatGPT's user interface: instead of writing lengthy responses based on a concise prompt, it is better for domain experts to train AI machines to have representative AI robots in each field: GPT-AI.

GPT-AI is a decentralized web3 project developed and created independently using CHATGPT artificial intelligence. The goal of GPT-AI is to enable everyone to have and train their own AI robots, eventually forming a huge scale of AI applications, transactions and rental platforms.

For example, if you are an image processor, designer, nutritionist, fitness coach or a chef, you can teach your AI robot your best skills and knowledge, continuously training it, accumulating data, optimizing its data structure, making it more professional. Such AI will be the most popular presence in all industries of Web3, and you can serve other users by renting or selling AI robots, thus earning commissions for yourself. This is the huge demand value that has been released by the combination of Web3 community and AI, and the value generated after solving the demand is returned to the users who keep training GPT-AI robots.

The decentralized and distributed features of Web3 provide better support for GPT-AI. In the Web3 ecosystem, all data and applications are stored on a decentralized blockchain network, which is public, transparent, and immutable. The distributed data architecture makes it easier for GPT-AI to access and share data while ensuring data security. In addition, the smart contract function of Web3 can also provide GPT-AI with more flexible and efficient transaction and training mechanisms, making the application and sale of GPT-AI more convenient.

GPT-AI is like a blessing, with its collaborative nature and more mature and humanized professional knowledge after training, its more suitable for all the Web3 user groups than the potential threats of automation and replacing humans.

Learn more: https://gpt-ai.io/

Media ContactCompany Name: GPT-AIContact Person: NICKEmail: Send EmailCountry: United StatesWebsite: https://gpt-ai.io/

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