General AI is great. Everyday people can load up ChatGPT and get fairly good results. But when a business requires a model to perform specific tasks custom to that business at a near-perfect rate, then firms encounter issues. As a result, the biggest companies in the world are specializing in AI models, applications, infrastructure, and other systems, to suit their needs.
Companies first tried hiring prompt engineers, with some roles paying up to staggering $335,000 a year for simply writing AI prompts. For many, this isn’t enough as specialized models need to be built to address specific business needs. Ernst & Young, for example, has built an in-house AI platform, in collaboration with Microsoft, to ensure high-quality outputs and reduce privacy risks.
Neurochain is offering an accessible alternative for companies that don’t have the funds, personnel, or time to build an AI-system from the ground up. With the largest AI dApp store in the world, businesses can browse a wide-library of specialized AI models—sorting through them using an intelligent categorization and tagging system.
Community members are encouraged to develop, refine, and enhance specialized models in exchange for a slice of the model’s royalties. This creates a continuously evolving hub of AI solutions for businesses to leverage.
That said, however, businesses often have requirements so niche that community-made models don’t perfectly suit their needs. In this scenario, a business would normally have to hire a team of developers and engineers to create the perfect model for them. With cost starting at $50,000 for a very basic system, this is not an accessible option for the majority of businesses. Fortunately, on Neurochain AI businesses can request AI models to be developed to their specific needs and Neurochain AI will source the best developers from the community to build such systems at a fraction of the cost.
Security is one of the biggest concerns when it comes to firms using AI systems, this is often a factor in building models in-house. A survey found that 85% of companies are concerned about AI-related security and privacy risks with 73% having experienced GenAI-related security incidents. This is often a valid concern when it comes to centralized AI-models, such as ChatGPT, as there is no visibility as to where and how OpenAI is using the data entered into ChatGPT by the users.
Neurochain AI, however, uses a decentralized architecture that ensures optimal data privacy and security. Data is disseminated across multiple nodes with end-to-end encryption. This reduces the likelihood of data breaches or unauthorized access as there isn’t a single point of failure but multiple nodes with scrambled fractions of data. Additionally, on Neurochain AI each AI solution is built specifically for that business and there is no sharing of data between different company’s AI models which in itself eliminates the problem where one company’s data can be used to train the models for another company.
More importantly, the decentralized architecture helps to reduce the AI model hosting costs which over time can accumulate to thousands or even tens of thousands of dollars per month. Once AI models are built and deployed, the next step is hosting them on a reliable infrastructure to compute the responses every time there is an interaction with the model. NVDIA’s staggering profit growth since ChatGPT initiated the surge in AI compute requirements, is a testament to the skyrocketing need for computers to power AI models. Neurochain AI employs the GPUs owned by the community and connects them into a reliable and especially scalable network at a fraction of the cost compared to alternatives.
For the largest companies in the world, the future is still to create hyper-specialized and secure in-house AI systems. But this could cost up to $15 million, for most companies this simply isn’t possible. Instead, small and medium-sized firms will be looking at cheaper, more accessible options to specialize AI solutions to their needs while boosting security. While the GPT store is a great option, its centralized system raises some security, specialization, and cost concerns. For that reason, Neurochain AI feels like a startup that is quickly emerging as the best option to address these key challenges while offering a comprehensive platform to use or build custom AI solutions.
It’s clear that the future will require businesses to use tailored AI solutions specific to their needs, the only question is how to do it without breaking the bank.
Disclaimer: The Industry Talk section features insights by crypto industry players and is not a part of the editorial content of Cryptonews.com.
Read the full article here
