Biggest Problems in AI

While AI has seen an unprecedented growth since the launch of ChatGPT 3, a myriad of complexities are preventing AI to become an integral part of the digital world.

The current state of the AI industry presents multiple challenges:

• Hardware Infrastructure. The lack of AI-focused infrastructure and the cost of adapted infrastructures causes developers to dedicate more time to infrastructure setup rather than product development, due to challenges in configuration, data management, resource scaling, and pricing.

• Data. Acquiring and preprocessing high-quality data for AI algorithms is a significant challenge, involving privacy concerns, data labeling, cleaning, and ensuring representativeness and diversity.

• AI Models. AI model training and tuning is a challenging process that demands computational resources and time.

• Deployment and Integration. Deploying AI models from development to production is a complex process involving integration, scalability, security, and ongoing performance maintenance challenges.

• Custom AI Solutions. While we’ve seen a steep increase in ready-to-use AI solutions, most of them are limited to generating texts, images, voices, and some calculations. Moreover, the majority of them are built on a single company’s private infrastructure and are highly dependent on the policies, pricing, operations, and practices of a single provider. This is not a reliable strategy for the majority of corporations that need custom solutions on a reliable infrastructure.

At NeurochainAI we've set out to solve the majority of these problems through a decentralized, transparent, and collaborative open-source platform powered by the community.