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Why AI Bots Don’t Work and How We Fixed It

Let’s be honest, we’ve allhad an experience chatting to an AI bot that has gone rough. That’s the main reason why majority of the industries are still using the good-old selection-based bots that simply annoy rather than add value.

So why has AI not yet solved the issues of customer support for all the banks, utility companies, healthcare providers, and other industries that handle a huge volume of customer support?

In short:

  1. Data preparation for bot training is an expensive and tedious task
  2. Al Bots tend to hallucinate and produce inaccurate results.

Here’s how we fixed it at NeurochainAI:

First, we're employing the community to “scrape” data for a specific bot training. For example, a customer of ours in xxx space is looking to add an AI customer support bot to their website. They’ve spent months (and a substantial sum) on data collection only to arrive to the conclusion that developers or customer support agents are not the best at data collection and preparation for AI bot training.

Another example is our partner Ovetrip which is looking to create the most degen Web3 gaming character which should not be limited by the degeness of its creators.

We’ve built a tool and onboarded community to collect and validate data for both of these and many more use cases. Check it out.

How does it work:

  1. We launch a data collection challenge on our data launchpad
  2. We invite the community to participate in exchange of rewards
  3. Community collects and validates the data for a specific task
  4. Our customer reviews and gives a final approval of the dataset
  5. We use the validated dataset for AI bot training

Here are the key benefits of using community-powered data collection and validation for training AI Bots:

Community-Powered Data Collection and Validation

Cost Efficiency: By crowdsourcing data collection and validation, companies can significantly reduce the financial overhead associated with hiring dedicated teams for these tasks. This approach distributes the workload, lowering costs related to labor and operational management.

Enhanced Data Quality: Community involvement in data validation helps ensure a high level of accuracy as it incorporates diverse perspectives and expertise, enhancing the reliability and quality of the data sets.

Faster Dataset Compilation: Community participation accelerates the process of data collection and validation. A larger pool of contributors means data can be gathered and refined at a much quicker pace compared to traditional methods.

Community Engagement and Growth: Engaging community members in the project promotes a sense of ownership and loyalty. This naturally attracts new members curious about the technology and willing to contribute.

Data Security and Privacy: All of the data is already public and available across various company-related sources like website, social media accounts, blog posts, PR articles, and others. So there are no data privacy or security concerns.

Community Education: By collecting and validating data for AI Bot training and also being able to chat to the bot, the community participates in the AI revolution while naturally learning about the key principles of how it operates. Education of the community is paramount for the future built on AI-driven digital solutions

Ok, so once we solve data collection and validation for AI Bot training, we’re still left with the hallucination problem. AI hallucination happens when AI systems come up with false or misleading info that doesn’t match their training data or the facts of the real world. This problem shows up a lot in generative models, like the ones used for processing language or creating images.

Using RAG (Retriever-Augmented Generation) advanced architecture mitigates hallucination by ensuring outputs are grounded in source material by pulling in relevant information dynamically during the generation process. Open AI and Google both use RAG vectorized databases to reduce AI hallucinations by 95-99%.

Here are the key benefits of using RAG vectorized databases:

Using RAG Vectorized Database for AI Training

Reduction of AI Hallucinations: Implementing a RAG (Retriever-Augmented Generation) vectorized database has shown to reduce AI hallucinations by 95-99%. This dramatically increases the reliability and trustworthiness of the AI's outputs.

Enhanced Learning Efficiency: The RAG architecture helps in efficiently querying and retrieving relevant information from the database during training, which enhances the learning process and effectiveness of the AI model.

Scalability and Flexibility: RAG databases are highly scalable, allowing the AI to handle growing data sizes seamlessly. This adaptability is crucial for projects as they evolve and expand.

Improved Accuracy and Relevance: By leveraging vectorized searching, the AI can access the most relevant information for any given query or task, thereby improving its accuracy and performance in real-world applications.

Cost-Effective Maintenance: With the improved accuracy and reduced error rates provided by RAG databases, the costs associated with AI training and maintenance decrease. Fewer corrections and less retraining translate to lower operational costs over time.

These benefits collectively make a compelling case for adopting community-powered data collection and validation, along with RAG vectorized databases, in AI development projects. This approach not only optimizes performance and costs but also enhances the community engagement and project visibility.

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